This brings us to the hard question for B2B2C founders: how do you prioritize and balance the “B” (business) and the “C” (consumer) in your model? In practice, trying to do both is how startups lose focus and alienate their core buyer. Like in any multi-sided vertical business model, you need to ensure you have at least one stakeholder prioritized at any point, and ultimately, locked in. Andres Robelo, Founder & CEO of Playbypoint, spent ten years learning this in racket sports — tennis, pickleball, padel — and built a playbook for navigating the tightrope.

Check out our latest episode of Verticals with Andres. And continue below to get our vertical playbook, inspired by the Playbypoint — Navigating Vertical B2B2C.

Watch Full Episode Here

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A Pickleball Story: From Hobby to Obsession

Andres founded Playbypoint in 2016 after a consumer frustration: he couldn’t book a tennis court from his phone. His instinct was marketplace — be the OpenTable of courts. His first customer was a Miami municipal park, which took nine months of lobbying city commissioners for a free pilot.

The marketplace thesis broke fast. Booking was only 10% of a club’s operation. The supply side wasn’t digitized — membership management, lesson scheduling, junior academies, F&B — so there was nothing to aggregate. Playbypoint pivoted from connecting consumers to courts to building an end-to-end OS for the business. Today they serve over a thousand clubs across 37 countries, growing 80% year-over-year, backed by Blueprint Equity. Pickleball’s massive US explosion hasn’t hurt either.

The B2B2C Alignment Map

Most founders assume that more consumer orientation means a stronger B2B2C flywheel. But that’s not always the case — founders should instead look to understand what kind of market they’re playing in. Consider how “C” (consumer)-oriented you should be, based on how supply- or demand-constrained your “B” (businesses you serve directly in your vertical) is.

The strongest B2B2C roughly follow the diagonal — their consumer investment matches their market’s constraint. Zocdoc and Affirm are highly consumer-facing because their businesses are demand-constrained: providers need patients, buyers need financing. Consumer value and / or aggregation is the moat.

At the other end, Appfolio and Playbypoint are business-first because their markets are supply-constrained: courts are hard to scale, demand for housing is high. Our point is that your B↔C positioning should be informed by whether your vertical is supply- or demand-constrained. Andres understood this instinctively — here’s how he built a strategy around it.

The Vertical Playbook: Navigating B2B2C

Step 1: Pick a Side — and Mean It

A thousand clubs means millions of players, and the B2C pitch is sexier on a fundraising deck. Andres deliberately chose not to chase it. Shifting to a consumer model would put Playbypoint in competition with its own customers — a structural misalignment that erodes trust in SMB verticals. His positioning: if the platform enables operators to monetize and retain their players, the consumer value follows. The B sells the C. That’s the sequence.

Step 2: Is Your “B” Supply-Constrained or Demand-Constrained?

Most B2B2C founders assume the business needs more demand — so they build consumer-facing discovery and lead gen. Andres discovered the opposite. Racket sports clubs are supply-constrained: courts are physical real estate, and demand outstrips capacity. At 6:59am, 200 people are queued for a 7am booking window. So Playbypoint built demand-limiting tools — capping reservations per player, enforcing membership-tier access, managing waitlists. A Mindbody would never build these because the TAM looked too small. That specificity is what makes the product irreplaceable. On the alignment map, this is why Playbypoint sits in the bottom-left quadrant alongside AthenaHealth — and why it would be a mistake to push it toward the top-right by over-pivoting on the consumer.

Step 3: Use “C”-Side Data to Make Your B Smarter

Playbypoint‘s consumer layer generates enormous data: player preferences, booking patterns, churn signals, social connections. The key is routing that intelligence to the operator, not around them. Andres is now launching a marketing and retention kit — the company’s first true add-on — that uses player data to help clubs proactively retain members. AI collapses the build time (a matchmaking algorithm that would have taken months now takes days), but the value accrues to the B. That’s what separates a real B2B2C flywheel from a pass-through.

Step 4: Monetize Through the B’s Workflow, Not the C’s Transaction

Playbypoint is 80% SaaS revenue, 20% payments — the inverse of what most vertical SaaS founders target. Andres deliberately avoided over-indexing on payments because it’s a race to the bottom. SaaS stickiness built on deep workflow — the customizable rule engine hardened across a thousand clubs — is harder to dislodge. When a competitor recently offered clubs cash to switch, Andres didn’t match. He kept compounding domain depth. The switching cost isn’t the contract; it’s the thousand edge cases baked into the system.

The Takeaway for Vertical Founders

The racket sports market has been growing wildly. From 2.5M US pickleball players to over 25M in under a decade, with a new wave emerging in padel. Playbypoint caught that wave — but only after eight years of bootstrapped grind. But when the TAM exploded, Andres’ business / consumer positioning was well-oiled. His central lesson is B2B2C discipline: pick the B, understand its constraints, use the C to make the B smarter, and resist monetization shortcuts that look good on a pitch deck but erode the relationship holding the whole model together.

Thanks for reading the Verticalist! Subscribe to our show, Verticals, wherever you watch or listen. Dive deep into Vertical AI strategy with a new founder, every Wednesday.

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The data tells a clear story: across 4,395 VC financings totaling $186B in 2025, vertical startups captured 53% of deal volume and 30% of capital deployed. That share would be even higher if not for a handful of horizontal mega-rounds: OpenAI’s $40B, Anthropic’s $13B, plus several other GPU cloud and foundation model raises. Strip out the 12 deals sized $1B+ and vertical raises took in 51% of 2025 capital.

Some new trends emerged, while others held strong. Healthcare and Financial Services—no surprise—remained the twin pillars of vertical investing, combining for nearly 1,100 deals. 2025’s foremost emergent verticals, however, were Manufacturing & Industrials, Legal, and AEC & Trades. Manufacturing saw a 41% increase in deal count from Q1 to Q4. Legal produced two of the year’s largest rounds, with Clio ($850M) and Filevine ($400M): even if neither of these companies is AI-native, both are quickly becoming AI-powered as competition scales fast. And AEC & Trades saw both early and late stage enthusiasm, anchored by CompanyCam’s $415M round that made it Nebraska’s first unicorn.

On the exit side, 2025 delivered $131.1B across 158 vertical exits—a number skewed at the top by Ansys and a handful of mega-buyouts, but broad at the base: Healthcare alone generated 43 exits worth $17.1B, and the IPO window cracked open with 18 vertical offerings including Via and Hinge Health. Including horizontal deals, 2025 exits totaled $234.9B across 254 transactions—vertical companies captured 56% of total exit value. Excluding Ansys (the $35B acquisition that threw all the numbers off), vertical liquidity was almost exactly on par with non-vertical.

This report examines 2025 in full: deal volume and capital deployment trends, vertical-by-vertical breakdowns, stage dynamics, top investors, and geographic concentration—plus profiles of twenty thematically notable vertical deals to know. We conclude this report with Euclid’s perspective on the undercurrents and structural shifts we believe will define Vertical AI in the year to come.

I. Financing Overview

The US and Canadian VC market produced 4,395 software and AI financing deals of $1M+ in 2025 (excluding non-relevant deals), deploying $186B in aggregate capital. Deal volume across quarters was relatively even: Q1 and Q2 combined for 2,271 deals (52% of the year), while Q4 saw a cooldown to 1,010 deals—the lightest quarter and an 11% decline from Q1.

Capital deployment told a different story. Q1’s $60.5B was inflated by OpenAI’s $40B Series F—strip that out and Q1 looks closer to $20B, making the quarterly trajectory even more—a pattern consistent with our thesis on vertical AI market sizing. Vertical companies captured 19% of Q1 capital, then surged to 34% in Q2 as non-vertical mega-rounds dried up, before settling at 32% in Q3 and climbing to 42% in Q4. The full-year split—$56.2B vertical versus $129.8B non-vertical—still understates the vertical story thanks to a small handful of foundation model and infrastructure rounds (OpenAI, Anthropic, GPU cloud companies) in the non-vertical capital pool. On a per-transaction basis, verticals averaged $24.0M in deal size versus $63.1M for non-verticals, reflecting the latter’s concentration in capital-intensive model training and infrastructure.

The Vertical Share Is Growing

By deal count, vertical’s share held steady in the low-to-mid 50s: 52% in Q1, 53% in Q2, 53% in Q3, and 55% in Q4. The Q4 figure—more than half of all VC deals targeting a specific industry—represents the year’s high-water mark and suggests the vertical share is still expanding.

This wasn’t a sudden shift. In 2024, vertical applications already led deal count—especially in exits, where over 80% of PE-backed software acquisitions targeted vertical companies. But 2025 confirmed the trend quantitatively: the funding market is reorienting around industry-specific applications, and the Q4 acceleration suggests this isn’t cyclical.

Deal Size Distribution

The 4,395 deals break down into five size buckets that reveal the market’s shape. $1–5M deals dominated at 1,733 transactions (39%), the earliest-stage formation layer. $5–15M rounds accounted for 1,357 deals (31%), the first scaling tier. $15–30M and $30M+ rounds made up 591 (13%) and 506 (12%) deals respectively, while $100M+ mega-rounds totaled 208 deals (5%).

Vertical companies held the majority at every deal size except $100M+, where the split was 45%/55%—essentially a coin flip driven by infrastructure and foundation model mega-rounds. The most vertical-skewed size tier was $30M+ at 58%, with $1–5M close behind at 54%. This makes intuitive sense: vertical companies build moats through industry-specific data, integrations, and domain expertise that compound over time, making them fundable at scale. At the $100M+ tier, infrastructure and horizontal AI companies (GPU clouds, foundation model labs) absorb capital at a rate that no individual vertical can match.

Quarterly momentum within size buckets tells another story. $100M+ rounds grew from 39 in Q1 to 55 in Q2, then held at 57 in Q3 and Q4—remarkably stable after the initial ramp. But the $1–5M tier—the new company formation layer—dropped from 503 in Q1 to 362 in Q4, a 28% decline. This could signal a tightening at the earliest stages, though Q1 2025 may have been inflated by deals delayed from late 2024.

II. Vertical-by-Vertical Analysis

Not all verticals are created equal. Healthcare and Financial Services are GDP-dominating, highly diverse markets with hundreds of deals each, while Agriculture and Life Sciences remain more niche. The more revealing analysis is relative momentum—which verticals are gaining share, and where is the capital concentrating?

Healthcare (597 deals, $11.8B)

Healthcare was the most active vertical by deal count and the also the largest vertical by capital at $11.8B (slightly ahead of Financial Services at $11.7B). The stage mix skewed early: 38% of Healthcare deals were in the $1–5M tier, and the median deal was $6.8M. The big stories here were clinical AI documentation (Abridge’s $316M Series E, Judi Health’s $400M Series F), value-based care infrastructure (Strive Health’s $550M Series D), and the steady drumbeat of digital health buyouts—Modernizing Medicine ($5.3B), CentralReach ($1.9B), and VaxCare ($1.7B) were among the largest healthcare exits. Healthcare also led vertical exit count with 43 transactions worth $17.1B.

Financial Services (501 deals, $11.7B)

FinServ was the second-largest vertical by count and nearly tied with Healthcare by capital at $11.7B, exemplifying the embedded fintech thesis we outlined in “Vertical FinTech.” ($500M). The vertical also produced two IPOs: Figure Technology Solutions and Wealthfront. Deal activity was remarkably consistent quarter to quarter, suggesting structural depth rather than hype-driven surges. Ramp’s back-to-back Series E rounds ($200M in June, $514M in September) at a $22.5B valuation punctuated the year, alongside Plaid ($575M) and Wealthsimple ($536M).

Emerging Verticals

Manufacturing & Industrial — 110 deals, $8.6B

Deal count rose 41% from Q1 to Q4 (22 to 31 deals), and the vertical produced the year’s second-largest financing—Project Prometheus at $6.2B. Jeff Bezos’s return to operational company-building with a “physical AI” thesis for chip packaging, automotive assembly, and aerospace is a strong signal. On the exit side, Synopsys’s $35B acquisition of Ansys was the largest vertical exit of the year. Manufacturing had the highest share of post-2022 companies (57%) of any major vertical, confirming that AI is creating a new generation of industrial startups.

Legal — 97 deals, $3.3B

Two of the year’s 15 largest vertical rounds were in legal—Clio’s $850M and Filevine’s $400M. Both are case management platforms adding AI capabilities, and both are scaling aggressively through acquisition (Clio’s $1B vLex acquisition, Filevine’s push into corporate legal). 43% of Legal deals were in the $1–5M tier, with the category gaining more share of early-stage vertical deal count from 1H to 2H than any other. Despite the maturity, a wave of new AI-native legal startups is entering the market.

AEC & Trades — 106 deals, $2.1B

The trades—construction, roofing, HVAC, plumbing—are a classic vertical software opportunity: fragmented end markets, low technology adoption, high workflow complexity. CompanyCam’s $415M round and $2B valuation (making it Nebraska’s first unicorn) is the poster child. ServiceTitan’s December 2024 IPO opened the aperture — and set the stage for a pipeline of formation-stage talent. Deals were evenly distributed across quarters (with special strength at the $5-15M deal size), suggesting sustained interest.

Other Verticals of Note

Retail & CPG — 181 deals, $2.4B

Retail saw a notable front-loading of deal activity, dropping from 53 deals in Q1 to 39 in Q4. Capital deployment showed a similar trajectory, declining from ~$700M in Q1 to ~$400M in Q4. This likely reflects both macro headwinds in consumer spending and a shift in investor focus toward B2B applications. The exits, however, were healthy: 15 transactions worth $2.7B. On the financing side, Retail gained more share of early-stage vertical deal count than any category save Legal.

Supply Chain & Logistics — 133 deals, $4.2B

Supply Chain showed continued resurgence in 2025, coming off a venture-cycle low in 2022. The per-deal average of $31.6M reflects several large growth rounds in fleet management, warehouse robotics, and freight intelligence. Q1 was the strongest quarter at 36 deals and $1.0B in capital, with a steady decline through Q4 (24 deals). The exit environment was active: 12 transactions worth $5.8B, including several large PE-backed logistics technology acquisitions.

Energy & Climate — 60 deals, $965M

Climate tech’s intersection with AI remains nascent but promising. The 60 deals included both pure-play climate software (carbon accounting, grid optimization) and AI-for-energy applications. The vertical’s modest capital figure reflects the prevalence of early-stage deals—most Energy & Climate companies are still raising sub-$15M rounds. But the opportunity is real: as AI’s energy demands grow, expect a feedback loop between energy tech and compute to drive larger rounds in 2026.

III. Stage Dynamics

The $1–5M tier—the earliest-stage layer—dominated every vertical, but the concentration varied meaningfully—a pattern we’ve explored in “What’s My Stage Again?”. Education had the highest $1–5M share at 56%, followed by Real Estate and Hospitality and Agriculture at 50%. Healthcare, by contrast, had one of the lower early-stage shares at 38%, reflecting the category’s relative maturity—more companies are raising larger rounds as they scale.

The $30M+ and $100M+ tiers are where vertical differences sharpen. Financial Services had the highest $30M+ share at 16%, reflecting a deep bench of growth-stage fintech companies. Legal’s 9% $100M+ share—the highest outside of Financial Services—reflects outsized Clio and Filevine rounds. AEC & Trades showed an unusual distribution: 42% of deals fell in the $5–15M bucket (the highest of any vertical), suggesting the category may be moving beyond the earliest stages of vertical AI formation and into more mainstream legibility.

One of the most telling stage signals is the “newness” of each vertical’s company base. We looked at the share of funded companies founded in 2022 or later—the post-ChatGPT cohort. Manufacturing leads at 57%, followed by Public Sector (56%) and Legal (53%). These are verticals where generative AI created net-new startup formation, not just incremental improvement to existing companies. The cross-market baseline sits at roughly 46%, meaning these three verticals have meaningfully higher shares of post-ChatGPT companies than the broader market.

The deal size distribution also reveals which verticals have deep, multi-layered ecosystems versus those still in their earliest innings. Healthcare’s balanced profile (38/31/15/13/3) is the hallmark of a mature vertical with companies at every lifecycle phase. Financial Services is similarly broad, with the highest $30M+ concentration (21%) of any major vertical—the fintech wave of 2019–2021 produced companies now reaching growth stage. Conversely, Real Estate’s 51% concentration at $1–5M and 0% at $100M+ signals a vertical still in early formation: lots of new entrants, few scaled incumbents.

The quarterly stage data surfaces one additional insight: the $100M+ tier grew from 39 deals in Q1 to 55 in Q2 and held at that level through year-end. This suggests that mega-round activity—often viewed as frothy—actually reflected persistent demand at the growth stage, driven in part by sovereign wealth funds, asset managers, and crossover investors entering AI for the first time. The smaller deal tiers, by contrast, showed more cyclical behavior, with the $1–5M bucket declining 28% from Q1 to Q4.

A few additional analyses help us shed light on how vertical companies deploy capital across sectors and stages. Looking at average deal size per employee (FTE)—a proxy for capital intensity—Aerospace & Defense leads ($476K), reflecting the R&D-heavy nature of the category. Manufacturing & Industrial follows at $421K, consistent with the physical-world infrastructure requirements of industrial AI. Education, raising on average less than $200K per employee in 2025 rounds, was the leanest, consistent with its lighter distribution model and lower customer acquisition costs (and perhaps also speaking to lower investor demand, despite great rounds from breakouts MagicSchool and SchoolAI).

Median valuation step-ups—the post-money markup from one round to the next—reveal which verticals command the strongest investor conviction. Aerospace & Defense leads overall at 2.15x, followed by Manufacturing at 2.08x and Real Estate at 1.96x. At the early stage ($5–15M tier specifically), Education shows the highest step-up at 2.5x. Financial Services, despite attracting the most capital, shows a more modest overall step-up at 1.80x—suggesting that larger round sizes dilute the per-round markup even as total value creation remains strong.

Capital velocity—measured here by median dollars raised per year since founding—aims to capture the appetite for capital vertical startups demonstrate as they scale. Life Sciences, unsurprisingly, leads at $4.8M per year. Closely following—perhaps somewhat counter-intuitively—are Media & Entertainment at $4.5M and Agriculture at $4.2M. Education trails at $1.6M, consistent with its smaller round sizes and more measured growth trajectories.

Capital Efficiency—measured here by median dollars raised per year FTE—captures how much leverage a business has created on its financing to date. While imperfect—given it does not capture the ebbs and flows of spend over time—we can see that Life Sciences dominates, with A&D and Agriculture a close 2nd / 3rd. In comparison to Capital Intensity, this suggests greater investment requirements over time rather than immediate investor demand. As with both Velocity and Intensity, Education trails at just $342K raised per employee.

IV. Momentum: Stage-Level Trends

While the aggregate vertical share—53% of deals, 30% of capital—is a useful headline, we wanted to drill down into comparative dynamics at the stage level. By segmenting deals into five size buckets ($1–5M, $5–15M, $15–30M, $30M+, $100M+) and tracking vertical share by quarter, a more nuanced picture emerges: vertical AI has decisive early-stage and Series A / B momentum, but has yet to generate the volume of mega-deals that allow it to consistently maintain parity with horizontal at growth.

The $1–5M tier—the domain of pre-seed and seed—showed the strongest vertical momentum of any bucket. Vertical share rose from 53% in Q1 to 60% in Q4, an 8-percentage-point gain that represents the most significant forward-looking signal in this dataset. When three out of five new venture-backed companies are building for a specific industry, it says something about where founders see opportunity. The post-ChatGPT generation is not building general-purpose tools—they are building AI for healthcare, legal, construction, manufacturing, and logistics.

Moving to seed / Series A: the $5–15M tier ranged from 47% in Q1 to 58% in Q3, while $30M+ held between 56% (Q2) and 60% (Q4). Neither showed a clear directional trend. This stability suggests that once vertical companies graduate to their $5–15M+ round, the competitive dynamics at that stage are relatively balanced between vertical and horizontal players. The $15–30M tier was comparatively volatile—59% vertical in Q1, dropping to 41% in Q2, then recovering to 54% in Q4. The Q2 dip likely reflects a burst of horizontal AI activity versus any sort of vertical pullback.

The most striking divergence was at $100M+. Vertical share fell from 51%, an 18-percentage-point swing that represents the only bucket where non-vertical companies decisively overtook vertical during the year. This is almost entirely explained by the concentration of mega-rounds in foundation model companies, horizontal AI platforms (OpenAI, Anthropic, xAI, Databricks), and infrastructure providers that raised capital-intensive rounds in the back half of 2025. At every other tier, vertical maintained or expanded its lead.

Our momentum data produces a clean narrative: vertical making leaps and bounds at formation ($1–5M), holding steady through the scaling stages ($5–30M), and ceding ground only at the mega-round level ($100M+) where the magnitude of horizontal AI raises distorts the picture. For investors focused on the next cycle, the $1–5M signal is the one that matters most—if it takes 5–7 years for an early-stage cohort to reach maturity, the companies being formed today will define the vertical AI landscape of 2030–2032. We expect 2026 to be another year of prolific vertical AI formation.

V. Financing Highlights

Below, we pull out 15 highlights of vertical businesses that raised in 2025—we selected some from the biggest deals above, and mix in others we think are most interesting. Our profiles range in deal size from $190M (Peregrine) to by far the biggest deal at $6.2B (Project Prometheus). Healthcare dominated the list with five companies (in line with its 30% share of the top 20). Legal and Manufacturing & Industrial also placed multiple highlights. Five of the fifteen are first or second rounds, suggesting that new company formation at massive scale is accelerating in verticals where the AI opportunity is freshly legible. Here are the deals we found most revealing, and characteristic of 2025:

Project Prometheus — $6.2B (Mfg. & Industrial, Q4)

Jeff Bezos’s return to operational company-building made headlines, but the thesis is what matters: “physical AI” for manufacturing, targeting chip packaging, automotive assembly, and aerospace operations. Co-CEO Vik Bajaj brings a career that spans Google Life Sciences (now Verily), GRAIL, and Foresite Capital—an unusual background that fuses deep science with operational scale. The team has pulled over 100 hires from DeepMind, Tesla, and OpenAI. At $6.2B, this is the largest-ever first financing for a vertical AI company—and it’s not close. It’s larger than the next four deals on this list combined. The round is a statement that the next wave of AI value creation will happen on factory floors, not in SaaS dashboards, and that the market for physical-world AI is large enough to justify deployment-stage capital before a single dollar of revenue.

Clio — $850M Series G (Legal, Q4)

Clio’s round ($500M equity + $350M debt) funded the simultaneous $1B acquisition of legal research platform vLex, creating an end-to-end research-to-operations platform at a $5B valuation. Founded in 2007 by Jack Newton and Rian Gauvreau, Clio was the first to commercialize cloud-based law practice management when on-premises systems dominated. The combined deal represents both the largest legal tech financing and the largest legal tech M&A transaction in history. What stands out in the data: at 5x revenue and a 1.38x step-up from its prior round, this is a mature, capital-efficient business using leverage strategically—the $350M debt component signals confidence in predictable cash flows. With 1,672 employees and 17 years of compounding, Clio is the anti-hype case study: a vertical SaaS company that defined its category long before AI became a funding catalyst, and is now using AI-era capital to consolidate it. NEA’s Tony Florence led the round alongside Goldman Sachs, TCV, Sixth Street, and JMI Equity.

NinjaOne — $500M Series C Extension (ITSM, Q1)

NinjaOne’s $500M Series C extension at a $5B valuation—a 2.37x step-up from $1.9B just twelve months prior—is the kind of round that reveals the power of vertical focus in what looks like a horizontal market. Co-founded in 2013 by childhood friends Sal Sferlazza and Chris Matarese, the company built a cloud-native endpoint management platform for IT teams and MSPs at a time when incumbents like Kaseya and ConnectWise were running on legacy architectures. The raise, led by ICONIQ Growth and CapitalG (Alphabet‘s investment arm), also funded the $262M acquisition of SaaS backup leader Dropsuite—a buy-and-build move that mirrors Clio’s consolidation strategy. Notable in the data: NinjaOne is one of only two companies on this list based in the South (alongside Fleetio in Birmingham), it has zero debt, and it remains founder-led and controlled. With over 35,000 customers across 140+ countries and 2,000 employees, this is a vertical platform executing a horizontal expansion playbook.

Fleetio — $454M Series D (Supply Chain & Logistics, Q1)

Fleetio’s $454M Series D—co-led by existing investor Elephant Partners and new investor Goldman Sachs Growth Equity—valued the combined business at over $1.5B and funded the simultaneous acquisition of Auto Integrate, the premier maintenance authorization platform in North America. The combined entity now services over 8 million vehicles and processes more than 13 million repair orders per year through 110,000+ repair shops. Fleetio was founded in 2012 in Birmingham, Alabama by Tony Summerville, with CEO Jon Meachin (Brown undergrad, Morgan Stanley and Parthenon Capital alum) running the business. This deal stands out for its 2.36x step-up and the sheer physical scale of the network it acquired. While most vertical AI companies on this list are primarily selling software or AI, Fleetio is building a two-sided marketplace for fleet maintenance—a structural asset that gets harder to replicate with every shop that joins the network.

CompanyCam — $415M Series C (AEC & Trades, Q3)

CompanyCam is a story of pure founder-market fit. Luke Hansen built the platform out of his family’s roofing business in Lincoln, Nebraska, solving the problem of photo documentation and job site coordination for contractors. The $415M round from B Capital valued the company at $1.99B—Nebraska’s first-ever unicorn. At a 3.21x step-up, this is one of the highest valuation jumps on the list, suggesting rapid revenue acceleration. The investor roster reads like a conviction trade: Insight Partners, JMI Equity, WndrCo, and Blueprint Equity all participated alongside B Capital. CompanyCam represents the broader AEC thesis—fragmented industries with low tech adoption, where mobile-first AI tools can compress workflows that previously ran on clipboards and phone calls. With 308 employees, it’s also one of the leanest companies on the list relative to its valuation, implying strong unit economics.

Filevine — $400M Series F (Legal, Q3)

Filevine’s $400M Series F at a $5.25B valuation makes legal tech the only vertical to place two companies in the top six deals of 2025. Co-founded in 2014 by Nathan Morris, Filevine provides case management and workflow automation software for law firms, with a particular strength in litigation and personal injury practices. Accel and Insight Partners co-led the round alongside The Halo Fund. The 1.74x step-up is more modest than CompanyCam‘s or Ambience’s, but the absolute valuation—$5.25B on its 8th round—places it alongside Clio as proof that legal tech has matured from a niche into a major vertical software category. The deal is also notable for its investor composition: Meritech Capital, StepStone Group, and Album VC joined, bringing a mix of crossover and institutional capital that typically signals pre-IPO positioning. With 700 employees in Salt Lake City, Filevine is a reminder that vertical unicorns don’t have to be in San Francisco.

Abridge — $316M Series E (Healthcare, Q2)

Abridge raised its Series E just four months after a $250M Series D, reaching a $5.3B valuation with an implied EV/Revenue multiple north of 697x. CEO Shivdev Rao is a practicing cardiologist who co-founded the company with CMU researchers Florian Metze and Sandeep Konam. The platform transcribes and summarizes clinical conversations, producing documentation, patient summaries, and educational content. Andreessen Horowitz led the round alongside Khosla Ventures, WndrCo, and newcomers Archerman Capital and the California Health Care Foundation. What stands out: the 0.35-year gap between rounds is the shortest on the list, suggesting either extraordinary growth or a competitive capital raise intended to lock out competitors like Ambience Healthcare. At $7.60M in revenue and 488 employees, Abridge’s sky-high multiple reflects the market’s belief that clinical AI documentation is a winner-take-most category—and the willingness to fund the race at nearly any price.

Periodic Labs — $300M Seed (Mfg. & Industrial, Q3)

Periodic Labs emerged from stealth with what may be the largest seed round in history—$300M led by Andreessen Horowitz at a $1.3B pre-money valuation. Co-founded by Liam Fedus, a former VP of Research at OpenAI who co-created ChatGPT, and Ekin Dogus Cubuk, who led the materials and chemistry team at Google DeepMind (including the GNoME project that discovered 2 million new crystals), the company is building “AI scientists” paired with autonomous robotic laboratories. The investor list reads like a tech hall of fame: Jeff Bezos, Eric Schmidt, Jeff Dean, Elad Gil, NVIDIA’s NVentures, Accel, DST Global, Felicis, and more. The thesis is that the internet’s text corpus is exhausted as training data; the next frontier is proprietary experimental data generated by AI running physical experiments. Periodic is targeting superconductors first, with early commercial work on chip heat dissipation for a semiconductor manufacturer. Among the 15 deals, this is the most audacious bet on founder pedigree over business traction—a $300M check written on a company founded in 2025 with no revenue and roughly 100 employees.

Ambience — $243M Series C (Healthcare, Q3)

Ambience Healthcare’s $243M Series C at a $1.25B valuation represents a 3.47x step-up—the second-highest on this list behind Peregrine‘s 6.58x. Co-founded in 2020 by Michael Ng (MIT Sloan, ex-Morgan Stanley, ex-Calera Capital), Ambience builds an AI-powered medical scribe that captures clinician-patient conversations and generates clinical notes in real-time, embedded directly into EHR workflows. Andreessen Horowitz’s Julie Yoo and Oak HC/FT‘s Vignesh Chandramouli co-led the round, alongside a deep healthcare-specialist syndicate: Sequoia, Kleiner Perkins, General Catalyst, OpenAI Startup Fund, and Optum Ventures. This is one of two clinical documentation companies on the list (alongside Abridge), highlighting the intensity of competition in a category where switching costs are high and health system contracts are sticky. At 200 employees and four years old, Ambience is executing a faster land-and-expand playbook than Abridge, with institutional health system partnerships as its primary channel.

Forterra — $238M Series C (Aerospace & Defense, Q4)

Forterra’s $238M Series C, led by Moore Strategic Ventures, positions the company as the leading autonomous ground vehicle platform for U.S. defense. Originally founded as Robotic Research in 2002 by Alberto Lacaze, the company rebranded under CEO Josh Araujo (ex-Jefferies, ex-Lazard, ex-USMC infantry officer). Forterra builds self-driving military land systems and robotic swarm coordination platforms. The round came on the heels of a landmark streak of government contracts: the first-ever DoD ground autonomy production contract (ROGUE Fires with the Marine Corps), a $114M prime contract for autonomous breaching systems, and the Army’s GEARS and UxS programs. Forterra also acquired tactical mesh networking company goTenna, adding a communications layer to its autonomy stack. Among the 15 deals, Forterra is the oldest company by founding date (2002) and the most evolved from a business-model standpoint—it has real production contracts and program-of-record revenue, not just R&D grants. The investor syndicate (Salesforce Ventures, Franklin Templeton, Hanwha Asset Management, NightDragon) reflects the growing crossover between defense tech and institutional capital.

Teamworks — $235M Series F (Media & Ent., Q2)

Teamworks’ $235M oversubscribed Series F, led by returning investor Dragoneer, pushed the company past a $1B pre-money valuation—minting a unicorn in the sports technology vertical. Founded in 2004 by Zach Maurides, a Duke offensive lineman who built the first version as a sophomore-year class project to manage his own chaotic practice schedule, Teamworks has grown into what it calls “the Operating System for Sports,” powering over 6,500 elite teams globally across professional leagues, NCAA programs, Olympic sports, and military organizations. The round is a combination of primary and secondary capital. What makes Teamworks unusual on this list is its acquisition-driven expansion: the company has completed 13 acquisitions (nine in the past three years alone), including INFLCR (NIL content), ARMS Software (compliance), Smartabase (performance), Zelus Analytics (predictive intelligence), Telemetry Sports (computer vision for football, used by 80% of NFL teams), Opteamal (athlete monitoring), and most recently Sportlogiq (AI-powered hockey analytics, used by 97% of NHL teams). The Sportlogiq deal alone brought 80 employees and 10 AI researchers with 180+ published papers. This is a roll-up strategy executed with venture capital rather than private equity—rare in vertical SaaS. At ~450 employees across 11 countries and headquartered in Durham, NC, Teamworks is a textbook founder-market-fit story: Maurides understood the coordination problem in athletics from the inside and has spent two decades compounding that insight into a platform that spans talent acquisition, player development, game preparation, and operations. The $411M raised to date and Series F stage also make it one of the most mature businesses on this list.

Lila Sciences — $235M Series A (Life Sciences, Q3)

Lila Sciences’ $235M Series A (part of a broader $350M close that brought total funding to $550M) valued the company at $1.26B and represents the second “AI for science” bet on this list alongside Periodic Labs. Incubated at Flagship Pioneering—the venture studio behind Moderna—Lila was founded in 2023 by Geoffrey von Maltzahn, a Flagship General Partner with a PhD in biomedical engineering. Braidwell and Collective Global co-led, with NVIDIA‘s NVentures, In-Q-Tel (the CIA’s venture arm), Analog Devices, and institutional LPs including the Abu Dhabi Investment Authority and State of Michigan Retirement System participating. While Periodic Labs targets materials science through autonomous physical experiments, Lila is focused on life sciences and chemical discovery through what it calls “AI Science Factories”—autonomous lab facilities where AI agents design, execute, and iterate on experiments. In-Q-Tel’s participation signals national security interest in accelerating materials discovery for computing, energy, and infrastructure. At 195 employees, Lila has demonstrated early results including novel protein therapeutics, green hydrogen catalysts, and carbon capture sorbents.

OpenEvidence — $210M Series B (Healthcare, Q3)

We would have included OpenEvidence’s $210M Series B at $3.5B valuation on this list even if it hadn’t more than tripled its valuation with further raises in the interim. Founded in 2017 by Daniel Nadler—whose previous company Kensho was acquired by S&P Global for $700M in 2018—OpenEvidence built an AI-powered medical search engine that has been adopted by over 40% of U.S. physicians, processing 8.5 million consultations per month. Google Ventures and Kleiner Perkins (with Chairman John Doerr personally involved) co-led; Sequoia, Coatue, Conviction, and Thrive followed on. The data tells the story: at a 35x EV/Revenue multiple on $100M of revenue, this is the most capital-efficient company on the list by a wide margin. Its implied valuation-to-revenue of 35x compares to Abridge‘s 697x. The 3.35x step-up and a mere 36 employees make OpenEvidence the leanest operation here—fewer than one-fifth the headcount of most peers. The freemium model (free for verified physicians, monetized through advertising) bypassed traditional enterprise healthcare sales cycles entirely. Nadler’s TIME100 Health recognition and partnerships with the AMA, NEJM, and JAMA Network suggest a platform with both distribution and credibility moats. With only 0.4 years between its Series A and B, OpenEvidence is on a trajectory that has since seen it raise a $200M Series C at $6B and a $250M Series D at $12B.

Commure — $200M Series E (Healthcare, Q2)

Commure’s $200M Series E, led by General Catalyst, is the infrastructure play in an otherwise application-heavy healthcare contingent. Co-founded in 2017 by Tanay Tandon, Commure builds an interoperable data platform that unifies fragmented healthcare datasets—an integration layer that sits beneath the clinical applications companies like Abridge and Ambience are building on top. This is Commure‘s 9th round and the data shows a raised-to-date figure of $2.1B—by far the most cumulative capital of any healthcare company on this list. The 0.47-year gap between rounds suggests continuous capital deployment. With 492 employees in Mountain View, Commure occupies a different strategic position than the clinical AI documentation companies: it’s a platform bet on healthcare data infrastructure rather than a point-solution bet on any single clinical workflow. General Catalyst‘s Pranav Singhvi led alongside LTS Growth, Maverick Ventures Israel, and Shasta Ventures.

Peregrine — $190M Series C (Public Sector, Q1)

Peregrine’s $190M Series C, led by Sequoia Capital‘s Andrew Reed, valued the company at $2.56B—a staggering 6.58x step-up, the highest on this list by a factor of two. Co-founded in 2018 by Nick Noone, a former Palantir executive who headed the U.S. Special Operations (SOCOM) business unit, Peregrine builds a data intelligence platform for law enforcement and public safety agencies. The company was built while embedded inside the San Pablo Police Department, and its platform was deployed for Super Bowl LIX security coordination in New Orleans. The growth metrics are striking: revenue reportedly tripled from $3M to $10M in 2023, then tripled again to $30M in 2024. Agencies using the platform have reported measurable outcomes—a 40% decrease in open homicide cases in Albuquerque, a 21% reduction in violent crime in Atlanta. At 311 employees and just $63M raised prior to this round, Peregrine‘s capital efficiency is notable. The 6.6x step-up reflects both the growth rate and the scarcity premium of govtech companies that can actually land and expand across fragmented municipal and state agencies. Peregrine is the only public sector company on this list, and the only one with Palantir DNA in its founding team.

Themes Across the Top Deals

Four patterns recur across the year’s most notable financings. First, the convergence of AI and physical-world operations: Project Prometheus, Periodic Labs, Lila Sciences, Forterra, CompanyCam, and Fleetio all apply AI to industries where the value sits in atoms, not just bits: factory floors, autonomous ground vehicles, robotic labs, or repair shop networks. Physical-world companies account for a substantial share of the list.

Second, these highlights underscore the power of vertical founder-market fit. Shivdev Rao (cardiologist building clinical AI), Luke Hansen (roofer building construction tech), Jack Newton (17 years building legal tech), Zach Maurides (Duke offensive lineman building sports operations tech), Nick Noone (Palantir SOCOM lead building public safety tech)—most successful vertical AI companies are led by people who understand the industry’s workflows from the inside, or who bring a directly transferable prior success.

Third, the capital advantage of category creation: companies with a strong claim to dominating a core function or major segment within their vertical—Clio and Filevine in legal, Abridge and Ambience in clinical documentation, OpenEvidence in medical search, Peregrine in public safety intelligence—attract capital on fundamentally different terms than companies competing in crowded horizontals. Multiple deals on this list feature step-ups above 3x, a signal that investors are paying for category leadership and the expansive terminal value that can accompany it.

Fourth, a new theme emerged in 2025: the AI-for-science mega-round. Periodic Labs ($300M seed) and Lila Sciences ($235M Series A) represent a conviction that the next source of valuable AI training data isn’t the internet—it’s proprietary experimental data generated by autonomous laboratories. Both companies raised at $1B+ valuations with minimal revenue, staking their cases entirely on team pedigree and the structural argument that physical-world data is the new moat. This is a category that didn’t exist on last year’s list, and it may define the next cycle.

VI. Exits

The 2025 exit market produced 158 vertical transactions worth $131B, headlined by Synopsys’s $35B acquisition of Ansys. While M&A dominated with nearly two-thirds of transactions by number, liquidity generated by the category ($89.4B) trailed Buyout/LBO ($90.0B) marginally. The year saw vertical 18 IPOs, composing $4.3B in aggregate deal value.

Including non-vertical exits, the total market was $235.8B across 254 transactions—vertical companies captured 56% of total exit value. Excluding Ansys, vertical share of value was 49%.

The quarterly pattern was uneven. Looking at all deals: Q1 produced the most exit value at $73.8B (thanks in large part to the X merger), while Q3 generated the highest exit count at 74 transactions. Q4’s combination of 54 exits and $60.8B in value included the Groq acquisition and several large PE buyouts, suggesting the exit environment was accelerating rather than cooling as the year ended. Vertical share over the course of the year was volatile by quarter, swung one way or another depending on mega-deals or lack thereof. Vertical had a dominant share in Q2 and Q3 (73%, 89%), while capturing barely over a third of value (35%, 36%) in Q1 and Q4.

Manufacturing & Industrial led exit value at $67.3B, driven by Ansys ($35B) and Altair Engineering ($10.5B). Healthcare generated the most exit activity with 43 transactions worth $17.1B, led by Modernizing Medicine ($5.3B), CentralReach ($1.9B), VaxCare ($1.7B), and Iodine Software ($1.3B). Financial Services contributed 25 exits worth $14.1B, led by NinjaTrader ($1.5B), Enfusion ($1.5B), and Hidden Road ($1.3B).

The IPO Window

After a two-year drought, the IPO market reopened meaningfully in 2025 with 24 total relevant offerings, driving $12.3B in value. Including both vertical and horizontal companies: Figma ($1.2B) and Circle ($1.1B) led on the horizontal side, while vertical standouts included Figure Technology Solutions ($788M, Financial Services), Via Transportation ($493M, Public Sector), Hinge Health ($437M, Healthcare), and MNTN ($187M, Media & Entertainment). The diversity of verticals—and a strong thread of B2C usage / B2B monetization—suggests an IPO market with a high bar for traditional software or consumer tech business models.

Exit Path Trends

Buyout/LBO transactions accounted for 20% of exits but a disproportionate share of value. Silver Lake ($4.5B Altera), EQT ($3.0B Neogov), and TPG ($1.1B SynXis) were among the most active acquirers. This confirms a continuing pattern: PE firms view vertical software as ideal targets—recurring revenue, defensible market positions, and operational leverage from AI integration. Over 80% of PE-backed software acquisitions in 2024 were vertical; that trend appears to have continued through 2025.

The most revealing exit metric may be the simplest one: the median vertical exit in 2025 was approximately $500M. For the many seed and Series A funds that have scaled up AUM since 2021, driving superlative returns via such median outcomes demands discipline on entry price that seems quite rare these days. At a $10-20M post-money seed, a $500M exit represents 25-50x gross valuation appreciation—exceptional. At a $50M+ post-money “seed” (increasingly common in consensus Vertical AI), the same exit is 7-8x gross. Still great, but tough math for a >$100M AUM fund.

The top 10 exits by value in 2025 accounted for over 60% of total vertical exit value—a reminder that no category escapes power-law dynamics. Below the mega-deals, PE remains a leading exit path and IPOs remain selective — the “middle market” of vertical exits ($250–1B EV) is arguably the healthiest and most under-appreciated segment of the liquidity environment, at least in VC land.

Top Exits & Other Trends

The quarterly exit cadence showed meaningful variation. Q3 led with 74 exits and $72.2B in value, while Q4 produced 54 exits and $60.8B. There was a boom in Healthcare exits mid-year: 12 in Q2, 19 in Q3, but just 7 and 5 in Q1 and Q4, respectively. Financial Services saw its own 1H concentration, with 17 exits versus 8 in the back half of the year. Along with Ansys, Manufacturing & Industrial had a spike in Q3—with 6 exits, half of its 2025 deals closed in that quarter.

The exit size distribution is worth noting. The top 10 exits by value accounted for over 60% of total vertical exit value, reflecting the power-law dynamics inherent in software M&A. Below the mega-deals, the median vertical exit was approximately $500M—large enough to generate meaningful returns for growth-stage investors but small enough that PE firms remain the natural buyers. The middle market of vertical exits is arguably less dependent on public market conditions or strategic M&A cycles.

VII. The Investor Landscape

General Catalyst was the most active lead investor in vertical deals in 2025, leading 24 round representing $1.8B in value. Andreessen Horowitz followed closely at 22 leads over $1.9B worth of rounds, spanning Healthcare (Abridge), Legal (Clio), and Financial Services. WiL (19 leads) and Khosla Ventures (17 leads) rounded out the top four, with Khosla maintaining its earlier-stage focus at a $40M average deal size. Across the top 15, NEA’s average deal was largest by a wide margin at $154M, while First Round Capital had the lowest average deal at $11M.

At the $1–5M tier, Y Combinator was the most active at the earliest stage of vertical financing (7 leads), alongside Gradient (5) and HealthX (5). European-based funds also has a strong presence here, with Pi Labs and Nina Capital both clocking 4 leads.

The $5-15M band was anchored by First Round Capital with 9 vertical deals led in 2025, those startups capturing over $100M in aggregate. Next were Susa, Madrona, and Menlo, with 6 leads each, followed closely by Pear and Costanoa with 5 each.

In the $15-30M bucket, Base10 was the unequivocal vertical leader, with 11 leads at an average deal size of $18M. noa—the sole European investor in the top 15—was the next-active here with 8 leads. SignalFire—the third-place VC in this band with 7 leads last year—stood out with the highest average deal size of the cohort ($30M), and over $200M captured by its investees. Honorable mentions include Bonfire (4) and Building Ventures (4).

Moving to Series A and beyond—deals $30-100M in value—it’s perhaps surprising to see that a16z did not take first place in the vertical league table. That honor went to General Catalyst, with 24 leads (though a16z closely trailed with 22). Companies backed by these two firms raised $3.8B in total in 2025. The next tier of prominently vertical investors included Khosla (17), Insight (14), Bessemer Venture Partners (13), and Accel (13). While Sequoia may not have been as prolific this year (7 vertical leads), they registered the largest average vertical deals ($94M), with a16z and IVP close behind.

Late-stage vertical ($100M+) saw six multi-deal leads, all with a relatively tight mean deal size in the $110-180M range: NEA (10), Kleiner Perkins (7), Oak HC/FT (5), B Capital (5), DST Global (4), and Ribbit (3).

Concentration patterns within specific verticals are revealing. In Healthcare, a16z, General Catalyst, and Khosla accounted for a combined 18 of the top 50 lead positions. In Manufacturing & Industrial, a small number of growth-stage investors (including Coatue, Tiger Global, and GIC) drove a disproportionate share of capital through mega-rounds. Financial Services was the most fragmented—no single investor led more than 4% of deals, reflecting the vertical’s maturity and the breadth of sub-categories (e.g. payments, lending, insurance, wealth management, compliance).

The most popular verticals amongst this group of VCs was — no surprise — Healthcare (55) and Financial Services (38), together representing nearly half of the total deal lead count. Interestingly, Supply Chain and AEC & Trades were next, composing another ~14% of lead rounds. Most of the rest of the verticals fell in the 2-5% range, with Retail & CPG, Manufacturing & Industrial, Public Sector, Education, and Insurance clocking 5+ (and in that order of incidence).

Corporate venture activity in vertical AI also warrants mention. NVIDIA Ventures, Salesforce Ventures, and Google Ventures were active across multiple verticals. Perhaps more interesting was industry-specific CVC activity: health systems (Mayo Clinic, Kaiser) investing in clinical AI, financial institutions (Goldman Sachs, JP Morgan) backing fintech infrastructure, and energy companies (Shell, BP) funding climate tech.

A final interesting 2025 pattern was the (re-)emergence of non-traditional leads in big vertical rounds. BlackRock co-led Applied Intuition’s $600M round. Franklin Resources (Franklin Templeton) led Plaid’s $575M financing. Qatar Investment Authority participated in Applied Intuition. Mubadala co-led Crusoe’s $1.4B raise. While asset managers in pre-IPO rounds is familiar (reminiscent of 2021), the uptick of sovereign wealth involvement is perhaps more notable — especially in light of their parallel interest in backing US VC funds as LPs. Along with growing federal embrace of AI and defense startups in the US, the intersection of government and technology warrants continued focus in 2026.

VIII. Geographic Distribution

Vertical deal activity in 2025 was concentrated in established tech hubs but showed meaningful geographic breadth. California led with 667 vertical deals (29% of all vertical activity), followed by New York at 406 deals (17%). Massachusetts (119), Texas (110), and Florida (98) rounded out the top five. Washington, Canada, and Illinois each contributed 60–100+ vertical deals, reflecting the nationwide diffusion of vertical software.

The more revealing metric is vertical share by state. Among states with meaningful deal volume (30+ deals), industry-proximate states dominated: Arizona (71%), Ohio (70%), and Tennessee (67%) posted the highest vertical shares. Illinois (59%), Colorado (55%), and Florida (54%) all exceeded the 55% average reference line. The most striking finding: California, despite leading in absolute deal count, had the lowest vertical share of any major state at just 41%—reflecting its deep concentration of horizontal AI infrastructure, foundation model, and developer-tooling companies. New York (53%) and Massachusetts (54%) tracked near the mean.

Examining vertical share by region uncovers some additional interesting findings. The Midwest leads in terms of vertical proclivity (59.6% of its deals). It joins a relatively tight high-mid-fifties grouping with the Mid-Atlantic, Mountain West, and Southeast. Overall variance between regions is actually relatively low, with Canada and the West representing the two greatest points of contrast.

Canada’s lower vertical leaning is somewhat surprising, given Toronto’s outsized vertical presence (the home of Constellation Software since 1995). We believe, however, that the country’s industry-specific focus is concentrated in that one metro—and that much of its “vertical reach” involves investment in US-based companies.

Finally, it shouldn’t come as a surprise that the West trails in vertical share by region, with a paltry 41.3%. As a global Mecca of technical talent—frontier AI and otherwise—the Bay Area drives the predominance of infrastructure, dev tooling, and foundation model / lab financing. It’s important to remember, however, that the clear plurality of vertical deals are still inked in California (667 in 2025). New York follows behind, with 406 deals. These two are the clear leaders by vertical deal volume, with a yawning gulf between them and the next tier: Massachusetts with 119 and Texas with 110.

In our view, San Francisco and New York City—considering volume, concentration, and subjective deal quality—remain the twin dominant vertical hubs, with strong and growing tailwinds behind the latter. Non-coastal hubs have the highest verticality. But whether they are as quick to embrace AI-native approaches at the formation stage as the hubs remains to be seen.

IX. What to Watch in 2026

From our vantage point at Euclid, a handful of structural dynamics from 2025 set the table for the vertical VC and startup landscape in 2026:

The Seed-to-A graduation test

In 2025, the goalposts for Series A shifted. Seeds now look like what Series As did 5-10 years ago. The result has been widening gap between seed and A, with contracting graduation rates between then. Seed-stage vertical AI companies raised at a historic clip in 2023–2025, but many will hit this gap in 2026–2027: strong product, real customers, not yet at the milestones today’s “true Series A” investors require. The sectors with the highest formation rates (Manufacturing, Legal, Public Sector, AEC) may end up being the most exposed.

We expect capital supply to adjust, but unevenly, as capital supply (VC funds and allocators) responds. That likely involves a drawdown in the current over-concentration of capital at “mature seed,” where too many funds compete for the same $3–8M checks—that competition has led to GPs looking earlier to get their ownership, and thus too many companies over-funded for their stage. In 2026, we expect many of them to hit that wall—perhaps creating great opportunity for VCs with “tweener” strategies, but also creating significant risk for the tier of excellent companies that haven’t hit exit velocity prior to the few-million-ARR mark.

Deep tech’s maturity cycle

Project Prometheus ($6.2B), Figure AI ($1.5B), and Applied Intuition ($600M) collectively raised $8.3B in 2025 for companies building AI that interacts with the physical world. They aren’t outliers—Periodic Labs raised $300M for autonomous robot-driven research facilities, Thinking Machines Lab raised $2B for scientific AI tools, and Physical Intelligence pulled in $1.1B for robotic foundation models. In total, robotics startups raised over $6B in just the first seven months of 2025, with capital concentrating into fewer, larger rounds. Manufacturing & Industrial led in share of 2025-funded companies founded 2022+, with a 57% share. Figure alone went from a $2.6B valuation to $39B in 18 months, while its robots were still performing controlled demos at a single BMW plant.

There’s an important distinction between hardware companies and Vertical AI as we define it. While it may be an outmoded term, Vertical AI is defined by the software layer, operating on fundamentally better economics than hardware businesses: lower capital intensity, faster iteration cycles, higher gross margins, and the compounding moat of domain-specific data. Physical AI is a multi-trillion-dollar opportunity that extends across the economy and will change the world as we know it over decades. In the short term, “deep tech”—at least, the version that conflates hard with valuable—is looking overextended. In the long run, we’re bullish on the vertical software layer bringing AI to physical action.

Shifting winds of liquidity

With Blackstone, Thoma Bravo, Clearlake, and Vista all executing multi-billion dollar vertical buyouts in 2025, the PE-led exit environment for mature vertical SaaS companies is robust. That cycle’s health depends on interest rates and debt markets, but the demand side is proven—and we expect that to continue to ramp.

Perhaps the bigger question, pertinent to the health of the venture capital ecosystem, is public market liquidity. 2025’s IPOs were a start, not the floodgate-opening many are waiting for. The backlog of late-stage vertical companies—many of which raised $200M+ rounds in 2025—will likely face go / no-go decisions on public listings in 2026–2027. Next year specifically, the vertical IPO pipeline looks sparse: beyond EquipmentShare’s January debut, Plaid is the only highly-rumored candidate of note, although there are many potential entrants. Overall, we estimate the IPO narrative in 2026 to be dominated by horizontal infrastructure & AI (e.g. Databricks, Anthropic, OpenAI).

A clarion call for AI defensibility

The defining tension in vertical software today is breadth versus depth, and the extent to which that facilitates defensibility in a fast-moving era of AI—we touched on this in a recent essay, called “Software Is Dead — Long Live Software”. Horizontal AI tools are getting better at surface-level tasks across industries, which means vertical players need to focus on the depth advantage their industry focus often lends—the surface areas of workflow, regulatory, and data idiosyncrasies that makes each sector unique. Emergent leaders in verticals that were seen as big, attractive land-grabs for solution-first AI—Harvey in Legal, Rogo in Finance—will come under increasing fire as the AI labs take low-hanging-fruit opportunities to extend their core competencies to industry. OpenAI and Anthropic have already made overtures in those two verticals. And while we haven’t yet seen the canonical example of vertical SaaS market leaders making the leap to Vertical AI leader, legacy systems of record are certainly ramping up sharp-elbowed tactics—we expect both startup displacement and defensive M&A to ramp up dramatically. In any case, the Vertical AI startups that will define 2026 will need to take specialization and long-term moats seriously.

Report Methodology

This report covers 4,395 US and Canadian VC financings of $1M or greater in software and AI, sourced from PitchBook’s transaction database with supplemental company-level data from Harmonic. The dataset spans calendar year 2025 and includes closed deals (at time of close, not announcement).

Deals not primarily software are excluded from the analysis—specifically: pharmaceutical and biotech, pure hardware and device companies, four-wall retail and services businesses, and capital-intensive infrastructure plays where software is incidental to the business model. The line between “software” and “not software” is increasingly blurry in an era of AI-enabled services; our general rule is that companies deriving the majority of their value from a defensible software or model layer are included, while those primarily selling labor, physical goods, or regulated products are not.

The remaining universe is classified as either vertical (defined primarily by the end market) or not vertical (horizontal tools, developer infrastructure, AI infrastructure, general-purpose foundation models, and 95% of crypto/blockchain). We don’t make an explicit distinction between B2B and B2C, though B2C is typically only classified as vertical when it is a market network connecting vertical stakeholders (e.g. lawyers, architects) with consumers, or the product is monetized vertically despite consumer usage (e.g. payors, realtors).

Exit data covers 158 vertical transactions (254 total) and includes M&A, buyout, and IPO events. To maintain focus on liquidity-generating exits, the following transaction types are excluded: recapitalizations, PIPEs, public-to-private transactions, acquisition financing, asset acquisitions, secondary buyouts, and reverse mergers (with the exception of SPACs, although none were recorded this year).

We selected 18 representative verticals which, while imperfect, cover most of the universe. AEC = Architecture, Engineering & Construction. CPG = Consumer Packaged Goods. We occasionally refer to Manufacturing & Industrial as Mfg., and Aerospace & Defense as A&D.

A final note on data completeness: market data providers’ coverage of recent early-stage deals (pre-seed and seed) is inherently lossy. As a result, the true volume of late-2025 early-stage activity is likely higher than what appears here. This bias, if anything, likely understates the share of early-stage Vertical AI activity.

Thanks for being a subscriber and supporter of Euclid Insights! The Vertical Report is published annually by Euclid: a VC firm backing Vertical AI founders from inception. For questions, please reach out to the Euclid team via LinkedIn or DM here.

Lights dim for startup presentation #612.

A founder in a quadruple-oversized parachute pants saunters to the stage. She hits the clicker, and on the main screen, “Healthcare is dead” appears in all caps. “I was the reigning world Yu-Gi-Oh card champion from ages 8-14. Now, I’m dropping out of college to build the AI-native operating system for Coordination of Benefits in health insurance, a $4B problem.” 3 minutes later, the company raises $15M at a $60M post.

What’s wrong with this picture? We would argue, just one thing.

YC’s Great Inversion

Y-Combinator’s founding ethos was built around a specific kind of founder. Paul Graham launched the program in 2005 to back “younger, more technically oriented founders.”1 In a 2018 TechCrunch interview, Graham placed the ideal YC founder age in the late twenties, and the average founder age held steady around 29 from 2015 through 2022. Graham explicitly warned against funding overly young founders, calling it “premature optimization”: he argued they should accumulate real-world context before committing to build against it. “In college,” the thinking went, “you should be figuring out what the options are, not picking one option and running with it.”2

In recent years, YC has bucked its own traditional wisdom. By the end of 2024, the median age of cohort members had dropped to 24, down from 30 in 2022. As of last summer, acceptances of 18-22 year olds were up 110% year-over-year. 2025 was the first year in YC’s history that the majority of founders were 25 or younger.

While YC CEO Garry Tan positions it as “refocusing YC on its original DNA”, it’s in reality a bit of a departure — one perhaps best explained by his second rationale: the AI revolution itself. As Jared Heyman of Rebel Fund posited: “Since generative AI is such a new technology, younger founders are at no disadvantage understanding and building around it.” One could take this a step further and imagine that younger founders might be less hampered by traditional conceptions of software success, unbound to approach problems in fresh ways. It seems YC is betting so, with an average of ~1.5 years of experience in its most recent YC cohorts.

A study conducted in late 2024 by Data-Driven VC found that the average unicorn founder started their first unicorn at age 35. The median age was 33. Younger founders are overrepresented in unicorns relative to the general startup population (with < 30-year-olds punching above their weight ~2-to-1), and unicorn founders overall averaged 8 years of work experience.

The picture gets more nuanced when you look at what else changed alongside age. The Rebel data shows these younger founders are more technically credentialed than their predecessors. The “technical” cohort has dominated since 2023 — the year following ChatGPT’s launch. While YC always leaned towards engineers, the historical average share was a relatively steady ~61%. So the YC founder profile is markedly different in the modern AI era: younger, with dramatically less work experience, and more technical.

It’s easy to see the rationale for younger, more technical founders in this era of application-layer AI. The argument is twofold:

1. Technical: When a technology platform shift is first underway, the stack is highly dynamic and volatile. Therefore, those with the technical chops to navigate it will have an advantage. A 40-year-old coder with a decade-plus from FAANG may outstrip everyone classically, but the 23-year-old who’s been building with LLMs since GPT-3.5 has intuition for what these models can and can’t do.

2. AI-Native: In the dynamic environment above, founders more “native” to the stack may be free of constraints in thinking from older paradigms. The modern version of an old quote attributed to Henry Ford: “If ever I wanted to kill opposition by unfair means, I would endow the opposition with experts.”

Let’s examine each of these arguments to see if YC’s implied bets make sense.

The Technical Argument

Over the last 18 months, the founder-facing AI stack has become drastically more legible and accessible. Previously — from the release of ChatGPT in 2022 to arguably early last year — the founder-facing AI stack was a relative mess, requiring meaningful ML, data engineering, and infrastructure experience to construct anything non-derivative. During that 2022-2024 period, the model layer was much more of a monopoly; data management and retrieval were significantly more manual; and AI “agents” didn’t exist conceptually, much less have vetted frameworks for orchestration.

That legibility arrived in fits and starts. OpenAI’s Chat Completions API (early 2023) made models programmable rather than just promptable. Function calling (which followed shortly) in OpenAI was the first reliable primitive for connecting LLMs to external tools. Structured Outputs (August 2024) then pushed schema adherence from <40% on GPT-4 to 100%, compared to <40% with the prior GPT-4 model from OpenAI. It was a watershed moment for making the model layer a dependable building block for application-layer founders.

Another massive wave of interoperability and usability breakthroughs kicked off in 2024, with Anthropic’s launch of Model Context Protocol (MCP). Quickly adopted by OpenAI, Google DeepMind, and Microsoft, it was described by Bessemer as “the USB-C of AI.” It gave AI systems a universal spec for accessing external APIs, tools, and real-time data. Before MCP, every tool integration was custom plumbing that required a dedicated engineer. Now, AI agents — which in mid-2023 were still hallucinatory, token-burning science experiments — have production-grade orchestration frameworks built on top of a shared infrastructure with standard connectors. And the rest of the stack has followed, from RAG to vector databases, to deployment and visual AI pipeline editors.

Most important, of course, is the fact that LLMs themselves have made AI applications easier to write by leaps and bounds. By 2025, GitHub Copilot will have generated 46% of the code written by its 20M+ active users and made developers 55% faster in controlled studies. Cursor, its closest competitor, crossed $500M in ARR in 2025. A quarter of YC's W25 batch had 95% AI-generated codebases. And non-technical Claude Code and Codex vibe-coders have become so ubiquitous that it’s hard to keep up.

Our point is that over the last four years, the AI stack has become vastly more legible. Shawn Wang — aka swyx, former leader of developer tooling at AWS, Two Sigma, and three unicorns — was early to this thread of thinking in his now-storied essay, The Rise of the AI Engineer: “A wide range of AI tasks that used to take 5 years and a research team to accomplish in 2013 now just require API docs and a spare afternoon in 2023.” His implication was that the critical aspect of technical fluency in the applied AI stack is moving away from research and engineering, and toward product and design. Since 2023, our view is that this effect has only accelerated. Technical talent in application-layer AI remains important, and that will probably never change in early-stage startup builds. But hyper-focus on that one quality made more sense when the stack was significantly more nascent and less legible. The current leader of YC himself recognized this shift last year:

The ability to be successful is no longer limited by technical ability. The only thing that’s sort of the limit is can the founders get in the heads of customers. — Garry Tan

The Experience Argument

The other justification for a shift to younger founders is that they are less hampered by pre-existing norms related to either startup architecture or the industry status quo. Keith Rabois has been perhaps the strongest advocate of the anti-expert viewpoint over the last decade:

People with domain expertise learn what you can’t do, not what you could do. The most important companies are usually founded by people who don’t know much about what they’re getting themselves into. — Keith Rabois

The LLM era version of this argument goes further: AI itself has compressed the cost of iteration and the speed of learning to the point where deep industry experience is less important. As one commentator put it: “With ChatGPT or Claude, technical founders can now tap into domain expertise without needing any industry experience.” In a world where products can be developed at unprecedented speeds, perhaps velocity is becoming such a moat that it more than compensates for imperfect initial domain knowledge.

The challenge with this argument is that everyone is working with the same tools these days, and the markets one can identify as compelling from the outside are widely visible. What happens when every YC batch has three founders trying to build a copilot or voice AI intake solution for a particular industry? That’s approximately the situation we’re in today. We discussed the dilemma in a past essay, “The Dispatcher Problem”:

The result of cheaper AI is persistent deflationary pressure on Vertical AI offerings, which are predominantly attractive due to the consumer surplus enabled by LLMs. Pulling data out of documents? Answering inbound phone calls? Drafting perfunctory compliance reports? Products like these can be excellent wedges today when infrastructure and know-how are scarce, and adoption is low. Soon, they will be table stakes — as excess margin is competed away by several well-funded, credible, nicely growing startups in every category. Any startup that hasn’t developed a moat in the meantime will be a casualty.
— The Verticalist

How is one to develop a moat if you don’t have a unique, earned insight that allows you to see a problem and a consequent solution that others don’t? Of course, there will always be a portion of founders who have the drive, intelligence, and luck to navigate to the right outcome regardless. But generally, experience — if not true domain expertise — is the path to the “secrets” Thiel popularized in Zero to One. That is especially true in space, like Vertical AI, in which platforms embed within and even thrive on industry complexity, rather than abstracting it away as much as possible.

Perhaps more importantly, the data aren’t particularly supportive of the anti-experience camp. Returning to the case of YC: an analysis of their ~100 unicorn founders found an average of 8 years of work experience upon entry to the program. The founders who built Airbnb, Stripe, Coinbase, and DoorDash weren’t fresh out of school, as the majority of YC founders are today. Antler’s analysis of the wider AI unicorn founder pool found that, while they have gotten younger — the average age dropping from 40 in 2020 to 29 in 2024 — the mean work experience remained 8 years. Even Rabois himself acknowledges that, a preferable complement (at least in enterprise applications) would be pairing “a founder who’s very naive, very hungry” with “somebody who’s got a lot more experience.” Even if it’s just a few years at a high-growth startup, there is intuitive value in having at least enough experience to seed an earned insight.

When it comes to vertical platforms, the debate around the importance of founder domain expertise is particularly important. Not only for investors trying to understand what drives success and for future founders trying to plan their paths, but also as a lens into the core primitives of lasting success in business models. What backgrounds matter in vertical platforms? And how have LLMs changed that answer, as it pertains to the fast-growing world of Vertical AI?

What Backgrounds Drive Vertical Success?

The cleanest way to test whether domain expertise matters isn’t to ask VCs what they believe — it’s to look at where they actually deploy capital. Given the importance of this perennial question to what we do here at The Verticalist (and at Euclid), we decided it was time to rise above the anecdotes and generalist unicorn data points and run some serious numbers.

Over the last month, we conducted a comprehensive analysis of founder backgrounds to get to the heart of what drives success in the vertical. While we ran a similar analysis in 2024, looking at exited founders, this approach was a bit too retrospective, given the long timeframes for big outcomes in venture and the fast pace of AI. So this time, defined success a bit more broadly, setting our sights on high-velocity VC financings.

Our analysis contemplates founder backgrounds across 673 vertical software and vertical AI companies that raised rounds of $15M or more in 2025, classifying founding teams by whether they had prior experience in the vertical they’re now building for. And to speak to the Rabois case — pairing of “athletes” with “domain experts” — we consider all founders, not just CEOs. We traced every company that every founder had worked at prior to founding each startup.

Although there’s plenty to unpack, the findings aren’t ambiguous.

Overall, the two-thirds (66.1%) of leading vertical startups have at least one member of the founding team with vertical experience. That share is notably higher (~71%) in Vertical AI specifically. To further understand both the impact of vertical domain experience and its interplay with startup type (AI vs. SaaS), we can examine deal size, the quality of VCs leading the rounds, and “capital velocity” (defined as dollars per year raised since founding).

Across every metric, startup founders with prior vertical experience hold an edge. Most striking is the impact of founder experience on AI-forward vertical startups.3 Vertical AI founders with vertical backgrounds raised more than 2x the average deal size ($100M vs $45M) compared to non-vertical backgrounds. In Vertical SaaS, mean deal sizes for vertically experienced teams were only 21% higher. The tight banding of median deal sizes suggests a fatter right tail — vertical-background founders in AI are landing outsized rounds that pull the mean significantly above the median.

We also wanted to look at the “quality” of VC investors attracted to cap tables.4 While no perfect measure exists for this, we used subjective brand perception as a proxy to assign “VC Scores” to each startup in the dataset — and it was consistently higher for vertical background founders across every subset. While the delta was most modest in Vertical AI, we expect this is an artifact of the data.5 A dollar-weighted VC score heavily favors vertical experience.

Analysis by Stage

For rounds of $15-50M, vertical-background founders are a modest majority—61-63% of companies. At $50-100M, however, they account for over two-thirds. At $100M+, it’s nearly three in four. The bigger the check being written, the more likely the founding team has prior vertical domain expertise.

One possible interpretation of this data supports the theory that vertical experience confers advantages in distribution and product expansion. While product velocity and vision dominate early on, later-stage fundraising rewards enterprise sales traction, regulatory navigation, and buyer trust — all of which are earned through true expertise and credibility. This, in other words, is what you’d predict if domain expertise were a durable advantage. Interestingly, it creates a through-line to our prior analysis of exited founder backgrounds, which found a >80% share of domain expertise by exit dollar.

Analysis by Vertical

As measured by VC quality, domain experience seems to have the biggest impact in sectors that are viewed as more clubby, credentialed, and opaque to laymen. Legal, for example, sees the greatest blended lift from industry backgrounds: 89% of funded legal AI companies have vertical-background founders, with a 100% median deal-size lift and a 22% VC-quality-score advantage. Harvey, EvenUp, Spellbook — a solid chunk of the companies defining the category post-LLM — were built by founders who practiced law or worked deeply in legal operations. Public Sector shows the most extreme VC-quality lift of any vertical (+75%). Like in Legal, it’s seen as a traditionally difficult procurement environment in which insider credibility and connections are critical.

Meanwhile, exposure to real estate and retail (which often blurs into commerce generally) has exposure across a broad array of companies, backgrounds, and life experiences. In those categories, branded funds tend to prefer outsiders.

Analysis by Age / Work Experience

The older a vertical founder, the more likely it is that they have domain experience. It’s not surprising we see this effect more-or-less monotonically, considering experience takes time to accumulate. Over two-thirds of vertical founders with 9+ years of work history have domain experience. Comparing Vertical AI vs. Vertical SaaS yields more interesting results. At every experience level, Vertical AI founders are more likely to have vertical backgrounds than their SaaS counterparts.

As covered above, domain experience is valued differently in Vertical AI vs. SaaS — that effect is amplified when we analyze by years of experience. Successful vertical AI founders with 0-5 years experience are 57% more likely to have domain experience than SaaS founders in the same cohort; amongst all with <9 years of experience, domain experience is 26% more likely. This suggests that the Vertical AI market is already self-selecting for domain expertise more aggressively than SaaS has been.

Another finding, runs against the narrative that domain expertise is the primary factor of success in Vertical AI: young founders attract more “brand” VCs. The 0-5 years-experience cohort achieves an average VC Score 20% higher than the 15+ years-cohort. Are we seeing quantitative evidence backing YC’s recent shift away from experience and towards youth in the AI era? That argument would hold that the proven lift afforded by vertical experience is merely correlation, and that youth — or rather, the free-thinking, fast-moving dispositions that often accompany it — is the rightful primary driver of top VC backing and ultimately success.

It’s worth remembering that validation of VC picking takes a long time, making the names on one’s cap table a very uncertain measure of ultimate performance — especially in a world where AI is theoretically changing the nature and profile of startup success. Perhaps brand-name venture firms have adopted the same mode of thinking as YC.

Here, all we’re interested in is the data — and that tells us that the youth-supremacy narrative falls apart on three counts:

1. If youth were the primary driver of success in Vertical AI, you’d expect young founders without vertical backgrounds to outperform old founders with vertical backgrounds. But they don’t. Those with 9+ years’ work experience have higher capital velocity across the board. Old vertical founders raised bigger rounds than young founders without domain experience. And although young founders with vertical backgrounds marginally underperformed peers without them on VC Score, they outperformed on all other measures. Youth may be preferred by brand-name VC, but it doesn’t substitute for domain experience.
   

2. The VC quality premium for youth is consistent across AI and SaaS, but the domain expertise premium is not. Young Vertical AI founders are 20 points more likely to have vertical backgrounds than young vSaaS founders (55% vs 35%). If youth alone were the success factor, you’d expect that gap not to exist — young founders would succeed regardless of domain background. Instead, the market is filtering young AI founders for domain expertise more aggressively. The ones who make it past the Series A threshold6 disproportionately have it.
   

3. Over 90% of top vertical rounds were raised by founders with >5 years of experience. We’ve spent so much time in comparative, cohort base numbers that it’s important to remember: by volume, super young founders represent a small minority of the overall dataset. The majority of founders with successful Series A+ raises in 2025 had 15+ years experience. Examining the trend by sector, it’s clear that the more regulated and “credentialed” a vertical is, the more seasoned the founder is likely to be; as much as fresh founders transforming the stodgiest industries is in vogue right now, the actual founder splits suggests that winning founders’ experience levels align with the average ages in their respective industry. Not a single A&D founding CEO with less than 6 years’ experience raised a Series A+ in 2025.
   

So why would young, successful Vertical AI founders be so much more likely to have domain experience than their peers in Vertical SaaS? We feel the most likely explanation is the same we shared above: building AI products that automate complex industry-specific judgment requires deeper domain understanding than building software that mirrors and digitizes processes. Velocity, free-thinking, and AI-nativity matter greatly — but in Vertical AI, a founder’s knowledge advantage in their domain is increasingly important.

Analysis by Prior Function

Most successful vertical startup founders aren’t technical. Over a third (35.1%) of CEOs who raised $15M+ vertical rounds were previously executives (prior CEOs, presidents, or founders of other companies). When you add in Ops/Strategy/Finance (14.6%), Product (15.0%), and GTM (6.1%), non-technical business leaders account for more than seven of ten successful vertical startup CEOs. Although non-CEO engineers (including CTOs) represent just 8.9%, they achieved the highest median capital velocity ($7.8M/yr) among non-financial backgrounds. This runs directly counter to the prevailing narrative that technical founders dominate AI-era startups. In vertical markets, the CEO who understands the customer’s workflow, regulatory environment, and buying process has a structural advantage over the one who understands the model architecture.

The most intriguing finding may be the IB / Consulting cohort. Despite being a small group (~4% of successful vertical CEOs), they lead on every key metric: the highest capital velocity ($9.8M/yr), the highest VC quality score (1.69), and the highest median deal size ($50M). These are founders who combine analytical rigor with the pattern-matching and relationships that come from advising industries before building in them — the ex-McKinsey partner who spent three years studying healthcare workflows before founding a clinical AI company. The VC / Investor cohort (8.7%) is also notable as a growing category, with similar dynamics to IB / Consulting: former VCs raise at the second-highest velocity ($8.9M/yr) and command the second-highest median deal ($40M), leveraging their connectivity, if not domain expertise from deal experience.

The “From Industry” cohort represents founding CEOs who launched their startup immediately after an operating role at a strategic in their vertical. Luke Hansen, the founder of CompanyCam (which became a unicorn with the 2025 deal that landed in our dataset) is a perfect example — he was a 13-year exec at White Castle Roofing in Omaha, Nebraska, before going the founder route. These pure domain experts, however, had the lowest VC scores (1.38) and slowest capital velocity ($5.5M/yr). Does this run counter to our argument that vertical experience is critical to success?

At Euclid, we prioritize two founder characteristics in Vertical AI: (1) earned insight into their problem and vertical (most analogous to “domain expertise”), and (2) an innate understanding of what success looks like for a venture-scale business. The latter is difficult to derive without exposure to either high-quality startups or comparable high-stakes environments. Thus, we believe that optimal domain expertise tends to be either: (1) a background with a combination of software / AI plus industry, or (2) industry insight gleaned through prior vertical startup / AI success. That said, we have backed and will back backgrounds outside of any one mold, because no such rules are universal — a healthy chunk of the best founders break all the pattern-matching, and are better for it.

The Democratization Paradox in AI

Per the anti-expert argument, if AI truly democratizes domain knowledge — enabling technical founders to iterate and learn industries in weeks rather than years — then the vertical-founder advantage should shrink in vertical AI relative to vertical SaaS. AI-native, low-experience founders should be pulling ahead on attracting dollars and VCs, as the outsider disadvantage compresses.

Our analysis, however, shows the opposite. In Vertical AI, >70% of leading 2025-funded startups have founders with prior experience in or around their industry — and those founders raised deals 2.25x larger, with greater overall capital velocities, and higher VC scores. Every metric points in the same direction: the more AI-native the company, the more domain expertise matters, not less.

If that finding is indeed structural, we believe it relates to the core function of AI-first solutions compared to traditional software. Vertical SaaS digitizes processes — it moves a workflow from paper to screen. Vertical AI enables something fundamentally different: automating context and judgment. It doesn’t just present information or offer a UI for abstracting knowledge into a database. Vertical AI, writ large, aims to do the work: generate documents, triage decisions, and even act autonomously.

When the product is the work itself, vertical nuance — industry buyer trust, regulatory nuance, in vivo edge-cases — becomes more important. Those are precisely the things that domain experience confers that iteration and obsession alone would take longer to unearth.

The argument against domain expertise predicts convergence between insider and outsider founder outcomes as AI capabilities improve. The data shows divergence — and deltas between Vertical SaaS and Vertical AI suggest the gap may be widening, not narrowing. As the AI stack becomes more legible, the greater rate-limiting factor to scale — to distribution and defensibility — becomes knowing what to build, for whom, and why they’ll trust you to build it. As the ceiling of Vertical AI rises with technical possibility and accessibility, the floor of domain expertise rises with it.

So What's Wrong With the Picture?

Certainly not the parachute pants or the Yu-Gi-Oh résumé — between your two authors, Euclid is rich with MC Hammer and Magic the Gathering phases that may or may not still be active. Coordination of Benefits is indeed a $4B problem, and one worthy of solving. Eye-catching proclamations like “Healthcare is dead” will be effective as long as we have human brains. $15M at a $60M post is clearly rich for a seed-stage company but, for better or worse, not wildly rare in the current YC milieu. Our issue with the picture is subtler: the implication that youth, AI-nativity, and audacity compensate for a complete lack of earned insight into a vertical as complex as health insurance. And while our example is tongue-in-cheek, there are examples at least as striking in every batch these days.

YC’s shift toward younger, more technical founders made sense in 2023, when the AI stack was volatile and the builders who could wrangle it had a genuine edge. But the stack has matured — dramatically. Structured Outputs, MCP, production-grade agent frameworks, and AI-assisted coding have eroded the technical barriers that once justified prioritizing engineering fluency above all else. In a world where every vertical is getting pounded by a dozen low-hanging-fruit AI ideas, we’re not even sure pure velocity — the wall-spaghetti approach of move fast and sling AI — has much life left in it. Garry Tan himself seems to agree with those points in some of his more recent interviews:

The ability to be successful is no longer limited by technical ability. The only thing that’s sort of the limit is can the founders get in the heads of customers.

Our data suggests the market has already internalized it. In Vertical AI specifically, 71% of founders raising $15M+ rounds have prior domain experience — a share that increases at later stages and in more regulated verticals. Young founders are not shut out; they’re just filtered more aggressively for domain knowledge in AI than in SaaS. Youth isn’t a disqualifier; in fact, it’s absolutely preferred. But because the product is the judgment in Vertical AI, and judgment has to come from somewhere — and that’s why experience matters more.

Some GPs have publicly shared their own philosophies in support of founder expertise in Vertical AI. a16z’s David Haber, in Context is King piece, argued that “AI dramatically expands what’s technically possible, but it doesn’t tell you what’s actually useful… [industry judgment] can’t be automated like code and is earned only through experience.” Greylock’s Christine Kim put it more bluntly: “pure technologists attempting vertical AI are at a disadvantage to founding teams who have both domain experience and a technology background.” Brian Feinstein, Kent Bennett, and Sameer Dholakia at BVP concluded in their Vertical AI Roadmap: “outsiders face steep learning curves that burn months and capital understanding nuances insiders grasp intuitively.”

None of this means YC is wrong to back young founders per se. They may have simply leaned into the profile because it’s their historical domain. If they are better sourcers and pickers amongst young, technical founders — and if they drive better returns for the program — then that’s where they should play. They are now so ubiquitous, however, that some in the VC and startup ecosystem interpret the latest Y-Combinator filter as a proxy for “founder profile of the future.”

That model is incomplete if youth and technical skill are treated as substitutes for domain insight rather than complements to it. The best version of our young, pantalooned founder — the one who raises $15M and then actually builds a lasting company — pairs AI-native intuition with hard-won knowledge of how benefits actually adjudicate, where the edge cases live, and why the buyer across the table should trust her to automate their highest-liability workflow.

So to the investors seeing YC’s recent shift toward youth and technical talent, and interpreting them as the primary founder rate-limiters in the AI era: caveat emptor. Our data says distribution, trust, and domain judgment are catching up fast — and in Vertical AI, the market may already be placing them first.

And to founders wondering what to make of all this, perhaps Paul Graham himself said it best over a decade ago:

What you need to succeed in a startup is not expertise in startups. What you need is expertise in your own users…. here is the ultimate advice for young would-be startup founders, reduced to two words: just learn.

We built Euclid from the ground up to serve category-defining Vertical AI founders. By and large, those are builders who pair AI-native vision and ability, with earned domain insights, and first-hand knowledge of what great looks like. The data says they win more often, raise more capital, and attract better partners. We think that in Vertical AI, their advantage will only widen.

Everything’s getting cheaper, easier, and more accessible — except judgment.

Thanks for reading The Verticalist!

Euclid is an inception-stage VC built for Vertical AI founders. If anyone in your network is thinking about leveraging their domain experience to build in AI, we’d love to help. Just drop us a line via the comments below or on LinkedIn.

On this week’s Verticals, we dug into how Camber — a Series B healthcare AI payments startup co-founded by Christophe Rimann and backed by a16z — is building exactly this. Episode here and full case study below.

Watch Full Episode

When Vertical Fintech meets AI Services

Camber is a healthcare AI payments startup that has raised $44M from a16z, Craft, Zigg, and others. Their vision: durable, hard-to-replicate financial infrastructure a la Stripe, but with the defensibility of a Vertical AI platform intaking massive volumes of first-party data.

Does every vertical need its own financial rails? Perhaps not, but the evidence is compelling that many do. At minimum, healthcare is a prime candidate. It lives and breathes on payments between fragmented, complex, regulated parties. As healthcare shifts out of hospital systems and into outpatient settings — behavioral health, PT, ENT, cardiology — unit economics can quickly break down as the cost of financial execution balloons. A hospital, for example, can justify a human chasing the last 10% of a $10K claim. A behavioral health clinic submitting $150 claims by the tens of thousands cannot. The long tail of underpaid claims becomes structurally uneconomic. Providers think they’re collecting at 97–98%; the underlying data says 92–93%. They can’t staff their way to a higher number, so millions of dollars evaporate into deadweight loss.

Christophe Rimann — a former McKinsey healthcare consultant who previously built a licensed crypto broker-dealer — saw that gap and spent five years building a foundational model purpose-built for claims adjudication. Not an agentic wrapper on legacy RCM software, or even RCM software re-imagined for LLMs. Camber’s team, rather, envisioned a vertically-specific system trained on billions of dollars of expert-labeled claims data, capable of reverse-engineering payer denial logic before a claim is ever submitted. Today, Camber’s first-pass “clean claim” rate runs around 95%, compared to 80–85% for large clinics operating without it, and as low as 60% for some $500M+ providers. For larger clients, that delta can translate into millions in annual EBITDA uplift.

Our interview with Camber was made possible by Parafin. Gusto recently partnered with Parafin to embed a line of credit directly into their payroll workflow, so SMBs can cover cash crunches in seconds. Learn how Parafin’s embedded fintech can solve your problems here.

The ROI-First Future of Vertical GTM

Camber is strategically interesting not only because of its infrastructure play, but also because it is rethinking monetization for the AI era. The company prices on outcomes, not seats. This isn’t disruptive for customers since they largely compete with offshore BPOs, not software vendors. Despite the fact that adoption takes doing — Christophe estimates 30–40% of enterprise value lies in change management — their gross margins run at upper-quartile SaaS levels. As Christophe put it: “Most of our providers think of us as a services company.”

Like much of enterprise vertical AI these days, Camber takes many pages from the Palantir playbook. “Sell services, execute through software / AI” is potent in healthcare — like many other real-economy verticals — because the buyer doesn’t want to think about the technology. They want to know their collections number went up and they want it solved. Camber structured their initial customer engagements to prove ROI. That has enabled them to run a 60-day pilot-to-conversion cycle… in a market where RFIs run 18+ months.

This is one of many suggestive data points — across a growing number of Vertical AI startups in the current AI Services wave — that outcomes-based pricing can collapse sales friction when the pain is acute enough. In some sectors, the grip of traditional vertical SaaS cycles is loosening.

Healthcare’s Stripe Moment?

The Stripe analogy is Christophe’s own. It’s admittedly not perfect: much of what makes Stripe what it is comes down to its horizontal universality. Moreover, if something is truly “rails,” shouldn’t it be agnostic to outcomes, rather than predicated on them? But there are a few ways in which mental model is apt.

Thirty years ago, every large credit card purchase involved a back-room phone call to the issuing bank.1 Today, that complexity is fully abstracted and automated. Healthcare insurance claims (and the associated payments) are stuck in that pre-abstraction era — resolution requires dozens of steps, stakeholders, and manual interaction. Camber is betting it can facilitate a similar evolution in healthcare, and compress it into a few years.

Of course, none of that would be possible without the inter-connectivity and process automation LLMs have enabled. By layering AI on top of a proprietary data network, Camber now processes roughly 25% of daily claims volume in its beachhead vertical within behavioral health. Multiple EHR vendors already white-label Camber as their payments layer. One in three of their SMB deals involves the provider switching their entire system of record just to access Camber — a striking inversion of traditional thinking around the defensibility of SoRs and vertical moat-building assumptions.

Payments as a Shadow System of Record

Camber’s success to date is just one case study, and a developing one. But there’s an interesting emerging lesson for Vertical AI founders and investors in healthcare, and other deeply regulated, payment-heavy verticals.

We recently shared our concept of the “shadow system of record” — a strategy by which founders can grow a thriving business in verticals where dominant, controlling incumbent systems of record (like EHRs in healthcare) have historically sucked the oxygen out of the room. The concept is simple, even if identifying opportunities is hard: establish a net-new control point and grow unhampered in the incumbent’s blind spot.

The payments layer may be a prime candidate for shadow SoRs in some sectors. Could EHRs become the interchangeable commodity, while the claims & payments network becomes the stickier infrastructure? Certainly, that would be a meaningful structural shift from the traditional vertical SaaS playbook.

It does echo a pattern we’ve been tracking: the next chapter of vertical software is being written by companies that capture value from non-software budgets, leveraging wedges impossible before the current generation of AI.

Camber has raised $50M — with a Series B led by a16z last year — employs ~90 people and is expanding from behavioral health into five new verticals this year. The open question, as Christophe frames it, is whether they can scale the go-to-market — and whether the claims landscape itself shifts beneath them in the next 18 months. That uncertainty is real — as is the case in highly regulated markets. But the benefit of being more than just a pure Stripe-esque rails play or a pure external Vertical AI Services play, is that Camber drafts on real, rarefied first-party data as it scales. The data moat flywheel they’ve built over five years of expert-labeled claims outcomes2 is compounding — and that’s not something anyone can vibe-code.

Thanks for reading the Verticalist! Don’t forget to subscribe to our show, Verticals, wherever you watch or listen. Dive deep into Vertical AI strategy with a new founder, every Wednesday.

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This episode is sponsored by Parafin, the embedded finance infrastructure powering capital products for vertical platforms. Backed by Ribbit Capital, Thrive Capital, and GIC (with a $100M Series C bringing their valuation to $750M), Parafin supports customers like Amazon, DoorDash, Jobber, and NMI. Click here to learn how Parafin can support you.

The AI agent boom promised to transform freight. Voice agents that negotiate with carriers. Document agents that process customs paperwork. Scheduling agents that book appointments autonomously.

The demos have been phenomenal. Retention… not so much.

As Deepak Chhugani tells it, logistics companies adopting these tools are hitting the same wall: change management is brutal, every broker’s workflow is different, and customers blame the vendor when things break. The result is a growing cohort of well-funded agent startups struggling to renew contracts. Perhaps a story not unfamiliar to AI-native founders working to bring agentic solutions to complex legacy industry.

Deepak is the Founder / CEO of Nuvocargo, an AI-native logistics platform that has raised >$85M from Tiger Global, QED Investors, NFX, and Y Combinator. His answer to the retention problem is in line with our recent essay: don’t sell the agent. Between Mike Powers at BuildVision (on Verticals here) and Chris Hladczuk at Hanover Park (on Verticals here), it’s a story we’re hearing more and more.

Nuvocargo sells the outcome. They are literally a licensed freight broker — one of their three offerings with ~40% gross margins — touching 300k carriers. The distinction, as Deepak illustrates, is that his partnership with clients goes even deeper than traditional brokers can. As opposed to a pure vendor, they build a deeply internal relationship with clients, not only further aligning incentives, but also making the service harder to replace and easier to extend:

• Freight brokerage: “Quote me a rate from Dallas to New York.” Traditional vendor: transactional, commodity-prone, external. Price is the main differentiator, which is why so many freight brokers blew up post-2022.

• AI-managed supply chain partner: “Here’s my $50M freight budget. Run it for me.” An incentives aligned partner with high internal access to customers. Plug into the customer’s ERP, replace 15 SaaS tools, and execute the right strategy for the customer — not just the the most profitable point-in-time deal.

On this episode of Verticals, we unpack exactly how selling the outcome works at Nuvocargo, Deepak’s path to where he is today (from investment banking in Mexico City to YC) — and how his framework breaks down into “Four Ps” that all founders building Vertical AI Services should ask themselves to stress-test their models.

The Vertical Playbook

The 4 P’s of Vertical AI Services

A litmus test for founders building defensible AI Services companies.

Performance

Can you deliver measurably better outcomes than the incumbent? Nuvocargo runs 12+ narrow, task-specific AI agents inside NuvOS, each performing a single function: scheduling appointments, processing documents, negotiating carrier rates. If one fails, a human unblocks it and the pipeline proceeds. Critically, the customer never engages with the agents. Instead, they see results: seamless delivery and — for one early customer — a 12% reduction in a multimillion-dollar freight budget in year one. If you can’t point to concrete ROI, you’re selling novelty.

Profit

Can you structurally lower your cost to serve through AI? Deepak draws a sharp line between his model and the boom-bust cycle of traditional freight brokers, who hire aggressively in good times and fire in bad ones. By embedding AI agents inside NuvOS rather than relying (fully) on headcount, Nuvocargo is shifting its P&L from a labor-heavy service business toward a consumption-based model. As Deepak puts it, the question every AI services founder must answer honestly: does your technology actually make each unit of work cheaper?

Pricing

Can you price in a way customers already understand? Managed transportation is an established business model — multibillion-dollar 4PLs have charged shippers 2–4% of freight spend for decades. Nuvocargo adopted the same fee structure rather than inventing a novel pricing model that would force customers to justify a new budget category internally. The pitch is simple: pay the same rate you’d pay the legacy provider, but get a modern technology stack executing on every shipment. As Deepak shared, he learned the hard way that squeezing every dollar of value upfront kills deals and discounts your ability to expand over time.

Partnership

Can you embed deeper than a vendor to become infrastructure? This is where Nuvocargo’s model diverges most from the standalone agent playbook. When the company integrates with a shipper’s ERP and begins consuming purchase orders directly, Deepak’s team isn’t just quoting a lane — they’re questioning whether a shipment needs to go at all. The customer replaces 15 SaaS tools, multiple broker relationships, and the headcount to coordinate them. That’s a very different switching cost than canceling an “agent.” As we’ve written on the commoditization risk facing AI services, startups that remain external vendors competing on cost will watch margins compress. The ones that become an orchestration layer — owning data, integrating with the customer’s stack — build moats with compounding potential.

The Takeaway for Vertical Founders

Performance. Profit. Pricing. Partnership. These four tests separate durable AI services businesses from “agents” and LLM wrappers. Nuvocargo passes all four not because the AI is more sophisticated, but because the business model was designed to leverage AI invisibly: delivering outcomes customers already know how to buy, at structurally superior margins, with customer “internality” that builds real stickiness.

Thanks for reading the Verticalist and for tuning into the show! Drop us a line below with your thoughts. And don’t forget to subscribe to Verticals wherever you watch or listen:

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It’s becoming increasingly clear that the picture-perfect “AI replacing workers” story doesn’t add up. Evidence linking AI to hiring slowdowns has been muddled at best, while productivity claims have been hard to measure (some have even been disproved by available data). The “AI-Washing” of job cuts — executives using AI automation to try to flip the negative connotations of layoffs into a positive shareholder narrative — has become pervasive.

None of this means that LLM-era AI isn’t fundamentally transformational, nor that its economic potential is capped. We’re just in the early innings of adoption. It is easy to forget that there was a 20-year gap between the commercialization of the personal computer (by IBM in 1981) and the peak of the digital productivity boom at the turn of the century.1

The single-mindedness of the current AI automation narrative is easy enough to explain: incentives, incentives, incentives. The loudest voices on AI’s labor impact have had the most to gain from speaking up — from the execs running frontier labs to CEOs right-sizing their workforces in the post-2021 era to VCs stirring up hype to justify ever-larger AUMs.

But it’s becoming a problem.

The Church of Agenticism

Hyperbolic AI narratives have now graduated from high-level economic pronouncements to product positioning itself. Every AI-powered feature is now an "agent." Conference panels want to discuss "managing your AI workforce." Every enterprise buyer is being told they need "digital co-workers." Founders are giving their agents names, personalities, and even human faces. Some startups are gauging candidates by how “agentic” their mindsets are.

The term agent — an inherently vague word meaning “one who acts” — is starting to collapse under the weight of its own ubiquity. While its rise was fueled, at least in part, by the desire to make AI more accessible and urgent for enterprises, it may be having the opposite effect. When everything becomes an “agent,” it's nearly impossible for buyers to tell what any given product actually does, how it fits into existing operations, how they should interact with it, or why it matters to their bottom line. In Vertical AI specifically, this is disastrous.

While there are verticals in which exploratory or performative AI adoption is real2, most industry buyers don’t care about AI — they care about results. Good Vertical AI is a capability infrastructure: a technological foundation that expands what organizations can accomplish, well beyond what is possible through any combination of humans and traditional software. Buyers and stakeholders need a clear way to understand the function of Vertical AI: its potential to unlock new business opportunities and strengthen industry-specific workflows. By focusing on form with metaphors like “agent”, by contrast, we risk obscuring the real opportunity, alienating the buyers who will drive adoption.

Agentic systems — AI that can accomplish complex, multi-step workflows in pursuit of goals — represent a genuine and meaningful capability shift. It is the desperate desire of all innovators to coin new “categories” that capture mindshare and warm audiences to their product, and for good reason. Metaphors are the heart of product marketing: key cognitive tools that shape how buyers evaluate, how organizations adopt, how new markets develop, and even how products themselves evolve.

We oppose Agenticism because it peddles a risky myth: that buyers won’t need to alter their business practices to enjoy the great benefits of AI. This has happened before.

GM Builds the “Factory of the Future”

On October 20, 1984, The New York Times published an article titled, “GM Factory of the Future Will Run with Robots.” Roger Smith, then GM’s CEO, argued that automation would help the company compete with increasingly formidable Japanese competitors. Smith bet his tenure on automation: GM would invest over $40-45B in factory robots in the mid-80s. It wasn’t GM’s first shot at owning the narrative of the future, but it would be their most expensive by a long shot.

Smith’s premise was simple: replace costly, unionized labor with tireless machines, keep the workflow and assembly line the same, and profit from the resulting productivity gains. Better, cheaper, faster! What actually happened, however, is a cautionary tale about the difference between adopting and buying technology. GM placed robots exactly where human workers had been. Job roles stayed the same. The pace and sequence of the assembly line were unchanged. The robots acted as obedient replacements for labor, integrated into a system designed around human limitations. 

Unfortunately, GM’s implementation was widely panned as a disaster. Smith’s robotic factories struggled to match the productivity of their human-run forebears. Robots sometimes painted each other instead of cars or welded their doors shut. Manufacturing costs increased.

Toyota, working with similar robotic technologies, asked a fundamentally different question: what becomes possible when a new capability is introduced into the system? That shift in perspective changed everything. Plant layouts were redesigned. Work cells were reorganized. Quality feedback was made more immediate. Human workers moved from task execution to overseeing the system. The factory transformed into a coordinated human-machine environment, not just a human workflow with machines attached.

Both companies had access to the same tools. Only one reconsidered the architecture around them. Ultimately, GM’s implementation fell short because it focused on swapping people for machines rather than on infrastructure to enhance capabilities. The long-term divergence in productivity and competitive advantage developed from that initial framing choice.

Stanford economist Paul David documented the same pattern a century earlier in his landmark study of factory electrification. In the late 1800s, factories ran on steam—a single massive engine turning an overhead drive shaft that powered every machine in the building through an intricate web of belts and pulleys. The entire factory layout was dictated by proximity to that shaft. When electric motors first became available, factory owners did the obvious thing: they ripped out the steam engine and bolted an electric motor in its place. The shaft kept turning. The belts kept running. Productivity barely moved.

It took thirty years before a new generation of manufacturers realized that electricity’s real advantage was distributed power—a small, independent motor on every machine, freeing the factory floor from the tyranny of the shaft. Suddenly, layouts could follow the logic of production rather than the logic of power transmission. Single-story factories replaced multi-story ones. Assembly lines became possible. Workers gained autonomy over their own machines. The productivity gains were transformative, but they required abandoning the architecture that steam had imposed.

The “agent” discourse repeats the same automation mistakes for the modern era. When AI is cast as a co-worker, the unit of adoption becomes the role, not the workflow or the system in which that workflow is embedded. Executives begin asking HR questions, and the functional value derived is simply cost. What’s the defensibility of selling cheaper work? Perhaps the next widget seller will undercut your pricing even further.

Funny enough, this wasn’t even GM’s first time making this mistake around technology. In 1919, the company entered the tractor business with a machine called the Samson Iron Horse, which was steered with leather reins so farmers would feel more comfortable than with a steering wheel. It flopped immediately, and GM exited agriculture by 1922, having learned nothing it would remember when the robots arrived sixty years later.

The Co-Worker Problem

The co-worker analogy seems harmless and helpful because it makes complex technology feel familiar. An entrepreneur can sell a vision of augmenting or even replacing specific staff members. It’s simple to understand and, hypothetically, easier to sell. It’s also straightforward to pitch the vision to investors and stakeholders: a large amount of money is spent on labor performing a specific task(s) that we can automate. Easy to explain, easy to grasp.

Perhaps unsurprisingly, the “factory of the future” powered by robots promised by the GM CEO was praised at the time and elevated him to the status of the press darling. He became a champion of U.S. manufacturing, catching the public's imagination and becoming a media hero.

Described as an "innovator," "visionary," and "21st century futurist," Smith was named Automotive Industries’ Man of the Year and Advertising Age's Ad Man of the Year, honored with the Financial World Gold Medal (best CEO in America), and designated by The Gallagher Report as one of the ten best executives in America. With such acclimation, it seems little wonder that "GM completed the 1980s in a state of arrogance.”3

It turns out that selling the replacement of labor with robots (or AI) to the press and investors was the only successful part of the GM initiative. The reason the “co-worker” metaphor is flawed is that it draws attention to staffing rather than to system design. It produces local gains (e.g., less headcount or individual employee productivity) while leaving the underlying structure untouched. Now consider how this plays in vertical markets—construction, healthcare, logistics, legal, food services—the industries where AI has the most transformative potential and where the “agent” framing does the most damage.

These buyers don’t wake up wanting an agent. They face a scheduling problem, a compliance bottleneck, and an intake workflow that’s losing money. Their procurement process relies on PDFs and phone calls. The customers most likely to benefit from Vertical AI are often the least tech-savvy, the most skeptical of hype, and the most allergic to buzzwords. Calling your product an “agent” shifts the focus from the problem to the technology. It’s the same category of mistake founders make when they lead with their product, or worse, their tech stack, rather than the customer’s pain.

Unsurprisingly, the mistake with GM extended beyond the metaphor; the entire automation process was flawed. It exhibited a poor understanding of the assembly line and the workflows that comprised it. It viewed labor as fungible units to be replaced, while assembly plants are especially complex environments. As the old adage goes, there are “few ways to lose money faster than automating a process you don’t understand.”

GM failed not because its robots were bad, but because it treated them as direct replacements for people. Leveraging automation necessitates understanding both the intended and actual “work to be done.” Successfully deploying Vertical AI requires a similar insight: you must understand the work so you can re-architect it to maximize its benefits from AI. Otherwise, you risk creating tools that sound great in a pitch deck but are ultimately misaligned with the actual process being automated — and incapable of realizing the transformative potential of AI.

Capabilities, Not Agents

The “agent” metaphor replicates this misalignment on a large scale. It presents AI as a replacement for labor, when the true opportunity—especially in vertical markets—is to broaden what a business can achieve. Consider the two categories of work in which Vertical AI is best positioned to operate. The first is administrative: scheduling, recording, invoicing, intake—back-office work that is time-consuming, inefficient, and likely not done well. The second, and far more interesting category, is the work not done: bids not submitted, calls not answered after hours, patients not seen, inspections not performed. This is the biggest unknown and the most compelling opportunity. It does not show up in a headcount reduction model. It shows up in revenue growth.

The distinction matters enormously for how we size these markets and how we sell into them. Much of the narrative around AI’s value has centered on direct labor replacement, given the immediate profit potential for buyers. But we believe AI’s greater impact—and the larger market opportunity—will be its ability to enhance workforce productivity rather than simply substitute for it. To bring this into focus, consider the hidden revenue lost in “work not done.” For instance, in industries like construction or legal services, missing out on even 10% of potential bids or after-hours service requests can translate into billions in unrealized revenue. If AI-enabled systems could capture just a fraction of these missed opportunities, the value unlocked could dwarf the savings from labor-reduction models. Making the “work not done” visible gives buyers a concrete, numbers-driven reason to rethink how they adopt technology.

This is the Toyota playbook, applied to vertical markets: when a new capability enters the system, the system should reorganize to exploit it. Freeing up administrative work may require less headcount in the back office, but tackling the work that remains should have the opposite effect—enabling businesses to take on more projects, more customers, and more patients, and to grow their revenue. More business will likely require more headcount, not less. In a steady state, the more productive firms will combine enhanced labor productivity with AI to grow and outperform.

When you frame AI as an "agent"—a digital worker—you implicitly promise substitution. When you frame it as a capability, you promise expansion. The second framing is more accurate, more attractive to buyers, and more aligned with how technology has actually transformed industries throughout history.

A concise comparison crystallizes the difference:

The richest upside comes from treating AI not only as a path to speeding up the status quo, but also as infrastructure to unlock new ways of doing the work altogether.

The "Moving Boundary” Between AI & Humans

There is a subtler strategic point here that the agent discourse obscures entirely. As AI capabilities improve, human roles won’t disappear; they migrate. The customer support representative is pulled into the boundary region where a customer is distressed but not saying why, where empathy requires overriding policy. The urban planner is freed from traffic modeling to mediate between incompatible visions of what the city should be. The construction project manager stops manually tracking submittals and starts managing more complex, higher-margin jobs. Even in AI services, the human transitions from rote execution to the paradigm of action that models learn from at the edge.

In vertical markets, this migration is the entire value proposition. The pitch is not “we’ll replace your people.” The pitch is “your people will be able to do more of the work that actually matters—and more of it.” That is a very different sale, and it requires a very different story than “agents replacing humans.”

The Vertical AI companies gaining real traction understand this intuitively. Abridge doesn’t sell health systems as an “agent.” Instead, they redesigned the documentation workflow so that a patient conversation automatically becomes a structured clinical note inside the EHR. The physician’s role migrates from hours of after-hours charting to reviewing and signing off on AI-drafted notes. Clinicians using Abridge report 86% less effort on documentation and 60% less after-hours work. That’s a much more powerful value prop (and business case) than selling a note-taking agent.

In legal, EvenUp has built a claims intelligence platform for personal injury firms that automates demand drafting, medical chronology creation, and case analysis across the entire lifecycle of a claim. Firms using EvenUp report that their drafting output has tripled and that average settlement timelines have been cut by a month, all without adding headcount. The attorney’s role shifts from documentation to higher-value cases within the firm.

In construction, BuildVision helps manufacturers, distributors, and suppliers automate the manual back-and-forth of the selling process—ingesting and normalizing PDFs, product specs, and unstructured data that previously lived in email threads and phone calls. The sales rep doesn’t get replaced; they get freed from the drudgery that kept them from responding to more prospective jobs, and even taking on higher-margin, more complex jobs. None of these companies leads with “agent.” They lead with the workflow, and the results follow.

GM’s CEO lacked this strategic vision. Plant closures and layoffs characterized his leadership. Replacing humans with robots was the end goal. He famously told auto workers: “Every time you ask for another dollar in wages, a thousand more robots start looking practical.”

Toyota’s engineers, on the other hand, understood that once new capabilities enter a system, the system either adapts to exploit them or resists and diminishes them. The business process (and eventually the organization) becomes a moving boundary between what the technology can reliably handle and what humans must still provide. Leading such an organization requires attention to the evolution of that boundary, not a static model of which “digital workers” occupy which roles.

Similarly, agents or, in Toyota’s case, robotics automation, alter the economic value of human capabilities. When an agent reliably handles a task, the economic importance of the skill required to perform it decreases, while the value of other human capabilities increases. This dynamic appears similar to augmentation but is fundamentally different. Augmentation relies on the idea that the worker remains central to the workflow, viewing AI as an enhancement of their capabilities. As this essay explains:

As agentic capabilities improve and force the structure of the work to reorganize around them, without proactive human-agent systems design, workers no longer define the workflow; they occupy the segments where agentic capabilities fail, get bottlenecked, or hit their limits. The effect may look like augmentation from a distance, yet the underlying logic is different: the system expands outward through agentic execution, and humans are continually reallocated to the frontier where reliability breaks and human capabilities like judgment, interpretation, and governance create distinct value.

Words Matter More Than You Think

Our anti-Agenticism is more than a semantic quibble. AI has a public perception problem today. Only 13% of respondents in a recent survey believe artificial intelligence will do more good than harm. The “stop hiring humans” billboards, the flippant commentary from tech CEOs about mass job elimination, the casual framing of AI as a replacement workforce—all of it feeds resistance in exactly the markets where AI can have the most impact.

The industries with the worst productivity—construction, healthcare, transportation—are also the ones with the strongest cultural attachment to human labor and the deepest skepticism of technology. These sectors account for 37% of total hours worked in the US economy but contribute only 24% of output. Their productivity CAGR has been below 0.5% since 2005. Construction has actually gotten less productive. There is no credible path to restoring historical productivity growth without a digital transformation of these markets. And there is no path to digital transformation if the people running these businesses think AI is coming for their livelihoods.

The “machines versus people” framing is simply bad business. This dynamic has caused labor tensions, slowed down technology adoption, and harmed organizational performance for over a century. The answer to disruption is not restraining innovation. But it is also not flippant to discuss job losses or to sell your technology as a human replacement. Founders should sell the broader promise of Vertical AI: technology that boosts productivity and drives growth, not just cuts costs.

GM CEO Roger Smith was a highly intelligent executive with good intentions who failed because he misunderstood the leverage of automation. For Smith, robotics represented the Holy Grail, the ultimate solution that could fix all of GM's problems at once. Unfortunately, he was wrong. By the time Smith retired in 1990, GM had shifted from the lowest-cost producer in Detroit to its highest-cost producer, largely because of the push to acquire technology that never paid off. Toyota, on the other hand, prospered. By 1990, they had surpassed GM in profit per vehicle by a factor of three, despite producing fewer cars. By 2008, it overtook GM as the world’s largest automaker, a position GM had held for 77 consecutive years.

The companies that succeed in Vertical AI will be those that, like Toyota, understand the moving parts of the system, not those selling a shinier robot for the same assembly line. If you’re a founder working in vertical markets, the practical and immediate implication is to drop the word “agent” from your pitch deck, landing page, and sales calls. Focus on the workflow you’re redesigning, the capacity you’re unlocking, and the outcome you’re delivering.

If we could leave you with one thought, it’s this:

Toyota never sold a robot — they just shipped a better car.

Thanks for reading The Verticalist!

Euclid is a VC backing Vertical AI founders from inception. If anyone in your network is working on a new startup in this space, we’d love to help. LinkedIn is the best place to reach us.

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Today’s Episode

Fund administration isn’t the sexiest industry — in fact, it’s hard to find many CFOs and managers who love their vendor. But it underlies $100T in global assets and is an attractive business in many ways: monetization scales directly with AUM + generally high customer lifetime values. Today, the fund admin revenue pool is dominated by legacy incumbents like SS&C, a $20B+ public company. They had little incentive to modernize. Until now.

Enter Chris Hladczuk, Co-Founder and CEO of Hanover Park. Before writing a line of code, he cold-called 50 CFOs. When half of them started cursing at the mention of their fund admin, he smelled blood in the water.

That customer mindset, paired with the conviction that Vertical AI could reshape services delivery, became the foundation for a company that just raised a $27M Series A led by Emergence Capital and scaled to $15B of assets on platform in twelve months.

It didn’t take Chris long to realize this couldn’t be a traditional AI-powered software play. Fund administration is high-trust with near-zero tolerance for error — historically meaning human-heavy services. AI is well suited to repetitive, numbers-based workflows, but one hallucination can lose you a $1B client, and there’s no shortage of edge cases quarter to quarter. Cognizant of that, Hanover Park is decidedly human-in-the-loop Vertical AI services. But what does that mean for margins? For product automation over time? How do you stage gate work product and ensure your AI services model scales over hundreds of billions of AUM?

Those finer points of Vertical AI Services are what we’re talking about in today’s episode. Chris shared his path for operationalizing human-in-the-loop — an important reminder that as far as LLMs have taken us, the economy will need humans more than ever to adapt AI to high-integrity fields like financial services. Stick around to hear Chris’ journey cracking a venture-scale market where software failed — from avoiding avoiding the pitfalls of AI services, to landing an outstanding Series A partner in Jake Saper at Emergence.

The Hanover Park Backstory

Chris’s path to fund admin wasn’t linear. After investment banking at Goldman Sachs and a CRO stint at fintech company Meow (scaling 10 to 1k customers), he declared “B2B SaaS is dead” on a podcast in September 2024. While that take was widely dismissed at the time — and might still be hyperbolic — its spirit has been increasingly validated, as AI has transformed the meaning of software.

His thesis: if building software gets cheaper, the moat shifts to whoever owns the outcome. In fund admin, that means not selling software to accountants, but doing the accounting — software, agents, and humans vertically integrated under one roof. He co-founded Hanover Park in 2024 alongside CTO Nick Puljic (Columbia CS, previously co-founded Stock Unlock via YC W22), and the pair set out to build a purpose-built ERP for private funds from the ground up.

At Euclid, we don’t think that owning the outcome offers any inherent moat — nor that services is commonly a fast track to vertical defensibility. But we do agree that the line between services and software is blurry in the age of AI. As we’ll discuss in this episode, the proof is in the pudding: can you internalize with a customer as deeply as any software ever could? Can you build internal IP giving you an automation and / or accuracy advantage? Is your bet on long-term margins credible?

Chris’ answers taught us a lot — catch the full episode to hear them.

The Vertical Playbook

Human-in-the-Loop Vertical AI

Step 1: Build the ERP Internally

Chris’s core architectural bet: own the system of record. Hanover Park built its own general ledger, document extraction engine, and reporting infrastructure from scratch. The system of record determines what agents can actually do — competitors who layer AI atop QuickBooks or a white-labeled legacy ERP hit a ceiling. As Chris frames it: without the ERP for the fund, you’re just a UI on top of somebody else. Don’t just bolt agents on top of someone else’s moat.

Step 2: AI Prepared, Human Reviewed

In a zero-fault-tolerance domain, the worst pitch is “trust me, it’s AI.” Hanover Park‘s paradigm: agents handle the transactional grunt work — cash reconciliation, document parsing, journal entry preparation — while human CPAs review and approve. One customer reported it felt like 25 people on their account when they had two. By automating low-level work, Chris can pay accountants more, give them equity, and recruit what he calls “the Navy SEALs of fund accounting.”

Step 3: Bundle Features, Monetize Products

Unlike the typical wedge-then-expand playbook, Chris refuses to sell any product standalone. Portfolio management, LP portal, KPI reconciliation, and money movement all come bundled with core fund admin at no extra charge. The logic: unbundled products are worse because they lack the source-of-truth data in the accounting system. The bundle is both a competitive moat and a retention mechanism that compounds switching costs.

Step 4: Consider Industry Pricing

Hanover Park charges basis points on AUM — not SaaS subscription fees. This aligns revenue directly with customer success: as a fund grows, so does the contract. Closed-end fund structures mean worst-case duration is ten years, and the best-case is a GP raising a $10B successor fund. Revenue quality mirrors subscription businesses without the artificial per-seat ceiling.

Step 5: Customer Selection as Strategy

Chris actively turns away funds below $100M. In a service where one unhappy CFO can poison a referral network, discipline on customer selection is existential. His North Star isn’t NPS — it’s whether every customer is a “raving fan.” That selectivity, paired with an always-get-on-the-plane founder-led sales motion, builds the trust required to rip out a CFO’s most important vendor.

The Takeaway for Vertical Founders

Hanover Park offers one blueprint for the AI-native services wave: build the system of record for internal use, vertically integrate the human + agent stack, price on outcomes, and bundle relentlessly. In a high-stakes use case like fund administration, there’s no room for hallucinations. The margin for error is low, as is the threshold for losing trust. At the same time, in such fields, if AI can be leveraged to deliver a critical service more reliably and accurately, that’s a trust-builder like no other.

So far, $15B in assets says Hanover Park is doing just that. Especially if gross margins grow and human-in-the-loop yields better automation over time, multiples could look increasingly software-like. This episode is a reminder that the most defensible AI Services companies aren’t necessary replacing humans — they’re making a tight cohort of them dramatically more valuable.

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Thanks for reading the Verticalist and for tuning into the show! Drop us a line below with your thoughts. And don’t forget to subscribe to Verticals wherever you watch or listen:

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Keep Reading…

It’s become something more than that — a publication about what happens when AI meets industries with real complexity. About messy workflows, rare domain expertise, and AI adoption beyond Silicon Valley. And most of all, about vertical architects — the founders reimagining industries from the ground up.

Hence: The Verticalist.

If you’re a founder building in Vertical AI, an operator with an earned insight you’re translating into a startup idea, or an investor tired of pattern-matching on the wrong patterns… this is for you.

Same team. Same thesis. New name.

Thanks for reading. More soon.

— Omar & Nic

2025 was the year Vertical AI moved from hypothesis to reality. After a 2024 defined by AI infrastructure buildout and foundation model wars, capital flowed decisively toward industry-specific use cases. We spent months dissecting every software and AI venture deal of $1M+ across the US and Canada — 4,395 financings and 254 exits, analyzed by vertical, stage, investor, geography, and more — to understand exactly what happened, and what it means for 2026.

Here are a few of our most interesting findings, in brief:

The Year in Numbers

The US and Canadian VC market produced 4,395 software and AI financings totaling $186B in 2025. Vertical startups captured 53% of deal volume and 30% of capital deployed—though that capital gap is almost entirely a function of a small handful of infrastructure mega-rounds (OpenAI’s $40B, Anthropic’s $13B, and a few others). Strip out just the 12 deals sized $1B+ and vertical takes 51% of capital.

Deal volume was relatively even across quarters—Q1 and Q2 combined for 52% of the year’s deals—but capital deployment was lumpy. Q1’s $60.5B was inflated by OpenAI’s Series F alone; remove it and the quarter drops to roughly $20B, making the real quarterly trajectory far more balanced. Vertical’s share of capital climbed steadily: 19% in Q1, 34% in Q2, 32% in Q3, and 42% in Q4.

On the exit side, 2025 delivered $132.7B across 158 vertical transactions—plus another $102B in non-vertical exits. M&A dominated by count, but the story is more nuanced than that, as we’ll get to below. Healthcare led exit volume with 43 transactions, and the IPO window cracked open with 18 vertical offerings after a two-year drought.

Our full report covers vertical-by-vertical breakdowns, stage dynamics, investor league tables, geographic distribution, profiles of fifteen notable deals, and our 2026 outlook. What follows are five findings from the data that we think reframe the conventional wisdom and set the stage for another banner year in Vertical AI.

1. The Remarkable Consistency of Mega-Deals

Most people assume late-stage venture is the volatile tier—big rounds, big markdowns, boom-bust cycles. In 2025, the opposite was true.

The $100M+ tier of vertical financings was the most stable bucket all year. Since Q1, we saw 55-57 deals every quarter. Meanwhile, the $1–5M tier of deals — pre-seed and early seed—dropped from 503 deals in Q1 to 362 in Q4, a 28% decline. The bottom of the market pulled back far more than the top.

Who’s sustaining that stability at the top? The investor data tells the story. The mega-round tier ($100M+) was anchored by a small group of multi-deal leads: NEA (10 leads), Kleiner Perkins (7), Oak HC/FT (5), B Capital (5), DST Global (4), and Ribbit (3). Their average deal sizes clustered tightly in the $110–180M range.

Beyond the frequent flyers, the mega-deal lead base broadened: BlackRock co-led Applied Intuition’s $600M round, Franklin Templeton led Plaid’s $575M, Qatar Investment Authority and Mubadala showed up in multiple rounds. Asset managers in pre-IPO rounds is reminiscent of 2021 — the uptick of sovereign wealth involvement is perhaps more notable, especially in light of their parallel growing interest in backing US VC funds as LPs.

2. A&D Momentum: Conviction in Chaos

When people think “hot vertical AI categories,” they think healthcare and fintech — and of course, they’d be right (especially regarding the former). But the 2025 data tells us that another category may be dominating the hype cycle.

Aerospace & Defense led all verticals in both capital intensity per employee ($476K per FTE) and median valuation step-up (2.15x). It beat Manufacturing ($421K, 2.08x), which got most of the headlines thanks to Project Prometheus ($6.2B) on the funding side and Ansys ($35B) on the exit side. It beat Financial Services, which despite attracting the most aggregate capital posted a modest 1.80x step-up (turns out bigger rounds dilute per-round markups even when total value creation is strong).

The contrast with Education is particularly stark. Education had the lowest capital intensity ($200K per FTE), the lowest capital velocity ($1.6M raised per year since founding), and the lowest capital efficiency ($342K raised per employee)—bottom of the table on every capital metric. And yet Education posted the highest early-stage step-up (2.5x in the $5–15M tier). Despite great emerging stories in SchoolAI and MagicSchool, the category isn’t yet a proven absorber of capital at scale. A&D is the opposite: capital-hungry and valuation-rich, despite its unproven track record as a generator of historical VC liquidity.

Forterra’s $238M Series C—profiled in our full report—is a case in point. Unlike some other A&D high-flyers, they have production contracts and program-of-record revenue, not R&D grants. Backed by an interesting investor syndicate (Salesforce Ventures, Franklin Templeton, Hanwha, NightDragon) that reflects the reanimation of crossover investing. Beyond any one deal—and equally relevant to VC and LP funding—the intersection of government and technology a is dynamic we’re watching closely heading into 2026.

3. CA: America’s Least & Most Vertical State

Amongst all US states in 2025, California posted the lowest ratio of vertical deals, 41% — yet it was also home to the highest absolute number, at 667 deals.

This makes CA simultaneously the least and most vertically oriented in the country. Non-coastal standouts—Arizona (71%), Ohio (70%), and Tennessee (67%)—posted vertical shares that make California look like it’s playing a different game. While New York, at 53%, tracked near the national mean in terms of verticality, it was the clear #2—home to 406 vertical deals in 2025, with signs of the most momentum.

The explanation, in CA’s case, is straightforward: dominant concentration of capital-hungry horizontal AI infrastructure, foundation models, and dev tooling pulls the denominator up. And while it makes sense that company formation is strongest in industry-proximate geographies—contractors in Arizona, manufacturers are in Ohio, healthcare operators in Tennessee.

The regional data adds another layer. The Midwest leads in vertical proclivity at 59.6%, followed by a tight cluster in the mid-fifties: Mid-Atlantic, Mountain West, and Southeast. The West trails at 41.3%. And Canada’s lower vertical share—somewhat surprising given Toronto’s legacy as home of Constellation Software—appears driven by concentration in a single metro, with much of its vertical reach involving investment in US-based companies.

San Francisco and New York remain the twin dominant vertical hubs by volume and deal quality. And while the data show that non-coastal metros embrace verticality, they have two big hills to climb: (1) bridge the massive gulf of general startup gravity; and (2) keeping pace on AI-nativity in this dynamic new environment.

4. Manufacturing is the Momentum Leader

We examined the share of 2025-funded companies in each vertical that were founded in 2022 or later—the post-LLM cohort. Manufacturing & Industrial led in “AI-native” share at 57%. Not healthcare. Not legal. Manufacturing.

More than half of all manufacturing AI companies funded last year didn’t exist before ChatGPT. The cross-vertical baseline is 46%, meaning manufacturing’s new-company share runs 11 points above average. Public Sector (56%) and Legal (53%) also exceeded the mean, but manufacturing’s lead reframes the narrative around the dominance of white-collar verticals in AI-native opportunity.

Deal activity data supports the formation data. Manufacturing deal count rose 41% from Q1 to Q4, the steepest acceleration of any vertical. The category produced the year’s second-largest financing—a $6.2B round we profile among the full report’s fifteen deal highlights.

At the same time, it’s important to remember that as AI and robotics merge into “Physical AI,” core differences in business model can be blurred. Much of this activity in Manufacturing — at least from a capital standpoint — is going into hardware-dominant, high marginal-cost businesses. At $421K in capital intensity per employee, Manufacturing & Industrials companies are raising more to meet the physical-world infrastructure requirements of bringing AI to factory floors, supply chains, and industrial operations — and they are spending more too.

5. Liquidity, Liquidity, Liquidity

In 2025, buyout and LBO transactions accounted for 20% of vertical exits by count—but generated about as much liquidity as M&A. PE firms view vertical software as ideal acquisition targets: recurring revenue, defensible market positions, and operational leverage from AI integration. The majority of PE-backed software acquisitions in 2025 targeted vertical companies. Silver Lake, EQT, and TPG were among the most active acquirers.

Meanwhile, IPO activity last year was meager — but perhaps a start. 2025 produced 9 vertical IPOs (of 15 total) generating $4.3B in aggregate value (just under 40% of all IPO value). Something after a two-year drought, but thin relative to the backlog of late-stage companies that raised $200M+ rounds over the last 12-24 months. Expect more tough go/no-go listing decisions in 2026–2027.

The most revealing exit metric may be the simplest one: the median vertical exit in 2025 was approximately $500M. For the many seed and Series A funds that have scaled up AUM since 2021, driving superlative returns via such median outcomes demands discipline on entry price that seems quite rare these days. At a $10-20M post-money seed, a $500M exit represents 25-50x gross valuation appreciation—exceptional. At a $50M+ post-money “seed” (increasingly common in consensus Vertical AI), the same exit is 7-8x gross. Still great, but tough math for a >$100M AUM fund.

The top 10 exits by value in 2025 accounted for over 60% of total vertical exit value—a reminder that no category escapes power-law dynamics. Below the mega-deals, PE remains a leading exit path, IPOs remain selective — the “middle market” of vertical exits ($250–1B EV) is arguably the healthiest and most under-appreciated segment of the liquidity environment, at least in VC land.

What’s in the Full Report

The findings above are drawn from roughly a quarter of the complete Vertical Report. The full publication includes:

• Vertical-by-vertical analysis of 18 sectors: deal count, capital deployed, stage mix, median deal sizes, and quarterly momentum for each—from Healthcare (597 deals, $11.8B) down to Energy & Climate (60 deals, $900M).

• Stage dynamics deep-dives: deal size distributions by vertical, capital intensity, valuation step-ups, capital velocity, and capital efficiency metrics.

• Profiles of 15 notable deals, from a $190M public-sector round to a $6.2B industrial mega-financing—covering step-ups, investor syndicates, founder backgrounds, and what each deal reveals about its vertical.

• The full exit landscape, including >150 deals worth $130B+ broken down by type, quarter, vertical, and size—plus the IPO pipeline and PE acquisition patterns.

• Investor league tables, from the most active seed leads to the dominant growth-stage firms—plus corporate venture and SWF trends.

• Complete geographic analysis by state and region.

• Our 2026 outlook on four structural dynamics: the seed-to-A graduation test, deep tech’s maturity cycle, shifting liquidity winds, and the coming fight for AI defensibility.

The Vertical Report is published annually by Euclid: a VC firm backing Vertical AI founders from inception. For questions, reach out to the Euclid team via LinkedIn or DM.

This week, we’re taking a look back to recap some highlights to date — and the key lessons we learned from each. From Slice and Broadlume, to Qualia and ServiceTitan, there’s a masterclass here for you. We’ve also added links to essays and playbooks from Euclid Insights relevant to each episode, so you can dig deeper.

If you haven’t, please take a moment to subscribe to Verticals on YouTube below (it’s only place we post full episodes and shorts). It’s been an incredible first quarter… and we look forward to bringing you many more.

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Ilir Sela, Slice (Ep. 1)

“Independent, Not Alone”

Our very first guest framed the thesis for the entire show. Ilir built Slice into a billion-dollar platform for independent pizzerias by running the reverse franchise playbook: give operators the shared stack and economies of scale of a Domino’s without forcing brand uniformity. The moment that stuck? Slice’s AI voice ordering hits ~90% transaction completion—but humans still clear 98%+. For low-margin shops, that 8-point gap is existential. Until AI clears the bar, humans stay in the loop. Cold pragmatism that every vertical AI founder selling into cost-sensitive end markets should internalize.

Jeremy Yamaguchi, Cabana (Ep. 2)

The AI-First Roll-Up Math

Jeremy laid out the honest economics of buying, operating, and AI-enabling legacy service businesses from his POV running Cabana. The takeaway: you’re running three businesses at once—an operating company, a tech company, and a PE shop. The magnitude of AI margin uplift is still unproven, but the structural advantage is clear: if you own the P&L, you can force AI adoption in verticals that would otherwise resist it for a decade. We unpacked the model further in our AI-First Roll-Up essay.

Todd Saunders, Broadlume (Ep. 5)

Community as Moat

Todd—whom we’ve called the “grand vizier of flooring”—explained how Broadlume turned community, content, and first-party data into durable defensibility in an industry most VCs wouldn’t touch. He parlayed that strategy into a strong exit to PSG — after having given up nearly $10M in ARR to undertake a risky pivot... incredible story. His return for our 2026 Predictions special only reinforced the point: the operators who know their verticals cold are the ones building platforms that last.

Mike Kopko, Pearl Health (Ep. 6)

Healthcare’s First Inning in AI

Healthcare is the largest category in Vertical AI by deal count and capital deployed. Mike — the founder / CEO of Pearl Health — made the case for why it’s barely started: a quarter of the US economy, byzantine money flows, a huge chunk of vertical investment (as we’ve covered in past Vertical Reports) and enormous room for AI to align incentives in value-based care. His key point for founders: sell provable time-to-value and measured ROI for discrete use cases—not “AI.” In non-tech-forward verticals, forward-deployed resources aren’t overhead; they’re the product.

Sam Youssef, Valsoft (Ep. 8)

The Compounding Machine

Sam runs Valsoft, a 130+ portfolio-company, $750M+ revenue vertical software aggregator in the tradition of Constellation Software. And the bigger the vertical, the less interesting it usually is for them. Large markets attract aggressive VC dollars and irrational competition. Smaller verticals—forestry, niche manufacturing, obscure back-office workflows—tend to be calmer. Rational budgets, rational pricing, rational competitors. That’s Valsoft’s domain. While he’s not alone in the world of vertically-focused PE shops (as we covered a while back in our Buyout Frenzy piece), Sam takes a uniquely single-minded approach. The episode was another reminder that “small TAM” markets often harbor enormous, defensible value once you’re inside the workflow.

Dan Friedman, Moxie / Boulton & Watt (Ep. 9)

Business-in-a-Box Done Right

Dan’s Moxie—a platform helping aesthetic clinicians launch, run, and grow compliant medspa businesses—is a case study in an important business model: business-in-a-box. There are many variations: we called it the Synthetic Roll-Up, others like Ilir at Slice (see our first episode above) see it as unbundling the franchise. Dan’s core lesson: start with off-the-shelf software and services to earn trust, before worrying about making the entire tech stack proprietary. Just as important is picking what not to do early. He also discussed how compliance was a unique moat for Moxie; operating across 50 different state regulatory frameworks is just one way they could help. This episode is also worth it to hear about Dan’s unique incubation model in Boulton & Watt.

Rahul Hampole, ServiceTitan (Ep. 10)

The Vertical Fintech Playbook

Rahul ran fintech at Yelp, Plaid, and now ServiceTitan. His message was surgical: optimize for customer value first, take rate second. Have 20 of your best customers on a text channel—they’ll tell you transparently what’s keeping them up at night. That feedback loop is your early warning system for which fintech products to build next. For vertical SaaS players focused on SMBs, aspire for 80–100 basis points on card transactions as a starting point—and fight for it. Generalizing this episode left us with a nice framework for finding PMF (in line with our own product-market fit guide, “Nobody Wants Your Product!”) and considering when to launch new platform products.

Nate Baker, Qualia / Fractal (Ep. 14)

TAM Is Not Fixed

Nate challenged one of the most persistent investor objections in vertical software: the TAM is too small. Having built Qualia into the backbone of title agents across America (500K+ professionals, 600 employees) and launched 150+ vertical SaaS companies through Fractal, his view carries weight. In vertical markets, TAM expands with each new layer of value you create. Companies that seem to have a $5K ACV ceiling routinely break through to $50K+ by solving adjacent problems. The episode is a great real-world case study building on the quantitative frameworks we explored in our Vertical AI market sizing series. Nate’s boldest claim: every business that isn’t seriously reevaluating its product offerings right now will be cooked.

Michael Saltzman, EvolutionIQ (Ep. 15)

From $50K First Year to $750M Exit

Everyone talks about how the revenue bar has permanently elevated. Michael’s story is the counterpoint. EvolutionIQ’s first year: one design partner, ~$50K total revenue. A few years later, 8-figure ARR and a $750M exit to CCC. The playbook: sell guidance (not automation) into a workforce that believes it already has strong expertise. Build a “digital twin” of data rather than writing back into core systems. And make results—not pitch decks—do the early selling. Architecture that can adapt as models improve was the real moat. For founders: this is not only an incredible exit story, but also a reminder of what initial design partnerships in enterprise Vertical AI can really take.

Mike Powers, BuildVision (Ep. 16)

Give Away the SaaS, Capture the Decision Layer

Mike is betting that in the age of AI, the value of the “interface” goes to zero. BuildVision gives away the workflow software for free, captures the network, then monetizes the transaction, the risk, and the capital flow in commercial construction procurement. His take on certain popular AI point solutions was provocatively bearish: Claude can already do a lot of what companies have raised hundreds of millions to solve. The defensible asset isn’t the wrapper—it’s the proprietary data moat and the decision layer (or what many are now calling the Context Graph). As always, Mike is a poster child for the importance of domain expertise — both lived and learned — for AI distribution into traditional industries like construction.

Darren Fike, Santé (Ep. 17)

AI as a GTM Weapon

Darren’s approach to using agentic AI to collapse switching costs might be the most underrated tactical insight from the show. In the $80B U.S. liquor retail market—90% independently owned—every prospect has been on the same system for 10+ years. Santé uses AI to download a prospect’s data, show the system pre-populated on the demo, and collapse the psychological distance between “considering” and “we can use this today.” Recently, this method yielded Darren 30 deals in a single month, zero feature requests. It’s a really unique case study on how founders are not only building novel products with AI, but also leveraging it to solve some of SaaS’s traditional friction points (for further reading on this, check out “Vertical AI’s Integration Problem”).

Nick Tippmann, TipTop VC (Ep. 18)

SaaSpocalypse & Distribution Tactics

In the span of weeks, $400B of SaaS enterprise value evaporated in the midst of the “SaaSmageddon”—the category's largest non-recessionary drawdown in over 30 years. Nick, founder of TipTop VC, joined us to make sense of the carnage. The bears say SaaS is dead. We disagree—AI is still software, and the market is expanding. But the question shifts from "will software survive?" to "who wins?" We shared our answer in our recent post, “The Dispatcher Problem.” Nick thinks distribution is a big part of the answer too — and shares his top tips (pun intended) on early Vertical AI GTM in the back half of this episode.

On behalf of both Euclid Insights and Verticals, we’re so thankful to have you as a reader, watcher, and / or listener — at least in our careers, there’s been no more exciting time to be a part of the growing Vertical AI community.

New episodes of Verticals drop every Wednesday — Subscribe on YouTube or Spotify!

And if you’re an operator ideating on something new in Vertical AI, the Euclid team would love to hear from you. Drop us a link on LinkedIn or our DMs here.

Look at the $5–15M range in the chart above—in any given month, there are pre-seeds, seeds, and Series As all landing in that band. The word “seed” is being applied to $500k SAFEs and $15M equity rounds. It spans pre-product, pre-revenue teams and companies with millions of ARR. Functionally, the term is now useless.

What actually determines stage nomenclature? It’s honestly hard to say.

Confusion by Design

VC round sizing is — and has always been — more vibes-based than deterministic.

1. Round order (“your first round is a pre-seed, your second is a seed”) never made much sense. Extensions, rolling closes, and opportunistic follow-on slugs throw off the sequence—especially in large datasets.

2. Traction sounds right in theory, but anyone crunching numbers either doesn’t have access to the full data set (e.g., managers analyzing portfolio data) or doesn’t have access to fundamental performance data (e.g., analysts like Carta). Proxies like headcount are too imperfect to be helpful, though Carta’s data does show Series A teams now average just 16.8 employees—down from 25.9 in 2021—reflecting the AI efficiency thesis in real time.

3. Self-reporting is what most structured datasets ultimately rely on, drawing from press or share names in filings. There’s always been a founder (and VC) incentive to report the “youngest” round possible—to seem more precocious. We’ve known founders who did three consecutive “seeds” even though they were hot commodities, purely for positioning. There’s nothing wrong with that—in fact, sometimes it’s the smart move to improve the chances that the right funds will think of you on first glance (though it won’t fool anyone sophisticated at the end of the day). Self-reporting is as inaccurate as it sounds.

4. Round size was historically the most reliable heuristic. Rounds of $1M, $3M, $10M, $30M, and $80M mapped cleanly to pre-seeds, seeds, Series As, Bs, and growth. It was never perfect, and some share of rounds were always way out of band. But in today’s market, that mapping has almost completely collapsed. In the last 12 months, we estimate pre-seeds have grown by 50%. $10M seeds are de rigueur. While perhaps eye-catching in their own ways, neither the $6M Series As nor the $600M Series As are abnormal.

There’s no good single heuristic and we’re not arguing it should be otherwise. But these stage names do play a role in high-level sorting and self-selection in our ecosystem. Founders use the terms to signal to the market the sort of round they intend to raise. VCs use them to signal to founders the sort of investments they like to make (and to LPs, where in the capital stack they play).

Our argument is that we have reached a moment where the terms need to shift, in line with market realities already baked in… even if they haven’t yet permeated the vernacular. But before getting to our prescription, let’s touch on the pathology; how did seed get where it is today?

The Origins of Modern Seed

Round sizes — and hence round names — are ultimately set by VCs, who allocate based on deal flow and AUM. Investment amounts are informed by the same fundamental factors behind all investor decisions: fear and greed. Both are manifesting in new ways that have steadily warped the seed market since 2021.

On the fear side: there’s too much capital in certain bands of early-stage VC. Multi-$100M+ “seed” funds have too much AUM to do $1–2M pre-seeds, but rarely have the brand to compete with platform funds for true Series As. They’re squeezed—so they take the deals they’re good at and expand them. At the same time, AI has inflated what “great” looks like: per Bessemer’s State of AI 2025 report, a “Shooting Star” must eclipse $100M in ARR in 4 years. In that environment, some investors have chosen to gravitate earlier: avoiding scrutiny against rising milestones by backing pre-revenue companies that could be 10x growers rather than ones already demonstrating a solid 3x. Especially as AI has driven a wave of capital efficiency — in ability to grow on fewer dollars and achieve profitably, if not in overall dollars raised — that have the potential to diminish reliance on mid-early-stage funders. This raises a tentative fear of a rise in inception-to-Series-A stories that could make traditional seed even less valuable.

On the greed side: mega funds are absurdly large—so large that returns targets traditionally associated with early-stage venture capital are hard to imagine at their AUM. But they still have the brand to convince founders they’re valuable partners at any stage. That said, the VC brand league tables are ever-shifting. We wrote about this in The Seed-Stage Reckoning and The Mind-Virus of Big Venture. While it seems like the same names have been dominant forever, that’s simply not true outside of a handful — it just especially seems that way as capital has concentrated into fewer, larger funds, following the 2022 downturn. Historically, it’s not uncommon for these perception shifts to occur as a result of drastic changes in AUM that move incentives, competencies, and (eventually) brand association out the stages at which that firm originally built its success.

The combined effect: round sizes have inflated across the board, and the relationship between capital raised and company stage has broken down. A $3M raise and a $30M raise can both describe pre-product companies with zero revenue. The amount of capital has become a significantly less reliable proxy for stage, traction, or quality.

This is the backdrop for our central argument: what VC calls “seed” is no longer a coherent stage. It’s a catch-all for everything from formation to Series A. We need a new perspective — most especially at the earliest stages, where institutional capital has only begun to play.

The Case for a Stage Shift

Periodic shifts in stage nomenclature are nothing new in the startup and venture capital ecosystem. Implicitly, they are happening continuously — explicitly, consensus seems to update every 3-10 years.

Before 1990, the landscape was simpler: friends-and-family money gave way to seed (more often “angel” rounds then), followed by Series A, B, and growth. Once coined, “Seed” meant what it sounded like—a small check to get started. From 1990 to 2005, through the dot-com boom and bust, the basic architecture held, but the scale of growth-stage capital ballooned. The bubble peaked at ~$90B deployed in 2000, a figure that wouldn’t be matched for two decades.

The first real structural shift came in 2005–2015, when Y Combinator, micro-VCs, and the SAFE note collectively redefined what “first round” investing looked like. Seed became institutional. It moved from angel territory — individuals writing $25–100K checks on a handshake — to a category with dedicated funds, standard terms, and follow-on expectations. Aileen Lee coined the term “unicorn” in 2013, and the implicit benchmark for what a seed company should become began to rise.

By 2015–2021, the ecosystem responded with two new stages. Pre-seed emerged around 2015 to describe $500K–$1.5M rounds for the earliest companies — the stage that seed used to occupy before it moved up. At the other end, pre-IPO rounds appeared as historically non-VC entrants moved in and companies stayed private longer, creating unique class of big-check capital that has since shifted to fewer, bigger, mostly foundational AI companies. Every existing stage shifted roughly one position later: what had been seed-stage companies were now raising Series As. What had been Series A companies were raising Bs.

This brings us to the present. Since 2021, the same rightward drift has continued—and accelerated. Today’s seed ($3–12M, institutional, often requiring traction) is functionally the old Series A. Today’s pre-seed ($750K-$4M, increasingly institutionalized) is the old seed. And that leaves a vacuum at the very beginning: the true formation stage, where a founder has conviction and a thesis but minimal product, minimal if any non-founding team, and little-to-no traction beyond design partnerships.

The stage for which the seed was invented no longer has a name.

Enter Inception

Inception is not a rebranding exercise. It describes something structurally new: for the first time, institutional funds—not accelerators, not angels, not friends and family—are providing genuine 1-on-1 partnership at true formation stages. Inception investing typically contemplates a $500K–$3M check, written pre-traction, pre-product, often pre-deck, and sometimes before a team is fully assembled. The investor’s value at this stage must go beyond the check: it’s operational co-building during the months when the company’s direction is still being set, iterating at high velocity, and arming the business with high-quality resources and guidance.

This stage is distinct from what pre-seed has become over the last decade. Pre-seed was supposed to fill this gap, but it has followed the same path as seed before it: it got institutionalized, bloated, and crowded. The median pre-seed round has roughly doubled since 2020. Funds that call themselves “pre-seed” now write $1–4M+ checks into companies that — often, if not always — have a product and early users. The label drifted from its original meaning, just as the seed did.

Many funds are talking about this vacuum and laying claim to inception. Few are actually dedicated to it, and even fewer are built for it. The gap between rhetoric and reality is revealing. If they’re unwilling to write a check before traction — or their fund model doesn’t support the time, concentration, and hands-on engagement that true formation-stage investing demands — they aren’t inception investors, at least as we see it. It’s also important to distinguish that, while accelerators nor incubators do check the box in terms of stage appetite, what we are talking about here is a net-new stage of institutional venture capital: the desire to build an active GP-founder relationship, bringing what once was only available to startups reaching Series A to true formation.

We believe an inception fund fundamentally differs from a pre-seed or seed fund. Deal sourcing requires novel origination. The portfolio must be concentrated enough to allow high-levels of support post-investment. The GP’s time per company is measured in days per month, not hours per quarter. The failure modes we often see at this stage are over-funding a company, which cascades into overspending by the startup before it is ready for scale. Another is writing a check and disappearing. This is the stage where VC-founder alignment matters most, because the investor’s behavior in the first six months shapes the company’s trajectory more than any follow-on round will.

Inception is the frontier of innovation in the venture capital industry — and to do it well at an institutional level requires not only the right risk appetite, but differentiated approaches to sourcing, winning, and supporting founders designed for a stage where top-of-funnel is not a deal, but people.

What Founders Should Ask VCs

If “seed” is a useless label and every stage has shifted, the first question for any founder raising capital is more basic than it sounds: where are you, really? Not what round number you’re on, not what your last investor called it, but what stage of company-building are you actually in—and what kind of investor is built for that stage?

Once you’ve identified where you actually are—not where you aspire to be in a year or two—three questions can help you cut through the noise with prospective investors:

1. What does success look like for your fund?

After a VC invests, what does success look like for them on both a fund level and portfolio-company level? A several-$100M seed fund writing $5M checks needs multi-billion-dollar outcomes at double-digit ownership for a single deal to matter. A mega-fund writing early checks needs decacorn outcomes for that first investment to represent more than an option. This is the crux of what we explored in The Quiet Death of VC-Founder Alignment—fund economics shape investor behavior far more than any outward positioning will reveal.

2. What would the ideal next step for us be?

For our company, what do you think an optimal next round would look like? What are some examples of that step for companies in your portfolio? How many companies in their last fund achieved that, and what did the typical trajectory look like along the way? Of course, if 70% of a S1 portfolio doesn’t make it to S2, that’s a meaningful signal about the kind of bets they’re making and their ability to help support their success. If they start talking about leading your Series As, that should raise a flag (what if they don’t?); if they expect you to skip straight to Series A, raising $20M+ or bust next, what does that mean for your optionality? A VC’s answer will speak volumes.

3. Are you valuable for my stage, or the next?

Every fund is optimized for a specific phase of company-building, and its value is typically sharpest in that phase. A fund set up to accelerate companies with a few million in revenue will excel at scaling out teams, optimizing go-to-market, and prepping for the maturity required of a business looking to launch into it’s scale-out phase. That's genuinely useful—if you're at that stage. But if you're a month out from formation, you need something entirely different: help iterating on the idea itself, perhaps making a founding engineering hire, warm introductions to seed (and potentially Series A GPs) for the next round, and bringing on your first advisors. Moreover, signal matters. And yes, usually that means brand and network. But consider that a big brand on your cap table at inception, that doesn’t participate in the next round, is a glaring red flag.

Make sure the investor you're choosing is valuable for where you are — and has the fund model to deliver that value — whether it’s tactical, tangible, or signal-based. You will, in all likelihood, bring further investors onto your cap table down the road: take care to partner with funds when and where they are most impactful.

Inception is the Frontier of VC Innovation

In just the last two decades, total VC AUM has expanded by an order of magnitude. The naming conventions we’re clinging to—pre-seed, seed, A, B—are artifacts of a time when the entire US VC industry deployed less in a year than a single mega-fund now manages. Every decade, the ecosystem has added a stage to account for what the previous terms no longer describe. We’re at that inflection point again.

Our case for inception isn’t simply about a label; it’s an argument about what the earliest stage of venture investing should look like. Not accelerators running cohorts of 200. Not angels writing checks on a napkin. Not pre-seed funds & seed funds masquerading as the same “product.” Inception is an institutional commitment to 1-on-1 partnership at the moment a company is being born—when the investor’s judgment, time, and operational engagement matter more than their capital.

With the exception of small partnerships that truly operate as a unit (rare above a few hundred million AUM) and programmatic funds like YC, VC is more of an individual sport than most realize. The GP on your board, answering your calls, by your side for a decade — that’s what matters. As we argued in Software Is Dead—Long Live Software, AI is reshaping not just which companies get funded but the entire business model landscape VCs invest in. The firms that adapt — by building their funds for the inception stage, not just talking about it — will earn the right to partner with the next generation of breakout founders from day zero.

Round names won’t ossify, nor ever be perfect. The age of Inception has just begun and deserves its own distinct consideration as a stage — but will eventually evolve too. Certainly, the capital efficiency enabled by modern AI is changing definitions faster than anyone can write. One piece of advice, however, is timeless: founders and allocators who continually interrogate the strategies and incentives beneath the nomenclature and find the right match for their goals will navigate this market with far more clarity than those who think one size still fits all.

Thanks for reading Euclid Insights! Additional sources here.1

Euclid is an inception-stage VC built for Vertical AI founders. If anyone in your network is working on a new startup in this space, we’d love to help. Just drop us a line via DM or in the comments below.

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This Week’s Episode

There’s a good chance growth equity brings to mind a time-tested framework: analysts screen the top of the funnel, cold-call their way through, compete to sniff out hidden winners first, and slowly escalate conviction up to GPs. Along the way, the CRM is a filing cabinet — a retrospective store of data. Thesis and conviction live in hallways and IC meetings. And it works. But sometimes — and perhaps especially in this new era of AI acceleration — there’s risk of winners slipping for the wrong reasons… because someone once said “construction tech is too cyclical” or “gross margins below 85% are unacceptable” once and it stuck.

Dave Pandullo and Scott Hoch, General Partners at AQL Growth — a growth-stage fund launching from Frontier Growth focused exclusively on vertical software and vertical AI — decided to launch their firm with a different backbone. They built Eagle Vision, an internal AI sourcing platform that encodes their investment thesis into a system of judgment at scale. Their results are early but compelling: partners on planes before the first call, and a portfolio company (Albi) sourced, scored, and closed end-to-end.

This week, we share their story and a peek into their playbook. And we get their view on the future of systems of record, RCM, scribes, and much more.

This Week’s Guests

Dave & Scott of AQL Growth

AQL didn’t start with a blank sheet of paper. Frontier Growth has been investing in software since 1999, and the team’s vertical conviction traces back to 2007, when Frontier backed Daxko — club management software for YMCAs and JCCs that looked, at the time, like a “tiny little market.” They exited too early. Daxko went on to become the dominant platform in sports and recreation, now north of $250-300 million in ARR. It made a lasting impression: niche verticals that look small from the outside often harbor enormous, defensible TAM once you’re inside the workflow.

Dave — who previously co-founded a mobile telehealth software company — and Scott, a 20-year growth equity veteran who came up through investment banking, spent years building pattern recognition across verticals inside Frontier’s broader strategy. Trades, sports and entertainment, state and local government, outpatient healthcare — eight investments in the last 12 months alone. The thesis tightened: modern systems of record evolving into systems of action, AI-native wedge solutions, capital-efficient growth, and verticals with a right to win. The problem was that this thesis lived in partner conversations and judgment calls — not in a system.

The spark came when Scott ran a CRM audit and discovered, to his and Dave’s dismay, that it was “a filing cabinet with the wrong information in it.” They couldn’t identify their top 100 targets. They couldn’t answer basic questions: Who is our ICP? How many of them are there? How do we stack-rank them? So they reframed the problem entirely. Capital, they reasoned, is increasingly commoditized — it’s a product. Founders are the customer. And like any good vertical software company, AQL needed to know its total addressable market. That realization — treating themselves as a product company, not just a fund — became the genesis of Eagle Vision.

The name AQL itself, derived from Aquila (Latin for eagle), encodes the ethos: sharp vision, intense focus. They write $5-30M minority checks into post-product-market-fit companies at $2-3M+ ARR, and they needed their sourcing engine to match the precision of their strategy.

The Vertical Playbook

Building an AI-Powered Investment Engine

Step 1: Encode Your Thesis

AQL’s first attempt at AI sourcing “completely whiffed.” They fed a model their website, strategy, and past investments and asked it to evaluate targets. The output was unusable. The problem wasn’t the model — it was that their thesis lived in partner conversations and judgment calls, not in a system.

So they reversed the approach. Instead of asking AI to evaluate companies against a vague mandate, they codified exactly what they believe: modern systems of record evolving into systems of action, AI-native wedge solutions, capital-efficient growth, and verticals with a right to win. They then encoded those criteria into a scoring algorithm, treating founders as their ICP and capital as their product. As Scott put it: the goal was “not to replace our judgment, but to encode what we really believed.”

Step 2: Scan at Scale

With a codified thesis, AQL ran 25,000 software companies through Eagle Vision’s filters. The output: a TAM of 6,500 vertical software companies fitting their strategy. Overnight, they went from not being able to identify their top 100 targets to knowing exactly who their ICP was and how to stack-rank them. The system simultaneously generated IC-level content for each target — customer industry analysis, jobs-to-be-done breakdowns, ROI modeling, vertical and software value chain mapping — that Scott described as “deeper and richer than IC presentations I had seen in my 20-year career.”

Step 3: Build Your “Attractiveness Index”

Knowing which companies fit the thesis wasn’t enough. AQL needed to know which verticals were worth the bet — and how attractiveness shifted over time. They built a Vertical Attractiveness Index (VAI) scoring markets across multiple dimensions: density of modern systems of record, market sizing and white space, data moats around the solution type, competitive landscape, customer satisfaction with incumbents, product roadmap trajectories, M&A activity, capital raises, and competitive hires. Critically, the VAI is dynamic — recalculated as competitive moves, funding events, and talent shifts hit the market. The CRM went from a static filing cabinet to a living intelligence layer.

Step 4: Flip the Funnel

The traditional growth equity funnel runs: analyst screens → cold outreach → slow escalation → senior engagement. Eagle Vision inverts it. Because the scoring algorithm so tightly encodes partner-level judgment, “we’re getting partners on planes before we’ve even had a conversation with a company,” Dave explained. The new flow is: identify, prioritize, engage, sell, add value. AQL’s investment in Albi — a business management platform for damage restoration — was the proof point. Eagle Vision identified and prioritized it; the team moved fast, shared deep industry knowledge with founder Alex Duda on first contact, and saw past surface-level retention metrics that scared off other investors. Nine months post-close, retention had dramatically improved — exactly as the model predicted.

The Takeaway for Vertical Founders

AQL’s Eagle Vision isn’t a product they plan to sell — it’s their investment thesis encoded as software. But the meta-lesson applies to any vertical operator: the best AI won’t replace domain expertise, but rather systematize it. The most data or the biggest models certainly help. Startups that also abstract subjective judgment into scalable systems — turning that tribal, rarefied knowledge into an edge — are building truly powerful moats.

Keep Reading…

Why all paths aren’t equal in the search for AI-proof vertical moats.

Watch the full episode on YouTube

Jefferies coined it the “SaaSmageddon.” In the span of weeks, $400B of SaaS enterprise value evaporated — the category’s largest non-recessionary drawdown in over 30 years. ServiceNow down nearly 50% from its peak. A penny stock that sells karaoke machines announced a freight AI product and triggered a double-digit sell-off across logistics software.

The bears say SaaS is dead. We disagree — AI is still software, and the market is expanding. But even if the pie grows, the league tables will look different. In a world where five to ten credible vertical AI competitors exist in nearly every category, the question isn’t will software survive — it’s who wins? History has a clear answer: distribution. There were a hundred Toast-like companies. One captured 50-60% market share — not with the best product, but with feet on the street.

In this episode, we dive into the SaaSpocalypse with Nick Tippmann, the Founder & Managing Partner of TipTop VC. TipTop is an Austin-based first-check fund investing in vertical software and AI. Previously, he was the founding CMO at Greenlight Guru (zero to eight figures of ARR, $120M PE investment). Tune in to hear Nick’s his view on the public market turmoil, his journey as an operator-turned-vertical-VC, and the GTM motions separating the best-performing Vertical AI founders in our respective portfolios.

This Week’s Guest: Nick Tippmann

From Founding CMO to First-Check VC

Nick grew up entrepreneurial in small-town Indiana — always wanting to start a business like his dad and grandpa. By his senior year at Indiana University, studying entrepreneurship, he realized the question he’d come to college asking (”how do you start a business?”) wasn’t specific enough. The real question was how to build a high-growth, venture-backed tech startup — and most of those words were foreign to a kid from rural Indiana. Then he discovered the early VC blogging wave: Brad Feld, Fred Wilson, Mark Suster, Paul Graham. Three out of four had been founders first, then became VCs. That planted the seed. For the next 14 years, he operated with a dual mindset: build something great as a founder, then eventually make the jump to venture.

The building phase was Greenlight Guru. Nick joined as technically the first employee and founding CMO of the medtech vertical SaaS, which helped medical device companies get FDA approval and stay compliant post-market. Over an eight-and-a-half-year ride, he wore every hat the company needed: marketing, rev ops, product (once PE came in), and eventually strategy and corporate development. That cross-functional grind — from demand gen to M&A — that cross-functional experience taught him what many VCs learn secondhand: go-to-market isn’t a department, it’s a system. Greenlight Guru scaled from zero to eight figures of ARR and attracted a $120M private equity investment before Nick moved on.

That move was TipTop VC, an Austin-based first-check fund writing $100-400k checks alongside leads like (our fund) Euclid. Nick’s LPs and supporters have experience across vertical winners like Toast, Shopify, Procore, ServiceTitan, Mindbody, and others — operators who’ve built and sold the exact type of company TipTop invests in. For Nick, that collective doubles as a proprietary diligence and support network, giving portfolio companies direct access to founders who’ve already navigated the vertical playbook. And he’s got some nice early breakouts in the portfolio, including GC AI, which went from $1M to $10M ARR in under a year and raised a $60M Series B at a $555M valuation.

Vertical Playbook of the Week

Roundup: 7 Vertical AI GTM Hacks

Step 1: Instrument RevOps Like a Product. The fastest growers treat their GTM engine with the same discipline as a product backlog. Weekly demand gen meetings. Every channel owner reports their number with a red/yellow/green status — if you’re red, you present your action plan and report back in five days. Channels stop working in 60 to 90 days. The companies that win detect that early.

Step 2: Sell Revenue-Connected Wedges, Not “AI.” Below the enterprise, most vertical buyers don’t care that it’s AI — in blue-collar verticals, it can be a turnoff. The winning wedges connect directly to recovered revenue: scheduling AI that recaptures 10-30% lost to cancellations, voice AI that captures missed leads after-hours. You’re not selling artificial intelligence. You’re selling money back.

Step 3: Sell Way Ahead of Product. The best growers in TipTop’s portfolio consistently sell before the product is ready. They’ve done deep enough customer discovery to find the hair-on-fire problem where buyers will pay in advance: “I just want someone to solve this so bad, I will pay you right now.“

Step 4: Give Free Value-First Analysis. Use AI to generate a detailed free analysis for prospects before they’re customers. Santé (a portco of Nick’s whose founder was a recent guest on Verticals) takes this furthest — downloading a prospect’s data file and showing the system pre-populated with their historical data on a live demo. AI makes this kind of forward-deployed sales engineering scalable for the first time.

Step 5: Build Community-Led, Founder-Brand GTM. Industry associations. Educational webinars. Founder as figurehead on LinkedIn. The goal isn’t lead gen — it’s getting the industry to buy your 12-to-36-month roadmap, not just your current features. In a crowded vertical, brand and trust compound as moats in ways product alone cannot.

Step 6: Go Old School. Hammering the phones. Going to the random plumbing conference. Boots on the ground, handshakes. In vertical markets where distribution is a structural advantage, traditional sales often outperforms the “cool” growth motion. Toast won this way.

Step 7: Price on Micro-Outcomes, Not Macro Bets. Don’t start by telling a customer you’ll take 10% of every closed deal. Break the outcome into micro-conversions: the value of a booked meeting, an answered missed call, a rescheduled appointment. Test small value units first, then expand as predictability improves.

The Takeaway for Vertical Founders

The SaaSmageddon panic assumes AI shrinks the software market. At Euclid — and on Verticals — we think it dramatically expands it; especially in spaces where digitization remains low and buyers are leapfrogging from pen-and-paper to AI-native platforms. But expansion doesn’t mean everyone benefits. Nick’s message is simple: there is no silver bullet. The companies emerging from this moment are winning because they instrument early, measure relentlessly, and execute the fundamentals with more discipline than anyone else. When product differentiation narrows and the market is in panic, distribution is the moat.

Thanks for reading Euclid Insights and for tuning in to Verticals! Drop us a line below with your thoughts. And don’t forget to subscribe to the show wherever you watch and listen.

Keep Reading

For more on the “SaaSpocalypse,” check out Euclid’s most recent essay — we analyze the recent public-market SaaS downturn and answer the question: is SaaS really dead?

$400B of SaaS enterprise value evaporated this month. ServiceNow is down nearly 50% from last year’s peak, CRM is down 40%, and even MSFT approached a 30% drawdown. Nowhere is safe: even rumors of evidence of “AI eating X” are triggering sell-offs. A penny stock that previously sold music jukeboxes and announced a freight product caused a double-digit sell-off in logistics companies.

Rarely can a broad public market sell-off be attributed to one cause. And, of course, this is no exception. It’s first important to recognize all the reasons outside of “AI is killing all software” we might be seeing trades out of the space:

1. Discrete Disruption: Beyond Cowork, both Anthropic and OpenAI announced initiatives targeting the legal and financial services industries, which hit players in those spheres (such as Thomson Reuters, LegalZoom, and CS Disco) hard. Moreover, some individual software names simply failed to deliver: for example, a miss from the normally reliable Tyler Technologies (down 15%). Perhaps not too much for a 60-year-old software & services business with longstanding monopolies in govtech, now facing a brave new world of public sector AI adoption.

2. Hyperscaler Capex: Massive increases in AI-related capital expenditure from Microsoft, Alphabet, Meta, and Amazon — forecasted to exceed $660B combined in 2026 — have spooked investors regarding when these investments will yield significant monetization. MSFT, for example, was hit thanks to both rising CapEx and Azure deceleration. We have written about this in our past essay on AI infrastructure and the idea of an “AI Bubble” here.

3. Sector Rotation: A general zeitgeist of overvaluation and need for a correction in technology equities (though with software now trading at roughly 4.9x EV/Revenue, it feels like overcorrection if for that reason alone). Some also speculate that stronger-than-expected economic data (e.g., the January payrolls report showing 130k jobs added) led investors to rotate from “capital-light” software into “capital-intensive” cyclical sectors like energy and industrials.

4. Size-Based Volatility: Small- and mid-caps generally pull down more in a market rout. Historically, this is especially true in software, a category whose aggregate EV is dominated by a few massive platforms at the top. The very long tail of sub-$5B EV software publics tends to get hit hard. During the Great Recession, NetSuite and Salesforce saw their NTM revenue multiples collapse by ~92% and ~80%, respectively.2 While — counter to popular opinion — vertical software companies have a ~20% higher mean Enterprise Value than their horizontal peers, the industries targeted by this market reaction have more vertical players with perceived un-diversified exposure.

Rational or not, the sell-off has led some VCs to publicly declare that in the wake of AI, SaaS revenue durability is challenged, if not extinct. The most bearish of “hot takes” is that vibe-coded applications will make core systems of record — or perhaps all application-layer software — obsolete.

Before getting into long-term viability, let’s dig a bit further into the numbers of the “SaaS flash crash.”

We analyzed the recent public-market SaaS drawdown, and while vertical names have been hit harder this month than their horizontal peers, the declines from last year’s highs are fairly similar. Lastly, we tried to identify which vertical sectors were hardest hit; perhaps unsurprisingly (given OpenAI and Anthropic news), the hardest-hit sectors are Legal and Healthcare, although the small n in each bucket means two hard-hit names have a particularly large impact (CS Disco in Legal and Doximity in HC).

As we highlighted above, the strongest correlation is company size (i.e., market cap). Small caps have been hit the hardest — while the delta between large and mid-cap is more modest.

Ultimately, a discrete 10% drop in software stocks tells us approximately nothing about the future of SaaS as a business model or technology paradigm. To use the famous quote attributed to Ben Graham: “in the short run, the market is a voting machine, but in the long run, it is a weighing machine.” 5 years from now, equity trend will surely be more instructive as to AI’s impact on SaaS. We think, however, there are a few valid conclusions we can draw in the meantime.

SaaS is Very Much Alive

While we absolutely agree that AI poses a material risk to incumbent software vendors, we do not share the consensus that “SaaS is dead” (nor that a jukebox company is a material threat to the freight market). While we hate to lead our counter-argument with pedantry, the simplest refutation of the “death of software” argument is this: AI is still software. And application-layer AI delivered in the cloud is still Software-as-a-Service.

Naturally, every major technological paradigm shift comes with new terminology. VCs, eager to signal thought leadership and capture mindshare, are as guilty of flooding the zone with new acronyms and techno-jargon as anyone else. While AI has come to replace SaaS as a shorthand, the technology delivery model SaaS was primarily meant to convey remains just as prevalent in the LLM era—even if other attributes typically associated with SaaS (such as heavy UI or seat-based subscriptions) are receding.

EvenUp, Abridge, Harvey, OpenEvidence, Rilla, MagicSchool: all AI, all SaaS. If we started calling AI “next-gen SaaS,” it might clear up a lot of confusion. When people hail the death of SaaS, many conflate the rise of a new generation with the demise of the larger underlying business model, which is stronger than ever. Calling AI the software-killer is like calling the iPhone the end of computing. In fact, like in that case, by making software easier and more accessible, AI is likely its biggest accelerant since the advent of cloud infrastructure itself.

As we wrote in our essay last week, though: just because a pie is growing, doesn’t mean everyone gets to eat. Even if SaaS growth continues unabated, the league tables in 5-10 years will almost certainly look different.

Who is Vulnerable?

In our piece last June on the threat to Vertical SaaS from AI, we highlighted three categories that we felt were particularly vulnerable:

1. Hyper-Narrow: Developers of extremely niche tools (in use case, more than necessarily market size) that are easily replicable with AI support. These are the nearest-term, clearest losers.

2. Obsoletion by LLMs: SaaS offerings that LLMs naturally subsume (e.g., Chegg, Grammarly, Dragon). That said, companies with the resources and willpower to re-invent themselves AI-first may thrive. Duolingo is talking this talk.

3. Hesitant Incumbents: Legacy SaaS providers who simply don’t offer AI functionality at parity quickly enough, or hold on too tightly to outmoded cash-cow products. Incentives matter. We envisioned these slow deaths in our essay on the development of intelligence systems.

We define hyper-narrow here to mean more of a function of an individual product’s scale, complexity, and defensibility versus narrow in scope. A vertical system of record, regardless of TAM, would not meet the definition. A horizontal customer service tool would. Regardless, we concede there’s genuine pressure on every app-layer company, as AI-native challengers are growing faster than ever before. And if past platform shifts are any indication of the future, it is almost guaranteed that the vast majority of incremental value will go to AI-native disruptors rather than legacy vendors. Incumbents move slowly. They can’t attract the best AI talent. They face the classic innovator’s dilemma: deciding whether to cannibalize their core business. A small subset of legacy SaaS companies will be able to weather the storm. Many will not.

And there’s a real structural threat you might call the “front door” risk. Classic software gets reduced to middleware, with agents capturing all the incremental value on top. Aaron Levie, CEO of Box, put it bluntly: every piece of enterprise software has to become a platform play because agents will do vastly more work with tools and data than people ever did. The system of record gets pushed down the stack — and that means lower growth potential and a smaller profit pool. It’s worth, of course, pointing out that the “integrate and surround” form of system-of-record disruption is by no means new. As we explored in our analysis of Vertical Software’s Integration Problem, incumbents facing this dynamic have either adapted (buy or build) or faded in relevance.

Not capturing the next phase of explosive growth doesn’t mean all growth disappears. If software were truly in its late-cycle period, it would be growing at close to GDP (it’s at least 2-3x that). In fact, Gartner expects software spending to accelerate in 2026 — the highest figure in half a decade. There is still significant growth left, even in classic cloud migrations: while 95 percent of enterprises have a cloud footprint, the share of enterprise workloads in the cloud only crossed 50% in 2025. That figure was just north of 30% eight years ago.

We’ve seen this exact movie before, and recently. Remember when the shift from on-premise to cloud was supposed to kill the enterprise software giants? Oracle, SAP, and Microsoft were all declared dinosaurs — legacy vendors that would be disrupted by nimble cloud-native startups. Instead, the successful incumbents pivoted by buying and building cloud businesses; today, they are bigger than ever. Microsoft’s revenue rose from $62B in 2010 to $282B in 2025. Oracle went from $12B to $57B over the past two decades; its cloud software business is 3x larger than the on-premise business ever was. The on-prem-to-cloud shift certainly killed many incumbents, but more importantly, it supercharged the growth of the enterprise software market. Cheaper to build, cheaper to sell, and cheaper to maintain is deflationary but also market-expanding. Global software markets were ~$200B twenty years ago; today, they're more than five times that size.

Parker Conrad, CEO of Rippling, reached the same insight, noting the impact of dramatically lower software development costs over the past two decades. AWS replaced self-hosting. Frameworks, open-source ecosystems, developer tools, and higher-level programming languages abstracted away complexity, making it possible to build in days what used to take months. The result was a Cambrian explosion of software businesses. But this did not make software companies less valuable. It increased competition, for sure. It made incumbency advantages weaker. And of course, it shortened the replacement buying cadence for many enterprises. But in aggregate, the net result is that it made software more valuable. The SaaS era that followed produced trillions of dollars in enterprise value precisely because building got cheaper. More problems could be addressed. More workflows could be institutionalized. More industries could be digitized. More customer segments (whether vertical or smaller businesses) could be sold software more efficiently.

We traced this pattern in our earlier piece on whether AI threatens vertical SaaS, examining the history of “layer commoditization cycles” from FORTRAN to cloud computing. Each cycle follows the same arc: innovation automates core aspects of a process, pushback follows as incumbents fear obsolescence, and then the market thrives with newfound productivity. LLMs are the latest iteration, and, as in every prior cycle, there will be losers, but the market as a whole grows.

The Vibe Code Fallacy

Benedict Evans made the point around the complexity of building software in his recent Stratechery interview: “I never thought that the hard part of building a piece of software that manages the thing for the other thing deep inside a big company was writing the code.” The hard part is discovering the problem, designing the right workflow, building a go-to-market strategy, and earning customer trust. And then you layer in the sheer volume of applications: the average number of SaaS tools across all buyers is 275. That number jumps to over 600 for enterprises.

It’s easy to dismiss the complexity of every SaaS app as consumable by AI, but as Evans points out, the same analogy can be applied to traditional SaaS: “they’re all a thin-SQL wrapper. They’re all just databases.” Extending the argument further, Evans adds: “It’s like the dumb Hacker News comment, ‘Airbnb is just a CMS.’ But yeah, every social network is just a CMS.”

If AI makes code cheaper, it makes the non-code moats — domain expertise, distribution, customer relationships, and what Evans memorably called “a throat to choke” — relatively more important, not less. And in the age of AI, that throat to choke becomes ever more valuable. When the output is probabilistic rather than deterministic, customers need someone accountable for the result. This is precisely why we believe the durable moats in vertical software — workflow entrenchment, proprietary data loops, and trust — compound rather than erode as AI advances.

One persistent fantasy is that enterprises will simply vibe-code their way out of buying software. Evans called this idea “delusional” in his interview, and we completely agree, especially in vertical markets. Companies aren’t in the business of building complex internal tools. Models improve extraordinarily quickly, and it would be naive to underestimate what vibe coding will achieve in two or three years. But the fundamental attitude of “let’s build internally versus pay someone to manage it” won’t change. Put aside the complexity & quality argument for a moment; the total cost of ownership will still almost always favor buying over building.

While this is especially true for vertical market companies, even cutting-edge tech startups “outsource” much of their stack. Why? Because it’s cheaper than dedicating staff to such tasks, and they can move faster on solving core problems. The net result is that cheaper-to-build software is usually cheaper to own, resulting in more prospective customers and larger markets. Companies will continue to outsource software to vendors — whether those vendors are legacy SaaS providers or new AI-native startups. We thought one particular curious anecdote around is that vibe-coding breakout Loveable just became a customer of HubSpot.

We used the analogy of super-sneakers in our earlier essay: imagine shoes that give every basketball player a lifetime of conditioning overnight. High-school bench warmers become NCAA-competitive. Even NBA pros improve. But the impact is logarithmic — the Currys and LeBrons already have a lifetime of training, and much of their edge is basketball IQ. The higher-level you already are, the less it helps. And everyone gets the shoes. The same is true for software businesses.

It’s also important to remember that digitization remains low in most vertical markets. These industries moved from pen-and-paper to Excel to off-the-shelf vertical software. And just like the SaaS revolution, many prospective vertical customers that were left behind will jump straight from having no software for certain workflows to buying AI-native platforms. We explored this adoption pattern in greater depth in our piece on Diffusion of AI across Vertical Markets.

We’ll share an anecdote that reflects a similar challenge we often see, even among the largest prospective vertical buyers, and one most tech-forward people have little exposure to. We were talking with a multi-billion-dollar construction materials business that had three different ERPs. Why? They were growing quickly, both organically and inorganically, and when they couldn’t figure out how to migrate off their multi-decade-old system, they decided to keep it installed for certain regions and workflows, buy a new system for organic growth, and eventually not bother replacing the third system they inherited from M&A. While they have no plans to change this IT stack, they are actively evaluating and purchasing new pieces of Vertical AI. These industries are not leapfrogging to prompt-engineer their own tools anytime soon, but they are now active buyers of new tooling. That same company just bought its first CRM (though they’d call it something else), along with a voice-first revenue acceleration stack.

What Comes Next

Not all software is created equal, and the panic is treating it as monolithic. Ben Thompson made a key observation in his Stratechery interview: “The destruction and the value creation are not simultaneous in time. First, there’s a lot of destruction, the unbundling aspect, and then people are freed up to figure out the new thing, and that’s when the value creation happens.”

We are still quite early in the destruction phase, despite the market panic and stock prices cratering. Some vertical software companies will die, though we think the durability of those names is far higher than that of their horizontal peers. Some of the next generation of vertical AI companies will be built on the rubble, but most of them will be built on green fields. The market, by definition, cannot expand if it’s just ripping and replacing legacy vendors. And it’s not hopeless for existing legacy vendors: incumbents with distribution and trust advantages can capture their fair share of AI revenue, too. The recent history of vertical profit premiums suggests the market already prices in the durability advantages that vertical incumbents enjoy.

As we explored in Dude, Where’s My Moat?, the immutable primitives of software defensibility are workflow and data. Speed is a compounding advantage in the early days of a category, but it is not a durable moat. At scale, almost all companies slow down — and that’s where data gravity, brand and trust, and platform lock-in take over. For Vertical AI, the path to defensibility runs through what we call the System of Intelligence: becoming the authoring layer for high-value workflows, building data loops from customer usage, and expanding into adjacent products that compound switching costs. The startups that build toward these moats early will be the ones still standing when commoditization arrives. Equally, the incumbents who survive—and perhaps even thrive—long-term are those who leverage brand and data gravity to build a durable moat.

As we noted in our earlier analysis of the AI threat to Vertical SaaS, the specific threat level is industry agnostic. It comes down to a product’s scale, complexity, and defensibility. Hyper-narrow tools are the nearest-term losers. Products whose core function LLMs naturally subsume — Chegg, Grammarly, Dragon — are next. And hesitant incumbents who don’t adopt AI fast enough will bleed share to AI-native challengers. But vertical technology isn’t any more affected than SaaS as a whole — in many ways, it’s more defensible.

Nicolas Bustamante of Fintool shared a simple framework for evaluating the strength of Vertical Software incumbents:

1. Is the data proprietary?

2. Is there regulatory lock-in?

3. Is the software embedded in the transaction?

He explains, “Zero ‘yes’ answers: high risk. One: medium risk. Two or three: you’re probably fine.” The idea that CRMs, property management systems, or ERPs will fall into a black hole is not something we have seen happen. Workers shifting off document management or project management tools? We’d certainly expect that to occur on a medium-term horizon.

The bears assume that AI will shrink the software market. We think it dramatically expands it, even more so in vertical categories. As we wrote in Vertical AI & the Productivity Paradox, the vertical AI opportunity should be measured not just as labor replacement, but as labor enhancement plus market expansion:

Consider a hypothetical industry where AI can double profit margins by automating labor. Now, imagine the same sector where incremental profits are reinvested into growth. We have the same productivity metric, but the same industry is now >20% larger. Re-investing savings over a five-year window results in a market nearly five times the size with the same productivity. Of course, firms in such a market would likely reinvest only some of their savings into growth, but the net result is a larger and more productive firm (and market).

Foundation model verticalization is real and accelerating. Companies with weak moats and hyper-narrow tooling are exposed. But defensibility is preserved when outcomes require coordination with real-world systems and build the data loops and workflow entrenchment that compound over time. As we wrote in our piece last year on Diffusion of AI across Vertical Markets, we view the primary limiting factor today as not technological, but human capital:

It is these comprehensive end-to-end agentic transformations that are essential to fully revolutionize vertical markets and fulfill the economic potential of LLMs. As such, for founders developing in vertical AI, the hard work must be highly specialized and market-centric. No doubt, technology will continue to advance, but we need vertical builders to see and build the future.

At Euclid, we’re investing in founders who see how AI can transform industries with the lowest digitization and the largest productivity gaps: the complex, gnarly, high-accountability coordination between AI and the real world. This is where the next trillion dollars of enterprise value gets created. And history suggests it won't stop at just one.

Software is dead. Long live software.

Thanks for reading Euclid Insights! Additional sources here.3

Euclid is a VC partnering with Vertical AI founders at inception. If anyone in your network is working on a new startup in this space, we’d love to help. Just drop us a line via DM or in the comments below.

The core driver is a rapid decline in the cost of intelligence—at least at a given level of intelligence (as we’ve written, ballooning reasoning complexity has meant token spend continues to grow, even as the cost of a token shrinks). This effect is unlikely to slow down due to fierce competition among well-funded labs (OpenAI, Google, Anthropic, etc.), not to mention major advances in hardware and software efficiency.

As with all economic-technological paradigm shifts, however, the ultimate flow of value remains uncertain. If agents replace traditionally manual services, one might think they would capture the spend that would’ve otherwise gone to the manual services. The scenario playing out, however, more closely models the commoditization of those services. In other words, services people would have paid $100k for just a few years ago will eventually fetch only a fraction of that amount, as agents reduce the cost of delivering them.

The result of cheaper AI is persistent deflationary pressure on Vertical AI offerings, which are predominantly attractive due to the consumer surplus enabled by LLMs. Pulling data out of documents? Answering inbound phone calls? Drafting perfunctory compliance reports? Products like these can be excellent wedges today when infrastructure and know-how are scarce, and adoption is low. Soon, they will be table stakes — as excess margin is competed away by several well-funded, credible, nicely growing startups in every category. Any startup that hasn’t developed a moat in the meantime will be a casualty.

Some believe that as the marginal cost of intelligence collapses toward zero, the key value proposition of enterprise technology will shift from providing tools that assist human labor to delivering outcomes that replace it. We believe this is certainly true: Vertical AI can handle more end-to-end workflows than Vertical SaaS alone. This warrants significantly more customer value and willingness to pay, tapping into significantly larger budgets. We disagree, however, with the now-prevalent view that service delivery — a customer relationship that mirrors an external vendor rather than an internal platform integral to the business — is the prevailing paradigm of AI-powered software.

Rising TAMs Don’t Lift All Startups

But isn’t this an unalloyed good, if plummeting intelligence costs ultimately greatly expand the TAM for AI services? In some ways, yes — the flaw is in thinking that this opportunity expansion will necessarily accrue to the same points in the value chain for providing those services. The impact of PCs & spreadsheets on the accounting industry is a great example. This research report from Morgan Stanley describes the impact:

As adoption of this technology grew rapidly throughout the 1980s, especially after the introduction of Microsoft Excel in 1987, we saw a reduction in the number of Americans working as bookkeepers and accounting/auditing clerks (from ~2 million in 1987 to just above 1.5 million by 2000) — but we also saw a significant increase in Americans employed as accountants/auditors (rising from ~1.3 million in 1987 to ~1.5 million by 2000) and management analysts & financial managers (from ~0.6 million in 1987 to ~1.5 million by 2000).

Spreadsheets didn’t just automate bookkeeping — they shifted value up the skill curve, from rote labor to higher-order analysis. Ride-hailing shows an even more dramatic version of this phenomenon: not just reallocation within the value chain, but wholesale elimination of entire intermediary layers. We think AI will do both — and Vertical AI companies need to understand which side of that shift they’re on.

To explore why, let’s broach a concept no tech-ecosystem article on AI is complete without: the Jevons Paradox. It states that, as technology increases the efficiency with which a resource is used (and lowers its cost), the total consumption of that resource increases rather than decreases. Uber is, of course, the canonical startup-land example.

The global taxi market (inclusive of all ride-hailing) grew from roughly $69B in 2019 to $271B in 2024. Pre-Uber, estimates of the global traditional taxi market were in the $30-50B range. So total spending on “getting a car to take you somewhere” has grown roughly 5-8x over 15 years, even as per-ride prices were cut by about half (although in the post-VC-subsidy era, costs have reverted by 10-20%).

As Uber grew the TAM, however, it transformed the flow of value in that market. Historically, revenue from ride-hailing was captured by a handful of stakeholders: owners (both owner-operators and regulatory-monopoly beneficiaries like NYC Medallion owners), brokers (taxi agencies, dispatchers, garages), and taxi drivers employed by such brokers. Almost all of these stakeholders were disrupted and cut out of the value chain, except for drivers, who were able to pivot to a different “employer,” albeit with much higher competition and greater fungibility today. Agency revenue was subsumed by Uber and Lyft. Medallions were crushed: after NYC’s peaked at ~$1m in 2013, they fell to <$100k today (though there are signs of limited recovery, largely thanks to government intervention).

So, yes — technology enabled an ancient, established, low-growth industry to grow by over 500% in a decade. That, in itself, is amazing and an enduring vote of confidence in our collective innovation economy. But in the case of ride-sharing — and in many other instances of market expansion through consumer surplus — growth was accompanied by a major shift in the flow of value, rather than a broad benefit for all existing market participants.

The same Jevons dynamic is now playing out in enterprise AI, and the parallels are familiar. The cost of a quantum of intelligence — holding model quality, context, and reasoning complexity steady — is dropping rapidly. The spend required to deliver GPT-3.5-level inference dropped by more than 280x between November 2022 and October 2024. That frontier-level cognition remains expensive is irrelevant — set real-world tasks either get done (or they don’t). But they are getting done. In 2023, using an LLM to read and categorize every single incoming email for a mid-sized company might have been cost-prohibitive — today, at ~$0.40 PMT (per million tokens), it’s a negligible expense. AI coding tools have contributed to a world in which 41% of code is now AI-generated or AI-assisted, further lowering barriers. The cost of producing a unit of “cognitive work” is collapsing at a rate that makes Uber’s per-ride savings look modest.

And, as Jevons Paradox would predict, total AI spending is exploding. Enterprise AI revenue went from $1.7B in 2023 to $37B in 2025 — a 22x increase in two years. Global AI spending is projected to exceed $2.5 trillion in 2026. Gartner recently moved up its forecast that AI will account for one-third of all IT spending by two full years. But as with taxis, the question is not whether the pie grows. The question is who gets to eat.

The Dispatcher Problem

A popular thesis holds that as the marginal cost of intelligence falls toward zero, the winning business model is AI Services (or, as some have put it, “Service-as-Software”). The idea is that such startups deliver outcomes that replace labor or outsourced services, rather than selling tools that augment them. Foundation Capital frames this as a $4.6T opportunity: whereas IT budgets comprise 1-2% of GDP, labor and traditional services account for more than 15%. The math is seductive. If your AI can do the work of an accountant, a paralegal, or a compliance analyst, shouldn’t you be able to price against the fully-loaded cost of that employee?

In theory, yes. In practice, startups shouldn’t expect to rip, replace, and capture such budgets long-term simply by offering an analog product. This comes back around to our point that services are, by definition, commoditizable (a dynamic we explored extensively in our earlier analysis of layer commoditization cycles). Today, the appeal for replacement is obvious — it’s cheaper and faster. By and large, however, the startups growing through distribution of these AI alternatives do not own the IP that enables this economic arbitrage (the cost differential between an LLM-delivered output and a human-delivered one). The labs that own LLMs do. Without durable moats of their own, an AI Services startup is a reseller of intelligence — and, in our view, basic workflow orchestration, RAG, or domain-specific fine-tuning don’t count as durable moats.

This is the taxi dispatcher problem applied to AI. Before Uber, a taxi dispatch agency captured margin by providing ride-matching; drivers received steady work, and ride-hailers received consistent service with a single point of contact. There was some degree of defensibility in aggregating supply (driver density in a region) and demand (awareness in that region). When a platform emerged that could not only match supply and demand more efficiently but also massively expand supply and lower the cost of expansion by outsourcing car ownership—critically, offering lower costs to riders in the process—the dispatcher’s ability to compete evaporated.

The dispatchers didn’t lose because they couldn’t compete with Uber’s take rate — and this point deserves emphasis. Today, Uber takes ~30% of driver revenues on average, which is not wildly different from the 30-50% that traditional taxi agencies, medallion lessors, and dispatchers collectively extracted from drivers under the old model. Uber’s moat didn’t come from extracting less; it came from consolidating every intermediary function — dispatch, payment, matching, reputation — into a single platform that owned the network. When you are the network, you don’t need to compete on take rate. You are the infrastructure.

Similarly, an AI Service company whose primary value is “we deliver this service cheaper by using LLMs” is a dispatcher sitting on a proprietary margin advantage that doesn’t belong to them. It belongs to the cost curve of inference — and that curve is controlled by model labs, hyperscalers, chip manufacturers, and energy producers. When models get cheaper (and they will), or when a competitor plugs into the same model API and undercuts on pricing (and they will), AI Services startups’ cost advantage compresses toward zero. There are ~35k AI wrapper apps globally today, with significantly more competition—thanks to lower entry and software development costs—than in prior technology eras.

In other words, yes, AI will allow companies to deliver services at radically lower cost. That is resulting in impressive top-line growth for many AI Services offerings as businesses make the obvious choice to switch to cheaper alternatives. But the ability to deliver a service cheaply is not the same as the ability to retain the margin from delivering it. The consumer surplus created by collapsing intelligence costs will be enormous. Who captures that surplus durably is the defining question of the current moment in enterprise AI.

Embededness & Defensibility

The companies that will capture and retain the surplus from collapsing intelligence costs are those that can build defensibility beyond the cost curve. Historically, in enterprise technology, that defensibility has come from a consistent set of sources, which we discussed at length in our essay, “Dude, Where’s My Moat?“ There, we evaluated the relative importance of various moats for Vertical AI, based on the stage of the company:

At the inception stage, most advantage derives from moats that quickly degrade: domain expertise and speed & execution. Partnership and integration relationships are a durable moat, but they become less relevant at scale. The most critical moats at the growth stage are usage and data loops, as we described above. In all likelihood, we believe that a defensible Vertical AI business at scale would have, at minimum, moats in data gravity, brand & trust, and/or platform lock-in.

Setting aside the universal advantages that stem from the founding team (expertise and velocity), these moats all share a common origin: they arise from being deeply interconnected with the customer’s business. And this point brings us to the crux of our thesis. The most important axis for evaluating a Vertical AI business is not “service vs. software”; it is internal vs. external.

By “internal,” we don’t necessarily mean a product with a traditional SaaS UI that the customer “logs into” every day. In fact, the long-standing software-industry consensus that value is correlated with direct, hands-on keyboard usage is, in our view, dead.1 We mean, rather, is the AI company embedded in the customer’s operations in a way that makes it structurally difficult to remove? Does it hold proprietary data that the customer generated? Does it connect the customer to counterparties, suppliers, or ecosystems that would be painful to rewire? Is it integrated into adjacent workflows in a way that removing it would cause cascading disruptions?

In contrast, “external” solutions mirror traditional services vendors. The customer calls on the AI Services startup when they need a task done — akin to a BPO, albeit faster and cheaper — but may turn to a different one next week, if it offers a better deal. The customer shares what’s needed to get that job done, but likely little else. Ongoing interaction outside the “work to be done” is limited. They deliver real value. They will grow quickly, while cost differentials are large and adoption is nascent. But by sitting on borrowed margin—cost arbitrage enabled by the model layer, not from their own IP—they are exposed to the same competitive dynamics as everyone else, including not only other AI Services startups but also deep-pocketed SaaS incumbents and (perhaps eventually) even the buyers themselves.

Even if they own more end-to-end workflows or guarantee outcomes, Internal AI doesn’t just deliver a service; it becomes a deeply embedded partner to its customers. AI, in fact, offers a much greater surface area to do so than the SaaS model ever did — a theme we explored in “Emerging Playbooks in Vertical AI,” where we traced the evolution from authoring layers to Systems of Intelligence. Internal AI platforms can embed themselves in every workflow, accumulate proprietary data every step of the way, and use that positioning to build advantages (and switching costs) that compound over time. As the model layer commoditizes further, internal AI will be insulated because the customer isn’t just paying for an inference portal, but for a System of Intelligence & Action that rivals the stickiness of traditional software Systems of Record.

To make this framework concrete, we can map the Vertical AI landscape along two axes: internal vs. external (how deeply embedded the product is in a customer’s operations) and wedge vs. platform (the breadth and depth of the product offering today). This produces four quadrants, each with distinct risk profiles and trajectories:

In the upper-right quadrant — Durable — sit Internal AI platforms: Systems of Intelligence & Action with a clear path to compounding moats, usually having evolved from an initial wedge into a multi-product platform deeply embedded in customer workflows. Companies like Abridge and EvenUp exemplify this trajectory. The upper-left — Rare — captures external-facing platforms: often consultative, high-ACV plays that may be dog-fooding an internal AI product. These can work, but high customer concentration and limited embeddedness make them unstable. The lower-left — Commodity Risk — is the danger zone: external wedge products with extreme early growth potential but existential risk from competing on borrowed AI margin. The lower-right — Precarious — represents internal wedges with high early growth potential that can extend into defensible platforms, but face meaningful risk from AI-forward incumbents who may replicate the wedge.

Importantly, the blue arrows on the chart illustrate the two valuable transition paths: from external to internal (deepening embeddedness) and from wedge to platform (building product breadth). The wedge-to-platform transition is a time-tested model for building durable Vertical Software businesses; newer is the approach of startups attempting to make both jumps, starting with an external, highly scalable AI Services wedge.

Our overall point is not that AI Services built on LLM infrastructures are inherently a bad initial wedge (or business model). Rather, we believe they must either forge their path to defensibility—most likely through some form of customer internalization—or face eventual commoditization.

Vertical AI: A Sanctuary from Commoditization

This framing of what wins in AI reminds us that success in the current era — while inherently disruptive — must pursue many of the same goals as traditional SaaS companies in terms of customer relationships. It is also a key reason we believe Vertical AI is so powerful. The unique dynamics of every industry offer fertile ground for building differentiated solutions that become deeply internalized by customers — as we argued in “The Future of AI is Vertical,” where we first laid out the thesis that vertical markets would be the near-term winners as LLMs’ performance increases and costs decrease.

The best vertical SaaS companies — Veeva, Procore, Toast, ServiceTitan, etc. — didn’t win because they were cheaper than the alternative. They won because they became the system of record that more closely mirrored users’ very particular needs. With millions in consulting spend, an enterprise might shape a Salesforce or NetSuite to suit its needs, but why do that when there is a system built for you? Along the way, Vertical SaaS platforms captured proprietary industry or first-party data (clinical trial data, job costing data, restaurant sales data) that made the product better the longer you used it. They connected fragmented, vertically unique ecosystems (pharma and clinical sites, GCs and subcontractors, restaurants and delivery networks) in ways that created network-effect moats. As we discussed in our two-part series on market sizing in Vertical AI, these platforms often start with narrow beachheads before expanding into much larger TAMs through product layering — a dynamic that AI is accelerating.

The Vertical AI startups — whether they consider themselves AI Services or not — that will durably capture the surplus from collapsing intelligence costs will follow the same playbook. The wedge may be a service delivered more cheaply. But the moat will be a system built on top of that wedge, leveraging their internal positioning with customers to develop moats—the proprietary data, the network effects, the multi-product platform, and the industry “brain”—that make it infrastructure to be relied upon, rather than just another vendor.

Those that never make that leap — that remain external providers of AI-delivered services, competing on cost — will face the same fate as taxi dispatchers. They’ll watch their market grow 500% while margins compress toward zero.

Winners Will Embrace Commoditization

In a Vertical Collective Roundtable late last year, a founder shared an insight around AI commoditization that stuck with us:

[Many] think that race to the bottom is a bad thing… we think it’s the opposite… the real unlock is new value creation.

This may seem contradictory — we’ve argued that cost competition alone is fatal. The distinction is intent. Racing to the bottom on price is deadly if that’s all you do. It’s powerful if it’s a deliberate wedge to win positioning from which to build the moats described above.

Some Vertical AI startups should embrace and accelerate the race to the bottom in pricing that will come with AI Services commoditization. They have the opportunity to attract many customers by offering shockingly low prices that traditional players cannot match. Yes, they cannibalize the per-customer “outcome” revenue opportunity. But they also win fast growth, industry trust, and the right to serve that customer in other ways.

Moreover, by weakening the position of market leaders that can’t compete, the Vertical AI startup can create a competitive vacuum, establishing a pole position from which to expand. Serving up that surplus on a silver platter can be a trust-building hack like no other. We covered variations of this strategy—which we call “Nuking Pricing Power”— in our prior essay on product commoditization, “A Guide to Disrupting Incumbents:”

Develop or support a lower-priced (or free) version of your complement, incentivizing fast adoption and decreasing pricing power of your complement.

A simpler way to put it is: if your product is going to be commoditized, you might as well do it yourself to win the market.

The Value Hypothesis

Every paradigm shift in enterprise technology produces a land grab — and, inevitably, a shakeout. Cloud computing launched thousands of SaaS startups between 2005 and 2015; most were absorbed, acqui-hired, or zeroed out, while a small cohort graduated into durable, category-defining platforms. We expect the same pattern in Vertical AI, but with greater ultimate market opportunity, faster potential growth, creative new monetization models, greater early capital efficiency, and — for all those reasons — unprecedented levels of competition.

The wedge that enables much of the current generation of app-layer startups is cheap intelligence. The trap for AI Services founders is mistaking a scalable wedge for a defensible business. The companies that will endure are those that use the current window, while cost differentials are large, adoption is nascent, and incumbents are slow to embed themselves so deeply in their customers’ operations that switching becomes structurally painful, not just inconvenient. As we wrote in “Early-Stage VC in the Age of Vertical AI,” the profiles of Vertical AI company-building are forcing investors and founders alike to reimagine what success looks like. This is no exception.

This is not a new idea. It is, in fact, the oldest idea in enterprise software, rediscovered. What’s new is the surface area: SaaS companies could embed in a few workflows and capture data from the screens users interact with. AI-native platforms can embed in every workflow, capture data from every interaction — whether a human is present or not — and build compounding intelligence that makes the product better the longer it runs. The opportunity to build “load-bearing infrastructure” has never been greater. Neither has the temptation to settle for being a “cheaper vendor.”

As Benchmark Capital and Wealthfront co-founder Andy Rachleff argued, the “value hypothesis” of a startup — the what, the who, and the how of demand — is "seldom correct" on the first attempt, because founders must discover who is truly desperate for their product… not just who says they're interested. This is also why we’ve argued that when megafunds suggest that market winners are obvious within two years of founding, these are logical, if not self-serving, positions for their fund models, but not how markets behave or category-winners emerge.

Customers are always interested in cheaper services, and AI can help deliver them. What customers truly want — and what they’ll pay to retain — is a system that knows their business better than they do: one that compounds institutional knowledge, connects them to their ecosystem, and becomes more internally valuable with every interaction. Building that system is harder than reselling cheap inference. But it’s the only thing worth building.

Thanks for reading Euclid Insights! Euclid is a VC partnering with Vertical AI founders at inception. If anyone in your network is working on a new startup in this space, we’d love to help. Just drop us a line via DM or in the comments below.

Most vertical founders building a system of record (or action) eventually faces the same problem: your product is better, your demo is clean, the prospect is nodding along… “But we’ve been on this system for twelve years.” Rip-and-replace friction left a big graveyard of vertical SaaS startups. While Vertical AI offers fresh approaches to dealing with it, the psychological barrier buyers have around “having done things the same way for 10+ years” is equally present.

In the $80B US liquor retail market—90% independently owned, averaging $2.5M annual revenue per store (roughly 3x a typical restaurant)—that objection is universal. Darren Fike, Founder & CEO of Santé, is taking a meta approach to the issue by incorporating agentic AI into his product purely dedicated to mitigating the costs of switching off status quo platforms. This episode is a great reminder that AI isn’t just a product strategy—it’s a GTM weapon that can work especially well to overcome vertical market adoption challenges that SaaS alone never could.

Support Verticals by subscribing on YouTube, if you haven’t had a chance. Keep reading below for our episode roundup and Vertical Playbook: AI-Powered Rip-and-Replace!

The Santé Story: From Stockroom to C-Suite

Darren’s path to founding Santé didn’t start in a conference room. It started on the floor of a liquor store in Tribeca. While working full-time in ad tech and fintech—including stints at Fast and Bond—he took a nights-and-weekends job at a wine store in NYC to pressure-test a thesis: that liquor stores were underserved by horizontal POS platforms.

What he found was worse than expected. Every day, five deliveries arrived with paper invoices. Staff spent roughly three hours manually keying that data into Square. Multiply that by 25 distributor relationships—the industry average, with high-end stores managing over 50—and you have a back-office workflow that’s practically begging for automation. He built an AI-powered receiving tool as a point solution, acquired ~20 customers in NYC, and within three months, six or seven of them asked for the same thing: “Can you just be our whole POS?”

Following YC and a small pre-seed gave them the early runway to start scaling and they’ve been heads down since. Santé launched its first POS customer in September 2023; by Q3 2024, the team closed 30 deals in a single month with zero feature requests — the clearest PMF signal a vertical founder can get. Today, a team of six AEs and three BDRs each close 20-30 deals per month through pure cold calling, with 95%+ retention driven by Santé’s position as the full system of record. Landmark wins include the highest-grossing liquor store chain in New York State, one of the top independent locations in California, and a PE-backed group in Massachusetts — all onboarded without product gaps. They’re 50 FTEs today and growing.

Darren’s 10-year target: MindBody-level dominance (50-60% share), which at projected ACVs with embedded fintech and AI would put the company at in IPO-scale valuation ranges.

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The Vertical Playbook: AI-Powered Rip & Replace

Step 1: Let Regulatory Complexity Be Your Moat.

Alcohol is the only controlled substance in the U.S. that isn’t federally regulated. Each state sets its own tax rules—carbonation-based taxes, county-specific delivery fees, bottle deposits—creating a compliance labyrinth that horizontal players like Square, Toast, and Clover won’t touch. As Darren frames it with a Gmail analogy: why would a platform serving millions of merchants throw off its core UX to accommodate tax logic that only matters to liquor stores? This counter-positioning is Santé‘s structural advantage. It’s not a feature gap; it’s a strategic disqualifier for horizontal incumbents.

Step 2: Ship a Wedge, Then Let Customers Pull You Into the Platform.

Santé didn’t start by pitching a full operating system. The AI receiving tool—scanning and digitizing paper invoices—was the wedge. It solved an acute, daily pain point with minimal adoption friction. But the pull toward platform was organic: customers needed POS, e-commerce, marketing, DoorDash/Uber Eats integrations, and loyalty programs all in one place. The lesson for vertical founders: your wedge earns the right to become the system of record. Don’t pitch the platform on day one.

Step 3: Build AI Agents That Collapse Switching Costs.

This is the centerpiece. Santé routinely displaces six to eight legacy systems. Five are industry-specific incumbents; several have recently been acquired by PE shops and are being sunset—creating what Darren calls “generational timing,” with over 26% of the market actively shopping. To exploit this window, the team built AI agents that crawl a prospect’s legacy system, extract all data—inventory, loyalty points, gift cards, 10-15 years of customer sales history—translate it into Santé‘s schema, and spin up a fully populated account. On a live demo, Darren drops a file onto a prospect’s server, triggers the bot, and minutes later says: “Here’s your Santé account with everything from your current system.” The result: switching anxiety evaporates. Sales cycles have compressed from 42 days to 23.5, with top reps closing under 18. The number one reason stores don’t switch, Darren notes, is complacency—not dissatisfaction. AI agents neutralize that inertia by making the cost of switching functionally zero.

Step 4: Use Internal AI to Scale Onboarding, Not Headcount.

The agents aren’t just a sales weapon. Post-sale, Santé supplements every onboard with scripts and agents that handle the repetitive migration work, freeing the (deliberately small) post-sales team to focus on relationship building and customer education. This is how they maintain 95%+ retention while onboarding at high velocity—AI handles the data plumbing, humans handle the trust.

The Takeaway for Vertical Founders

The conventional wisdom is that rip-and-replace is a structural headwind you manage, not a problem you solve. Santé flips that. By deploying AI agents as internal tooling—not just as the product—Darren has turned the industry’s highest-friction moment (migration) into his strongest sales asset. For any vertical founder staring down entrenched legacy incumbents, the playbook is clear: don’t just build a better product than the system of record. Build the tools that make leaving the old one painless. The switching cost moat only works if the other side can’t build a bridge. AI is the bridge.

For more on Vertical AI GTM strategies, explore our essays on Emerging Playbooks in Vertical AI and Vertical AI’s Integration Problem, or browse the GTM archive.

Few sectors are as complex or fragmented as commercial construction. It’s a massive, multi-trillion-dollar ecosystem where every project has several integral decision-makers and points of value. For a Vertical AI founder bringing value to that entire universe, the question of “who do you charge, and how?” is notoriously difficult to answer.

On this episode of Verticals, we sat down with Mike Powers, Co-founder and CEO of BuildVision, to discuss how he is answering that question by rewriting the playbook for multi-stakeholder industries.

Below, we’ll dig into Mike’s strategy for Multiplayer Vertical AI success. But first, some background on the founder and business making it happen. Naturally, we’re a bit biased at Euclid, as BuildVision’s first investors — we’ve written a bit more on the deal here. But we think you’ll find the story as compelling and insightful as we did.

PS: Going forward, we’ll just be posting the intros to our Verticals episodes here on Euclid Insights — full episodes will live at YouTube (and Spotify / Amazon).

It would mean a ton if you could please take a quick second to subscribe.

We appreciate your support!

Huge thanks to Parafin for sponsoring this episode, and for helping make Verticals happen. Embedded finance is one of the biggest untold stories in Vertical AI, and it’s growing faster than ever. Whether you’re a founder or an investor, Parafin’s story is worth checking out.

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I. Vertical Titan

Mike Powers, Co-Founder & CEO of BuildVision

Mike has lived the problem of construction procurement, from multiple stakeholder vantage points, for over a decade. His career began at Turner Construction, one of the country’s largest general contractors, working in their strategic procurement arm. There, he saw firsthand how leveraging buying power for strategic equipment—critical infrastructure like chillers and switchgear—could turn a cost center into a profit center.

Mike eventually transitioned to the tech side, joining bid-management platform BuildingConnected, initially as an SDR. There, he had a quick ascent, and ultimately found himself running enterprise sales for a hyper-growth Series A business. Shortly after, BuildingConnected was acquired by Autodesk for a bit under $300M. He would stay on at the construction software giant for a while — and while overseeing a $200M sales organization there kept him busy, the idea for a dedicated procurement platform never left him.

In 2023, just three days after his son was born, Mike left Autodesk and officially launched BuildVision. The platform serves as a modern procurement solution for general contractors (GCs) and OEMs, streamlining the sourcing, purchasing, and tracking of complex, engineered equipment. But BuildVision isn’t just a digital clipboard or “procurement CRM.” It’s a true “multiplayer” platform designed to serve both GCs and OEMs.

The “Multiplayer” Architecture: The Event Ledger

Most vertical software starts as “single-player” tools—improving the workflow for just one stakeholder (e.g., a project manager at a GC). BuildVision, however, was built from day one to be the connective tissue between the General Contractor (Buyer) and the OEM/Rep (Seller).

• The “Event Ledger” Concept
  Mike describes BuildVision’s ultimate goal as becoming the “event ledger for equipment.” Instead of just tracking a purchase order, the platform records every state change of a critical asset: how it is engineered, specified, sourced, financed, transacted, and delivered to the job site.

• Free Utility to Build the Network
  To make this “multiplayer” ecosystem work, you need density on both sides. BuildVision aggressively removes friction by making the core workflow tools free.

  • For Buyers (GCs): Tools to ingest 3,000+ pages of construction documents, structure data, and send RFQs are free.

  • For Sellers (OEMs/Reps): Tools to ingest emails, process documents, and drag-and-drop proposals are also free.

  • The Strategy: By commoditizing the “table stakes” SaaS features, they capture the data flow across the entire network, creating a proprietary data moat that is hard for point solutions to replicate.

Monetization: Aligning with Outcomes, Not Seats

Instead of charging a flat subscription fee (like the $7-10k ACV Mike saw at BuildingConnected), BuildVision monetizes where value is actually created or risk is reduced. Mike calls this “capture” — taking a percentage of the value flowing through the platform.

Here are the three specific ways they monetize outcomes:

1. Procurement

BuildVision acts less like a software tool and more like an automated procurement agent. When a General Contractor needs to make a complex decision—like selecting an alternate chiller manufacturer to save money or time—BuildVision helps execute that buy.

• The Model: They take a transaction fee (ranging around 3%) on the equipment purchase.

• The Value: The GC is happy to pay this because BuildVision is often de-risking the purchase, ensuring engineering specs are met, or improving lead times—outcomes that directly impact the project’s profitability.

2. Finance

Construction is plagued by slow payment cycles (Net 90 or Net 120), which squeezes OEMs. BuildVision leverages its position as the “ledger” to solve this cash flow problem.

• The Model: BuildVision connects capital providers directly to the OEM to pay them on Net 1 terms (immediately) instead of months later.

• The Value: In exchange for immediate payment, the OEM accepts a discount. BuildVision captures that spread, effectively “selling dollars” to manufacturers who are desperate for liquidity—but doing so in a way that benefits all parties.

3. Support Sellers

Because BuildVision normalizes unstructured data from emails and PDFs, they can offer their customers insights they’ve never had before.

• The Model: They monetize by providing OEMs with forecasting data and “win/loss” analysis.

• The Value: Manufacturers can see why they are losing bids (e.g., over-specifying equipment) or get accurate forecasts of upcoming project demand, allowing them to adjust pricing or production dynamically.

Giving the SaaS Away

Mike Powers is betting that in the age of AI, the value of the “interface” will go to zero. By giving the “SaaS” away, BuildVision captures the transaction and the data, allowing them to monetize the outcome (buying better, getting paid faster) rather than the tool. This “decision layer” is the company’s core asset and moat, valuable to all stakeholders across the ecosystem.

II. Vertical Playbook

Building for Multiplayer Ecosystems

The most compelling part of Mike’s strategy is how BuildVision navigates the “stakeholder maze” of Vertical AI in a complex industry ecosystem. Here is a breakdown of how they are executing a next-generation vertical strategy — with a focus on how to balance monetization and adoption.

1. The “Free Utility” Wedge to Build a Network Moat

In the age of AI, where “vibe coding” a point solution is becoming easier, defensibility requires a network moat. BuildVision takes a contrarian approach: they give away the “table stakes” software for free.

Whether it is an AI tool that scrapes 3,000 pages of construction documents to structure data or a workflow for sending RFQs, BuildVision offers these utilities at zero cost to both buyers and sellers. As Mike puts it, “What we consider table stakes tools are free and will always be free... How do we create a best-in-class tool... giving that away on both sides of that procurement network marketplace?”.

This strategy accelerates adoption and data aggregation, allowing them to build a proprietary “event ledger” for equipment that tracks how assets are designed, procured, and delivered.

2. Monetizing Outcomes, Not Seats

If the software is free, how does BuildVision make money? By replacing work and underwriting risk.

Mike contrasts this with his days at BuildingConnected, where success was defined by $7,500 subscriptions and high-velocity sales calls. At BuildVision, they target the enterprise C-suite with a proposition tied directly to financial outcomes—increasing sales or reducing claims.

“If we don’t do any of that, we don’t make any money. But if we do that, we benefit from the upside,” Mike explains. By leveraging their data to help GCs make better alternate decisions on equipment, BuildVision takes a percentage of the transaction (what they call “capture”), effectively monetizing the decision layer rather than the interface.

3. The Financial Layer as the Ultimate Unlock

Perhaps the most potent insight for founders is how BuildVision plans to leverage its position as a system of record to solve cash flow problems. In construction, payment terms are often painfully slow (Net 90 or 120).

Because BuildVision connects capital providers directly to OEMs, they can facilitate faster payments—offering Net 1 payment terms in exchange for a discount on the purchase order. “We can go sell them dollars for 95 cents,” Mike notes, pointing out that this financial arbitrage can generate significant revenue without needing to act as a traditional reseller.

The Takeaway for Vertical Founders

The lesson from BuildVision is clear: in complex, multi-stakeholder verticals, the future isn’t just about charging a SaaS fee for a better interface. It is about giving away the workflow to capture the network, and then monetizing the transaction, the risk, and the capital flow. As foundational AI models continue to improve, the “wrapper” value erodes, but the value of a proprietary data moat and verified outcomes — the “decision layer” — only increases.

Catch the full episode with Mike wherever you watch / listen. See you next week!

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As Keynes put it, “worldly wisdom teaches that it is better for reputation to fail conventionally than to succeed unconventionally.” The human evolutionary instinct for self-preservation in consensus is powerful. For venture investors, that cognitive dissonance is especially pervasive in peaks of fear and greed. And — between massive AUM expansions and generational tech paradigm shifts — we are amidst just such a moment today.

The rot of contrarian thinking in venture capital can be boiled down to two distinct concepts that, while occasionally overlapping, are increasingly at odds: fundability vs. investability.

Fundability is a measure of how well a startup fits with current venture consensus. It likely has a repeat founder or a founder from a known entity (Stanford, YC, Ex-Stripe). It operates in a category that is currently “in vogue” (Generative AI, Defense Tech, etc). Investability, on the other hand, is a measure of potential outcome and financial return.

The danger arises when VCs conflate the two — or don’t recognize the difference. We assume that because a deal is fundable (e.g., a central-casting founder, a hot market), it is investible. Conversely, we assume a deal is uninvestible if it is unfundable (e.g., due to an unknown background or an unsexy market). As Kyle Harrison noted in Build What’s Fundable, the risk is that we are building an entire ecosystem that rewards fundability.

How Capital Fosters Consensus

The growing conflation of fundability and investability today is no accident; it is a structural byproduct of market concentration.

The flight of capital into large platform funds that began following the 2021 downturn has continued unabated, making the current venture market likely the most concentrated in history. The downstream impacts are now beginning to surface.

In “We Have Met the Enemy, and He is Us,” we shared a view on how the industry has drifted from its roots as a market for independent thinkers, and how that would lead to a slow calcification into a consensus-driven machine:

Taken to its extreme, venture capital concentration will turn this market into something closer to a planned economy than a capitalistic network of independent competitors. A competitive round with the best firms bidding is, of course, an incredible indicator of potential—unless the bidding is rigged, because firms only bid as a result of other current or past bids. Many have already embraced the monoculture. If a hot YC batch emerges, if a pedigreed repeat founder raises, if a mega-fund plants its flag, everyone rushes in behind them. Demand dictates perceived quality; perceived quality dictates prices. Efficient market!

Since we wrote that piece last Fall, the market has arguably even grown more lopsided: A16Z just raised $15B, equivalent to ~20% of all venture dollars deployed last year. While ~$110B was deployed in total last year–higher than any year on record outside 2021 and 2022–the number of rounds closed (~3,200) was lower than any in the last decade. As a result, the valuation gap between the top “premium perception” startups and the median has grown to 4x.

This new reality has noticeably shifted upstream manager behavior as they seek to benefit from increasingly rich markups. In the process, platform funds have become consensus-building machines, and an increasing share of venture capital is allocated based on fundability rather than investability.

To some investors, optimizing for fundability can look rational. A non-GP partner at a platform fund, for example, might see a consensus deal as “safer”—more likely to produce a better short-term perception of performance, even if downstream returns never materialize. A similar thing can be said for emerging managers. EMs that live or die by conversion from Fund I to II, or II to III–with gaps between fundraises of perhaps two years–face significant pressure to produce markups, brand-name co-investors, and the appearance of momentum at any cost.

This Time It’s Different

Manager pressure to deploy capital into consensus deals is amplified by the current market, both due to the bigger rounds that AUM concentration has produced and the rationalization of “rule-breaking” that a boom cycle like post-LLM AI enables.

An unavoidable theme in modern early-stage VC markets is larger round sizes and higher valuations. Carta reports 36% growth in median seed pre-money marks over the last year. Particularly stark, however, are recent price spikes for “premium perception” startups. As of late last year, the 95th percentile seed pre-money valuation reached $80M, up 130% from the prior year. The “premium gap” between the median and the top 5% seed has grown to more than 4x.

One popular narrative today is that larger rounds are warranted in this new era of AI. There are various justifications: it grows faster, it’s hyper-competitive, it’s winner-take-all, the first-mover advantage is real, outcomes will be bigger, etc. Some are at least partially true (e.g., growth), while others are purely speculative (e.g., winner-take-all). Most important to the argument for larger seed/pre-seed rounds, however, is the implication that modern AI winners are identifiable earlier. In other words, as the story goes, mega-funds (and their followers) can drive alpha by allocating more capital, sooner, with fewer proof points. Some even argue that outsized first rounds improve companies’ likelihood of success.

Historical data suggest that this sort of “king-making” logic fails miserably at the earliest stages. We examined $1B+ exits in 2025 and traced them back to their initial rounds. First, the vast majority began with raises in the $500k-5M range (though, of course, there were exceptions, such as Statsig’s $10M round or Neon’s $28M round). Second, while platform funds led some of these smaller rounds (such as Regrello and Figma), they were underrepresented relative to their share of overall AUM. If most future winners were already “legible” at first check–enough to be “king-made”–one would expect to see a far higher share of outsized, platform-led rounds, with such rounds translating into significant outcomes at a greater rate.

Retrospective analysis, however, cannot account for AI, which is introducing dramatic changes to the venture capital world. If the top X% of AI startups are growing significantly faster, funds will inevitably compete by awarding them more capital, earlier. Pre-traction stages, however, are devoid of such signals, AI or otherwise. At first-check, we see no reason to believe the ground truth has changed: that most winners start outside consensus, and often remain there longer than one might expect.

So we must ask the obvious question: are early rounds today growing because opportunities are genuinely bigger and capital reliably confers advantage? Or because large-AUM funds must deploy ever-larger war chests? While the latter is clearly true, credible evidence for the former remains elusive. The obvious virtue of bigger rounds for platform funds is not increasing odds of success, but simply the ability to deploy substantially more capital.

How Consensus Erodes Innovation

The market is responding to this demand for “legible” assets by manufacturing them at scale.

One of the biggest challenges to accelerating venture capital deployment is the limited supply of high-quality assets. Free markets, however, respond naturally to such imbalances. An increase in demand for suitable early-stage assets from downstream funds eventually leads to an increase in formation-stage supply. Indeed, over the past few years, there has been an influx of new “inception at scale” models.

Such programs vary widely, from re-imaginations of community sourcing to internal programs at seed franchises to offshoots of large platform funds. Some of them have been (and will be) successful. The natural incentive of such strategies, however, is to optimize for fundability and hence conversion to seed–especially in a world where inflating seed round sizes provide increasingly juicy markups. While some programs are thoughtful, many tend towards “startup factories,” embracing volume over focus.

In our view, the growth of commoditized early-stage capital pools reduces the value of that capital in signal, brand, and network durability. In other words, as the VC asset class has expanded, the talent pool has diversified, and the number of categories capable of supporting venture-scale outcomes has increased, inception programs should speciate accordingly–greater specialization, creative new funding models, innovative forms of value-add, etc. Instead, we have seen many accelerators become less rational: increasing apertures, cohort sizes, and funds deployed. Typically, they champion the same marketing message: X% of our companies raise seed funding at a $XM+ valuation.

The result is convergence to the most legible, lowest-risk signals from inception. When accelerators attempt to serve everyone, they must default to consensus heuristics—pedigree, age, early traction speed, and alignment with prevailing narratives—because those are the only scalable filters. With little left to differentiate beyond price and branding, and with an increasing number of VCs moving earlier, the talent bar is harder to maintain. Y Combinator’s average founder age has dropped to just a few years out of college; only 17% of the founders in the 2025 batch were over 30, while nearly 60% were under 25. Other newer programs now explicitly target college dropouts, with some even extending to high-schoolers.

The recent explosion in spray-and-pray, inception-at-scale programs, however, feels less like thoughtful innovation designed for durable alpha, and more like a knee-jerk reaction to ballooning supply-side capital and "consensus deal” demand. To quote Kyle Harrison’s essay again:

I reflected on my understanding of why Y Combinator existed in the first place and why, for the first several years, it was so valuable. It represented the best on-ramp for what was, at the time, the opaque world of tech.

But then, you realize that the goal post has shifted. As the tech industry has become dramatically more navigable, YC became much less focused on making the world understandable, revolving, instead, around feeding consensus. “Give the ecosystem what they want.”1

The Fundability Trap

The key to the fundability game, in our view, is not to play it at all. The odds are stacked against most funds, especially emerging managers, and the data suggest a negative correlation between early-stage consensus (high prices) and performance. The investment team at Levels, a data-science-focused venture capital fund-of-funds, analyzed whether high entry prices at the seed stage helped or hurt individual deal returns. Specifically, they examined the probability of a home-run outcome (50x from entry price) for seed / pre-seed investments with valuations below or above $20M. They found that the lower-priced cohort is nearly 4x as likely to produce a 50x+ hit vs. its higher-priced counterparts. An AngelList analysis reached a similar conclusion: although higher-priced rounds were more likely to be marked up, there was no correlation between seed-round valuation and ultimate outlier returns (≥10x).

Multiple studies over the years have demonstrated how the confluence of a market hype cycle and consensus capital can eviscerate returns. One HBS paper examining data from 1965 to 1992 found that venture capital is less efficient in hot markets: capital over-invests in opportunities, too many competitive companies are funded, and the per-dollar return of the strategy declines. Another study from 2017 found that non-consensus investments produce better returns:

We find that consensus entrants are less viable, while non-consensus entrants are more likely to prosper. Non-consensus entrepreneurs who buck the trends are most likely to stay in the market, receive funding, and ultimately go public.

When venture capital focus converges on a consensus asset, market, or theme, it tends to inflate prices, accelerate valuations, and ultimately lead to more frequent and dramatic failures. Regrettably, as we’ve noted before, the incentive persists for VCs to chase popular trends for near-term momentum, even though this tactic demonstrably yields poorer long-term returns.

Despite that, most investors talk about venture as a game to be won solely by “access” to a small set of consensus deals. That framing pushes the industry into a zero-sum, finite game: capital concentrates into a handful of incumbents chasing the most “legible” deals, the opportunity set narrows around the same geographies and themes, and deal activity compresses even as dollar volumes remain large. It’s corrosive to the pursuit of contrarian thinkers and ideas.

If outlier outcomes depend on investing in unconventional or overlooked sectors (especially for non-platform funds), that message seems to be lost, at least with the current VC zeitgeist. According to a survey of 885 VCs (at 681 firms) on the most important component of generating returns, 49% said deal selection, 27% said value-add (a proxy for winning), but only 23% said deal flow. What’s fascinating about this survey is that it runs counter to what the data says (It’s the deal flow, baby!), and while we don’t have the historical data to prove otherwise, the conclusion that 77% of ‘alpha’ is from picking and winning seems to be a strong reflection of a market that has internalized the consensus-is-king message. Dan Gray at Odin covers this dynamic well:

In reality, selection is messy and inconsistent and value-add is questionable, while deal flow is fundamental and often underprioritised — despite research emphasising the importance. As a matter of simple maths, VCs can achieve better investment outcomes with a relatively minor improvement to their pool of opportunities versus a much greater improvement in their picking skill.

Identifying outliers is essential, but doing so by sitting back and picking is an incredibly noisy and difficult skill. Perhaps more importantly, your skill in picking and winning only matters if there are quality options available. This is why venture capital, especially compared to other private equity categories, shows remarkably persistent performance in funds raised by the same GP.2

The irony is that the incumbent firms will argue that “VC doesn’t need more funds, it needs more great companies,” while smaller, conviction-led GPs—often the ones doing the harder work of finding genuine outliers—face a more challenging fundraising environment. Of course, it is in their interests to paint venture as a zero-sum game centered around access to “legible” deals, to compress deal activity and reduce competition while growing dollar volumes. It follows that when megafunds argue that market winners are obvious within two years of founding or must clear some arbitrary growth hurdle, these are logical, if not self-serving, positions for their fund models, as we argued in our essay last Fall:

The concentration of capital in “asset managers” requires a corresponding concentration of capital in companies. You can’t raise your next fund if you can’t deploy the first. Tiger Global’s rise and fall demonstrated the pitfalls of the high-velocity, low-touch investment model. The only other option available—and frankly an easier one, given sourcing and underwriting don’t scale linearly with check size—is to concentrate capital in specific assets. What safer place to do it than in perceived winners in a market you can argue is winner-takes-all?

The underlying truth of venture hasn’t changed: greatness is unpredictable, success is idiosyncratic, and consensus is often a mirage. Chasing fundability over investability has never been a reliable path to outlier outcomes and fund returns. Most importantly, perhaps, is that while ‘consensus comfort’ has many short-term advantages for GPs and EMs alike, in the long-term, it is the most expensive thing you can buy.

2

https://www.sciencedirect.com/science/article/pii/S092911992300010X

The Odin Times

The Origins of Alpha

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3 months ago · 21 likes · 3 comments · Dan Gray

Investing 101

“Build What’s Fundable”

This is a weekly newsletter about the art and science of building and investing in tech companies. To receive Investing 101 in your inbox each week, subscribe here…

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5 months ago · 215 likes · 11 comments · Kyle Harrison

Jordan’s Writings

Venture Manager Selection: Capitalising on Arbitrage Windows

Markets age as cities do…

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a year ago · 51 likes · Jordan

Enterprise AI adoption isn’t “failing” because the tech isn’t ready. It’s slow because most founders are still trying to ship it like horizontal SaaS — and the enterprise vertical AI playbook is fundamentally different. And while founders are figuring out the enterprise GTM motion for Vertical AI, big companies are still figuring out how to test, procure, and implement it. The net result is that — while we’ve seen more PLG-like Vertical AI dominate the “league tables” — enterprise Vertical AI represents a multi-trillion-dollar white space opportunity.

Everyone talks about about how the revenue bar has irreversibly elevated in the AI era. “You’re dead if you don’t hit $XM in your first 12 months.” In it’s first year, EvolutionIQ worked with a single design partner, brining in ~$50k total. A few years in, however — once they optimized the product and enterprise workflow — they hit hitting 8-figure ARR shortly not long, with a very different growth profile.

This episode breaks down how EvolutionIQ became one of the first true breakout Vertical AI exits — selling to CCC for $750M — by doing something unfashionable: deep, hands-on iteration with at-scale carriers until the product was undeniably compounding value.

Michael Saltzman, co-founder and co-CEO, joins us to walk through the unsexy blocking and tackling of those earliest days of EvolutionIQ. He unpacks the core of the problem they attacked in insurance: why claims was a massively underserved problem, how (in a world where revenue acceleration gets all the attention) cost savings on a massive base can be just as powerful, how they eschewed the classic approach of writing back into core systems (skirting the perennial integration problem) and built a “digital twin” of the data instead, and how they made results (not pitch decks) do the early selling.

He also shared a lesson important for every founder today: how to build a platform that can adapt as underlying technology improves quickly. EvolutionIQ began pre-LLMs but, thanks to their flexible architecture — and more importantly, thanks to their incredible early technical talent from Google — they adapted and assimilated without slowing down.

Michael’s insights in this episode are crucial for any Vertical AI founder selling a truly enterprise product, structuring deep big-company design partnerships, or grappling with the trades offs of product complexity and early AI revenue milestone pressures.

This was one of our favorites to date!

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I) Vertical Titan

Michael Saltzman — Co-founder & Co-CEO @ EvolutionIQ

The backstory

Michael started as a mechanical engineer, then joined Bridgewater Associates where he spent years analyzing insurers and learning how they talk about their businesses — especially where the money actually goes.

At Stanford, he reconnected with a longtime friend, Tom, who’d spent more than a decade doing AI work at Google. They were drawn to industries with massive unstructured data — and insurance claims stood out as an “ugly stepchild” with massive potential: huge dollars, low product attention, and mountains of documents.

Their insight was simple: carriers were investing heavily in distribution and underwriting, while claims — where a huge share of revenue gets paid out — remained under-tooled. If you could turn unstructured claims data into consistent guidance, even small accuracy gains would compound at enterprise scale.

The hardest part

• Selling “guidance” (not automation) into a workforce that already believes it has strong expertise — and earning trust from the chief claims officer all the way to the frontline adjuster.

• Building an integration strategy that didn’t break core systems: every carrier’s data is different, so EvolutionIQ had to build an interface layer to normalize inputs across wildly inconsistent stacks.

• Creating a deployment motion where product and engineering were deeply involved, not just customer success — because value only shows up when the tool actually changes day-to-day behavior.

• Proving measurable impact quickly enough to unlock expansion: long sales cycles are real in insurance, so the first module needed to show results inside a year so it could “pay for” the next.

Memorable lines from Michael

• On first design partner: “At worst we would waste $50,000 of their dollars — and at best we’d build something they couldn’t buy.”

• “We didn’t write anything back to the CRMs at all.”

• “We didn’t even try to sell it until we had results from the first customer.”

What EvolutionIQ actually sells

The product isn’t a robotic process replacement. It’s closer to AI augmentation for claims professionals — an application wholly separate from their core systems of record that analyzes incoming claims and guides adjusters toward the best next actions.

In the complex sub-space of bodily injury and disability claims (their beachhead), value often comes from better decisions and earlier interventions. So it wasn’t just a matter of doing the same work faster. They had to sell hard ROI: we can empower your claims function with better risk decisioning that will directly lower your expense ratio and hence increase profitability.

The “digital twin” integration strategy

Instead of trying to rip and replace carrier systems, EvolutionIQ built a digital twin of the carrier’s claims data in its own format.

They normalize inputs through an interface layer, run models against that normalized view, and avoid disruptive write-backs — which reduces implementation risk and makes scaling across many carriers technically feasible.

Time-to-value and expansion economics

Their north star wasn’t “implement everywhere.” It was “prove impact fast enough that the customer can expand based on results.”

They aimed to demonstrate measurable business outcomes within the first year, enabling expansion to be driven by real claim performance — not by selling harder — and creating a compounding “module pays for module” dynamic.

Getting customer one

Most prospects told them to come back when there was something real. Then they met an innovative carrier willing to co-build.

The first commercial relationship was essentially a small, bounded bet: a modest initial spend, a shared commitment to iterate, and an understanding that the product didn’t exist yet — the carrier wanted to build it with them.

Building moats in enterprise Vertical AI

A recurring theme was compounding advantage: the more deployments, the more robust the interface layer becomes, the more the product can improve across the stack, and the more deeply embedded it becomes in decision workflows.

In enterprise vertical AI, the moat isn’t just the model. It’s the combination of integration leverage, deployment trust, and a product that actually changes how the work gets done.

II) Vertical Playbook

The “Digital Twin” Approach to Vertical Data

Why it works

All too often, core-system integrations — especially ones reliant on sharp-elbowed, legacy incumbents — are where Vertical AI point-solution wedges face major headwinds. Not because they’re impossible to navigate (plenty do) but because they risk derailing your roadmap and momentum: “developer ecosystem” certifications, walled-garden API costs, IT queues, managing competitive concerns once revenue gets to $5-10M ARR.

The “digital twin” strategy takes a completely different approach. You rely on customer access to pull data out of core systems, normalize and enrich it, and deliver guidance in your own product surface without integration reliance. That lets you iterate fast, reduce reliance on slow incumbents, focus on ROI, and meanwhile, build a durable asset: a translation layer that learns how to interpret the “reality” of customer data regardless of the customer or SoR it came from.

The tradeoff, of course, is adoption risk. You’re asking users keep yet another window open — in markets with SaaS fatigue, this can create friction. The only way that works is if the product is good enough that they want it open.

How to run it (EvolutionIQ-inspired blueprint)

1. Define the “canonical object” you’re twinning

• Pick the smallest unit of work you’ll improve (a case, order, ticket, claim, patient encounter, shipment, etc.).

• Specify the minimum fields / events / documents you need to drive decisions and measure outcomes.

2. Build a read-only ingestion path first

• Start by pulling data out of systems of record (APIs, exports, event streams, SFTP, DB views).

• Avoid write-backs and UI embedding in v1 so you’re not blocked by IT queues, vendors, or release cycles.

3. Create a translation layer into a canonical schema

• For each customer, map their messy tables / fields into your internal “truth model.”

• Treat this like a product: reusable connectors, mapping tooling, validation tests, and observability.

• Goal: make every customer “look the same” once data lands in your platform.

4. Stand up the “digital twin” as an internal system of understanding

• Materialize a clean, queryable representation of the canonical object + timeline + documents.

• Store the derived signals you compute (risk flags, next-best-actions, summaries, predictions).

• This becomes your independent data asset and lets models + workflow logic stay portable.

5. Ship a standalone “new window” that earns daily usage

• Build the workflow surface where your AI is actually useful: prioritization, guidance, triage, escalation, audits.

• Design for “side-by-side” use: fast load, low clicks, obvious next action, etc.

• Adoption hack: make it the fastest way to answer the question users ask 50x a day.

6. Integrate deeper only after you’ve proven pull-through value

• Once outcomes are clear and usage is sticky, selectively add write-backs, embedded widgets, or workflow triggers.

• Integrate where it reduces friction (SSO, deep links, task creation).

• Use ROI + trust to negotiate access and move upstream in the workflow.

Founder litmus tests

• Can you ship product changes without waiting on a core-system release cycle?

• Do you have a clear canonical schema — or are you re-building the data model per customer?

• If you never wrote back into the system of record, would your product still change daily behavior?

• Is your “new window” compelling enough that frontline users will keep it open, even with swivel-chair friction?

• Can you explain recommendations in a way that a claims leader can trust and defend?

What we debated on-air

• When “being inside the core” is actually necessary — and when it’s just a default assumption from the industry.

• How much of the moat is the model versus the translation layer and data semantics you accumulate over time.

• The real adoption tradeoff of a standalone UI: friction is real, but so is the leverage of owning your product surface.

III) Vertical Market Pulse

Enterprise ROI proof points mount in insurance

Travelers announces AI commitment, 20% profit hike | Claire Wilkinson @ Business Insurance

A growing set of large carriers and insurance players are moving away from “innovation theater” to reporting measurable ROI from AI adoption: straight-through processing, AI-supported underwriting review, and even AI voice agents for first notice of loss.

Takeaway: For founders, the bar is rising. It’s no longer enough to demo a model — you need a deployment story that survives production constraints, workforce adoption, and ROI scrutiny inside a regulated enterprise.

Insurance AI case study: labor & efficiency

Allianz to cut up to 1,800 jobs due to AI | Alexander Huebner @ Reuters

We have argued in the past that — viewed holistically — AI will be a net creator of jobs. That said, in particular roles and in the short term, there will undoubtedly be disruption… and receptionists and call centers have been some of the earliest to feel measurable pressure from AI.

The ROI covered in the previous pulse have begun to have equally tangible impacts on enterprise customer service. In insurance, high-volume, process-heavy operations are universal, making it a natural place to see developments like the one Allianz announced recently. Of course, some such announcements will be marketing inspired; always important to keep in mind.

Takeaway: beyond the positive savings to the company and the negative implications for call center workers, we would call attention to to a larger point. Winning products won’t just be “automation” that reduces headcount. They’ll be the systems that augment. Unless an enterprise is admitting it’s cutting service levels, few heads means each head must be more efficient. Winning Vertical AI focused on support staff will make humans materially faster and more accurate at processing the low-level tasks quickly and materially better at considering the high-impact 20% of decisions.

Insurance AI fundraising heats up in 2H 2025

Insurance AI deal roundup | Euclid Insights

Recent major fundraises in US insurance AI highlight an embrace of AI for both back-office and risk. In 2H 2025, two deals that stood out to us were Nirvana Insurance ($100M led by Valor for AI-driven trucking underwriting) and Liberate Innovations ($50M led by Battery for insurance reasoning agents). Smaller but significant rounds include Retireable ($10M led by IA Capital for combining AI-augmented retirement planning) and Quandri ($12M led by Framework for brokerage “digital workers”).

Next week on Verticals

Join us next week on Verticals to tackle Multiplayer Vertical AI with Mike Powers, co-founder and CEO of BuildVision!

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Some of the biggest opportunities in the startup and venture world occur when norms are changing too fast for most to update their priors. As we’ll hear from Nate Baker today, Vertical AI today is driving just one of those moments, as viable market sizing changes under our noses at an accelerating pace.

In an AI-driven vertical software world, traditional TAM math is a form of intellectual debt. It anchors you to yesterday’s workflows, yesterday’s labor costs, and yesterday’s definition of what software is “allowed” to do. As this week’s guest puts it, early-stage TAM sizing assumes a fixed market—when in reality, the best vertical companies expand the market by replacing services, collapsing labor, and continuously taking on more of the customer’s actual work.

That reframing matters more now than ever. LLMs are accelerating how quickly new wedges can reach revenue—but they’re also compressing timelines, commoditizing surface-level features, and exposing which products actually own the structured workflows underneath. In that environment, obsessing over TAM too early can blind founders to the real question: can this product keep expanding what it does for the customer as capability improves?

Of course, this doesn’t mean that TAM is completely dead and buried. It just means that traditional conceptions of TAM are—sticking to them can leave both investors and founders dead in the water. The reality is that VCs still need some heuristic to make sure an opportunity is big enough to support the venture scale needs of their fund math. Some of the biggest opportunities, however, are starting in beachhead markets that would have been traditional non-starters.

This episode is a deep walk through that tension with Nate Baker, who’s lived both sides: building Qualia into a core system of record for title agents—and then building Fractal Software into a factory for launching vertical SaaS companies at scale.

We talk about why Nate no longer believes in TAM at pre-seed and seed, why speed-to-revenue wedges are winning for now, and why clean, system-of-record-grade workflows remain the most durable foundation for AI automation. It’s an important conversation about the assumptions you may need to unlearn.

Don’t forget to subscribe to get weekly episodes like this in your inbox—available on YouTube, Spotify, and more—and check out our sponsor below to support the show. Happy listening!

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I) Vertical Titan

Nate Baker — Founder @ Qualia

The backstory

Nate started Qualia ~11 years ago to build software for one of the most under-appreciated roles in real estate: the title agent. The bet was straightforward and brutal: become the core system of record in a market with “unbelievable” regional complexity, then compound vertical platform value from that foundation.

Since its founding in 2015 by Nate and his co-founders Lucas Hansen and Joel Gottsegen, Qualia has streamlined an industry historically burdened by paper, manual coordination, and siloed systems. Critically, they had found a true “lightning bolt workflow,” as our prior guest Scott Wolfe Jr. of Levelset would put it: rather than building for just one stakeholder, Qualia brings together buyers, sellers, lenders, title & escrow professionals, and agents. Today, the platform processes transactions used by >1M real estate professionals and touches millions of homebuyers every year.

To date, Qualia has raised well over $200M from top investors like Tiger Global, Menlo, and 8VC, reaching unicorn status along the way. It’s expanded both organically and through acquisitions—most recently integrating Old Republic Title’s RamQuest and E-Closing platforms in a strategic roll-up designed to deepen its title production stack. That combination of capital, talent, and M&A has supported headcount growth into the 600+ FTE range, with core teams across SF and Austin.

What makes Qualia an especially interesting case study for Verticals is that it’s both a vertical SaaS and a vertical AI story. Nate is evolving the product rapidly into AI-enabled automation and structured process intelligence, infusing the real estate closing ecosystem with machine-powered document processing, data extraction, and collaboration tools. In other words, Qualia is a disruptor—but in another way, an emerging incumbent that’s using it’s market leverage to become a system of action. It’s an interesting lesson in how—while most incumbents simply don’t have the incentives and resources to do so—some companies of scale with “startup-minded” management can keep moving fast in the world of Vertical AI.

Having fallen in love with the model, however, just one or two vertical startups wasn’t enough for Nate. Six years ago, he also co-founded Fractal Software, a venture studio designed to launch vertical SaaS. Since then, Fractal has helped start ~150 vertical SaaS companies—initially skewed toward full systems of record, and now increasingly experimenting with faster AI-driven point solutions. Over the course of Fractal’s first first years, Nate has learned a ton about the inputs to vertical SaaS and AI success—and was kind enough to share those learnings with us today.

The hardest part

• Earning “system of record” trust when every state (and every customer) insists their workflow is uniquely complicated

• Staying disciplined on the core product while continuously expanding ACV, without distracting R&D into a mess of side quests

• Competing against legacy incumbents that are still on-prem—and can’t ship fast enough to keep up with AI-driven expectations

• Reconciling two truths at once: incumbents should win with distribution, but many won’t move quickly enough

• Staying ahead of the curve in AI: navigating a world where “six months from now, what’s in market today will be legacy”

Memorable lines from Nate

• “I fundamentally don’t believe in TAM sizing anymore at pre-seed, seed… the ability to grow the end market is limited only by your creativity and ability to build value for your customer.”

• “If you’re starting today, you have to prioritize speed to revenue.”

• “The primary constraint on rolling out LLMs is actually clean, structured workflows.”

• “Every business that is not seriously reevaluating their product offerings right now is going to be done in 24 months.”

Why “TAM” is the wrong question in vertical SaaS

Nate’s push isn’t that market size doesn’t matter—it’s that early-stage TAM math is usually anchored to old spending patterns (“reasonable ACV x number of customers”) and ignores what great vertical products actually do: they expand into adjacent workflows, replace services, and increasingly replace labor.

In other words: the real question isn’t “Is the initial wedge TAM big enough?” It’s “Can you durably keep expanding what you do for the customer as capability increases?”

Layer-cake strategy, and the common mistake it invites

The classic vertical playbook—core system of record first, then add modules—still works. But Nate flags a trap: teams can over-rotate into “multi-product” too early and lose the focus required to make the core product fly off the shelf with strong sales efficiency.

His heuristic: if the core product has strong sales efficiency and growth is compounding, don’t rush the adjacency just to chase incremental ACV. Expand, yes—but don’t distract yourself out of momentum.

Point solutions vs. system of record in the LLM era

This was the central debate: do you still bite off the hard ERP first, or do you start with a wedge and integrate backward?

Nate’s take is nuanced:

• If you already are the system of record, you’re advantaged—especially because LLM rollout depends on structured, interpretable workflows.

• If you’re starting today, the pace of change pushes you toward speed-to-revenue wedges first, then building back toward the system of record over time.

The “structured workflow” moat

One of the most tactical insights: LLMs don’t magically fix messy businesses. They need clean workflows and data you can trust. That’s why many point solutions struggle to move from “cool demo” to “deep automation”—they can’t access or shape the underlying system-of-record-grade workflow.

For founders, this reframes “data moat” into something more operational: can your product impose structure, not just read unstructured data? We’ll paraphrase a relevant quote from Niket Desai, co-founder of Laurel, around gauging whether your data / AI strategy is actually contributing to a moat: “are you adding net-new columns of data?” Or just derivative of existing systems of record?

The incumbent clock is shorter than they think

Nate described a common legacy mindset: “We’ll ship something similar in 12–18 months.” In the AI cycle, that pacing is fatal—because the product standard moves every quarter, not every year.

This is also where the on-prem vs. cloud gap becomes existential. If you missed the cloud transition, AI-native delivery becomes dramatically harder to execute and iterate.

Inside Fractal Software: how to launch 150 companies

Fractal started from a simple observation Nate and his co-founder Mike Furlong shared: in many verticals, company-building is less like inventing a new science project and more like constructing a commercial building—repeatable if you systematize research, recruiting, and early capital.

The reported outcome: Fractal could compress the timeline from “I want to start a company” to “founder in seat with an MVP and real customer motion” by ~a year.

Fractal’s highest-signal founder lessons

With a big enough sample size, patterns emerge. Nate highlighted a few:

• If co-founders are in the same city but don’t work together ~5 days a week, it’s a red flag for Nate. Remote teams, however don’t underperform co-located founders.

• Founder ability to sell matters hugely—especially when paired with care for engineering craft. This doesn’t mean they have to be without a stereotypical salesperson, or even be primarily commercial. They just need to be able to drive founder-led sales effectively themselves.

• Speed to MVP and speed to first revenue are leading indicators. Even 2 months in, lagging commercial movement is often predictive.

II) Vertical Playbook

Vertical product expansion: finding your pace

Inspired by Nate Baker, Co-Founder & CEO of Qualia

Why it works

In a fast-moving AI environment, time is a competitive weapon. A wedge product can get real distribution and real revenue while the tech frontier is still shifting. But the wedge only becomes durable when it connects to (or evolves into) the structured workflow layer that agents can automate reliably. As mentioned above, moving into a “layer cake” strategy too early can be a trap. Founders need a framework to gauge when then should double-down on the core vs. when it’s time to layer on new offerings to expand TAM and share-of-wallet.

How to run it

1) Pick a wedge that touches “work,” not reporting

Choose a starting point where the product can replace minutes of human labor, not just “track” it.

2) Get to revenue speed first—then deepen workflow structure

Use early customers to learn the real process variance. Your job is to turn “messy regional exceptions” into a clean, interpretable workflow.

3) Treat structured workflows as an AI prerequisite

If your product can’t standardize inputs/outputs, you’ll struggle to deploy LLMs beyond shallow copilots.

4) Expand ACV by taking on adjacent work (not adjacent features)

The layer cake compounds when each new layer either:

• replaces a service,

• removes headcount,

• or directly drives customer growth.

5) Don’t multi-product yourself out of PMF

If your core product isn’t selling efficiently, fix the core before you chase adjacency. Expansion is powerful, but distraction is fatal.

Founder litmus tests

• Does the wedge get you to meaningful revenue quickly without locking you out of the system-of-record path later?

• Can you uniquely access and structure the workflow data needed for agentic automation?

• Are incumbents technically capable of shipping at today’s pace—or are they stuck in “12–18 months” land?

• If the wedge becomes commoditized in six months, do you still have a compounding roadmap?

What we debated on-air

The open question: will the best vertical companies of the next era still start by rebuilding the system of record—or will they mostly start with fast wedges, then integrate backward as LLM capability expands?

Nate’s answer leaned pragmatic: if you’re starting today, prioritize speed to revenue. But if you can become (or already are) the system of record, it’s still the most advantaged position for durable AI automation.

III) Vertical Market Pulse

Are vertical voice agents the easiest “instant AI ROI”?

Toma’s AI voice agents have taken off at car dealerships | Sean O’Kane @ TechCrunch

AI voice feels like the new “hello world” of vertical automation: it’s magical in the demo, easy to adopt, and doesn’t require a full rip-and-replace of the core system. That’s exactly why it’s spreading fast across industries where phone calls are revenue (auto dealerships, field services, healthcare intake, etc.).

The founder implication: wedges that attach to obvious pain (calls, scribing, outbound scheduling) can get to real revenue quickly—but you still need a plan for where you’ll compound once the novelty becomes table stakes.

Is Agentic AI disrupting SaaS?

Will Agentic AI Disrupt SaaS? | Crawford, McLaughlin, Doddapaneni, Fiore @ Bain & Company

As agents get better at “doing work,” control points are shifting: who owns the source-of-truth data, who orchestrates the tools, and who owns the interface where outcomes get requested and verified.

The founder implication: “agent wrappers” without deep workflow integration can scale quickly, but the durable winners will either (a) own the system-of-record-grade data structures, or (b) become the orchestration layer that everyone else plugs into.

Embedded fintech is still expanding the “layer cake”

How Xplor & Parafin bring embedded financing to local SMBs | Team @ Parafin

We’re still watching vertical platforms turn “software spend” into “operating leverage”—and embedded financial products are a major accelerant. When capital is native inside the workflow, it’s not a feature; it’s a revenue line and a retention engine. Much more on this from our past episode on Vertical FinTech with Rahul Hampole, head of fintech ServiceTitan.

The founder implication: the best expansion paths are the ones that turn your product into a business partner (not a dashboard). If your platform can directly help customers grow revenue, manage risk, or fund expansion, your ACV ceiling changes.

Next week on Verticals

Tune in next week as we unpack Enterprise Vertical AI with Michael Saltzman. He’ll share his journey building EvolutionIQ through a $750M acquisition.