Every vertical software incumbent faces the same two paths ahead: rebuild the product for AI (and likely pay a vendor to help you do that) or buy a platform to spearhead your AI strategy. So far, few are taking the latter path. But that will change. In this episode, your hosts — Luke Sophinos, CEO of Odyssey and Nic Poulos of Euclid — debate M&A in Vertical AI. Why it’s been more of a trickle than a flood thus far, the pressure points that are influencing acquisitiveness, and how founders should think about the coming years.

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Today’s Episode

The SaaSpocalypse obituaries ran early. The IGV software index fell 24% in Q1 2026, its worst quarter since 2008, then clawed back most of the way since. Infrastructure software and consumption-priced names from MongoDB to RingCentral are riding the agentic wave, while 26% of horizontal apps are net-contracting, and vertical apps are faring variously based on their industry of choice and its AI exposure.

As it turns out, software customers aren’t really leaving. Our latest public comps review has vertical software at a 109% median NRR against 105% for horizontal apps. The market is uncertain, however, how long that will last. “Legacy” systems of record — plenty of them PE-owned — spent ten-plus years cobbling together end-to-end suites, and now they’re caught in a pincer. Vertical AI businesses that started as wrappers (e.g. Legora, MagicSchool) are coming in the front door and putting up growth rates that make SaaS look sleepy. And AI-native services are slipping in the back to do the customer’s work, disintermediating their direct relationship with the product.

At the same time, the AI that incumbents are adding to their products isn’t not working. Microsoft has sold 30M Copilot seats, Salesforce has grown Agentforce past $1.5B ARR, and nearly all AppFolio customers use Realm AI. But after three years, Copilot reaches <7% of Microsoft 365 seats and Salesforce’s organic growth is still about 6%. AppFolio’s growth — which largely comes from payments and screening — slowed from 18% to 14% last quarter, even with the success of its AI. Most incumbent AI bolt-ons are smart defensive moves: they dissuade against 3rd party AI wrappers and bundles model usage into a contract the customer already has. But they aren’t reinventing the core product, and are unlikely platforms for transformational new growth. So what happens when more disruptive Vertical AI visions come along? Long-term, it’s unlikely users want to work inside old SaaS systems of record at all. What happens when someone rebuilds them AI-natively, agents unchaining users from logging into anything?

Eventually, something will give. Incumbents understand that software customers are sticky and that it won’t happen overnight. But it remains an existential threat to those that don’t have the talent, velocity, and executive mandate to think big — and perhaps even to cannibalize cash-cow SaaS product lines. The most natural way to accelerate a proper AI growth platform would be an acquisition. Today, we ask: who, if anyone, is taking this approach, and why aren’t more doing so just yet?

The Ball & Chain of SaaS Success

There’s much more to software than a relational database and a decent UI. But when a Vertical AI startup that truly understands the specific workflow enables much of that clicking, data entry, dashboarding, etc. to be handled by agents, the system of record risks losing the “time in platform” stickiness that was once so important. And for an incumbent doing billions in topline, it takes incredible political capital to launch a product to risk what made everyone rich in the first place.

In our discussion, Luke shared his take on incumbent ability to move:

They’ve gotten so good at the SaaS playbook that that’s what they do. They’re still running four-week sprints and capacity planning. I’ve run this playbook successfully in three roll-ups because I’m a PE-backed CEO. It’s really hard to change.

It’s not laziness or incompetence. It’s incentives. PE-owned vertical software — as an example — spends a much thinner slice of revenue on R&D than its venture-backed peers. The CEO’s bonuses tie to stock price next year, not next decade. So margin expansion is easier than re-platforming. A play that risks a big portion of a healthy, probably even growing installed base might result in a CEO search firm on retainer.

Some might argue that current incumbent LLM-based products are merely wedges that will evolve organically into larger AI strategies. After all, looking at some of the early Vertical AI breakouts, there are plenty of cases in which wrappers worked. As MagicSchool’s CEO shared in a prior episode, the company broke out because educators were Googling “ChatGPT for teachers” when LLM adoption was brand-new and still felt off-limits at work. OpenEvidence offered a discovery and research solution for MDs that was simply more accurate and trusted when it came to medications. Both nailed vertical-specific distribution. And both quickly moved to build advantages on top of those wedges that put them on a more defensible path: MagicSchool with a platform moat and OpenEvidence with a network effect of pharma advertisers.

It’s hard to see how a wrapper helps an incumbent drive massive new adoption or build a new moat. More or less, these bolt-on products are chatbots that pipe questions or simple BI / data requests about existing SaaS ERPs through a model lab API. Most are leagues away from a headless system of record. The question really is, where do incumbents go from there?

Whatever it is, it would require speed. In the current AI era, it’s easy to look at the scale and growth rates of OpenAI or Anthropic and assume that all giant companies can grow that fast. But their growth is predicated on the LLM, a singular novel product. And while they iterate that product brilliantly, it’s not exactly proof that massive companies suddenly have a velocity and innovation advantage building and launching wholly net-new products. OpenAI itself gives us several examples of the opposite, between the flops on Sora and ads. Between burgeoning bureaucracies, sacred cows, varying investor and shareholder needs and pressures, the historical truth remains unchanged — success can be a ball and chain.

Why Aren’t They Buying?

So if acquisition is an obvious way to inject novel AI products, DNA, and velocity into an incumbent eager to stay ahead, why aren’t we seeing more Vertical AI M&A? We landed on two major reasons.

1. The targets are young

Per our annual Vertical Report, Vertical AI still mostly lives at Series A. Growth-stage AI-native companies are thin on the ground, and the handful that exist (EvenUp, Abridge, EliseAI) were founded years before ChatGPT. What we’re seeing in vertical is still mostly at-scale vSaaS, making the Vertical AI cohort particularly thin:

• Clio took vLex for $1B, and Waystar bought Iodine at a $1.25B enterprise value, both in 2025. SaaS buying SaaS.

• Autodesk agreed to buy MaintainX for ~$3.6B in May: about 26x forward ARR, and the biggest check in Autodesk’s history. That said, Autodesk is a rare example of an incumbent vertical software player who is just really good at M&A.

• Procore bought Datagrid in January. Datagrid had built construction agents mostly on Procore’s own data, so this one practically wrote itself. But it’s a good example of how incumbents should think about staying ahead.

• CCC paid $730M for EvolutionIQ, which we pulled apart in January as the template for what comes next — a public incumbent that isn’t necessarily paying up for an AI-native company before it hit scale.

Of course, these are just a few examples. But so far, we’ve mostly seen late-stage SaaS-SaaS merger type buys (Clio), newer-generation SaaS players (Procore) defending turf, or existing frequent acquirers (Autodesk) doing their thing in the AI era. More of that will come, but we expect the aperture of who buys to grow as does the Vertical AI market as a whole.

2. The buyers don’t have the cash

The narrative would run that stocks got hammered in Q1, equity isn’t free, cash feels scarce. For PE-backed, debt-laden platforms wired for cash, that’s easier to see. For most SaaS, however, cash hasn’t been a problem.

After 2022, many have been on unprecedented cash-generating tears. Veeva’s operating cash flow was $1.4B in FY26, up from $911M in FY24. Procore’s FCF rose 507% year over year in Q2. To the extent cash reserves are dwindling, the bigger reason is that they feel it’s better spent elsewhere. Toast’s cash fell this year mainly because of $412M in repurchases. AppFolio spent $125M on buybacks in Q1. Even Veeva bought back $170M, a first for them.

This year, software incumbents pouring into buybacks has been a defining feature of the year, seeing some of the largest buybacks in the sector’s history: Salesforce ($25B, its largest ever), Adobe ($25B, about 24% of its market cap), Intuit ($8B), ADP ($6B), and ServiceNow ($5B). All despite the SaaSpocalypse. While outside the application-layer realm, Nvidia — setting the stage for much of our economy these days — authorized a $150B increase just a few days ago, marking the largest in history. M&A makes little sense if you’re convinced the best investment opportunity is your own stock.

Not to mention, you don’t always need cash to acquire. Beyond financing, most such deals have equity components. So the “we’re broke” argument falls a bit flat, at least for larger public incumbents. Which brings us to rationale #3.

3. The buyers are proud

We’re squarely in the “build-and-partner” phase of AI adoption. Following the “we can do it ourselves” phase, which never really lasts. It’s a natural reaction from any company, most especially those used to a strong moat in their vertical that provided them plenty of time to replicate or elbow out competition. August’s Claudeforce announcement is a perfect example: rent the frontier, wrap it in your suite, tell the Street you’ve got it handled. The question is whether they’re still asleep or wide awake and simply… stuck. Chained to a legacy system with thousands of customers, where nobody gets promoted for putting recurring revenue at risk.

In the history of disruptive technology, there’s a recurring pattern that feels relevant here — you could call it the “Incumbent Adoption Cycle:”

Every platform shift has played out the same way inside incumbents: we’ll do it ourselves, then build and partner, then buy. Microsoft first answered the internet with MSN as a closed network, then licensed Spyglass’s browser code to ship Internet Explorer, and by 1997 had bought FrontPage, Hotmail and WebTV. Oracle’s Larry Ellison dismissed cloud as “fashion” in 2008, and after six years building Fusion apps in-house, bought RightNow, Taleo and NetSuite. SAP’s own cloud ERP never took off, so it partnered with AWS in 2011 and then bought SuccessFactors, Ariba and Concur. Each company bought once its own build had fallen behind, and bought only when the writing had long been on the wall — and the price tags hefty. Today’s vertical incumbents are somewhere between the first two steps. The questions are whether this time is different, and if not, when we get to the third step.

In Vertical AI, everything is moving faster. Building is proving a lot harder than the partnership press releases let on. Emergent threats are stacking ARR faster and the need for an AI story is growing. So our guess is that M&A will start to emerge sooner rather than later.

What Makes You Worth Buying

If the wave is coming, founders should ask: what kind of company gets bought, and at what prices? In our conversation, Nic laid out four ways Euclid is seeing to go after an incumbent. We’ve covered some of this ground in prior episodes. Let’s take a look at these competitive angles in an M&A lens.

1. Go head-on

NinjaOne picked MSP endpoint management, a PE-rolled-up category where Kaseya and ConnectWise each held about a quarter of the market. It built faster, leveraging cloud-nativity to go straight for the system of record. In June, it raised at a $12.3B valuation on ~$500M of revenue. It’s now number three in the category at ~10% share, growing 50%+ a year. Harder to afford from an M&A perspective at this point, but value creation is value creation.

2. Irreplaceable point solution

Plug into the SoR and get big enough inside their customer bases that cutting you off would dent their own retention and piss off customers. This is a classic setup for a strategic deal like Datagrid: agents built on Procore’s data, acquired by Procore. The key here is to own functionality that the SoR doesn’t, and cannot easily replicate. Either due to technical prowess, not being a core competency, or because an independent third party between stakeholders is necessary. Brellium is a good example of the latter, sitting between provider, patient, and payer on clinical compliance. Their product would be impossible without data from each party that none of them has any incentive to provide to the other.

3. Do the work, then become the SoR

Forward-deploy, automate every workflow you can get your hands on, build ARR momentum on a product that plays nice with SoRs, then build your own SoR (probably on the sly) once you have the capital, installed base, and data to do so. Luke explained in our episode:

You look like an employee at the company you’re servicing, but you’re just automating with AI as much as humanly possible in every single workflow.

EvenUp was drafting personal-injury demand letters before AI-Native Services was a category. It’s now valued at $2B+ and creeping toward the full stack of a plaintiff law firm. Obviously, the weaker or more fragmented your incumbents, the easier this is. In fund administration, Hanover Park went from $1B to $15B in assets under administration in under a year — but in a market where services, not SoRs, were the primary draw.

4. Build a valuable data asset

In the Vertical AI era — as we will share more on in our upcoming State of Vertical AI pieces — data moats are key. Those require, very often, some form of reinforcement learning, which in turn requires rewards data, or decision traces. Not all such rewards are simulatable (like coding) or purchasable (like annotated case law). And beyond incumbents, the model labs are acquirers here now too. So far they’ve paid for people, not datasets; OpenAI’s 2026 deals for Hiro (personal finance) and Torch (health records) were acquihires or feature grafts. As Figure and others show in robotics, significant dollars are going into producing this data from scratch. Platforms that capture it as exhaust of the job — where training data is otherwise visibly scarce and that data helps solve important problems — are building enterprise value beyond ARR.

The Price of Waiting

Which brings us to the proud buyer’s real problem. Emergent Vertical AI is learning to build the one thing some imagined only model labs could.

Harvey closed $550M at a $15.5B valuation this month, following its launch of Tenet. A proprietary LLM, post-trained on Moonshot’s open-weight Kimi K3. Cursor built Composer in coding, the labs’ home turf, before agreeing to a $60B all-stock sale to SpaceX. Coding is about the worst place on earth to try this. Endless data, all of it simulatable — but Composer was still quite serviceable, reportedly a contributor to the company’s hitting breakeven early this year. If an app-layer company can still get to near-frontier quality, without a massive pre-training budget, then proprietary data (in data sparse spaces) is much more valuable than we’re giving it credit for. That’s a tremendous margin and moat opportunity for Vertical AI. We discussed this further in The Open-Weight Unbundling.

Of course, it’s rare a Vertical AI company has the data to build any sort of advantaged in-house model from day one. So far, it’s been a growth-stage exercise. But for prospective acquirers and sellers alike, it’s a worthwhile consideration. Waiting to find out what a startup with a proprietary model costs, versus just the ability to build it down the road, will be costly.

The Takeaway for Vertical Founders

The M&A wave is coming. In fact, looking at history, it’s just about on schedule. Few believe everyone can simply build their own enterprise applications wholesale any more. We’re in the build / partner phase now, in which incumbents are still holding onto the dream that they can own everything AI in their space, without paying up. The dilatory factors here — the relative youth of Vertical AI, the lack of evidence that AI bolt-ons aren’t the answer, the confidence that buybacks are all they need — could all change relatively quickly.

For now, Vertical AI founders can stay focused on what matters. Nail distribution. Build customer depth. Navigate your incumbents carefully and have a plan to create independent advantage. And if all goes well, either sell at a premium once the buyers wake up… or get so big nobody can afford you.

See you next week.

Abstract

• Software prices whipsawed over the past year, but NRR held at a median of 109%. Customers haven’t left — at least not yet.

• The rebound has been uneven. Infrastructure has pulled away, and vertical software has been the least bought back.

• The market seems to be pricing verticals by industry. “Knowledge work” verticals fell much harder (without respect for AI addressability) as a group, while physical verticals saw a much bigger bounce-back.

• Our view on the market dislocation and what it means for founders & VCs.

Announcing Euclid Comps

With this piece, we’re also opening up Euclid Comps: an interactive comparables dataset covering ~140 public application-layer equities across all key financial (EV, revs, margins, multiples), operational (customers, ACVs, NRR), and AI (AI Tailwind Factors, AI-Addressable Work Score) measures. Plus tools to visualize your own cuts of the data. Updated quarterly and free for all subscribers of The Verticalist!

Back in May, we wrote SaaSpocalypse Now, which argued that public-market investors were pricing in the immediate revenue upside of AI, rather than the eventual steady state. In the eventual equilibrium, businesses that leverage valuable data, workflows, and distribution advantages to own AI diffusion in their domain will flourish. Those that cling tightly to SaaS product form factors and pricing models will atrophy. To see how we are tracking against this future, we revisited our software comp set following the late-summer earnings season to assess where we are in the cycle and how the long-term vertical advantages we identified this spring are tracking.

One effect caught our eye: prices whipsawed, but retention didn’t. Between New Year’s and April, our three cohorts fell together, by roughly a quarter to a third. Since late March, infrastructure is up 87%, horizontal apps 31%, and verticals 16%. Over the full year, the median stock in our set is still down 16%, while IGV, the market-cap-weighted software ETF, is down just 7%.

Net Revenue Retention (NRR) barely moved through all of it. Median NRR across our set — reported by just over half of the ~140 tickers — sits at 109% today, the same as in our early-2026 snapshot. That’s a few points below the 112–115% that software investors treated as healthy in the final decade of the SaaS era, but it’s steady. Of the 74 companies we tracked across both periods, 38 were flat, 19 improved, and 17 declined. Of course, general macro turbulence can push NRRs up and down a few points, and NRR is a lagging indicator: contracts renew annually, so real displacement would show up a few quarters after the fear does.1 But so far, customers aren’t leaving.

That stability, however, is the least interesting part of the story. There’s much more to be learned from looking at which stocks were bought back and which weren’t. There is one business attribute that appears more determinant of performance than the rest — and it has nothing to do with horizontal vs. vertical.

The Consumption-Based Tailwind

In SaaSpocalypse Now, we argued that investors were rewarding immediate AI beneficiaries while discounting the less visible strengths of vertical platforms. We also made a more specific claim:

Consumption-based access & pricing models were the clearest driver of software performance over the last several months.

Joe Floyd at Emergence put together an analysis that appears consistent with that view, showing that consumption-priced companies outperformed their seat-priced peers. Our own latest data, 6 months later, corroborates it across all ~140 stocks in our comps; the pricing model is the single biggest separator we found:

The outperformance of businesses with consumption-based pricing is fairly easy to understand. If you’re “infrastructure for AI,” and AI scales up extremely quickly without the need for human intervention, you benefit as companies test new agents and LLMs — if you happen to price on usage, you capture more of that upswing, faster and with less friction. That’s a big part of why infrastructure has pulled away from applications altogether. Non-infrastructure businesses with consumption-based models can see (and are seeing, anecdotally) the same benefit from AI tailwinds. There aren’t many of them public yet, outside of nascent revenue streams from subscription-heavy SaaS players testing the waters.2

Enthusiasm for immediate AI beneficiaries can coexist with durable demand for vertical apps. The retention data are consistent with that: existing-customer spending looks more resilient in verticals and infrastructure than in horizontal applications. That doesn’t establish AI as the cause, but it gives us evidence beyond stock returns to examine.

Sold as a Block, Bought Back Unevenly

Vertical software stocks have underperformed over the last year — with the slowest recovery from the SaaSpocalypse. They’ve seen a one-year return of −26% vs −17% for horizontal apps, and +29% for infrastructure. But revenue growth rates tell a different story.

Median vertical revenue growth was 12.7%, in line with horizontal applications’ 12.9%. By our estimate, vertical valuation multiples compressed by roughly 40% over the year, against roughly 30% for horizontal applications and almost nothing for infrastructure.3 Retention doesn’t explain it either: verticals retain at a 109% median against 105% for horizontal apps, and only one vertical company in our set (Docebo) sits below 100%. It appears the market has been marking down vertical software on narrative rather than fundamentals.

The obvious explanations don’t hold up. Verticals fell 25–27% whether they were under $2B, $2–10B, or above $10B in enterprise value. Old versus new-generation verticals fell by the same amount. Starting valuation barely matters — the expensive verticals didn’t fall much further than the cheap ones. Pricing model matters at the margin (verticals whose revenue scales with usage held up better than seat-heavy peers), but it doesn’t explain the gap with horizontal apps: comparing subscription businesses only, verticals are still down 27% against 21%.

The Pernicious Fear of Replacement

Industry, on the other hand, explains a lot. If you split our verticals into physical-operations businesses (supply chain, manufacturing, hospitality, retail) and everyone else, the physical-operations group is down 7%, and the rest are down 31%. Retention is the same in both, around 109% (though only three of the physical-operations companies disclose it).

At first glance, that looks like the market pricing AI risk sensibly. Physical work is hard to hand to an LLM; legal research and claims correspondence aren’t. To test it company by company, we built a new score for Euclid Comps: AI-Addressable Work, the share of what a company’s customers pay for that is language-in, language-out work an LLM can do today.4 It’s deliberately separate from our AI Tailwind score. High addressability is a threat to a company that sells seats to the people doing the work, and an opportunity for a company that owns the workflow and can charge for the work itself.

The score does sort the two groups. The physical-operations verticals score a median of 10%, the rest 20%. But inside the “everyone else” group, it explains almost nothing: the least addressable companies fell just as hard as the rest, and some fell harder.

Bentley Systems is a good example. Its infrastructure-engineering software runs on simulation and analysis engines where exactness is the product, so it scores just 10% on addressability. It beat estimates in Q1, yet it’s down 39% on the year, with coverage pinning the slide on the “seat compression” narrative: fewer engineers, fewer seats. Guidewire (30%) tells a similar story. It fell 36%, and some of its worst days came from sector-wide AI sell-offs, though a soft ARR print didn’t help. The market seems to have looked at the industry and priced the whole category as AI-exposed.

Horizontal software got a more careful read. Among horizontal apps, the least addressable fell 12% and the most addressable 25%. Twilio and RingCentral have users doing plenty of language work, but customers actually pay for the underlying network and usage. The market picked up on it, with Twilio up 179% over the year and RingCentral up 158%.

So in the same market, over the same year, horizontals got company-by-company analysis, and verticals got a broad brush. The market has made a broad assumption about the replaceability of LLMs in knowledge-work industries. It’s unlikely that such a coarse measure is accurate.

But AI replacement is a generalized fear that’s hard to overcome. The market sold off all software earlier this year, save names it was sure would get immediate boosts from the agentic boom (i.e., infra and consumption-based pricing). When it came time to buy back, that single big fear loomed5, making knowledge-work verticals less palatable.

A Smaller Vertical Premium, but Still a Premium

None of this means the market has stopped paying for vertical moats. When you consider the 100–109% retention band — a massive one by volume, comprising 40% of the companies that disclose NRR — it’s pretty interesting to note that vertical businesses out-trade horizontal apps by roughly 1.7x.

In that fat middle band, verticals trade at 4.8x EV/Revenue, while horizontal app peers trade at 2.9x. Why would the market pay a ~65% premium for the same retention rate, when there’s no substantive difference in other fundamental metrics (lower, in fact, from a growth perspective: 12.8% vs 13.4%)? A likely answer, in our view, is that the market still believes a vertical company at 107% NRR is more likely to stay there than a horizontal company at the same number. In a world still questioning to what extent historical conceptions of moats — vertical and otherwise — hold up, that’s a pretty significant nugget.

Across our whole set, retention tracks valuation multiples closely but has had almost nothing to do with this year’s returns.6 The market prices stickiness into the multiple and then trades on narrative. The market feels something is holding up about the vertical moat, even if it isn’t willing to pay the same premium it once did. The premium was higher a year ago, and it has shrunk for the group as a whole, even as retention underneath it held steady. If the market is misreading LLM replaceability in knowledge-work verticals, the premium is smaller than it should be. And we’d argue it’s not even about being vertical per se. It’s about specificity, and the nature of the lock-in it enables.

The AI Effect? Horizontal Apps Are Bifurcating

To break retention down, we split the 75 companies in our comp set that disclose NRR into three cohorts: infrastructure (security, observability, identity, data layer), vertical (industry-specific application software), and horizontal application (workflow tools serving broad buyer bases).

Infrastructure holds with a median NRR of 114%. Only one company in that group, PagerDuty, sits below 100%. 72% retain at 110% or above. We’ve discussed the reason (or at least the narrative) in past pieces — most recently, SaaSpocalypse Now. Agents need tools and piping (data, web search, security perimeters, VoIP connectivity, observability, etc.) and can use them on a consumption basis as they scale ad infinitum. The favorable sentiment is probably a bit too generously spread, but the AI tailwind here is real and easy to see.

Horizontal apps are in a very different position. With a median NRR of 105%, more than a quarter of the cohort falls below 100% — meaning customers are spending less year over year, net of expansion. ZoomInfo at 89%; Weave at 92%;7 EverCommerce, Yext, and Asana all contracting. From project management and help desk to BI and marketing automation, these products organize workflows a capable knowledge worker — or, increasingly, an AI agent — can approximate. When the core value proposition is “help humans do a task through a UI,” and that task is moving into the model’s strike zone, the UI stops being the moat.

But the cohort isn’t sinking together. The same share, 26%, retains at 110% or better: Figma at 136%, ServiceNow at 125%, Atlassian at 120%, Twilio at 116%. Horizontal apps are splitting apart, and that’s a big slice of the horizontal world on each side of the line.

Doing the Work: Mo’ Money, Mo’ Traces

For founders, AI-Addressable Work reads differently. The public market is pricing high addressability as a threat to incumbents. For a startup, it’s the point of attack: because AI-native products can do the work, rather than enable it as did SaaS, the LLM-addressable function itself is just the first arrow in your quiver.

However, such points of attack may also be commoditized soon, if they aren’t already. The same models are available to every competitor and every incumbent. And with the rise of open-weight models, the ease of distillation, and the resulting perpetual pricing pressure on AI infrastructure, any quantum of intelligence is highly deflationary. So that point of attack needs to build into a moat quickly because competitors and incumbents will be coming, and their price of entry drops weekly.

The good news is that customers appear to pay more for doing the work than for enabling it — and that effect is starting to show up in public numbers. CS Disco now earns 92% of its revenue on a usage basis, with per-use GenAI review at a record. Five9’s AI agent revenue is 15% of subscription and growing 78%. HubSpot has moved several agents to outcome-based pricing. More than 80% of Figma’s $10K+ customers consume paid AI credits every week. These aren’t ever the best examples of businesses truly owning outcomes and taking on what might’ve historically been services or in-house, human-owned deliverables — but there’s no shortage of breakout examples in the startup world, AI-Native Services (AINS) or otherwise.

Doing the work should not only increase ACVs but also give you access to more decision traces. For example, whether the draft was accepted, the claim was paid, or the retail product sold through. Those traces — especially byproducts of the job that are impossible to scrape and hard to buy — drive reinforcement learning, expansion into adjacent workflows, and appear to be the most compelling path to AI-native moats. A tool that only assists a human never sees the full workflow, and it very rarely sees the ultimate business outcome in full context. That’s how addressable work becomes a data moat, though it takes a strong vision to get there: knowing which workflow to own first, and why doing it produces data nobody else will have.

What’s Actually Changing

In one sense, the data lines up with the expected pressures of AI: software whose primary value is organizing information through a UI is under real pressure. It’s looking, however, like horizontal apps are the best exemplars of that soft underbelly. As defensibility of the middle of the stack — namely software UI — is commoditized, peripheral layers of the stack grow in importance. Below: infrastructure that AI agents have to route through. Above: vertical systems of record with domain-specific moats.

Equally important is what the data doesn’t say. We shouldn’t infer that AI is responsible for all the weakness we observe. The current data support a more specific conclusion: horizontal applications show weaker reported retention, widespread replacement of enterprise software is not occurring in any meaningfully visible way, and the market is pricing vertical software by industry rather than by business. Vertical moats are neither dead nor ignored by the market. The AI app-layer replacement cycle will not happen overnight.

If the market is using too broad a brush, the pockets of undervaluation likely sit around vertical companies with low AI addressability and strong moats that were sold along with their industry, and not yet rebought due to oversimplified fears about the AI narrative. If we’re right, the question is how long that dislocation persists — and of course, whether that’s a better use of capital than getting in on soaring infrastructure bullishness. If NRR begins to tank across knowledge-work verticals regardless of AI addressability, then something else entirely is afoot, and we’ll be the first to call it out. Strong opinions, loosely held and all.

As we suggested back in our initial piece on the SaaSpocalypse in February and again in May: much of enterprise software will not be trivial or quick to replace. That some incumbents — vertical or otherwise — may not be so swiftly dismissed is no reason for founders to fret, or for incumbents to celebrate. Software (including AI) undergoes a constant innovation-replacement cycle. And on the whole, the diffusion of LLMs is accelerating, both due to massive coding efficiencies and novel direct applications.

However, while the prospect of faster switching cycles is always compelling for would-be disruptors (or their investors), that’s not what makes the current AI age so exciting.

The Takeaway for Vertical Founders & VCs

Seven months ago, we summed it up in a prediction that’s only gained steam since: “The bears assume that AI will shrink the software market. We think it dramatically expands it, even more so in vertical categories.” New functionality means new value, means new markets and growing TAMs.

Compared to five years ago, today’s opportunity is just bigger. By a lot. In our estimation, ~5x bigger than total current SaaS spend.8 That means trillions of dollars that will largely be captured by companies not yet listed on a public stock exchange.

So, founders and VCs: if you don’t like what’s happening to software in your portfolio, stay focused. The prize ahead is worth it.

A chart from the essay did much of the work, going semi-viral on LinkedIn and Twitter and generating some… interesting discourse. Its claim: venture isn’t really an asset class at all, just a handful of managers who capture nearly all the returns. It’s a convenient argument. Unfortunately, the data in the chart doesn’t actually support it.

One disclosure first: arguing the countercase serves our interests as an emerging manager, regardless of what we think of a16z’s argument. We trust you to judge both arguments on their merits.

The Case for More Venture

a16z’s argument, compressed: value creation has moved to the private markets and returns are concentrated in a few managers. So the question isn’t whether to allocate more — it’s who you’re allocating it to. Guess who.

We would certainly not dispute the broader case for more venture exposure. Companies can now build enormous businesses before going public, leaving much of their growth outside the reach of investors buying publicly traded shares. SpaceX, Stripe, Anthropic, and OpenAI illustrate the scale of that shift. It is reasonable for LPs to revisit allocation frameworks built for a much smaller venture industry.

Buyout is under pressure, particularly in software. If AI weakens margins or retention, it also threatens the terminal value of assets bought on the assumption that recurring revenue was unusually durable. Robert Smith’s line that “software contracts are better than first-lien debt” is harder to take for granted.

a16z argues that venture is better suited to this uncertainty: it can back the companies driving the disruption, and a handful of winners can offset losses across the rest of the portfolio. The essay also argues that an LP can have a large legacy venture portfolio yet little exposure to the companies driving the current wave of growth.

None of this will surprise institutional allocators, and we agree with many of the lead-in premises. The argument depends on the next step: getting from “technology ate the economy” and “you are underweight to the ‘best’ managers” to “that’s why you should give the megafunds more money.”

Of course, not every established firm or GP agree with a16z. Menlo's Venky Ganesan shares a particularly impactful rebuttal. More on this later.

What the Chart Actually Measures

The essay’s structural argument rests on an analysis from Jackson and Strebulaev’s July 2026 NBER paper, “Human Capital in Venture Capital: Evidence From 100,000 Venture Capitalists.”

The headline finding is striking: 5% of venture capitalists account for 90% of investment profits. a16z’s chart turns this into “Venture Is Not an Asset Class, It Is a Handful of Managers.” We borrowed the construction and changed one word, for reasons that should be clear in a minute.

So what does the study actually measure, and how does it support the case the essay makes?

The Study Measures People, Not Funds

The result behind a16z’s chart ranks individual investors by cumulative investment profits, then treats that concentration as a finding about funds. The paper examines fund-level returns elsewhere, but that is not what this chart shows:

We study human capital in venture capital (VC) using a new dataset covering over 100,000 professionals affiliated with U.S. VC firms. Investment success is extremely concentrated: fewer than 40% of VCs with any investments are ever credited with a successful investment, and 90% of investment profits are generated by 5% of VCs.

a16z turns that finding into a claim about funds: “in continuing the theme of power law, it’s not just a smaller number of companies generating the returns. It’s a smaller number of funds with exposure to those companies.”

That conclusion requires a separate analysis. Concentrated profits among individual investors do not establish how those profits are distributed across funds or what returns those funds deliver to LPs.

When a few companies generate most of the gains, the investors in those companies will account for most of the profit dollars. The concentration figure alone cannot tell us how much is due to skill, access, luck, or time in the market. The authors examine skill and access elsewhere, but this chart cannot bear the fund-level conclusion a16z puts on it.

Dan Gray made a similar argument in a recent post, describing the “only a handful of startups matter” and “the power law keeps getting stronger” as a statistical mirage:

These statements are all economically illiterate, but there’s just enough of a statistical mirage to pass with VCs and create FOMO among LPs if delivered with confidence. Tragically, because of the high levels of institutional insecurity, even if a majority of other VCs suspect this is an unsustainable strategy, they’ll choose to participate anyway because of (the unfortunately named) principal–agent conflict.

The cumulative methodology adds another limitation. The paper tracks 12,151 VCs across 143,096 investments from 1996 to 2025. By aggregating across that entire period, the ranking gives investors with longer careers more years and investments over which to accumulate profits. It does not isolate investment skill from time in the market.

Profit Dollars Are Not Returns

The chart’s subtitle says the top 5% of VC investors account for 90% of returns. The paper says they account for 90% of net profits.

Net profit is the estimated value of an investor’s diluted stake in 2024 dollars, less the capital invested. The chart does not measure TVPI or IRR. It rewards absolute dollars generated without adjusting for invested capital or time. By this measure, $1B returning $1.2B ranks above $5M returning $195M: $200M in profit versus $190M, despite multiples of 1.2x and 39x.

Michael Jordan scoring 40 points tells you he had a great game. You still have to check the final score before declaring the Bulls the winner. Likewise, the paper’s 5% figure tells us which individual VCs were credited with the most cumulative profit dollars. It doesn’t tell us which funds delivered the best net returns to LPs.

For example, would anyone argue that later-stage Uber investors outperformed the first-round investors because they generated more dollars at a fraction of the multiple?

None of this is a flaw in the paper. The authors are measuring aggregate economic value, and they say so. The flaw is in the conclusion drawn from that measure, which the paper neither justifies nor implies.

The Missing Link to Fund Returns

The paper also tests whether funds with “successful” investors deliver better net returns. It finds little statistically significant evidence that they do.

Across six tests—three thresholds of prior success measured against net IRR and net TVPI—only one result is statistically significant. None of the IRR results is significant. The lone significant TVPI result appears at the five-or-more-successes threshold; by itself, that does not establish a practical manager-selection rule. Tests limited to smaller teams show a similar pattern.

“Successful” means having invested in companies that went public, reached a unicorn valuation, or were acquired for at least five times their total capital raised. The narrower finding is that prior individual success generally did not significantly predict better net LP returns in this sample.

The authors suggest that fees and carry may absorb the gains, or that the available data may lack the power to detect a relationship. Our conjecture is that large fund sizes dilute any individual investor’s effect on fund-level returns. Either way, the study cited as evidence tested “success” against LP returns and mostly found no relationship.

Brand Is Not a Selection Criterion

Later in the essay, a16z cites Accolade and PitchBook: of roughly 3,000 US venture firms, 20 have consistently delivered a 3x net return over two decades. Among 2,143 global funds from 2000–2018 vintages with reported DPI, 17% returned 2x, 6.7% reached 3x, and 2.4% reached 5x.

The essay presents these figures as support for increasing allocations to megafunds: “It’s worth saying out loud that only the best funds have the access, the power, or the muscle to repeatedly find, nurture, and exit a behemoth.”

The logic runs as follows: venture is a power-law business; only the best funds can repeatedly capture power-law outcomes and deliver consistent returns; therefore, LPs should concentrate capital in the megafunds with access to those deals. From the essay:

The fat-tail of outcomes is happening within a small group of companies held by a small group of funds. Top-quartile 5-year IRR is now double the median for the most recent vintage. Even more recently, the top decile funds by IRR have never been farther away from the top quartile:

Recent-vintage IRR is weak evidence of durable manager dispersion. These funds are still being deployed and marked; interim valuations, deployment pace, and stage mix can move the rankings substantially before positions resolve. The youngest vintages have not had five years to mature.

The period also includes the 2020–2022 fundraising surge, when capital reached firms that might have struggled to raise in a more normal market. A wider spread among recent funds could reflect that cycle as much as a durable increase in manager skill. The charts do not distinguish the two.

That is the narrower objection. The historical record also complicates the idea of a fixed circle of firms controlling venture’s biggest winners:

For all the talk of a closed circle of elite funds, the field of firms backing venture’s largest winners has widened substantially. In the 1990s, just 25 firms had at least one of the top ten deals in a given year, and five of them each captured more than 8% of all top deals over the decade. From 2005 to 2015, during a boom decade for new firm launches, 70 firms landed top-ten deals, and no single firm captured more than 8%.

Concentration does two jobs in this argument. First, a few companies generate most of the gains, making access to them essential. That premise then becomes a reason to concentrate LP capital in established firms. What is missing is evidence that those firms will continue finding the winners and convert that access into attractive net returns at today’s entry prices and fund sizes. The essay asserts that link rather than demonstrating it.

The price question is central here. In an August 2025 interview, a16z’s Martin Casado argued that startups that eventually succeed tend to raise at higher valuations in their early rounds than startups that fail. He also called the idea that non-consensus investing produces alpha “actually quite dangerous in the early stage.”

That is a claim about early prices predicting later success, not about prices rising once success is apparent. Even if it holds, an investor can pick a winner and still earn a poor return by overpaying.

The claim also assumes that early prices are a reliable signal of value. Price discovery depends on investors making independent judgments. When large funds price rounds using the same signals, a high valuation may tell you more about consensus than about the return still available.

Menlo’s Venky Ganesan made the counterpoint this month: investors can be right about a company and still be “wrong about the price by a factor of ten.”

As we’ve written before:

The aspiration of the efficient market is that many players with diverse views, resources, and means compete to drive discovery of a price aligned with intrinsic value. That “crowd-sourced” benefit falls away when there are too few buyers, or buyers who fail to decision independently. Tulip bulbs in 1635 Amsterdam didn’t turn out to have much more value than a common flower, but with hordes of unthinking investors chasing the same commodity, prices skyrocketed.

A market where a handful of large funds price off the same signals is the opposite of that crowd. It’s an inefficient planned economy. If everyone buys the consensus names, no one is differentiated, and price stops telling you much about value.

The second claim is harder to square with how returns work. Alpha is what you earn from being right when the market isn’t, and a consensus position is one that the market has already priced in.

We’ve Heard This Argument Before

We addressed a similar argument from Lux Capital’s Josh Wolfe in The Mind-Virus of Big Venture in 2024:

We expect LPs to adjust their allocations accordingly, leading to widespread contraction in the number of venture firms and consolidation of LP dollars into fewer, name-brand managers... VC is and will remain a rarified ecosystem where only a select cadre of firms consistently access the most promising opportunities.

Two years later, venture fundraising is more concentrated. That can be rational for an individual allocator: familiar names make a difficult selection decision feel safer. But those flows show where LPs put their money, not which managers will outperform.

Older fund-size studies complicate the claim that scale reliably produces stronger net returns. Cambridge Associates found that funds under $150M outperformed larger funds in 19 of 30 vintages from 1981 to 2010. In a review of roughly 100 funds in its own portfolio, the Kauffman Foundation found that none above $500M returned more than 2x net. These are historical cohorts, but the arithmetic still matters: the same exit moves a small fund’s multiple far more, so a larger fund needs bigger exits, more ownership, or more winners to match it.

Menlo’s Venky Ganesan sees the same pattern forming in the current cycle. The firms that missed the early rounds and are now catching up by writing very large checks very late are, in his view, the most likely to turn a miss into an actual loss. The winners of the next decade, he argues, will be the firms that treat position sizing as seriously as sourcing — a very different prescription from concentrating capital with whoever can write the biggest checks.

Today’s opportunity set may be large enough to support a few funds of that scale. It isn’t large enough to support an entire industry reorganizing around them.

Underwriting the Next Generation

We think LPs have good reason to take emerging managers seriously. A 2024 PitchBook study found that emerging VC managers delivered higher median IRRs than established managers in every vintage cohort from 1997 onward, though with greater volatility. Cambridge Associates also found new and developing managers consistently represented among the ten best-performing US venture funds in each vintage from 2004–2016, ranked by net TVPI as of June 2019.

The challenge is identifying the next generation’s winners as they emerge. That means underwriting the people, their strategy, fund size, and portfolio model. A familiar name doesn’t answer any of those questions.

So why single out this essay? Because this argument is winning. Its recommended destination for LP capital also happens to be the firm making it. Choosing the biggest names makes a hard selection problem seem simpler. But the evidence they cite doesn’t support the case they’re making.

The case for concentrating capital in the largest firms would turn venture into an asset-management business, where scale is the product and discovery is a legacy feature. Call it the Blackstonification of venture.

The next generation of storied firms is being built now by investors who see an opportunity to invest differently. Allocators help make that possible by backing their judgment before their names become the obvious ones.

Thanks for reading The Verticalist. Euclid is an inception-stage VC built for Vertical AI founders. If someone in your network is building in the space, we’d love to help — drop us a line in the comments or on LinkedIn.

References

• Jackson & Strebulaev, NBER WP 35501. Human Capital in Venture Capital: Evidence From 100,000 Venture Capitalists 2026

• Martin Casado interview. Newcomer, Martin Casado Isn’t Backing Down, 2026.

• Ewing Marion Kauffman Foundation, We Have Met the Enemy… and He Is Us: Lessons from Twenty Years of the Kauffman Foundation's Investments in Venture Capital Funds and the Triumph of Hope over Experience, May 2012.

• a16z: “Catching the Venture Bus'” September 14, 2026.

• Menlo Ventures: Venky Ganesan, September 2026.

• StepStone Group: “Venture capital: Shedding the ‘access class’ label,” April 2026. https://www.stepstonegroup.com/news-insights/venture-capital-shedding-the-access-class-label/

• Odin: Dan Gray, “Setting Rogue Agents on Venture Capital,” September 22, 2026.

Dom DiNardo is the Founder and CEO of Aforza, a Vertical AI platform for consumer goods companies now operating in 70+ countries. Dom’s career spans three tectonic shifts in enterprise software: he was the third employee at Salesforce UK in 2002, where he spent a decade building their global sales engineering organization from pre-revenue anxiety to a $200B+ juggernaut. He then joined Veeva under Peter Gassner, absorbing the vertical playbook that turned $7M in funding into a $50B+ public company. As Head of Europe at Vlocity, he helped scale the multi-vertical platform before Salesforce acquired it for $1.3B in 2020.

Dom founded Aforza in 2019 to tackle a market vertical SaaS never did: CPG. Since, it’s raised $22M from Bonfire, DN Capital, and Salesforce Ventures. It began in a seat-based world — but today, a third of the company’s revenue comes from its AI agent, Ava. Tune in to hear why AI-native and AI-first aren’t always synonymous… and why Dom’s foremost lesson from Veeva matters more today than it ever did in the SaaS era.

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Today’s Episode

Consumer goods is a $30B software market with no Veeva — no globally dominant vertical platform to serve its unique needs. This alone is interesting, given those needs are indeed quite idiosyncratic. Much of the work happens offline, in convenience stores and bars and supermarket aisles where refrigerators block cell signals and basements kill Wi-Fi. In distribution alone, players have to navigate complex promotion logic, discount rules, and order capture. And most of this is handled real-time by field reps who don’t have time to reference spreadsheets or call into HQ.

What led Dom to pick CPG? He spent three decades living through the evolution from horizontal SaaS to Vertical SaaS, working at the category defining companies of each. While his lessons compounded, there was always one key net-new insight that allowed the next era to do what the prior couldn’t — an insight that the predecessor might’ve seen as heretical. Veeva eschewed the universality of Salesforce, and it was perhaps the single most defining aspect of their strategy. Now Dom is betting that what worked for Harvey and MagicSchool in the era of Vertical AI 1.0 won’t for Aforza. Today, we discuss that breakpoint.

Vertical Intimacy: The Salesforce Anti-Lesson

Dom spent a decade at Salesforce, long enough to internalize the growth model: hire salespeople by the dozens, chop territories, reassign accounts, ram the machine forward. This isn’t to say Salesforce’s model wasn’t thoughtful when it came to verticals. They developed industry-specific sales forces — effectively internal consultancies — that helped enterprises mold the CRM to their unique purposes. It worked in an era where SaaS adoption was new. But it created a specific kind of friction: Salesforce was continually reliant on customers to re-educate AEs on the changing workings and needs of their industry. The core Salesforce product never accumulated real “expertise” in any single vertical because it was fundamentally designed to be malleable to any.

At Veeva, Peter Gassner ran the opposite playbook — one that today, sounds incredibly familiar. Focus on a single, large industry. Embed the sales team beside the customer and leave it there. Understand depth, the processes, the vertical alignment. Create a persistent feedback loop between the customer that drives product development, rather than just implementation, customization, or customer success.

Dom calls this vertical intimacy. At first, many felt Veeva’s go-to-market motion was more systems integrator than SaaS. But it produced a company that raised $7M, spent $3M of it, and went public at a $4.4B valuation.

Vertical Advantage Outside the Desk-Core

While forward-deployed is ubiquitous these days, there’s one element of vertical intimacy that we expect to see more of in Vertical AI. That is, the alignment of product form-factors and entry UIs with modality of work.

Some of the fastest-growing names in Vertical AI to date share a very similar product interface: a chat box. Harvey, MagicSchool, Legora, Open Evidence — even if the platform has since expanded beyond it, the initial customer touch-point felt like a GPT-wrapper. We discussed this in our original analysis of emerging playbooks in Vertical AI: for white-collar professionals who largely stick to a single location with a laptop nearby (e.g. lawyers, teachers1, doctors), chat is a natural entry point because it meets them where they are and how they work.

Aforza’s users in the CPG industry mostly operate outside the desk core — they’re in the field, selling beer and ice cream, in convenience stores in the East End of London and traditional markets in Kenya and Taiwan.

A guy who’s got to do 20 visits a day doesn’t want to sit and talk to ChatGPT. He just wants the answer in front of his face — where he’s going next, what he should do, and why he should do it.

As Luke pointed out in our episode, so far, the Vertical AI league tables have been dominated by white-collar, desk-core industries. One exception, Rilla (which we covered in Voice-First Playbooks in Vertical AI) saw success anchoring on voice, a non-chat interface that matched the nature of its end-users in the trades. The Vertical AI UI question is not “chat vs. no chat.” It’s “what is the highest-bandwidth way to get intelligence to and from a specific worker in a specific workflow?” It’s not always as straightforward as “do they work at a desk or not.” There are plenty of white-collar workers in CPG — it’s just that the roles that actually move the needle in terms of day-to-day distribution are in the field.

Nor is this form-factor consideration constrained to “desk vs. not.” In some cases, AI is able to take on the full work stream and execute the action. In other cases, AI must arm workers with the right information, at the right time, to do so… and capturing the right feedback from users for context along the way. The reality is that we are still very far away from removing the human altogether, in most workstreams.

So the question for Vertical AI product-builders considering human interface must aim to maximize bandwidth. We discussed this in our prior episode with Mike Droesch at Bessemer Venture Partners. What product modalities — visual, vision, voice, audio, tactile, text — create the highest bandwidth of desired information input and output when interfacing with users? And even before that: which users and roles should our product interface with to have the greatest potential impact on the underlying business, and where & how do they work?

Answering these questions requires the kind of deep vertical intimacy Peter Gassner first championed at Veeva two decades ago.

Dom believes that — compared to Vertical AI that properly embraces vertical intimacy — horizontal AI players (e.g. Copilot, Agentforce) face a structural disadvantage that goes well beyond interface design. They can’t build the use case library, because they don’t know the use cases. They can’t weave AI into the workflow, because they haven’t mapped the workflow. And they can’t price it simply, because they don’t know what the AI actually costs to run in a given vertical.

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Pricing: Know the Outcome, Hide the Token

Aforza was founded pre-ChatGPT. Combined with his years of experience across Salesforce, Vlocity, and Veeva, this puts Dom in a unique position to comment on Vertical AI pricing. We asked him a question on the minds of every founder whose product isn’t consumption-based-native:

How do you charge for AI when you started as a per-seat SaaS company?

Let’s take a look at Aforza’s pricing:

• Field sales CRM + AI — Per user, per month. The customer has 50 reps. They want a simple line item on a spreadsheet. Aforza knows the use cases and token consumption well enough to bundle the AI cost into a flat PUPM rate.

• Automated claim matching — Per item. The customer processes 5K promotion claims a year. They want Ava to match claims to promotions autonomously. Price: 5K times X dollars.

• Trade promotion planning — Pegged to business size. Scenario planning and promotion optimization, priced relative to scale, not compute.

In all three cases, the customer never hears the words “tokens.” Dom thinks most enterprise buyers see complex consumption-based AI pricing as “fairy dust” — impossible to forecast or budget. He believes that, in the enterprise at least, it’s on founders to do math and present deterministic pricing schema that allows customers to properly budget in advance. That’s no small task — nor a riskless one in a world where AI COGS are rapidly shifting and no founder wants to degenerate into a cost-plus model that dilutes their value capture as like-for-like tokens get cheaper.

The economics of pricing AI by outcome require knowing the outcomes. Salesforce, as Dom recounted, runs multiple trainings on pricing before each new release — and he bets they’ll never achieve AI pricing appropriately tailored to industry-specific ROI. Because they simply don’t have the vertical intimacy to know.

Pricing complexity might be the most underrated drag on enterprise AI adoption right now. Enterprise buyers in industries like CPG, where IT organizations run leaner than banks or pharma, need predictable costs before they’ll commit. In this phase of nascent AI enterprise adoption — where overwhelming demand lives alongside existential cost fears — Vertical AI businesses that can absorb compute variability and translate it into outcome-based (or at least consumption-aligned) pricing hold a real edge in procurement cycles.

The Takeaway for Vertical Founders

From Salesforce to Veeva to Vlocity to Aforza, Dom has seen how the enabling priciples of a business model evolve. Like the go-to-market model that allowed vertical SaaS to differentiate diverged from horizontal SaaS wisdom, and the interface that works for white-collar AI may be the exact wrong prescription for work outside the desk-core. Both breaks share a root cause: insufficient intimacy with the customer’s actual workflow.

Veeva walked so Vertical AI can run. Product can incorporate AI where users already work… which may be something beyond the prompt. Knowing vertical use cases well enough to price by outcome, not by token, can lend a pricing advantage in the enterprise. Teams can be so woven into customers’ operations that Vertical AI product becomes indistinguishable from how they work — rather than a product to be molded ex post facto.

Vertical intimacy is the one input a founder can't shortcut. It accrues over cycles beside customers, which is why the best Vertical AI founders are often people who've already spent time absorbing industry knowledge, even if peripherally. At some point though, it can become easy to lose sight of the goal: not just accelerating whats already there, but building something transformationally new. Which brings us to Dom’s final advice, after two IPOs and a >$1B acquisition, all of them other people's companies: don't leave your legacy too late.

See you next week.

Mike Duboe is a General Partner at Greylock, where he focuses on commerce, marketplaces, and consumer networks. Previously, he was the first growth hire at Stitch Fix, building the growth org from 20 through IPO. At Greylock, he’s backed transactional and network-drives businesses like Pepper (food distribution), Vori (grocery), and Highstock (excess international inventory). Join us in this week’s episode as we debate the future of AI commerce: what happens to the marketplace model when agents can handle transactions end-to-end, and why — even in commerce — attention may be all you need.

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Today’s Episode

For 28 years, Sriracha founder David Tran sourced every pepper from a single farm on a handshake. Tens of millions in procurement, no contract. While noble, it ended the way you fear it might: generational transition, a renege, a $23M lawsuit, a national shortage of our favorite hot sauce. This seems like a quirky anecdote. But the truth is, most B2B commerce mirrors Sriracha’s antiquated setup more than you think — other than the frictionless commerce Amazon has trained us to think of, it runs on relationships, phone calls, personal loyalty and preferences.

VC has seen the opportunity in B2B marketplaces over the last decade, with mixed success. Faire was the canonical example, with enough fragmentation on both sides and demand that looked consumer-esque. Mike backed several of these at Greylock: Pepper in food distribution, Vori in grocery, Rosnovi in raw materials. Many of his investments, however, skewed away from the initial marketplace model and ended up building, in effect, vertical software. Transactional revenue lines, in other words, or a great add-on once you had already built trust, but a very difficult entry point.

AI might be changing that order of operations. Certainly, as the cost to match buyer and seller agentically goes to zero in some markets, it is changing the marketplace business model. today we debate what that future marketplace looks like — and whether you and I would recognize it as a marketplace at all.

New Markets, Not Better Marketplaces

Some imagine the outcome of AI commerce will be improving existing marketplace experiences: better matching and personalization layered on top of existing platforms. As Mike shares, this vision might be looking in the wrong direction:

The way I think about it is AI is actually making more markets addressable to that model than previously were addressable. For transactions that were previously heavily human-to-human, high complexity—negotiations that may have lent themselves to more of a brokerage versus a streamlined marketplace—agents can handle a lot of that right now.

In 2012, Benchmark’s Bill Gurley released a now-canonical framework outlining ten factors for evaluating marketplace opportunities. Roughly, those were:

1. New experience vs. status quo

2. Economic advantages

3. Technology leverage

4. High fragmentation

5. Supplier sign-up friction

6. Market size (TAM)

7. Ability to expand the market

8. Usage frequency

9. Control of payment flow

10. Network effects

Some believe this framework systematically excludes most B2B verticals from serious consideration. And yes, enterprise purchasing is more complex and relationship driven in some cases. We feel skeptics tend to paint with too broad a brush here:

• High fragmentation
  B2B often does have fragmentation in niches (long-tail suppliers, VARs, agencies).

• Friction of supplier sign-up
  One of the stronger criticisms, but solvable (esp. if you have a peripheral product).

• Frequency
  Frequency isn’t always lower — sometimes it’s higher. Moreover, it can be offset by very large deal sizes and greater intent / context.

• Payment flow
  This is more a behavioral change problem than anything else with much of B2B payments being offline. B2BMs can also monetize may other ways.

• Size of market opportunity
  Easily the laziest criticism — B2B markets are massive, with higher take rates.

Agents have the opportunity to move a needle on many of Gurley’s pillars, in consumer and enterprise both. Not only by improving matching, but by absorbing enough negotiation complexity to make an otherwise idiosyncratic transaction marketplace-eligible in the first place. We explored the buyer side of this thesis in The Dark Marketplace, where we argued that agent-to-agent transactions would create new commerce surfaces invisible to humans. Mike adds the supply-side complement: AI doesn’t just enable automated purchasing. It has the potential to free up supply — the creation of supply catalogs in verticals where inventory was previously opaque by default — which in turn can attract new demand.

So AI will open many new verticals to commerce models. One element Gurley’s framework does not contemplate as directly, however, is the fact that many B2B transactions, like our Sriracha example, are still very dependent on human behavior. Twenty years of accumulated trust and service — and a neck to choke if something goes wrong — may prove much more difficult for technology to accelerate.

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The Commoditization of the Match

The core value of a marketplace — the thing that justifies take rates — is the match. Aggregate supply, aggregate demand, charge for liquidity. That was the logic from eBay to Uber. That may be changing. In Mike’s words:

It’s gonna be insufficient to just charge for the match. In many industries, agents could kind of do this without there being a marketplace interface. That is an area where this business model is exposed.

If agents on both sides of a transaction can find each other, negotiate terms, and execute — and in categories with standardized online catalogs, they increasingly can — then the match approaches commodity. The marketplace’s position as intermediary weakens, putting natural pressure on take rates.

If this is the case, it’s probably good for all parties as cheaper matching means more dollars for buying. But where does value re-accrue? As in most cases of commoditization, upstream and downstream.

Mike points to Highstock to illustrate the point. The company processes roughly $100M in merchandise for 50+ beauty brands. The moat is traceability, not matching: ensuring near-expiration products don’t end up on gray-market channels that dilute brand equity. They built proprietary infrastructure to trace items through multi-hop distribution chains, giving perception-sensitive brands control over the channels their product shows up in. An agent could theoretically find a buyer for 10,000 units of near-expiration moisturizer. It cannot replicate an upstream compliance apparatus that makes a $2B beauty conglomerate trust the platform with its excess.

Palm Street makes a parallel case in logistics. A consumer marketplace for live plants and exotic animals, it pulled sellers from Whatnot and other platforms by specializing in fulfillment that others — even with a clear liquidity lead — couldn’t match. Shipping plants requires specialization generalist tools simply won’t build: climate-controlled packaging, timing windows, species-specific care, etc. The moat is downstream delivery, not discovery.

The above examples also demonstrate why the AI commoditization of the match benefits vertical strategies. By design, Vertical AI can address edge cases unique to that industry use case, which feature much more heavily in needs up- and down-stream of the transaction itself. Compliance, logistics, SKU data, distributor features, credit, insurance: different use cases have different needs here. Traditional marketplaces won and lost on pure scale and liquidity, a la eBay or Amazon, which disincentivizes too much specificity. But when matching is table stakes, everything around it becomes the business — and that penumbra of revenue streams is especially well suited to vertical.

The Attention Tax

There’s another revenue stream peripheral to commerce we haven’t addressed yet. As we discussed with Mike, it’s not only growing in importance — in the AI marketplace era, it might be the whole ballgame. Let’s explore it with an example.

OpenEvidence is an AI healthcare research tool used by a purported two-thirds of physicians in the US. It hit $100M in annualized revenue at the outset of 2026. But it doesn’t monetize by charging doctors, or transacting the drugs its MDs look up. It sells ads to pharma companies. CPMs run $70 to over $1,000 — compare that to $5-15 on social media.

Vertical AI applications capture intent signals that traditional ad infrastructure cannot. As they say, if the product is free, you are the product — and never has that been more true with AI, which can capture more context, preferences, and judgment through its conversational interfaces than perhaps any product form factor before it. A doctor typing a clinical question into an AI tool is more targetable (both by subject, intent, and timing) than any search query or social feed scroll. Context density and specificity support orders-of-magnitude higher pricing.

There’s much richer semantic context and pretty high-intent stuff that you could target on. OpenEvidence is like the first real example where overnight you convert all that attention into massive ad revenue.

Of course, ad monetization on marketplaces is nothing new. We’re all too familiar with ads on Amazon, Instacart, Uber, and other consumer platforms — but it can be a powerful revenue stream in B2B. Toast runs a CPG-trade-spend product. Mike’s investment Pepper is building an ad network on top of its food distribution platform, tapping into trade spend that historically went unmeasured and unattributed. While consumer ad models typically need 100M+ MAU before the economics work, vertical apps can monetize at dramatically smaller user bases because the CPMs are much, much higher. And Vertical AI attention — with its greater context — has the potential to be more valuable still.

Skeptics of the AI marketplace ad opportunity will point to the risk of trust corrosion, perhaps most especially in B2B. The argument might run: a doctor using an AI tool should worry about whether an answer is biased by a sponsor. Mike argues the reverse: clearly labeled advertising can build trust. When it’s obvious what’s an ad and what isn’t, users can see that the default responses aren’t commercially motivated. In healthcare, for example — where the alternative is pharma reps with free lunches and opaque influence campaigns — transparent ads would be a significant improvement. Not to mention, a profitable one.

The Takeaway for Vertical Founders

In the AI era, marketplace value is migrating in two directions at once: downstream into operations and compliance, upstream into attention and advertising. The match — the historical core — is commoditizing. But as we have seen over and over again, when the core rate-limiter gets cheapened, the function becomes more accessible, and the overall market grows immensely.

For founders building in commerce-adjacent vertical AI, three things to consider:

1. Risk: Take rate pressure on the match.
   Solution: Build toward upstream and / or downstream value from day one. Even if the match gets you in the door, your moneymaker may lie in the periphery.

2. Risk: Double-sided fragmentation is less important.
   Solution: For those considering attention-based or ad models, concentrated professional attention plus high-value vendor spend is proving attractive. If both exist in your vertical, the embedded ad opportunity may be bigger than traditional software / AI or transactional commerce revenue.

3. Risk: You don’t have the specificity to own upstream or downstream.
   Solution: In many verticals, AI may be the unlock to transactions where matching was too complex or relationship-dependent. But if you can’t charge for that match, you need peripheral revenue streams — and these are often less generalizable than liquidity. Attention-drive monetization is working for OpenEvidence, while the general chatbots (from OpenAI to Instinct) are still figuring it out.

David Tran’s handshake lasted 28 years. The next generation of commerce infrastructure won’t be built by founders who dismiss that as inefficiency to be magically solved by AI. It’ll be built by the ones who understand exactly why it worked, what it takes to earn the same trust from an agent, and what that means for where value is accruing in the era of agentic commerce.

See you next week.

Quinn Litherland is the Founder and CEO of Revin, an AI voice and SMS platform deploying automated agents into home services operations across HVAC, plumbing, roofing, and remodeling. NYC-based, the company has raised just over $10M to date, across two rounds led by Stellation Capital and Toyota Ventures.

Tune in to hear Quinn lay out the two failure modes he sees killing forward-deployed strategies across Vertical AI, the specific operator profile that makes FDE actually work, and what happens when your agents outperform the customer’s best human rep on day one.

Watch the Full Episode Here

Today’s Episode

Forward-deployed engineering has been the strategy of the moment in Vertical AI for well over a year. It’s almost a universal feature at this point: sending technical staff on-site, embedding in customer workflows, learning the business from the inside, handling CS, even driving product. Palantir made the model famous, and now everyone is copying it.

The appeal is straightforward. You have to understand how a $50M HVAC company qualifies leads; how a local business manages a sales cycle across SMS, email, and voice; how manufacturing owners with 30 years of muscle memory react when X happens. That understanding is rarely written down — live, in situ is the only way.

Quinn and his team at Revin have built this motion from the ground up in a $650B+ industry. His forward-deployed team goes on-site, embeds in customer operations, and deploys AI agents that routinely become the top-performing “rep” in the call center on day one. Customers see $5M+ in net new revenue and start asking for more workflows, more automation, more of their front office run by agents.

But Palantir’s CTO made fun of the copycats for a reason. Most companies running forward-deployed motions are, in his words, “just sales engineers dressed up.” Quinn sees two specific ways the strategy goes wrong, which we’ll cover below. Before we get into today's episode though, a quick observation:

It seems FDE is becoming something different altogether. It’s a role historically thought of as primarily technical but with strong customer success mandates — implementation, problem solving, keeping the customer happy. Modern FDE is taking on roles traditionally associated with Ops, Sales, even Product. They are increasingly independent. What sort of background do you look for when your FDE essentially has to do a little bit of everything? More and more, they resemble mini-founders. Perhaps they’re due for a title upgrade.

Trap #1: Consultant Fever

You send smart, technical people into customer sites. They do great work. The customer is happy. Revenue grows. But you never institutionalize what you learn. Every new account — maybe every site or team — requires not only a fresh deployment, but a net-new feature. Your FDE team scales linearly with revenue. Your gross margin looks like Accenture, not Palantir. “It’s 100% an excuse not to build great product or repeatable systems,” Quinn says. “That’s the hard truth.” Your customer can’t live without you — but you also can’t get any leverage on the business.

This is the risk we flagged in Service-Level Disagreement: the arbitrage between service delivery and product automation is real, but is ultimately limited by ACV. If you’re still hand-deploying every customer in year three, you didn’t build a product company. You built a consulting firm that happens to use AI.

For some, this is a business model that makes sense (beyond the obvious McKinseys and Bains). Earlier this year, OpenAI spun out DeployCo and Anthropic launched Ode, both implementation arms designed to drive model consumption. Their incentive is to go in, deploy AI for $10M, and leave — or better yet, stay and continue to develop new use cases for their LLMs. For them, almost any version is a success: more tokens consumed, more API revenue, no middleman. For startups, this only works if you have a consumption-based product with sufficient gross margin that the customer can build more use cases around. If the only margin you capture above AI infrastructure costs is ephemeral implementation work, you’re a subcontractor for the model labs. Here’s how Quinn thinks about it:

No one’s coming to save you. The venture market’s so weird right now. Teams need to be a lot more intentional about where they’re spending their cash and how they’re building repeatable workflows versus having this grand vision but insane costs around the human beings they’re sending on-site.

Trap #2: Forward-Deployed Chaos

The second failure mode of FDE is organizational in nature. Half of your FTEs are running around customer sites, putting out fires, closing deals… and nobody is translating learnings into repeatable product. Poorly managed, the FDE motion can become an excuse for organizational chaos rather than a deliberate product strategy.

The solve begins with a tight “templatization” process. In other words, a disciplined pipeline of FDE, to product, to engineering. Today, when a $50M HVAC company signs up for Revin, the team already knows 99% of what their qualification process looks like, how job type matching works, and how to integrate with ServiceTitan. The onboarding that used to require on-site setup from scratch now runs through pre-built templates. A human still oversees it, but the repeatable scaffolding is in place.

“Every month, my FDEs need to be able to take on more and more,” Quinn says. The metric that matters is accounts per FDE, not headcount. If that ratio isn’t improving, you’re scaling a services org, not a product.

The second half of the solution centers on responsibility design. Quinn believes FDEs should sit under product, not engineering or sales. The field team generates real-time feedback every day. If that signal doesn’t flow directly into what you’re building, you’ve turned your most expensive employees into an expensive suggestion box. “If your engineering leader isn’t product-centric enough to learn from the team in the field, you’re always going to be surface level,” he says. The secondary implication of this is that defined roles for engineering vs. product may become important earlier than in the past. FDEs surfacing insights from their particular customer and building them is efficient. Unless those additions are constrained to an individual customer or deployment, however — hopefully an exception rather than a rule, per trap #1 — product roadmaps can get incoherent, fast.

Founders need to strike a balance that corrals chaos, without kneecapping the power FDE to drive natural product expansion. It can be a real problem when market pull is high, and resources are constrained. As Quinn shared, customers who see Revin crush 2-3 workflows want 6 more. Each new workflow may touch new systems, new data sources, hard technical problems the team hasn’t solved yet. Quinn is honest about the tension: “How do you make sure you don’t lose trust by overpromising, overcommitting, just being there and saying, yeah, we can build that?” Saying no to expansion revenue is painful. Saying yes before the product is ready — or before you’ve confirmed it’s something the broader ICP could use — risks triggering both traps at once.

Optimizing Your FDE Strategy

Quinn’s best deployment lead, Paul, ran a car detailing business in Austin before joining Revin. He bootstrapped it to ~$500k / month, sold the business, and developed a gut-level understanding of how operators in the space thought. He now manages accounts representing billions of dollars in roofing enterprise value. He could, Quinn says, “leave Revin right now and probably go run a roofing roll-up better than 95% of the folks in private equity.”

The FDE role is becoming an increasingly demanding one, requiring industry expertise, product intuition, sales ability, and empathy for operators who’ve often run their businesses the same way for decades. The person who fills this role can deploy agents on-site, manage upsell conversations, and feed product insights back to the team in the same week. The FDE is much more than an engineer who occasionally talks to customers, or a CSM who occasionally files tickets. They must understand two things simultaneously:

1. They’re there to help customers change the way they do business through AI.

2. There is real wisdom in their decades of praxis —  and that wisdom needs to be learned and incorporated if you want to be true Vertical AI.

Some startups are rebranding the FDE. We’ve heard: AI deployment strategist, forward-deployed architect / specialist, GTM engineer, and even AI coach. In Vertical AI, customers are thinking about this role differently. No longer is the FDE just an extension of customer support — in some cases, they are the breath of fresh air, someone versed in AI who will finally listen to them and help them build. They’re a conduit for the years of ideas this practitioner has had. “Who am I going to be working with?” is now part of the sales process.

Economics have to be central to your FDE considerations. In forward-deployed Vertical AI, CS is structurally more expensive — we estimate ~50% more than the SaaS baseline. But from what we’ve seen thus far, the ACV opportunity is also significantly greater, as solutions can do more of the work, and absorb consultant, advisor, even CXO roles. If the FDE has commission on renewals and expansion, and a $100K account can grow to $1M, you have a profit center disguised as a support function. Quinn points to Decagon as another company getting the incentive structure right: “How do you make sure your FDEs walk into the office every day thinking, I can make a million dollars this year if I renew every account and expand them?” Of course, pricing / customer (or ARPA) potential must be factored in. If your average ICP business grosses $500K in revenue, there is a natural ceiling to how much FTE spend makes sense. If you’re working with enterprises that have top-lines in the billions, a la Palantir, persistent logo-specific teams are easily justified.

What You Get When FDE Works

It’s become common to draw a distinction between the system of record (in Quinn’s space, ServiceTitan, Housecall Pro, Salesforce FSM) and the system of action. As Revin sees it, the historical system of action was humans. Now, agents have the potential to absorb much of that layer.

Quinn’s math: if Revin drives $5M in net new revenue to a customer’s bottom line and charges $70K, the product may be dramatically underpriced. The boost in willingness to pay for handling a workflow soup-to-nuts — versus asking someone to train up on a new dashboard to manage it themselves — is very real. Operators are already paying consultants and fractionals multiples, if not orders of magnitude, more than SaaS vendors.

While traditional vertical SaaS concerned itself with dominating the UI layer — “spend time in my platform” — FDE-led Vertical AI is focused on eating the action layer underneath. Take the Podium-ServiceTitan situation: ServiceTitan shut off Podium not because Podium was running agents, but because Podium was building a competing FSM. Quinn’s read: “As long as you don’t try to stab the bear, you’re probably going to be fine.” If you own the work, that may earn you the right to own the UI and the database. But as an early stage startup, that can risk provoking the ire of sharp-elbowed moneyed incumbents — some of which you may be better off partnering with. More simply: own the workflows, don’t worry about the rest. If a primary concern of FDEs is implementation, understanding, even playing nice with existing legacy SoRs is critical.

Quinn’s vision of where this could lead for Vertical AI customers: a $500M roofing company run by 5 people and a fleet of agents. The C-suite and key relationship owners — or wherever the centers of core competency lie — remain. Much of everything else can be automated. Quinn believes old-school operators, historically relegated to “legacy” in the SaaS era, are now discovering some of the “coolest, most badass ways to grow their business.” FDEs are the vanguard of that massive opportunity in Vertical AI.

The Takeaway for Vertical Founders

Quinn shared his three most critical hires for an AI-native vertical company:

1. FDE — an AI Deployment Strategist with the profile of an ex-founder who can go on-site, build trust, and deploy agents while identifying expansion opportunities.

2. Sales — an industry-native seller who knows every conference, every Facebook group, every distribution channel in the vertical, not a generalist SDR.

3. Engineering — visionary technical leadership who can scale systems for the long term (not just ship features) and help manage the increasingly tricky FDE-product-development interface.

Notice what’s missing: no product manager; no designer; no head of marketing. We asked Quinn for his top three, so this is not to say those roles aren’t important. For very early stage startups, however, the FDE can wear many of those #4+ hats. And that allows the customer to play an important role as well. As Quinn put it, our FDE is the product manager, the customer is the designer, and the agents’ performance is the marketing.

Done right, forward-deployed engineering can teache you which workflows to own, how to institutionalize them, how to price the value you create, and give you the customer perspective and language to understand how to sell and market. Use FDE as a crutch and you risk becoming a consulting firm (a dev shop, rather than a true AINS1). Use FDE with reckless abandon and you may end up with a sprawling roadmap and an incoherent product without compounding value. The best Vertical AI founders will use FDE as a CT scan — parsing granular customer operations, iterating to generalizable product, and unearthing the action layer of an industry.

See you next week.

Yoni Rechtman is a Partner at Slow Ventures, a generalist seed and pre-seed fund managing over $1B in assets. Yoni joined Slow from Tusk Ventures, where he spent five years investing in regulated industries. Slow has become well known for backing novel startup business structures: they seeded Metropolis, which acquired its way into the parking industry and is now a $5B physical AI company; they backed Phoebe, a three-sided agent network for home care; and they led Ando’s $4M seed round in restaurant staffing AI. Yoni also writes 99% Derisible and is behind many of Slow’s popular decks on rollups and growth buyouts. In this episode, Yoni gives us a preview of his next deck: a five-part taxonomy for the future business models that work in an era when software can become so much more than it once was.

Watch Full Episode Here

Today’s Episode

Most people, most of the time, want to buy outcomes, not workflow, not dashboards, not a nicer way to do the thing they didn’t want to do in the first place.

This is not a new observation, but the math behind it has changed. Software has historically captured 1–3% of a business’s revenue. Services capture the rest. That ratio exists not because businesses love paying consultants, but because software couldn’t actually do the work. It could only orchestrate it. The best software businesses in the world (Google, Visa, Uber, Facebook) are the exceptions: network effects businesses that sell outcomes rather than workflow. Everyone else sold a hammer and hoped the customer would swing it.

AI changes the physics. Software can now deliver the hammered nail, or the whole cabinet. The markets get 30–100x bigger. But the business models that got us here (high gross margin, passive retention, amortized R&D over long product cycles) break down along basically every line of the income statement. What replaces them is the subject of this episode.

Selling the Cabinet, Not the Hammer

Yoni’s starting claim is blunt: “No one has ever woken up and said, I want to buy workflow software.” And the data backs him up. A normal business spends 1–3% of revenue on software. If it’s only 1% on software, it’s 99% on everything else. The opportunity is capturing a share of the 99%.

Forward-deployed engineering (FDE) is the first model that does this. Instead of selling a product and training the customer to use it, you take responsibility for the outcome. “Rather than saying you change your business process around my software,” Yoni puts it, “I’m going to change my software around your business process, and I’m going to guarantee that it’s going to do that.”

The practical result: you stop selling through a software line item and start selling through a transformation line item. Enterprises right now want to buy AI transformation. If you show up with experts and own the implementation, you’re part of their future-proofing story. If you show up with a web app, you’re competing with every other web app on features.

The trade-off is obvious. FDE businesses have heavier delivery costs and harder scaling curves. But they can charge dramatically more, ACVs of $500K instead of $50K, because they’re getting closer to what the buyer actually wants: “the thing doing what I said it’s going to do.” As we explored in Service-Level Disagreement, the strategic question is whether you can graduate from the wedge to something with software-like defensibility before the arbitrage window closes.

A quick word from our sponsor, Parafin. Now past $100M in revenue and backed by a Goldman Sachs-led credit facility, Parafin powers embedded lending, cards, and insurance for vertical platforms like DoorDash, Jobber, and TikTok Shop. Purpose-built for platforms serving SMBs, no infrastructure required. Explore a custom program for your platform today →

Five Shapes of Post-SaaS Software

Yoni has been working on a deck, forthcoming by the time this publishes, that lays out five business models he thinks actually produce durable value for software companies going forward. The taxonomy:

1. Forward-Deployed

Services to differentiate software. Own the outcome, own the implementation. Palantir’s cultural shadow is long here for a reason. You decommodify the buyer experience by making promises and delivering on them, which is what software historically fails to do.

2. Neo Firms

Software to differentiate a service. Build a new kind of professional services firm where AI is the operating system, not a feature. The comparison Yoni draws is sharp: Crosby, which uses software internally to deliver legal services and owns the output, versus Harvey, which post-trains a model and presents it in a UI. “I feel much more confident that Crosby has value add above its inference,” he says, “and that’s unquestionable to me.” The dividing line is ownership: who is responsible for the work? If you own the liability and the outcome, you’re a different kind of company than if you’re reselling better prompts.

3. AI Rollups

Or other inorganic strategies, like growth buyouts. Instead of selling software to an operating business, buy the business and build software into its DNA. This is Slow’s signature thesis. Metropolis seeded in 2019, acquired Premier Parking (600 garages), then SP Plus ($1.5B), and now runs AI-powered checkout-free parking across thousands of locations at a $5B valuation. Vertical SaaS captures 1–3% of TAM. Owning the business captures 100% of the value software creates. As we noted in The Siren Song of Services, the math on capturing more of a customer’s spend at lower margins can be dramatically better than capturing a sliver at 80% gross margin, provided you have the operational DNA to execute.

4. Agent Networks

More on this below. It’s the one Yoni is most excited about and the one worth examining in detail.

5. Hardware-First

Hardware to differentiate software. Apple is the canonical example. The software is fine. It works because the hardware is extraordinary.

The conspicuous absence: plain SaaS with AI features. Yoni is bearish. “Replicating features is fairly easy,” he says. If your value prop is a better prompt between the foundation model and the user, you’re making a bet against foundation model progress. That’s been a losing trade for four years.

Deep Dive: Agent Networks

The most novel piece of Yoni’s framework, and the one with the clearest connection to what we’ve been writing about in The Dark Marketplace, is agent networks. The playbook goes like this:

1. Start in single-player mode.

2. Do real work for a customer: fill shifts, schedule caregivers, manage invoices. Charge for it.

3. Use that single-player utility to acquire a large population of customers on one side of a transaction.

4. Then layer on multiplayer, network effects that emerge from the connections between those customers.

The contrast with the last generation of marketplace businesses is instructive. Uber spent billions to overcome the cold-start problem, subsidizing rides, paying drivers to sign up. The agent network playbook inverts this: you get paid to solve the cold-start problem. Your single-player product generates revenue while simultaneously building the supply or demand side of a network.

Phoebe, a Slow portfolio company, illustrates the mechanics. They do labor orchestration for home care agencies, a scheduler agent that assigns shifts. In doing that work, they acquire all of the talent each agency manages. The next move is a talent agent that represents those caregivers across multiple agencies, then a family-facing agent that helps households find and manage care. Three sides, one network, and each new side feeds the others. Ando does something analogous in restaurant staffing: forecast demand, fill shifts, build an identity passport for workers that travels with them across employers.

Yoni distinguishes this from the 2019-era “come for the tool, stay for the network” playbook, which mostly didn’t work. “There was not a substantial underlying platform shift driving change,” he says. “It was basically just people borrowing from two different business models and mashing them together.” AngelList and HoneyBook are fine businesses. They didn’t produce the network effects their models promised because there was no new capability underneath. Just two models stapled together.

Agents provide the missing capability. A tool that can actually do work, not present a workflow for a human to operate, creates genuine single-player value worth paying for. And that means you can charge for customer acquisition rather than subsidize it.

The Data Moat Debate

We spent a chunk of the episode on whether data moats in vertical AI are real or a story founders tell investors. Yoni is more skeptical than most.

His argument: “We all have access to the same API keys.” Post-training and fine-tuning within a domain is a product with a short shelf life because foundation models are coming for that work directly. He points to Jasper AI, which had early access to OpenAI’s API and built a copywriting product on better prompts. “I don’t think Jasper exists anymore. And it’s kind of laughable that it would.”

I pushed back. If the data is truly proprietary (manufacturing processes, decade-long booking histories, internal operational data that no model lab will ever buy), it compounds. A vertical AI company embedded in a customer’s workflow accumulates context that an outsider can’t replicate with a better prompt.

Yoni’s counter: “The person that has it doesn’t own it. The customer owns it. So why would the margin accrue to me instead of to them?” If the proprietary data lives inside the customer’s systems and you’re extracting and processing it on their behalf, you’re describing an IT services company.

The productive tension landed on a distinction between what flows through the process and what the process itself becomes. Yoni’s view: “Process and product are distinct. Until they’re not.” If you can productize the process, not just the output, that’s where durable value lives. He drew an analogy to Bain, BCG, and McKinsey. They weren’t technology companies in any conventional sense, but they pioneered a technology, scientific management, and productized it at scale. The post-war management consultancies didn’t sell deliverables. They sold a way of operating.

Whether that is a moat in the traditional sense (earned data, switching costs, platform lock-in) is the open question. Yoni’s burden-of-proof standard is useful regardless of where you land: “I think the burden of proof is on you for why there’s a moat and terminal margin. Not on me to demonstrate that there isn’t.”

The Takeaway for Vertical Founders

The SaaS income statement is deteriorating on every line. Gross margins compress with inference cost. LTV shrinks as switching costs drop, because agents use software instead of people, and swapping API calls is easier than retraining a team. R&D amortization windows shorten because competitive pressure accelerates. None of this means software companies are worse. It means they’re different.

The question for vertical AI founders is which of these five shapes your company is actually becoming. Are you owning outcomes through FDE? Building a neo-firm that uses software to differentiate a service? Acquiring an operating business and doing a full AI-driven overhaul? Constructing an agent network where single-player utility funds the cold-start problem? Or differentiating through hardware?

“SaaS with AI features” is not on the list. Reselling tokens with a better prompt is not on the list. If your moat story starts and ends with “we have better evals,” you’re one foundation model or model lab harness update away from irrelevance.

The margins are a bit lower, market opportunity is much larger, and — despite the ongoing hype — blue oceans abound for those willing to build something truly differentiated.

See you next week.

Here are a few of the founders, operators, and investors joining us this year:

Please find additional details about the event below — they’ll also be continually updated on our main page here through the event date.

The Details

• Date: Wednesday, September 23, 2026

• Time: 1:00 - 6:30 PM (including panels, networking, and closing rooftop cocktails)

• Venue: One Kearny Club, 23 Geary St, San Francisco, CA

Panels

• Healthcare AI — Pearl, Camber, OpenAI

• AI Marketplaces — Greylock, Handshake, SuperMe

• Scaling Vertical AI — Owner, BuildVision, Shepard, GC AI

• GP Panel — Tidemark, Bessemer Venture Partners, Footwork, Homebrew

Sponsors

A big thank you to our anchor sponsors for making The Verticalist Summit possible: Stifel and Latham & Watkins. Thanks, and we hope to see you next month!

Sahill Poddar is the Co-Founder and CEO of Parafin, the embedded capital platform behind DoorDash, Jobber, Mindbody. Most recently, Parafin raised a $100M Series C at a $750M valuation, led by Notable Capital, and joined by Ribbit (backer since the seed), Thrive, Redpoint, and Pathlight. Since then, they’ve crossed $100M in revenue and raised >$500M in additional lending capital, including a recent $150M facility led by Goldman Sachs.

Before Parafin, Sahill led machine learning at Robinhood and ran revenue growth at Meta. He’s no stranger to quantitative undertakings — he began his career as a physicist at CERN, where he contributed to the work that discovered the Higgs boson. Thanks to Parafin’s success, Sahill now has a view into dozens of vertical platforms from the inside — an ideal vantage point for pattern-matching what’s working in Vertical AI.

Watch the Full Episode Here

Today’s Episode

In software, channel strategies tend to be most effective once you’ve hit ~$10-15M ARR. You build the product, acquire users, prove unit economics and then consider channels — learn how to sell it yourself before asking others to. Parafin did the opposite. From day one, the company took the embedded route — well before vertical embedded was a thing. With its capital products lively natively inside vertical platforms, it is able to touch hundreds of small businesses that ultimately receive the capital for each channel partner it adds. No consumer brand, no Google Ads, no outbound sales team targeting SMBs.

This is an unusual strategic choice: in early days, it can be expensive and risky. You need large platforms to scale quickly, yet they may hesitate to bet on a startup. Once in, you require partner buy-in to drive adoption amongst end-users. Each subsequent partner’s needs — and user bases — may not mirror the next.

In Sahill’s case, the problem was clear, universal, and non-core enough for its customers that it worked. But the real payoff of the channel-first GTM lies in information asymmetry. Parafin now underwrites across dozens of vertical platforms spanning restaurants, fitness studios, auto shops, home services, and payroll. That cross-platform view reveals patterns about which types of SMBs are healthy and why. And importantly for our episode, along with that comes an understanding of which platforms are building real defensibility and loyalty in their verticals, and which may be exposed to AI disruption that remains in its earliest stages.

The Audacity of a Channel-First GTM

Sahill went after the white whale first: DoorDash. “Everyone’s on the call,” Sahill says about the earliest pitch. The platform’s product, risk, legal, and executive teams all had to say yes. DoorDash was public. Parafin was a seed-stage company.

The reason Sahill was willing to take this harder path because he’d developed a thesis on why direct-to-SMB lending had been a graveyard — Cabbage, OnDeck, BlueVine — to date.

1. Adverse selection. “You don’t really want to give money to people searching for money,” Sahill says. Pull-based distribution systematically attracts lower-credit borrowers.

2. Cost of marketing. Paid ads in the lending space are costly, and perform poorly in many verticals.

3. Commoditization. Every SMB lending competitor underwrites on the same public data, leaving price as the only differentiator, inviting a race to the bottom. There’s always someone willing to cut corners to win on price.

Channel-first would eliminate all three problems at once. For Parafin, customer acquisition cost is zero1 because the platform already owns the merchant relationship. Underwriting runs on proprietary data (real-time transaction volume, retention patterns, seasonal behavior) that no one outside the platform can access. And offers are pushed to merchants based on actual performance, not pulled by the desperate. Parafin has had zero enterprise churn since inception.

The product advantage of true embedding is extreme, especially in lending. Parafin doesn’t just give its partners as referral link to a form, as did Cabbage with Airbnb, and many other SMB lenders before it. The platform-native nature of Parafin’s product enables a different experience: pre-approved offers and 10-second decisioning. By using the platform’s granular transactional data to inform underwriting, they deliver better loan performance and a lower-friction, higher-trust merchant experience. And that matters for channel partners. As we explored in a prior episode — Vertical FinTech, with Rahul Hampole at ServiceTitan — fintech products at the right moment can dramatically expand platform ARPA, NPS, and retention. The better Parafin does, the happier its platform partners.

A personal note
This week, the partner that makes this show possible is also our guest. Parafin has been the presenting sponsor of Verticals since we launched. Working with Sahill and his team has been a lesson in founder foresight, and we are incredibly grateful for their support. We’ve shared many call-outs highlighting Parafin’s success — the revenue & retention they can bring to your platform. But working with a partner on something like lending is just as much about trust and relationship as it is performance. In our experience, Parafin offers a rare combination of both. So whether or not you’re ready for the product today — reach out. They’re a long-term partner who want to see you (and the larger Vertical AI ecosystem) succeed.

Learn more about Parafin today →

What the Best Vertical Platforms Look Like from the Inside

Because Parafin sits inside the financial plumbing of dozens of platforms, Sahill has had an inside view into what works and what doesn’t in the vertical business model.

The best Vertical SaaS are collective bargainers for all their businesses when it comes to negotiating with their vendors for services. And the harder they bargain, the better they get.

The strongest platforms he’s seen don’t just sell software — they aggregate fragmented SMBs into an effective Group Purchasing Organization. They negotiate on behalf of their merchants with payments processors, lenders, insurance providers, and suppliers, then pass those savings through. More merchants means more bargaining leverage, which means better terms, which attracts more merchants. It’s a flywheel purchasing power, a virtual Group Purchasing Organization.

Product-wise, Sahill sees a bimodal distribution of user engagement across the platforms he works with. The majority of merchants either use 1-2 features or 6+. Few in the middle. Lending turns out to be one trigger that can move a merchant from the point-solution cluster to the platform cluster. A restaurant that takes a Parafin advance to buy a second oven starts paying more attention to the platform’s other tools — driving ARPA and retention.

If it’s Written Down, AI Is Coming for It

Sahill has a fairly straightforward view on the positioning of various verticals against model labs.

If it’s white-collar, AI is coming for it in a very hard way. But if it’s a platform meant for blue-collar businesses or that industry, it’s very difficult for AI to come for it. [In legal in finance] everything worth knowing is written in some document that AI can read.

The likes of Harvey, Legora, or Norm AI have had a faster path to efficacy precisely because most first-order data in legal and compliance is already digitized. But that availability is a double-edged sword.

Home services, fitness studios, and restaurants are a different story. The critical insights — a plumber’s no-show rate spikes on Fridays, pizza box logistics cost more than the POS system, gym class schedules should shift based on weather patterns — live predominantly in practice, and less so in documents.

Luke visualized a “screen time” heuristic: the fewer minutes per day operators in a vertical spend on a screen, the harder it is for a general-purpose AI to find trainable data. A teacher on MagicSchool might use software 30-60 minutes a day, but the value of the product is powering the 6 hours of non-screen work around it. Jobber’s AI appointment-confirmation bot may seem like a commodity — but the company’s foothold in home services might yield an edge over platforms that don’t understand the specific, predictable moments and reasons trades clients cancel.

Some of the most defensible Vertical AI platforms will access difficult-to-aggregate data — or better yet, create it de novo, as a function of their product. Parafin is leaning into their edge with Paraformer, a proprietary transformer model for time series analysis of SMB cash flows. It powers AI agents for fraud detection, lien checking, and entity verification. As we noted in Payments as a Shadow System of Record, such industry financial layers can become stickier than traditional systems of record because of their access to proprietary information across customers.

Sidebar: The Vertical AI Utility

Parafin is embedded fintech. You might also call it Vertical Infrastructure. What Sahill has built also reminds us of an emerging category we’ve been thinking a lot about lately at Euclid: the Vertical AI Utility. It’s infrastructure that scales with consumption, works whether the platform calls it directly, embeds it, or connects it via an agentic layer. It doesn’t compete with core application layer — it makes such platforms more valuable by doing something the platform can’t (or doesn’t want to) do alone.

Utilities aren’t glamorous, but they can be difficult to displace once they’re wired in. As the owner of a function few deem their core competency, they often have the breathing room — and data access, across many customers — to compound advantage.

As the likes of Stripe have demonstrated in broader fintech, that may give you a ubiquitous foot-in-the-door to offer adjacent infra as well. We believe that as Vertical AI markets grow, so will the need for such utilities. Many will be relatively industry-agnostic — or perhaps more customer-size-oriented, as is Parafin. Others, perhaps in spaces like manufacturing, healthcare, or finserv — will likely cater on a sector basis. It’s one to watch.

The Takeaway for Vertical Founders

Channel-first can be a cold-start problem from hell. You need a platform to trust you before you have proof; you’re building for someone else’s customer; the integration is deeper; and distribution & feedback runs through an intermediary.

But the compounding advantages are enormous. You inherit the platform’s distribution, trust, and — perhaps most importantly — data. These layers of stickiness have never been more important than now, in the era of Vertical AI. As Sahill put it, “if your platform is predominantly just UI and database and that’s all you got, yeah, it’s probably gonna be a tough road.”

Increasingly, defensibility comes from things you and you alone have earned the right to learn.

See you next week.

Rick Zullo is the Founder & Managing Partner of Equal Ventures, a seed-stage fund with over $200M in committed capital investing across energy, insurance, retail, and supply chain. Rick has been investing in vertical software, marketplaces, and services for 14 years. One of his earliest exits was Vettery, a staffing platform acquired by Adecco, and his portfolio includes companies like Equal Parts, Threeflow, and Odyssey.

Rick brings a Munger-inspired value-investing lens to venture, focused more on competitive advantage and long-term free cash flow than most in the asset class. In this episode, we dig into one of his core convictions: in a market flooded with AI-driven growth, much of the growth managers are are seeing is beta, not alpha. Caveat emptor.

Rick has also build a defined practice around services, well before “AI-native services” was a category. He’ll share his POV on why it’s never been easier to start a services company… and never been harder to build one that lasts.

Watch Full Episode Here

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Today’s Episode

Venture capital is strange business these days. AI-native startups are growing faster than software ever did. Services — historically anathema — are in vogue. Physical AI and defense tech are absorbing billions, despite hardware being a VC graveyard and defense LPA-restricted for decades. Valuations (and hence revenue multiples) at seed have detached from any rational framework. And the narrative has shifted so dramatically that some LPs are questioning their exposure to software and embracing “deep tech” (although the vast majority of that category is still software, making that dichotomy deeply confusing).

Naturally, the instigator of these shake-ups is AI. Rick Zullo believes many investor assumptions in this moment are misplaced. His argument: the current boom is producing the illusion of product-market fit at scale. When a technology wave lifts every company in a category simultaneously, you can’t mistake it for idiosyncratic company success. Conflating beta with alpha1 can turn out very costly.

Beta-Driven Product Market Fit

Rick has a term for what happens when a technology catalyst makes everything in a sector work at once: beta-driven product market fit. “Because of a technology or market catalyst, everything in a single category is working,” he explains. “That long-term means a lot of these companies are going to struggle as they become more well-capitalized, compete against each other, and invite more competition.”

He feels the AI-Native Services wave is a perfect example. When an LLM makes it trivially easy to automate insurance claims processing, and five companies all go from zero to $5M in six months, the growth looks like signal. But it might not be the signal founders are looking for. When the technology wave lifts your boat, it lifts competitors and incumbents too.

The fundamental distinction between software and services still matters. Historically, SaaS entered greenfield markets: the product was new, adoption required friction, and success in overcoming that friction yielded defensibility. Services companies operate in brownfield territory. “You’re largely trying to steal share from other competitors in the industry,” Rick argues. “That’s a very different game.” The incumbents have distribution, cost of capital advantages, and they’re implementing AI as aggressively as anyone.

The insurance TPA market is his clearest case study. Equal has studied it extensively and found what you’d expect in a mature services industry: tremendous multihoming behavior, low switching costs, and a cost curve that every player is riding down together. “Are you going to assume that because you have early growth, competitors aren’t going to respond?” In most services categories, they already are.

In a world where buyers are trying to figure out how to use AI, it makes sense to be more consultative. Forward-deployed everything is all the rage, for good reason. When used for the right reasons — to find a unique, defensible, compounding application of AI — it’s powerful. When the footing is win business and hope the product will come, it’s dangerous.

Summarized: SaaS revenue was hard to get, so strong growth meant product-market fit. If AINS revenue is easier to get, it’s likely easier to lose as well. So VCs touting their zero to $5M ARR as proof of market leadership and justification of valuation might be in for a rude awakening.

What if there was a partner that was built for vertical platforms, that could grow your revenue, and bring huge value to your customers? Enter Parafin — the benefactor behind Verticals. They are the leading embedded lending platform, working with top logos like DoorDash, Gusto, Amazon, and TikTok Shop. From working capital to Buy Now Pay Later, they have your customers’ backs. And their own AI-powered underwriting & analysis has the hard part covered. They’re smart, supportive, and growing like a weed. Get set up with Parafin today →

AINS & The Revenue Multiple Trap

If beta-driven PMF is the disease, revenue multiple investing is the symptom. Rick is blunt: “Revenue multiple investing in services has never worked.” The reason is mechanical. Services companies carry cost structures that software companies don’t, even fully automated ones. The question isn’t what your gross margin looks like in year one.

The long-term question is: what is your net margin? …At early stage, it’s really hard to tell what net margin is unless you understand the moat structure of that industry.

He sees investors underwriting AI services companies at 500x to 1,000x revenue, in some cases calling GMV “ARR.” Hidden costs compound the distortion: companies loading up on risk assets and keeping the cost of capital below the line, out of the gross margin calculation. In insurance, opex optimization barely moves the needle. What matters is loss ratio, and most AI-enabled carriers have no idea what their loss ratio will be until their book matures. “A lot of them are trying to outrun their book with growth,” Rick says. “We saw that play through in 2021.”

WeWork, for example, had strong revenue growth, positive early margins, and a compelling narrative. Benchmarked against any legacy REIT on unit economic ratios, it was terrible. Rick sees the same trap in AI services: companies that look exceptional in isolation and mediocre when compared to the incumbents they’re supposedly disrupting.

As we’ve argued many times before, the “death of software” narrative misses so much nuance that it’s more or less worthless. Rick believes the same effect is at play with the blind trust many VCs are placing behind AINS. Like in all categories, there are of course excellent businesses. But if the market broadly prices services revenue as if it carries the same margin structure and defensibility as SaaS ARR — when much of the time, it doesn’t — that is a potentially fatal mistake.

Venture’s Blackstone Problem

So why is capital flooding into categories with these competitive dynamics? Rick’s answer sits upstream. It’s an incentive problem that starts with VC fund economics.

“Big funds are starting to look a lot more like Blackstone,” he observes. “They’re looking a lot less like Benchmark.” When you manage $10B at 3-and-30, you need companies that consume hundreds of millions in capital. Physical AI, deep tech, defense, nuclear, autonomous vehicles: these are plays that require the deployment of billions. “It’s not [just] convenient,” Rick says of this alignment. “It’s actually extremely strategic.”

Taken to an extreme, the result is a market that systematically favors capex-intensive plays and penalizes capital efficiency. Veeva Systems, which raised $9M before its IPO and has been compounding cash ever since, “would not exist in this market,” Rick argues. Not because the opportunity isn’t there, but because no large fund can write a meaningful check into a company that doesn’t need one. As we explored in The Fundability Trap, the market increasingly rewards fundability over investability. Rick extends that critique to capital structure itself: funds that need to deploy billions will find theses that require billions, whether or not those theses create durable value.

Physical AI is the latest expression. Rick acknowledges that cornered physical resources (cameras in warehouses, sensors on shipping routes) can create genuine moats. He likes how Steel Atlas put it in their “data gusher” thesis: limited providers, expensive-to-replicate infrastructure, sensors that throw off valuable data, software that compounds on top. But the broader category gives him pause. Most physical AI companies pitching AI-optimized mining, energy services, or manufacturing face incumbents with lower cost of capital, deeper distribution, and decades of vendor relationships. A truly defensible, proprietary hardware wedge is one thing. Off-the-shelf hardware plus compute is another. GPU capex, unlike railroads or transmission lines, has a useful life of two to three years. The analogy to durable infrastructure is questionable.

At Euclid, we’ve discussed hardware as a moat in Vertical AI, and why unique datasets have become more important than ever to the AI application layer. We do find it interesting, however, that deep tech proponents point to data generation as a key source of defensibility for the theme. Even the Data Gusher piece above lists Palantir as the canonical success story of a “data factory” — a Vertical AI business that is 100% software, eschewing both proprietary hardware and infrastructure by design. Deep tech or physical AI may be a compelling way to deploy capital at high velocities, but its unit economics and data advantages largely remain to be seen.

The Quiet Compounder

Rick’s answer is his portfolio. Between a third and half of Equal’s Fund I companies are free cash flow positive while growing at rates above 100%. These aren’t companies in the venture zeitgeist. Most aren’t raising $100M rounds or getting profiled in The Information. They’re vertical businesses that found defensible positions in their markets and are compounding quietly.

Equal’s internal filter centers on one question: “Can this company hit $100M of gross margin within the next five to seven years?” Very much in the vein of the Bling Capital underwriting framework we explored with Kyle Lui a few weeks back. The GM filter alone eliminates most AI services companies raising at astronomical multiples, because it forces you to confront the margin structure rather than assume it away.

And it all comes back to fund economics. Rick’s “dragon number,” the exit value needed to return the fund at average ownership, is roughly $570M. A $1B outcome still returns 2-3x the fund. That is a fundamentally different game than a megafund that needs $100B outcomes to move the needle. Founders Fund, by comparison, has invested in 600 companies, and six really matter to them, at average entry points well above $1B on a dollar-cost-average basis.

There are many ways to draw up a 10x fund in venture capital: 4 vs. 40 investments a year, $5M vs. $500M fund size, 20% vs. 2% ownership. They can all work. But the math has to hold together — and that math determines what the managers have to prioritize, and therefore, founder alignment. We laid out all the numbers in our essay, The Quiet Death of Founder-VC Alignment.

Hope is not a strategy. Yet VC fund strategies that assume $100B exits or capturing double-digit percentages of all venture-backed exits abound. Rick sees a landscape in which most AI services companies have zero chance of monopolizing their sector, and most physical AI plays face incumbents with structural advantages. The contrarian position might be the most old-fashioned one: invest in models that have actually been proven to make money.

The Takeaway for Vertical Founders

If you’re building in Vertical AI and everything seems to be working — revenue is growing, customers are signing, VCs are circling — first of all, congrats. But second, take a beat. Beta-driven product market fit means the rising tide is lifting your boat alongside everyone else’s. The question, to paraphrase the Buffet quote we all know and love, is what happens when the tide goes out.

Most high velocity-startups prioritize early commercial success, and more than ever, we feel that’s the right move. Vertical AI adoption is still nascent, economy wide. You’ve got to get in, grow, and figure it out. But along the way, the founders who build durable companies in this cycle will work just as hard to answer the long-term questions. Here are Rick’s top three:

1. What is your path to not simply a market-leading position, but a monopoly?

2. What does your net margin look like at scale, not just gross margin?

3. What’s defensible when every incumbent in your sector is also implementing AI?

As we’ve explored at The Verticalist many times before, workflow depth and data gravity are earned. Early market pull is critical, and the right path to product-market fit. But don’t mistake it for a moat. If you’re not leveraging traction to build something structurally defensible, you’re running a race that gets harder every quarter. Assume you have more competition than is visible, because beyond startups, incumbents will take their shots too, even if they’re slower to materialize.

On the capital side, we also agree with Rick on this: pick aligned investors. Beyond personal simpatico, a fund’s economics should match your company’s trajectory, stage, and vision. If you’re building a capital-efficient Vertical AI compounder, the partner who needs you to raise $200M and exit for $20B is not your partner. The one who will celebrate a $500M exit at 15% ownership is.

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Alex Oppenheimer is Founder and General Partner at Verissimo Ventures, a pre-seed and seed fund investing across the US, Israel, and Europe. Before launching Verissimo in 2020, Alex spent years at NEA, where he became a specialist in SaaS mechanics. He’s written 80+ checks since, backing vertical software, infrastructure, and fintech companies at the earliest stages. Tune in for a sharp debate on whether the vibe coding wave is actually a threat to Vertical SaaS, or just to the cheap end of it — and where the new lines of build vs. buy are being drawn.

Watch the Full Episode Here

Today’s Episode

Software is dead. We’ve all heard it. AI has made it trivially easy to spin up a tool — a scheduling widget, a personal CRM, a dashboard — and the natural conclusion is that nobody needs to buy software anymore.

While the conclusion is wrong, it’s not for the reasons most people assume. What AI can replace and what’s worth investing in are two very distinct categories. The cheap end of the market (discrete, small-scope tools) and the complex end (truly enterprise-scalable platforms) are night-and-day. They have wildly different build times, maintenance costs, and ultimately, economics. Many have assumed that all of software should take a hit — in reality, enterprise solutions may have gotten harder to build in the AI era, with rising expectations of interoperability and customization. While the simple solutions, designed for prosumers to execute on a small, repeatable task, have more-or-less lost pricing power altogether.

Alex has been investing in SaaS nerly his entire career. He joined NEA in 2013 when calling yourself “the SaaS guy” was meaningfully differentiating. He’s since written 80+ checks and founded Verissimo, investing at pre-seed and seed across three continents. His vantage point as a first-check software investor with deep finance roots, an engineering degree from Stanford, and a penchant for analysis aren’t foreign to the venture world — but his grounding in those fundamentals even as AI takes the world by storm, re-writing conventions, is somewhat refreshing.

The Cheap End Is Exposed

Vibe coding may be a tongue-in-cheek term — but its impacts are very real, both tangible and psychological. People who never thought of themselves as developers are now shipping functional (if not scalable or enterprise-ready) software. the claim isn’t that software development is completely democratized, but that the mindset of entrepreneurial people — builders with ideas but lacking coding expertise or budget — has shifted. even if they don’t build all their solutions, their expectations for ownership, flexibility, and scope are wholly new. Halfway through the episode, Alex pulls up a photo of his barber rebuilding his entire booking and scheduling system in ChatGPT in between haircuts.

A guy who cuts hair is shipping software. Most barbers or mechanics or teachers aren’t — and probably never will — spend their time on technology. But 1% of them will. and over time, we imagine that will change the psychology of industry. When people see that it is possible for fellow domain experts to mold the software tools of their trade (if not own them), that shifts expectations.

“It works for me” is a long jump from “it works for thousands of users”, however. Edge cases, compliance, integration depth, multi-tenant architecture: the requirements compound at every order of magnitude of software scale. Anyone who’s shipped enterprise software knows this. Anyone who hasn’t dramatically underestimates it, because their only reference point is themselves —an inherently, you’re more likely to forgive your own product for being clunky.

I would like to see somebody rebuild a very complex, enterprise-ready solution, vibe code it themselves as a non-developer, and then scale it up to be a billion-dollar business. Just not gonna happen.

Around small tools (which can be vibe-coded, cheaper, even nearly free) but also around the systems of record in their space (which should be more interoperable, flexible, and customizable).

There is an 800-pound gorilla lurking behind the vibe-coding wave: economies of scale. Salesforce is able to spread its infrastructure cost across tens of thousands of users. Alex painted the picture of what is probably happening to most of those barber-developers who first try to scale their product out to many more users: “I churned my Salesforce subscription and now it costs me $80K a year — I had to hire three developers instead of just paying Salesforce $25K a year and being done with it.”

Beyond maintenance and product lift of solving edge cases for every new customer, you’ve got actual hard infra: your costs on Vercel, Supabase, etc. look free at first, then really expensive on a unit economics basis. Paying $1000 a month to support 10 users paying $50 a month isn’t sustainable — it works well if you scale to 100 users quickly, and it doesn’t if you grow linearly. Some builders will simply nail it and blow past the diseconomy of scale moment. Many more will tire out and keep a few select tools, pressuring their core system of record — whose price is getting commoditized down anyways — to enable integrations or open APIs.

A quick word from our sponsor, Parafin — they power embedded lending, cards, and insurance for the likes of DoorDash, Jobber, and TikTok, having extended $35B+ in offers to small businesses. Purpose-built for vertical platforms serving SMBs, no infrastructure required.
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Big vs. Small, Not Vertical vs. Horizontal

The line on build vs. buy isn’t vertical vs. horizontal — it’s big vs. small.

This holds regardless of whether the software serves healthcare, logistics, or construction. A lightweight scheduling tool for a single barbershop? Buildable. A revenue cycle management platform for a hospital network? Not buildable. And while the operating enterprises themselves are more deep-pocketed, they face the same hurdle of economy as vibe-coders. Is your core competency building and maintaining technology solutions — and if not, do you want to commit to making it one? If the answer is no, you are ultimately going to continue to buy.

If it’s a small, very tangible solution where you can write up a summary of what it’s going to do in one page, that’s probably something you can build. But if it’s not your core competency, and it’s important and it’s complicated — that probably makes sense to pay someone else to do.

The reason is the oldest one in economics: specialization of labor. Does it make sense for an HVAC business owner to spend half their time on what is effectively software development? No. It makes more sense to pay someone who spends 100% of their time on that solution and can amortize the cost across hundreds of customers — especially now that traditional, broad-based SaaS is under significant pricing pressure. If a system of record refuses to lower their price? Someone will fill that gap and offer a cheaper solution, but that someone will still be an organization that is committed to scaling as a primary focus.

Attio is a great example. They are a 7-year old horizontal CRM that has raised over $100M in funding to date from GV, 01 Advisors, Redpoint, and Point Nine. They began as a vertically specific solution focused on the venture capital industry, but went horizontal due to TAM concerns. In doing so, they cut prices down to about a third of comparable solutions and stopped catering solely to VC. But with perhaps the most open APIs on the market, they are arguably more successful selling into VCs today than they were then they were horizontal. Openness and price won out.

The Second TAM Expansion

Years ago, Alex created a widely-shared presentation called “SaaS 101” comparing Vertical SaaS to horizontal SaaS. The framework was clean: vertical players had smaller TAMs but lower CACs. They grew more slowly but more efficiently into what everyone assumed were niche markets.

As Alex puts it, however, what were considered “niche” markets at the time turned out to be anything but. Software’s steady march into the economy meant TAMs expanded under investors’ feet while they were still anchored to old sizing models.

Now, it’s happening again. AI is creating a second TAM expansion for vertical markets. AI taps into and replaces services budgets, not just software budgets. When a Vertical AI product can do the work of a consultant, an analyst, or a back-office team, the addressable market expands beyond prior software spend to include the total cost of labor and services.

Some verticals still sit at single-digit SaaS penetration. A few may skip the SaaS generation entirely and go straight to AI. The retrospective pattern matching that led VCs to dismiss vertical markets a decade ago — “too small,” “too hard to sell into”, “not enough historical wins” — is the same pattern-matching causing them to underestimate Vertical AI today. In a past episode of ours (Why TAM is the Wrong Question) along the same lines, Nate Baker suggested the question most are inherently asking is: “is the initial wedge TAM big enough?” But here’s the one you should be asking: “can you durably keep expanding what you do for the customer?” AI is widening that answer — and even time it does, another market perceived to be too small, crosses the threshold of meeting venture-scale standards tomorrow.

Survive to Iterate

Alex tells every pre-seed founder they should have one mantra above the rest:

We survive to iterate.

These four words run directly counter to what most large funds will tell early-stage companies. “When the big funds come in at really early stages, their advice mostly centers around scaling,” Alex said. “Hiring and being aggressive around sales, without really understanding that the product’s not baked yet. And if you’re truly an early-stage investor, the market’s probably not ready yet.”

You have to be there when the market is ready. That means being there before it is. Which means it won’t look interesting right now.

This connects to a broader dynamic both of us observed: the current fundraising market is bifurcated. The average founder pitch has gotten better (more resources exist, polish is higher) but the median hasn’t moved much. Alex put it well: “the average has gone up, but the median has gone down in terms of quality.” You can raise $10M on three slides at an $80M valuation. Or you can raise at a multi-billion dollar valuation on real traction. But the middle, where most real companies live grinding from early product to product-market fit, is a valley of death.

The founders who survive that valley in verticals are the ones with earned insight. They didn’t graduate and decide to be founders. They spent years in an industry, identified a specific problem, and had the patience to iterate through years when the market was still forming. That type of judgment usually can’t be replaced by technical fluency alone.

The Takeaway for Vertical Founders

The build-vs-buy question will be asked again every time tools improve. And every time, the same mistake will be repeated: people will confuse what they can build for themselves with what can be built at scale for an industry.

If you’re building something simple enough to summarize on one page, you’re competing with every operator who can now ship their own version in a weekend — technical or not.

If you’re building something complex enough to require deep domain knowledge, multi-party workflows, edge case handling, solid scalable architecture, and years of data accumulation — vibe coding just isn’t a comp (yet). That said, they journey will never be the same. Low-end tools in your space are losing pricing power. Baseline systems of record are faced with unprecedented pressure to lower pricing. Expectations around interoperability and customization are rising, fast. The value of truly proprietary data — especially those that have near-term, defensible impact on outcomes — is an order of magnitude more important. And the market opportunity in vertical you’re serving isexpand again, as AI reaches beyond software.

The new dividing line of build vs. buy is big vs. small. Build accordingly.

See you next week.

Jake Saper is a General Partner at Emergence Capital, where he’s spent 12 years backing companies at the frontier of enterprise software, including early bets on Zoom, Veeva, Ironclad, and Physical Intelligence. In 2024, Jake coined the term AI Native Services in his essay The Death of Deloitte, arguing that AI would fundamentally reshape professional services.

Emergence was also an early bettor on open-source AI infrastructure, leading investments in Together AI, which just raised $800M at an $8.3B valuation, and in OpenAI’s $4B deployment spinout, DeployCo.

Tune in to hear our in-depth discussion with Jake on why the open-weight surge is unbundling the model labs and the coming reckoning over where value actually accrues in Vertical AI.

Watch the Full Episode Here

Today’s Episode

Last week, Chinese model lab Moonshot AI released Kimi K3 — a 2.8T parameter open-weight large language model. Kimi K3 is the first open-weight model to crack the frontier to this extent — scoring first in a popular front-end test (ahead of both Fable 5 and GPT-5.6 Sol) and second only to Fable 5 on another measuring long-horizon knowledge work. And while not “free,” as open-source1 might imply, K3 achieves the above at roughly half the cost per task (and that’s on a relatively favorable Opus 4.8 comparison). K3’s full weights go public on July 27.

K3 is just the latest in a cascade of open-weight models. It is estimated that open models are used by 80% of startups and represent 25-50% of volume across major platforms like OpenRouter and Vercel. Together AI’s annual bookings crossed $1.15B as customers seek to cut ballooning inference costs. The gap between frontier and open-weight performance — once measured in years — has compressed to 4–6 months, and appears to be shrinking.

Jake and Emergence called this shift early, backing Together AI in 2023 on the conviction that enterprises would want the flexibility, security, and cost advantages of open-source models. In July 2026, the question is no longer whether open weights will be in demand, or matter — it’s what happens when the model becomes less of a moat.

The Tokenmaxxing Hangover

Tokenmaxxing describes a phase of eager, first-wave LLM adoption in which enterprises encouraged pure token usage volume from its employees, presumably to accelerate adoption, value, or just generally prove they were AI-pilled and braced for the future. Some of the results are unsurprising: Uber blew through its annual AI budget in a quarter, Amazon shut down its leaderboard because employees were setting up agents in pointless loops just to climb the ranks; founders bragged about monthly token spends in the millions.

Jake uses a fitting term for the aftermath, which is no doubt a catalyst for the open-weight surge: the tokenmaxxing hangover.

There was an era three months ago when there was a lot of pride that companies would take in how much their token spend was going up as an indicator of — turns out — a very poor indicator of value created by AI. The hangover treatment has been to more heavily scrutinize what those tokens have been spent on and to find effectively cheaper tokens to get the same or better outputs.

Companies that were routing everything through frontier APIs at premium prices are discovering that open-source models — used judiciously, in the right use cases — can deliver comparable quality at a fraction of the cost. This is all pushing the ecosystem toward a multi-model paradigm: frontier closed-source models for the highest-intensity tasks (a small portion of total runs) and significantly cheaper open-weight models for more quotidian tasks (the majority of workloads).

Three forces are accelerating this more diverse future of model use:

1. Open-weight model quality has caught up. Kimi K3 is months behind the leading closed-source labs, not years. And that trend appears to have accelerated. Few deny that foreign open-weight models — less exposed to US IP law — are distilled from frontier models, likely putting a natural limit on how small the gap can get for holistic performance. But the capability difference simply isn’t as noticeable as it once was, and in certain domains, it’s arguably non-existent.

2. The US federal government has shown a willingness to intercede. Both to gate powerful frontier models (as they did with Mythos) and, potentially, to crack down on Chinese open-source models almost certainly distilled from US models.2 Regulations incentivize companies to further control their AI infrastructure.

3. Enterprises are beginning to seek their own AI moats. For example — as we’ve discussed at length here at The Verticalist — capturing, protecting, and embedding proprietary data into their stacks. Beyond this, there’s the possibility of edging into the model layer: fine-tuning an LLM with open weights to their unique purpose. That makes open source not just cheaper, but strategically essential for anyone trying to build a defensible data asset.

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The Labs Respond

The frontier labs seem to be doing their best to couch panic over the open weight news in grave national security concerns. A tweet from Dean Ball, OpenAI’s “head of strategic futures” is making the rounds branding open weights as “inherently decelerationist,” likely to result in “AI communism” that he characterizes as a “dystopian hellscape.” The incentive for frontier labs to support government intervention to suppress direct competition requires no explanation. The deeper response, however, lies in their approach of new layers beyond the model.

OpenAI hired Jason Boehmig, the co-founder and former CEO of Ironclad, to lead its new legal vertical in June, a direct move into application-layer territory. Harvey and Legora have been among its largest customers. Both OpenAI and Anthropic are hiring vertical-specific leaders across industries. And the app layer hasn’t been the labs’ only expansion salvo. OpenAI’s DeployCo raised $4B backed by Emergence, TPG, SoftBank, and Goldman Sachs to embed forward-deployed engineers inside enterprises. AI implementation services are perhaps an even more peripheral adjacency to their model-layer competencies that would create TAM breathing room as infrastructure is squeezed.

While their call for open-weight regulation is louder, the layer expansion feels even more telling about how seriously the labs are taking the potential erosion of their core moats. How successful these new initiatives will be is a separate question we will be writing more on soon. But as we wrote about Claude for Science last week (and OpenAI’s legal product is not dissimilar), the vision is nascent — today, a core model plus a set of connectors. As we discuss with Jake, that’s not what made vertical software companies defensible. While Vertical AI undoubtedly moves faster than its SaaS forebears did, it’s not just a question of product development — those with the strongest ostensible moats (e.g. Veeva, Fair Isaac, Epic) have gone deep on unique workflows and parsing of first-party vertical data for years.

The labs’ dilemma is that their core business is under commodity pressure from open weights. Therefore, moving up the stack to applications and / or services looks like a natural escape hatch — and one they may be uniquely positioned for, if only due to their vast balance sheets. But that means competing with their own API customers in a very existential way. It’s a self-reinforcing loop: the labs go vertical, which makes their customers warier, which accelerates the shift to open source, which puts more pressure on the labs’ core margins, which pushes them further into vertical plays... and so on.

As they look to their own long-term defensibility, even enterprise operating businesses are catching on. As Jake put it, if you’re a pharma company and you give a model lab all your proprietary molecular research, you’re handing your most valuable IP to an entity that’s now hiring vertical leaders and building competing products. “I think for good reason, people are increasingly wary. And that’s another reason why open source is being utilized — so people can keep their data within their house.”

As we covered in The Dispatcher Problem, the big question is who captures value as the marginal cost of intelligence falls toward zero. The recent step-changes in open-weight capabilities and the shrinking frontier gap make that question more urgent. When the model is no longer a meaningful differentiator, value migrates to whoever controls the workflow, the data, and the customer relationship. Perhaps the value also flows to the third parties facilitating all these value-chain shifts (e.g. integrators and other AI services).

We’ve said it before and we’ll say it again — we believe the immutable primitives of app-layer defensibility are workflow depth and data gravity. With more margin to go around, cheaper tokens only make the Vertical AI opportunity more compelling. And in a world of model-layer commoditization, deriving defensibility from the app layer has never been more important. The model labs themselves seem to agree.

Controlling Your Own Destiny

The best Vertical AI companies aren’t waiting for the labs to sort themselves out. They’re already taking steps to cement their app-layer moats — and even to take incrementally more control over their AI infrastructure.

Harvey is a visible example. The legal AI company is training open-source models to encode law firm workflows while simultaneously running a multi-model stack across Anthropic, Google, and OpenAI. The goal is to ensure that no single provider has leverage over the business. As Jake put it, “You’re seeing a lot of the leading agentic software companies focus an increasing amount of their spend on fine-tuning their own open-source models versus relying on the closed-source labs.”

What makes this possible is the convergence we’ve been describing: open-source models are good enough for the majority of workloads, and fine-tuning them with proprietary data is feasible only when the weights are open (unless you want to fund ground-up LLM training yourself). Making proprietary evals3 more valuable than they’ve ever been.

Jake made a related point worth noting: the companies that own the full outcome — AI-Native Services, or AINS4 — may be in a unique position to gather that eval data. A fund administrator like Hanover Park (a past guest on Verticals) sees the complete lifecycle: the AI output, the human review, the client feedback, the audit result. A software vendor selling into the same fund admin sees a truncated slice.5 That’s a separate argument from the open-weight thesis, but it points in the same direction: value is migrating from model access to the proprietary data and workflows built on top of it.

The Takeaway for Vertical Founders

The open-weight unbundling is a control story.

A year ago, Vertical AI companies were largely dependent on one or two frontier labs for model access, fine-tuning, and the roadmap. That dependency is unwinding. Open-weight models today are good enough for many tasks. The cost advantage can run as high as 10x. And the frontier labs may be accelerating the process by making dependency more dangerous, as they seek to compete with their own API customers.

From what we’ve learned so far, the founders navigating the unbundling well are doing three things:

1. Going Multi-Model: running multi-LLM architectures that route by complexity and cost and function. Knowing what works, for what, and why is an edge.

2. Exploring Fine-Tuning: supercharging open-weight models with proprietary data, turning customer feedback into a compounding asset beyond simple reinforcement learning. The app layer strikes back.

3. Depthmaxxing: Maximizing channel checks, customer data depth / breadth, champion relationships, physical proximity (via forward-deployed X), ICP granularity, and general user obsession. Businesses are both greedy and fearful around AI today. They want partners who are on their side and will help them take advantage — but only businesses that go all-in on understanding them will earn their trust (and thus the right) to solve their big AI problems. Founders securing airtight footholds and relationships are finding they can offer more (and more upmarket) than they expected.

As the cost of intelligence falls and the weights open up, the bulk of LLM use recedes into infrastructure. What remains is the vertical knowledge, the proprietary data, and the willingness to stand behind the outcome. As we’ve argued in many past essays, what makes a Vertical AI business durable hasn’t changed — open weights just make the ultimate prize dearer and clearer.

See you next week.

The argument is as follows: AI is commoditizing an increasing portion of software, leaving proprietary data, context, real-world feedback, trust, and distribution as the remaining scarce assets. As a result, vertical startups are incentivized to own the entire value chain. This ambition now extends beyond traditional technology and business services to include AI-enabled home services, construction firms, senior living, and even hospital systems.

NFX concisely summarizes this view in “The AI Wrapper Is Dead”:

The wrapper generation is dead. The survivors and the next generation of startups will think vertically. ‘How do I own every piece of the value chain?’

No doubt, thin wrappers are vulnerable. However, “own every piece” feels like an oversimplification that describes several very different strategies. A company might expand to multi-product, own physical assets, combine hardware and software, operate as a service provider, or vertically integrate into another part of the market. These are radically different strategies with radically different economics.

Our view is that AI expands the feasible boundary of venture-backed startups without determining the optimal one. AI companies should focus on capturing segments of the value chain where data, workflow position, and high-value outcomes accumulate. Sometimes that requires vertical integration, but it often does not.

The form of vertical integration, moreover, matters as much as the extent of it. A company may own the entire stack yet still gather undifferentiated data, depend on the same models as everyone else, and operate with higher friction and worse economics than focused competitors.

Integration maximalism ignores a long history of dominant vertical application-layer platforms that achieved powerful scale and defensibility without expanding into every possible industry revenue stream. Even in the current era of AI, businesses will thrive — and may even be better off — by focusing on controlling the specific portion of work-to-be-done where learning can accumulate.

In Vertical AI, as with SaaS before it, the objective is simple: own the control point. Naturally, control points are industry-dependent and shift over time as technology differentiates some layers and commoditizes others. But the full-integrationist narrative suggests that LLMs have turned every vertical surface into a control point. This is its fatal flaw. In any B2B landscape, there are always a limited number of consequential loci in the customer’s workflow that can serve as the launching point for systems of records.1

Clay Christensen’s Other Theory

Clay Christensen is best known for The Innovator’s Dilemma. He had another, less famous theory of vertical integration and modularity. Christensen first described it in “Disruption, Disintegration, and the Dissipation of Differentiability.” In a 2006 interview with Bloomberg, he summarized the argument this way:

During the early stages of an industry, when the functionality and reliability of a product isn’t yet adequate to meet customer’s needs, a proprietary solution is almost always the right solution — because it allows you to knit all the pieces together in an optimized way.

But once the technology matures and becomes good enough, industry standards emerge. That leads to the standardization of interfaces, which lets companies specialize on pieces of the overall system, and the product becomes modular. At that point, the competitive advantage of the early leader dissipates, and the ability to make money migrates to whoever controls the performance-defining subsystem.

Put another way, when a market is young, it is by nature experimental:

1. Buyers are figuring out whether they should buy, build, or wait

2. Sellers are fragmented and inconsistent, without baked-in economies of scale

In this phase, assuming buyers determine there is value in the new technology, more proprietary approaches look attractive. From the buyer's perspective: given that the current sellers are small and expensive, perhaps we should just control our destiny and grab value ownership in this hot new space. It follows that vertical integration is more interesting for sellers — get consultative, solve the whole problem, own more. In this moment, technology buddies becoming operating businesses even makes sense. Why shouldn’t sellers become what buyers were, owning the fruits of their in-house tech.

As the category matures and standards emerge, however, modular offerings improve. Customers become less willing to pay extra for the integrated solution and begin to value interoperability. At that point, the early integrator is exposed. Low-cost providers assemble products from components, undercutting the integrator’s stack on specialization and price. Open source solutions emerge. And tech businesses who became operating businesses can no longer maintain internal technology autarky — if they want the best tech, they need to become tech buyers themselves. In Christensen’s theory, the long-term market profits are not captured by the integrator, but by whomever owns the “performance-defining subsystem.”

AI makes this theory newly relevant by expanding what a startup can plausibly do in-house. More of the stack can be automated, bundled, or absorbed. That makes integration more tempting, but it does not change where profits ultimately migrate.

In A Guide to Disrupting Incumbents, we shared how this applies to startups disrupting incumbents by commoditizing complements, undercutting pricing power, and using the surplus they create to earn distribution and trust. In that piece, we discussed the canonical example of commoditization with Microsoft and the PC market:

In the early 1980s, software wasn’t even considered a market separate from hardware. When PCs started taking off, IBM was so eager to be a first-mover that it launched its 5150 model with an open architecture: using off-the-shelf hardware, publishing the technical specs, and encouraging expansion cards.³ The PC landscape, including IBM and its fast-followers, would quickly descend into a knife fight, kicking off a race to the bottom on pricing. Meanwhile, Microsoft’s DOS—offering the preferred⁴ OS, a critical complement to PCs—skyrocketed.

Integration can win early and still lose the market later. The graveyards of once-prominent technology stalwarts bear this out. Vertical integration may be structurally necessary or serve as a temporary bridge to market entry before ecosystem maturation. It might also be a company-killing error. Commoditization will eventually affect the integrator’s stack. Vertical AI founders should focus on owning components with lasting defensibility and profit potential.

NVIDIA is the modern counterexample to integration maximalism. The company tightly integrated GPU architecture, CUDA, networking, and the software ecosystem around accelerated compute. But NVIDIA left semiconductor fabrication to companies such as TSMC. It was integrated, with co-design strengthening its control point and enabling access to the capital-intensive layer, where a specialist had better economics. That position helped make NVIDIA one of the most valuable companies in the world.

The Minimum Sufficient Stack

Integration creates a durable advantage only when it secures something scarce. The argument for capturing more of the value chain centers on defensibility. For example: a product that senses the world, captures first-party data, influences an action, observes the result, and improves. If the company owns the entire loop, competitors cannot access the same context.

However, vertical integration does not guarantee defensibility. A company may own the full stack yet still collect only commodity data. Long-term value depends on the quality and structure of the signal, which must be proprietary, dense, and difficult to replicate. A vertically integrated startup may own sensors that produce undifferentiated data. In such cases, integration increases capital and operational burdens without enhancing defensibility or long-term prospects. The company is committed to eventually competiting with startups taking advantage of falling input costs to undercut. Having made the argument for the necessity of integration, management is often forced to wage a losing battle that saps the strength of its core product.

The traditional lesson from vertical software remains relevant. Leading vertical software companies seldom win by capturing the entire value chain — they win by capturing the workflow control point. And the concept of “owning everything” extends as readily across operating lines as it does technological ones. Toast owns the POS and transaction layer, not the restaurant. Veeva owns critical commercial and regulatory workflows in life sciences, not the drugmaker.

The strength of these platforms lies in their ability to understand and monetize the workflow without inheriting the industry’s full cost structure. Vertical AI raises the bar because software can now do more of the work directly. But the strategic question is the same:

Durable AI-native companies succeed by building towards securing control points. In our earlier work on successful Vertical AI playbooks, we recommended that many AI-native companies should begin with an authoring layer — a starting point for a valuable internal workflow that generates revenue and immediate ROI. This was a product strategy argument.

Our view on vertical integration builds on that: at the earliest stage, Vertical startups need to own the minimum sufficient stack to capture the control point: the part of the workflow where that loop compounds.

There are many ways a company can secure a control point, dependent on industry and state of technology: workflow embedding, licensing, integrations, contractual rights, proprietary hardware, and more. What matters is capturing dense and repeatable signals. The rule is simple:

Previously, the key question was whether to build or buy. For Vertical AI, the strategic question is own versus access: which parts require direct control, and where is reliable access sufficient?

The shift from on-premise software to SaaS has already demonstrated this lesson. Most SaaS companies rent compute and storage. A SaaS product that offered no unique value beyond generic infrastructure was vulnerable, but the answer was not to buy a data center. The answer was to capture the layer with data and workflow gravity and ultimately outsource the rest to cloud infrastructure providers.

Vertical integration, in itself, confers no specific market advantage. Sensors, models, integrations, services, and hardware matter when they enable access to the control point. Past that basic threshold, the risk is that ownership adds friction without durability.

Control Points in the Wild

There are three independent variables to describe the components of vertical integration. Access means you can use an asset without owning it. Ownership means the asset belongs to your company. Control means the control point itself.

Four examples show how they combine in practice:

OpenEvidence: access without ownership

OpenEvidence sees the clinical question at the point of care, retrieves the relevant evidence, and presents an answer to the physician.

Commercial agreements with medical journal publishers secured access to licensed content. The control point is the clinical question at the point of care. It does not need to own the journals, the hospital, or the physician workflow end-to-end. It needs trusted access to evidence and repeated presence at the clinical question.

Its defensibility, therefore, depends on the durability of its content access, physician distribution, trust, and the value of aggregated demand, not yet on a fully closed clinical learning loop. In this case, licensed access, without ownership, may be sufficient for long-term durability — because ultimately it isn’t necessarily the content that provides differentiation. It is their superior understanding of physicians’ questions and answers in situ, and the resulting ability to provide the best physician experience.

Halter: ownership for control

Halter captures cattle data, directs movement through virtual fencing, and gives the rancher an application for managing their herd.

The collar is required because it provides access to the control point. Without the hardware, Halter would not have access to the signal, action, or outcome.

However, the hardware itself is not the moat. It is mission-critical today because it secures the observation-and-action loop and the rancher workflow where learning compounds. Over time, modular hardware components should be allowed to commoditize, provided this expands access to first-party data without weakening control point ownership.

Abridge: control without ownership

Abridge captures the clinical conversation, creates documentation, and connects the note to downstream charting, coding, and revenue workflows.

While the sensor is the microphone, Abridge’s control point is the authoring layer: the clinical note starts in their software, flows into the health system through deep integrations, and serves as the base layer for critical downstream work. That position captures clinician edits, approvals, coding changes, and revenue outcomes.

Abridge’s learning loop does not require any additional parts of the healthcare value chain beyond their control point and its downstream workflows. Even as clinical notetaking commoditizes, what compounds is that downstream position, not the transcription itself. In the medical scribe world, the question remains whether the underlying technology supports long-term defensibility of this control point, especially in the face of highly dominant and motivated incumbents — but the control point that matters remains clear.

The AI-Native Law Firm: ownership without control

Now consider a fully integrated AI-Native law firm: client relationships, lawyers, and delivery all in-house.

While owning the entire value chain may appear defensible, in practice, it often results in collecting undifferentiated data that is easily accessible to frontier model companies, traditional law firms using tools like Harvey, and other AI-Native law firms. We discussed this dynamic in our essay on AI-Native services:

Model improvements only make a service defensible to the extent that no competing service adopts the same LLM advances. That’s not a bet we’d take.

The strongest moats in software have always been those that are not easily replicated — proprietary data loops, workflow integration, distribution, network effects — not the infrastructure they’re built on, regardless of whether you charge for the tool or the output.

When Integration Is Actually Necessary

While cautioning against the reflexive urge to own the entire value chain, vertical integration can often be structurally necessary to win a market.

Integration can secure supply, reduce latency, improve quality control, establish a single accountable operator, or prevent a critical supplier from capturing product economics. These are real advantages, but they are mechanisms rather than moats.

Integration is most defensible when one of three conditions holds.

1. The category is not stable enough for modularity

If the handoffs between the model, workflow, hardware, and surrounding environment change constantly, separate vendors may not be able to coordinate fast enough.

In these markets, integration is necessary for rapid iteration. However, this supports integrating across unstable interfaces, not owning every adjacent business.

2. Performance is below the buyer’s threshold

A multi-vendor system may be too immature or brittle, and the buyer may opt for a single party to guarantee reliability. Integration may be necessary to establish trust. RLHF (Reinforcement Learning from Human Feedback) vendors likely fall into this category.

Even in these cases, companies should distinguish between layers required for current deployments and those likely to be defensible profit centers in the future.

3. Durable access cannot be secured without ownership

A supplier may refuse to provide the data. The sensor, literal or figurative, may not exist. A software vendor may make integrations nearly impossible or severely restrict the actions a product can take.

If the only reliable way to secure access is to own the relevant asset, ownership may be required.

These conditions are real but more limited than the integrationist narrative suggests. They also evolve rapidly, especially in the LLM era. Markets mature quickly, fierce competition drives interoperability, and buyers are quickly becoming more attuned to their needs. Modular entrants will inevitably target individual layers of the integrator’s stack, often after the integrator has established the category.

Modular competitors do not always win. But the advantage created by ownership is often time-limited unless the integrator continues to own the performance bottleneck, the control point, or both.

We are in a classic Christensen’s Young Market moment today. Buyers are struggling with understanding what to buy or build — and what is even worth adopting in the first place. To that end, it can make sense to be more experimental, to be more consultative, and to take on more than you otherwise might have to solve enterprise problems. And the reward may indeed be owning more than a traditional wedge, faster.

We believe, however, the prescription for Vertical AI founders is not to blindly commit to owning everything from the get-go. Toeing this line requires strong management discipline and taste, but the ones who do it well — testing everything while retaining focus — will benefit greatly. There are a number of strategies founders can use to maintain balance, manage a wider footprint, and test expansion.

Commoditize Complements, Don’t Adopt Their Economics

In A Guide to Disrupting Incumbents, we wrote about eating complements and nuking pricing power. A startup can weaken incumbents by making adjacent products cheaper, free, embedded, or otherwise less able to capture margin.

But commoditizing a complement does not require ownership, nor does it require adopting its economics or business model. Often, the better strategy is to make the complement abundant, standardized, and cheap so that value shifts back to the layer you control.

Startups can undercut on price or promote interoperability standards. The objective is to remove the bottleneck without permanently inheriting the complement’s cost structure.

If a healthcare AI company identifies services as the bottleneck, it may initially require human intervention. However, the goal should be to convert repeated service work into access to a control point, rather than simply becoming a lower-cost services provider.

The primary advantage of software is leverage. AI changes the boundaries of what startups can do, but it does not negate the attractiveness of software and asset-light business models. If anything, leverage matters more as baseline intelligence gets cheaper. Fast followers, perhaps even the frontier models themselves, will modularize your offerings.

Before founders ask what part of the stack they should own, they should ask a more important question:

If a complement hinders adoption, integrate only enough to reduce its pricing power, coordination costs, or reliability risks. If a layer does not strengthen the control point, consider renting, licensing, or exposing it through a modular interface.

Commoditize complements, but avoid adopting their cost structures. The specialization of function and labor is as close to a fundamental economic truth as we have — great founders pick their battles against this principle wisely, and recognize they are unlikely to win a protracted war.

The Control Point in 1841

After the Panic of 1837 exposed the lack of reliable commercial credit information, the abolitionist merchant Lewis Tappan founded the Mercantile Agency. The agency built the first B2B credit network: a network of paid correspondents across the country, mostly lawyers, bankers, and other local observers, who reported on merchants’ solvency, reputation, and creditworthiness. A young Abraham Lincoln was reportedly among the early network correspondents.

The merchants were the vertical integrators of their era: they bore the capital and operating risk. They held the inventory, warehouses, supply chain, and customer relationships. The Mercantile Agency owned something else: a valuable information layer critical to recurring, high-value business credit decisions.

The correspondent network gave the agency the data. That data was then compiled into useful decision-support systems for credit. Each new piece of information, including payments and delinquencies, improved the system, enabling an “asset-light” merchant to capture the control point. The Mercantile Agency eventually became Dun & Bradstreet, one of the most enduring business-data companies in the United States.

Christensen’s Law of Conservation of Attractive Profits applies to the Mercantile Agency as well as to Vertical AI: profit does not necessarily accrue to the company that owns the most layers. It migrates toward the company that controls the performance bottleneck.

The Mercantile Agency needed thousands of correspondents and decades of patience to capture its control point: the merchant credit ledger. They owned neither the inventory nor the warehouses, but rather the clearinghouse of business trade.

Vertical AI founders should look for the modern equivalent of that ledger. Own enough of the stack to make it accurate, durable, and compounding. Serve a domain so well, displacement becomes not only difficult but uninteresting.

If Tappan or Christensen could give you one piece of advice today, it might this: bundle and unbundle with care.

Thanks for reading The Verticalist. Euclid partners with Vertical AI founders at inception. If someone in your network is building in the space, we would love to meet them. LinkedIn is the best place to reach us.

Zach Rosen is the Co-Founder and CEO of Brellium, the AI compliance platform that audits every patient visit across ambulatory healthcare. Brellium serves over 250K providers across all 50 states, auditing more than 1M clinical records per month against payer, regulatory, and OIG standards.

The company has raised >$30M from First Round Capital, Left Lane, Kearney Jackson, and Menlo Ventures. A second-time founder, Zach struggled with a personal misdiagnosis that drove him to build a safety net for patient care.

Watch the full episode here

Tune in to hear why: the real compliance moat isn’t the public rulebook; how Brellium is building intelligence no EHR or model lab can replicate; and why compliance is a dead wedge in some AI markets… and a killer in others.

Today’s Episode

Compliance is having an AI moment. Norm AI just raised $120M at a $1.2B valuation led by Khosla Ventures, turning regulations into AI agents in legal and finance. But it isn’t the first time compliance has been in vogue. Veeva built a $40B+ business on compliance in life sciences, Avalara did it in tax, and Workiva in financial reporting. Boring on the surface, billions underneath.

But healthcare compliance is a different beast. The rules aren’t deterministic, the enforcement is inconsistent, and the stakes are orders of magnitude more personal. Zach Rosen has built Brellium to tackle a problem space where all three collide. This episode taps his experience to get at questions that matter for any founder eyeing compliance as a wedge: when does it become a moat, when is it just a feature, and how has AI changed the game?

Non-Determinism is Your Best Friend

The conventional wisdom on compliance as a Vertical AI wedge is straightforward: regulations are public, the data isn’t proprietary, the moat is thin. If you can Google the requirements, so can your competitor and so can the incumbent EHR.

Zach argues this framing fundamentally misunderstands what compliance means in ambulatory healthcare. The public bulletin is the starting point, not the finish line.

Take Aetna’s ABA requirements in Texas. You can find the 12 items on the payer bulletin: sessions must be individualized, care must be medically necessary, and so on. Simple enough on paper. But what does “individualized” actually mean? What does “medically necessary” look like against a specific patient chart? Two providers reviewing the same documentation will come up with different answers. And Aetna’s auditor may have a third definition entirely.

There are kind of fractals of knowledge. The closer you zoom in... it’s not as cut and dry. If it’s 45 words of medically necessary care that’s also individualized to patient, then all of a sudden you start to understand — wait, there are a bunch of edge cases here that I maybe didn’t account for.

This gap between exposure and enforcement is one we’ve explored in the past. And it’s the core of Brellium’s defensibility strategy. The regulations may be public. The enforcement and remediation patterns — written, tribal, and word-of-mouth — are not.

About a year ago, one of Brellium’s customers was placed in prepaid review and had funds clawed back because a payer in Georgia required backdated co-signatures from two separate providers on all documentation. That requirement wasn’t on any public bulletin until weeks after the enforcement action. The provider lost a substantial sum essentially out of the blue.

In the AI era, doing the work is as important as identifying it. That’s why it’s critical that Brellium doesn’t stop there. They intake that enforcement event, codify it, and push it to every other customer billing the same payer in the same state — the next provider doesn’t get blindsided. Multiply that across 250,000 providers, all 50 states, and dozens of payers, and you get a compounding intelligence layer that no system of record can replicate because EHRs don’t see how audits actually play out. These are decision traces only a platform that sees and does the work can capture.

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Compliance Products Are Secretly Training Products

Brellium sells compliance. That’s the “why to buy.” But the actual first-order value delivered is workforce training.

A real-time compliance flag can tell a provider to swap out one diagnosis code for another. That protects the documentation. It doesn’t change how the provider treats the next patient.

At the surface, we’re a compliance product and that is why most of our customers choose to partner with us. But actually if you dive deeper, the value that we provide — that’s actually first order, not the second order effect — is that we’re a training product.

Brellium’s weekly reviews track provider performance over time: what they did well, what they need to improve, why, and how. Providers who improve get placed into higher performance buckets that qualify for bonuses and promotion tracks. Providers who repeat mistakes get reinforced constructive feedback. It’s a closed-loop system that changes behavior, not just documentation.

This is a pattern we’re seeing across Vertical AI more broadly. The products building lasting defensibility aren’t the ones automating a task; they’re the ones making the human better at their job over time. Rilla does this for field sales in the trades. Brellium does it for clinical compliance. The product starts as a tool and becomes, over time, indistinguishable from the org’s training infrastructure. That creates switching costs no feature comparison can overcome.

Two Paths to Win in Vertical AI

Zach views the basic option set for nascent Vertical AI products as being an evolution of the Vertical SaaS playbook:

• Path A — Start with a wedge product, get customers to pay for it, then bolt on adjacent products to become the system of record / action. This was the HubSpot playbook, the Salesforce playbook, the playbook 90% of vertical software companies ran. Start with one thing, make it excellent, then add a dozen more over five years.

• Path B — Start with a wedge so good the system of record / action can’t possibly compete with you, because the wedge is powered by data that is proprietary (or that the incumbent can’t feasibly access). Ideally, build a network effect around that wedge. Gong ran this play for six years as the “best call intelligence tool,” plugged into HubSpot and Salesforce as a pure point solution, before pivoting into a platform.

The AI era has made these two paths diverge further. Path A is an order of magnitude easier now; AI makes building adjacent products and expanding a platform faster than ever. But Path B, building a genuine network effect, is marginally harder, because the incentive structure shifts away from perfecting the individual customer journey and toward aggregating data across many.

If you are obsessed with compliance in a highly regulated industry... you have two core options. If compliance is simple and deterministic, you should probably take Path A — use it as your wedge and then build adjacent products. Path two, which is where we’ve leaned, is when compliance is non-deterministic. And that network effect is actually passed through taking word of mouth information and or information that is not publicly available.

AI-native Path A plays might be starting with a different kind of platform wedge, or even a service. For Path B Vertical AI businesses, the type of data that is defensible may be changing in the AI era, but the core strategy mostly rhymes.

Brellium has bet on Path B. The enforcement intelligence compounds. A provider switching to a competitor loses access to enforcement data accumulated across Brellium’s entire network. The two-sided nature of their model — solving a costly and dilatory issue for both payors and providers, translating between them without need for communication — create an additional network effect.

The Provider-Payer Flywheel

Brellium’s go-to-market exploits one of healthcare’s unusual structural features: the provider and payer sit on opposite sides of the same compliance problem.

Providers want to avoid funds being clawed back. Payers want to enforce compliance because every dollar out of their bank account goes into a provider’s bank account. Both sides spend heavily arbitrating what payers call fraud, waste, and abuse, essentially non-compliant documentation and care.

Brellium started with providers: mid-market, lower burden of proof, faster to stack proof points. Once enough providers in a given payer-state combination were on the platform, the data told a clear story. Brellium-approved providers were measurably more compliant than the market average.

That became the pitch to payers. Once a payer endorses Brellium, it makes the next provider conversation more straightforward. Not easier, Zach is quick to clarify (“none of this is easy — you’re selling next gen products to a highly regulated market that is generally not incredibly excited about technology”), but more straightforward.

The flywheel accelerates because each new provider contributes enforcement data, each payer relationship validates the product, and both sides compound the network effect. It’s a version of the two-sided vertical platform dynamic, grounded in compliance intelligence rather than transactions.

Healthcare’s notoriously closed EHR ecosystems actually help here. Unlike most Vertical SaaS categories where the rational incentive is to integrate and play nice, healthcare EMRs are, in Zach’s words, “almost entirely closed ecosystems.” Brellium’s GTM playbook: partner with the #2, #3, and long-tail EHRs first (who are incentivized to fill gaps), build the customer base on the dominant platforms organically, then formalize distribution partnerships up the chain once the proof points are undeniable.

The Takeaway for Vertical Founders

Compliance is one of the oldest wedges in vertical software. Veeva, Avalara, Workiva: the playbook is proven. But in the AI era, the game is bifurcating:

In verticals where compliance is deterministic, with clear rules, consistent enforcement, and public standards, AI makes the wedge easier to build and easier to copy. Use it to get in the door, build fast, and expand into the platform. Path A, in Zach’s framework.

In verticals where compliance is non-deterministic, with subjective standards, private enforcement, and word-of-mouth rule interpretation, AI enables something different entirely. The product becomes an intelligence layer that aggregates how rules are actually enforced across an entire market. This Path B offers a shot at building something genuinely defensible. In Zach’s eyes, it also requires more: a stronger leadership team, a more dynamic product org, and a tolerance for the slower burn of network-effect compounding.

The compliance products that win the AI era won’t be the ones that can read the rulebook. Translating deterministic rulesets and workflows from analog to digital was a SaaS-era play that’s now little more than a foot in the door (if you’re lucky enough to have found an urgent, blue-ocean use case). The AI-native winners will be startups that solve for the hairiest compliance issues — which they can do only because they’ve seen when and why they’re broken, in real time, over and over again.

See you next week.

Adam Harris is the Co-Founder and CEO of Cloudbeds, the hospitality management platform operating across 157 markets with $20B+ in annual transactions and 550,000 daily logins. Harris built Cloudbeds over nearly 14 years from a fragmented stack of nine disparate systems into a unified system of record for hoteliers worldwide, and now, a system of action. A UC Berkeley-trained economist who started on Wall Street before crossing to the building side, he recently launched Signals, a conversational AI layer built on Cloudbeds’ proprietary hospitality data model — more than a copilot, it’s built on 14 years of hospitality pricing data and is evolving into a true system of action. Tune in to hear why he thinks the AI gold rush is producing more pirates than pioneers, what it’s taken for a an incumbent to go all-in on Vertical AI, and why 80% of mature market leaders will be too slow to survive.

Watch Full Episode Here

Today’s Episode

Every podcast and pitch deck in 2026 tells the same story: AI startups will disrupt slow-moving incumbents. The system of record is dead. The future belongs to whoever ships the fastest agent.

Adam Harris has heard it all. He runs a platform that will check in 57M guests in the next six months, processes $20B+ in transactions, and sits on 4B data points across 157 markets. Last month, Cloudbeds displaced 117 competing systems — Harris had never heard of most of them. His take on the disruption narrative is blunt: the treasure is the dataset, and right now, everyone else is just plundering for it.

The Pirate Problem

Adam doesn’t mince words about what he’s seeing in hospitality AI. “I call it pirate season,” he says. “Everyone is looking for the gold and they’re just plundering wherever they can take it.”

What makes someone a pirate is the shortcut: taking the treasure without doing the work to earn it. That takes two forms:

1. The visible one is the credential pirate, the operator who entered the industry six months ago and is now a conference regular and, in Harris’s words, one of the “big voices on LinkedIn.”

2. The substantive one is the product pirate — the startup shipping a Claude-powered revenue tool or a chatbot that hallucinates cancellation policies. Wrap a frontier model and call it a moat.

Unfortunately, for would-be pirates, access to true vertical data gold isn’t easy. Anyone can spend $20/month on an Anthropic subscription and spin up quick solves. What they can’t access is 14 years of booking patterns, rate decisions, guest preferences, and operational data across 20,000+ properties — not, at least, without winning access through customers, partnering with platforms that have been around the block, or creating a flywheel of their own. As we’ve argued before, the moat in Vertical SaaS has always been the compounding dataset. Incumbents are getting increasingly sharp-elbowed around their hard-won assets.

“If ChatGPT wanted to optimize for hotel transactions, they should come talk to me,” Harris says, “because I do it every single day.” When we asked him if he’s giving full data access to any AI-native startups, Adam responded with a resounding “hell no.”

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From Record-Keeper to Action-Taker

SaaS is metamorphosing. Harris frames it as a regime change: the value of a system of record is no longer the record itself, but what you do with it. “Salesforce kind of feels like a big dumb database to me,” he says. “I don’t want to log into Salesforce. I just want the data and the action point.”

This tracks with what we found in our analysis of the SaaS repricing: the public markets didn’t punish all software equally. They punished software that was a passive store of data with no path to becoming a driver of outcomes. The winners had consumption-based models, AI-augmented workflows, or both.

Cloudbeds’ answer is Signals, their proprietary AI layer. The stat that matters: 80% of hoteliers see the right pricing move but act too late. Not because they lack awareness, but because they’re running a 24/7/365 operation where humans physically cannot process every signal at the speed the market demands. Signals runs millions of permutations per hour across Cloudbeds’ 4B data points and furnishes the action ahead of time.

The distinction Harris draws is between probabilistic and deterministic outputs. “I cannot have a probabilistic output for whether a rate is $100 or not. It has to be $100 if it’s $100.” A thank-you note can tolerate hallucination. A pricing decision cannot. That’s why Cloudbeds built deterministic harnesses around their AI (confidence scores, source attribution, audit trails) before connecting any of it to customer-facing workflows.

The Two-Year Blueprint

The most counterintuitive thing Harris said on the show: Cloudbeds spent two years rebuilding its data architecture before shipping any AI products. In the time it’s taking some startups to go from zero to tens-of-millions of ARR, Adam has been methodically laying the groundwork for his transition to an AI system of action.

He compared the process to building a house. “Permitting and planning takes most of the time. The actual building of the foundation and walls—it comes along pretty quick.” Most AI startups skip the permitting. They start with the construction and then go back to fix what they should have planned from the start.

Harris places the agentic AI cycle at “phase one of five,” roughly where self-driving cars were before anyone trusted a Waymo. “They’re super cool,” he says of agents. “I love swarming them up. But there’s a lot of garbage out at times.” He’s bullish on the long arc but thinks the industry is confusing current model capability with production readiness.

Interestingly, the jury is still out for him on whether an AI-first Cloudbeds will command more or less revenue. He ran parallel experiments: traditional SaaS pricing versus performance-based pricing where they take a cut of the revenue lift they generate. The outcome: both models hit approximately the same number. The value was equivalent regardless of how they charged for it. That’s a signal the product is genuinely driving outcomes, not just billing for the promise of them.

The Takeaway for Vertical Founders

If you’re building against an entrenched incumbent with a real data corpus and an active AI strategy, your window is narrower than you think. Harris displaced 117 competitors last month. He’d never even heard of most of them. The graveyard of “AI-powered” point solutions is getting crowded fast.

But not every incumbent is Cloudbeds. Harris himself pointed at the PE-owned, margin-optimized platforms that are “running the same exact business they were pre-ChatGPT.” 80% of PE money is sitting on the sidelines right now. They maximized profit but neglected the data layer. Those are the ones where the data asset exists but nobody’s investing in it. That’s where the real opportunity for founders lives. Not in outrunning incumbents who are already moving, but in replacing the ones who haven’t picked their heads out of the sand.

And for incumbents? Harris’s advice is unglamorous but firm: get your data right first. Build the harnesses. Don’t ship AI slop just to say you have AI. The companies that resist the urge to protect their hoard and instead aggressively invest in the vault, won’t just survive in the era of Vertical AI — they’ll thrive. Incentives as they are, however, neither we nor Adam expect that share to be more than ~20% of legacy market leaders. See you next week.

Zain Jaffer is the founder and CEO of Blazel, an AI-powered marketing platform that helps companies build authentic presence on LinkedIn and generate leads. Before Blazel, Zain built Vungle into the fastest-growing company in mobile ads, scaling it from zero to >$400M in ARR before Blackstone acquired it for $780M — after which it went public at a $4B valuation. After a few years on the VC side, he jumped back into the founder seat with a $10M pre-seed for Blazel. Tune in to hear why he’s betting that Vertical AI companies can (and should) claw back control from the model labs by building or adapting their own capabilities. He makes the case that the economics of renting intelligence may not hold up as long as founders assume.

Watch the Full Episode Here

Today’s Episode

Every Vertical AI founder faces a version of the same question: how much of your intelligence stack should you own? The default answer — build on Anthropic or OpenAI, move fast, ship product — has been the right one for most early-stage companies. The models are excellent, API credits are abundant, early gross margins can be forgiven, and nobody needs to reinvent inference infrastructure at Day Zero.

But a growing number of founders are hitting a wall. Costs escalate unpredictably. Latency from multi-agent architectures compounds. The frontier labs continue their saber-rattling about moving into the application layer as every API call you make teaches them more about your workflow. Meanwhile, open source seem to catch up faster every cycle. Zain lays out why he decided to build Blazel’s own model capabilities, pivot away from an AI-Native Services model, and what his decisions reveal about where value will actually accrue in Vertical AI.

The $10M Trap

Zain’s journey toward model independence started with a pivot. Blazel began as an AI native agency — one of the first, before Sequoia, a16z, and YC started championing the category. The agency grew fast. Replacing existing marketing budgets was a frictionless sale: buyers already have a purchasing workflow, there’s no new vendor approval process to navigate, and you don’t even need a UI. Zain’s take on agency-style AINS: “It’s very easy to get to $10M ARR. I’m not kidding.”

The problem is what happens at $10M. You’ve hired a team of humans. Any AI you introduce threatens their jobs, and they become a barrier to the very automation that should be your competitive advantage. More agencies flood in because the barrier to entry is so low. The allure of hiring more humans before the LLM cost structures warrant them — to maintain growth and raise more capital — is hard to deny. And if you go to your VCs and propose cutting revenue to pivot toward a platform model, you face a high likelihood of resistance, even disillusionment.

We’ve discussed this dynamic in the SaaS-to-AI transition: incumbents are generally terrified to cannibalize their own revenue, and it gets harder with scale. Zain saw it forming and made the call to pivot before the agency got too big. “It’s very hard to pivot when you’re 10M ARR.” Zain also emphasized the difficulty of running a people and technology business simultaneously — at Vungle, he ultimately felt their decision to split focus between brand advertising and app installs was a major misstep. Competitor AppLovin kept singular focus on one ICP and one problem. Vungle exited at $4B. AppLovin is worth $150–250B.

The agency, in Zain’s telling, wasn’t the business — it was the product roadmap. Humans doing the work revealed exactly which workflows to automate and where domain-specific AI could outperform general-purpose models.

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When the API Bill Hits

Blazel’s content pipeline initially ran on Anthropic’s Sonnet. Multiple agents would generate content, other agents would edit out AI slop, then the output fed back as labeling data to train evals. It worked. Until the bills came.

“How did I spend $70K last month on Claude?” Zain recalls. “I wasn’t monitoring my tokens because it’s free credits.” The cloud credits that seed-stage companies get from AWS, GCP, or Azure mask the real unit economics. At API pricing — roughly 20x what you’d pay through consumer products — the math gets ugly fast for any company processing high volumes of domain-specific content.

Cost was one forcing function. Latency was another. Every agent call is a round-trip to the model provider. Chain four or five agents together — generate, edit, check for slop, evaluate, reformat — and the latency compounds. For a product that needs to feel responsive to time-strapped CMOs, that kills the experience.

But the existential forcing function is competitive. Frontier labs are building applications — or at least threatening to. Just by existing on their rails, you’re helping them accelerate your obsolescence.

The model providers themselves aren’t content sitting in the model layer. They want to go into the application layer… You are basically training the model provider. — Zain

Fine-Tuning Your Way Out

So Blazel did something that sounds, on its face, absurd: a pre-seed startup started building its own model capabilities.

The approach wasn’t training de novo models from scratch. The team fine-tuned open-source models using reinforcement learning and pre-training techniques, then built a harness of prompts and tools around the result. The labeling data came directly from the agency’s human editors: months of expert corrections that encoded what “good” content actually looks like in specific domains. This is why the agency phase mattered — it generated the proprietary training data that no frontier lab could easily replicate.

We got to the point where our own AI model was producing better content than what our team could produce. And we hired the best content people you can imagine. — Zain

A domain-specific model, trained on domain-specific evals by domain experts, beat a frontier model at a specific task — not because it’s a better model overall, but because it’s been shaped by data the frontier labs don’t have. Your content editors’ judgment, your customers’ voice patterns, your vertical’s quality standards. These artifacts can’t be scraped from the open internet.

We made this argument in Dude, Where’s My Moat? — workflow and data are the immutable primitives of software defensibility. That principle applies equally to the model layer. The companies with the deepest workflow integrations, that win them access to the most relevant proprietary data, will build the most defensible model capabilities. They do it not by outspending the labs or going toe-to-toe with them on compute, but by outlearning them on narrow, high-value tasks.

To work toward model independence, evals are key. “Labeling is a fundamental layer when it comes to AI and that’s where humans are absolutely important. For the LLM to be a good judge, it needs to have some intuition, and intuition comes from hiring humans.” They’re the mechanism through which domain expertise gets translated into both moats, whether at the application or AI infrastructure layers.

A Trillion Parameters Under Your Desk

While Zain’s bet seems quite contrarian at the moment, it’s increasingly plausible as we consider where compute is heading. Nvidia’s DGX Station, announced at GTC 2026, puts 20 petaFLOPS of AI performance and 748GB of coherent memory in a deskside form factor — enough to run a trillion-parameter model locally.

If running large models locally becomes cheap enough, the economic argument for API intelligence dependency weakens substantially. Companies can fine-tune and serve open-source models on their own infrastructure, keeping proprietary data in-house and eliminating per-token costs at the margin. The labs’ moat narrows to whatever capabilities can’t be replicated with open-source architectures, custom training data, and increasingly affordable compute.

Zain frames it as an existential risk for the frontier labs: “A model itself could actually become a commodity. I think that’s a threat to OpenAI and to Anthropic, which is why they’re trying to go” app-side. If the model layer commoditizes, value accrues to whomever owns the workflow and the data — and the remainders will be either have to make their bones on supply ownership, become Dispatchers to the cloud provider oligopoly, or get competed to death. The irony is that the frontier labs, continuously making noise about their path into the application layer, seem to agree. Even SpaceX — a business that has basically no B2B apps — claimed in their S-1 that the enterprise application layer represents >90% of their TAM.

The Takeaway for Vertical Founders

None of this means you should rush to fine-tune an open-source model at pre-seed or seed stage. The API-first playbook is still the fastest way to product-market fit. Use the credits. Ship the product. Learn what your customers actually need.

But be intentional about what you’re learning. Every human edit, every customer correction, every eval your team runs — that’s labeling data. It’s the raw material for the domain-specific model you may need to build sooner than you planned. Zain’s team didn’t set out to build their own model. They set out to build a marketing agency, and the data they generated while doing it became the foundation for something far more defensible.

Founders who want to best position themselves should consider treating API dependency as a strategic phase, not a permanent architecture — at least to maintain optionality and set the right product culture. Build on the frontier labs today, but capture the data and develop the evals that would let you peel off high-volume, domain-specific workloads when / if the economics demand it. The compute to run your own models is getting cheaper by the quarter. It’s too soon to imagine every startup with it’s own LLM. But the question is not whether Vertical AI companies will own more of their model layer — it’s when, and in which form factor.

Zain has lived the cycle. He built a company to a $780M exit by disrupting the mobile ad incumbents. He watched from the VC seat as SaaS companies failed to adapt to platform shifts. Now he’s back, betting that a startup with domain-specific data, proprietary evals, and fine-tuned models can build something the trillion-dollar labs can’t match in his vertical. The labs have scale. Vertical founders have specificity, earned insight, and (in the best cases) data frontier model providers can’t scrape or buy. In a world where compute is commoditizing fast, the model landscape of the future — at least in Vertical AI — might not be as homogenous as recent history has trained us to believe.

See you next week.

Adeel Khan is the Founder and CEO of MagicSchool, the leading generative AI platform for K-12 education. It grew from zero to 7m users — and >$10M in ARR — in less than a year. Adeel is a true domain-expert founder: he spent his career as a teacher, principal, and school founder. After launching MagicSchool’s MVP in May 2023, the company hit 1M users in just five months — all organic — and today partners with districts representing one in five children in America. And Adeel did it with a sales team that is 85-90% former teachers. MagicSchool has raised $65M across seed, Series A (led by Bain Capital Ventures), and a $45M Series B led by Valor Equity Partners. Tune in to hear how Adeel turned a career in classrooms into the fastest-growing Vertical AI in education ever — and why his hyper-vertical GTM strategy allowed him to turn an AI wrapper into a category-defining platform.

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Today’s Episode

In VC circles, K-12 education has a reputation as a startup graveyard. ACVs are small. Buying cycles are fickle and cyclical, following the school calendar. Decision-makers are risk-averse by necessity: incentives for innovation are limited and the downside of a bad technology bet is a room full of nine-year-olds staring at a broken screen. The sector has produced vanishingly few large software outcomes in the modern era, and the even EdTech VCs are often skeptical of the category.

MagicSchool AI — after launching a ChatGPT wrapper in 2023 in K-12 — went from zero to tens of millions of ARR, 7M teachers, and district partnerships covering one in five American children, all in less than three years.

The obvious question is how. The answer, in large part boils down to two things. The first is some combination of timing and luck, which frankly, all hyper-growth startups need. The less obvious — and more instructive — answer is that the founder leaned heavily into his vertical domain expertise. MagicSchool was built by teachers; and from inception to today, it’s sold by teachers. 85-90% of Adeel’s GTM team have backgrounds as educators — teachers sell it, implement it, and show up when a superintendent needs to understand what AI means for her tens of thousands of students. That hyper-vertical GTM was critical to MagicSchools breakout success.

Leaning into Your Domain Expertise

We’ve written extensively about how founder backgrounds correlate with Vertical SaaS success. The data is unambiguous: domain expert founders capture 84% of exit dollars and raise more, from higher-signal investors. Adeel Khan is a case in point — though he’d be the first to tell you it was far from obvious in early days.

When MagicSchool was raising its seed in 2023, the deck was stacked against it in every conceivable direction. Application-layer AI companies were, in Adeel’s words, “at their absolute bottom.” EdTech wasn’t anyone’s darling. And then there was the founder himself: a former school principal with no technical co-founder, no software background, no Stanford pedigree. As we explored in The Fundability Trap, VCs pattern-match on consensus profiles — and Adeel matched none of them.

Even the ed tech VCs... they were just like, we don’t have the pattern of a teacher founder or a principal founder. Whether or not they said that out loud, it was kind of a thing that I saw. — Adeel Khan, MagicSchool

Ironically, the very thing that made investors nervous — a non-technical founder who’d spent his career in schools — is what made the product work. Adeel’s first customers in Colorado weren’t buying software. They were buying him. His reputation, his understanding of their problems, his ability to walk into a CIO’s office and speak the language of someone who’d been burned by the last five EdTech vendors. Adeel isn’t the post-pedigree founder — he’s the strong-pedigree founders, just one without the typical pedigree VCs favor.

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Vertical GTM: Hire the Expert, Not the Seller

This is where MagicSchool diverges from the standard “domain expert founds company” narrative. Most vertical founders leverage their expertise at the top — in product vision, in early sales calls, in pitch decks. Adeel built his entire organization around domain fluency.

Their solutions architects — the closest analog to a forward-deployed engineer — are former district technology implementers, pulled directly from the schools they now serve. When a solutions architect walks into a superintendent’s office, they’re not demoing features. They’re describing problems they’ve personally lived. While acknowledging the trade-off, Adeel framed the logic this way: a former teacher can learn how MagicSchool’s prompt architecture works; a career SaaS seller can’t learn 15 years of navigating school board politics in an onboarding sprint.

The companies that invest in domain-fluent GTM teams compound trust faster than those that bolt on traditional sales orgs. As we’ve explored across our founder-led sales analysis, that difference shows up in pipeline velocity, win rates, and retention, especially early. It’s not the solution for every market. In some — especially those with faster-cycle, more trasnactional sales — pure sales athletes can work best. That’s what worked at Owner, a platform in the restaurant space, as CRO Kyle Norton shared in a prior episode.

In K-12, the buying decision is as much psychological and emotional as logical. MagicSchool’s competitors — and there are dozens, from legacy SIS providers to curriculum companies to horizontal AI copycats — have launched products that “even look like ours,” as Adeel puts it. Features can be copied. An organization that speaks the buyer’s language and knows how to grow into AI with them cannot.

Monetization: Just Get Them to Sign

MagicSchool’s monetization story is a case study in creative pricing for risk-averse markets. The product was entirely free until January 2024 — eight months after launch, with a million users already on the platform. Adeel’s reasoning was disarmingly simple: “The biggest companies in the world that I know were free.”

When monetization came, the team didn’t hire enterprise sellers or build a traditional pipeline. They invented a pilot structure designed to remove every possible objection: $1,000 for a single school, $2,000 for an entire district — regardless of size. Miami-Dade, with 350K students? $2,000.

The catch was in the contract. Each pilot agreement included the full renewal price, timed to the district’s natural buying cycle in July. The spring semester pilot was a zero-risk trial with a committed upsell baked in. “The goal was sign a contract with us. If you sign a contract with us, we’ll figure the rest out later.”

The open question — for MagicSchool and for Bain Capital Ventures, which led the Series A before renewals came due — was whether districts would convert at full price. Adeel’s COO predicted 50%. Adeel predicted nearly 100%. BCV landed somewhere in between.

Adeel was right. Near-universal renewal at full ticket price. The pricing strategy worked because of three things that don’t generalize easily: a million organic users already in the buildings providing bottom-up pressure, a product teachers genuinely loved (94% were enthusiastic fans, per internal NPS), and a team that could walk into a renewal meeting and speak the buyer’s language from lived experience. Strip out any one of those, and instead of a hyper-growth Vertical AI story, MagicSchool might’ve been proof-positive of the poor retention rates of most AI wrappers.

The GPT Wrapper That Won

In 2023, “GPT wrapper” was the most derogatory label in venture capital. Adeel concedes the point freely: “In the most derogatory way, we were certainly a GPT wrapper. And so was everyone else.”

A prevailing thesis at the time was that horizontal platforms — ChatGPT, Claude, Gemini — would eventually subsume every application-layer product. Users would learn to prompt; platforms would make it easier to customize the UI, harness, and skills. Killing the wrappers in the process. Even Adeel, at the time, thought it was a real possibility. And as it turns out, it wasn’t altogether wrong — plenty did die.

Three years later, however, the opposite has played out for MagicSchool. Getting real value from horizontal AI tools now requires connectors, projects, context ecosystems, custom skills, etc. And while some users can string these together in their free time, teachers have one or two hours a day at their computers and none of the expertise to execute. They’re not building context architectures for fun. And as MagicSchool grew, it found ways to become less vibe-codable.

The structural insight — one we’ve explored in Software Is Dead — Long Live Software — is that as the underlying models get more powerful, the complexity of extracting domain-specific value increases, not decreases. A horizontal platform doesn’t know yesterday’s quiz scores, the district’s curriculum standards, which students are on IEPs, or how their teacher-users are likely to think or decision. MagicSchool does (or at least gets closest). Adeel is adamant, the moat isn’t the model. It’s the context layer on top of it — and the domain experts who built it know exactly what context matters.

Adeel shared a great example: Jasper — front of mind for every AI founder in 2023 — never materialized in education. Why, given it’s clear overlap around the content creation value props MagicSchool et all centered around? Jasper’s users were marketers and writers who lived on computers all day, cycling between tools as better ones emerged. Teachers aren’t tool-hoppers. They adopt what works and stay put. The “GPT wrapper” wasn’t a liability. It was the fastest path to becoming the trusted, contextually aware platform that no horizontal product could replicate. And like in most great Vertical AI businesses, there were vertically specific dynamics key to this story that wouldn’t held up on other markets. Adeel saw them early.

The Takeaway for Vertical Founders

MagicSchool’s trajectory compresses about a decade of normal vertical software company-building into three years. Explosive PLG, creative monetization, near-perfect conversion, the transition from order-taking to strategic sales, a highly vertically trained GTM team, and the emergence of legacy competitors who copy everything except the one thing that matters — teacher’s love for the product.

The lesson isn’t “hire domain experts” — that’s obvious enough to be useless, and as mentioned, won’t extrapolate to every vertical. The lesson is that, when compounded across an organization, domain expertise is infinitely more valuable. One domain expert founder gets you early customers and a good product. An entire GTM team that’s lived the buyer’s daily reality gets you a trust advantage that copycats can’t reverse-engineer. The competitor can match your feature set. They can’t match the fact that your solutions architect used to run IT deployments in the buyer’s own district. And in MagicSchool’s case, it’d be very difficult to catch up on district lock-in and brand. First-mover advantage is provably unreliable — but it had real value here when paired with excellent continuing execution.

Adeel put it well when describing how schools choose technology partners: “So much of the work that we’re doing with Magic School isn’t just about the app or the product. It’s also about the people and the partnership.” This episode is a great reminder that in Vertical AI, the org chart can be a true, tangible moat.

See you next week.

We believe that judgment will prove to be the real prize in Vertical AI, separating great products from great companies. Agents can already follow rules, pull the right data, and generate useful documents. What they can’t do is handle exceptions: the case that doesn’t fit the template, where a human has to intervene based on market, customer, and counterparty-specific context. Those calls don’t live in an ERP, CRM, WMS, etc. They pile up in the exception queue, and they vanish as fast as humans clear them. Longer term, enterprise value in Vertical AI accrues to whoever captures the reasoning behind that human judgment.

We’re already seeing this pattern in the early vertical AI breakouts. Tennr owns referrals. EvenUp owns demand packages. Abridge owns clinical documentation. On the surface, the products look different. Underneath, they all do some version of the same thing: they sit inside a repeated, high-consequence workflow that leaves behind a reusable decision trace with every resolution. This is the exception queue, and we believe it’s the most valuable asset for each Vertical AI startup to capture.

Why Wedges Work

In evaluating wedges, we often think of a two-part test: on one axis, exception density; on the other, the cost of failure (or delay). The former, as you might expect, considers the frequency of workflow exceptions requiring remediation. The latter considers what happens when exceptions aren’t caught swiftly or handled properly.

A workflow with high exception density but low consequences may drive usage — and even near-term revenue growth — but usage is not a proxy for urgency or durability. It becomes a productivity tool, and productivity tools are fragile in a world where the frontier of intelligence is rapidly commoditized. A workflow with high consequence but low frequency can matter, but it often behaves more like a consulting project than software.

When the cost of failure or delay is high, the buyer has urgency. “Cost” can show up as unrealized revenue, idle labor, margin leakage, compliance exposure, or cash flow getting stuck. That is why delay matters as much as failure. A prior authorization that technically clears but arrives too late has still failed the workflow. A recoverable denial that is not appealed within the window still results in leaked revenue. A freight dispute that closed after the invoice ages out can still damage cash conversion.

When the cost of failure or delay is high, the buyer has urgency. “Cost” can show up as unrealized revenue, idle labor, margin leakage, compliance exposure, or cash flow getting stuck. That is why delay matters as much as failure. A prior authorization that technically clears but arrives too late has still failed the workflow. A recoverable denial that is not appealed within the window still results in leaked revenue. A freight dispute that closed after the invoice ages out can still damage cash conversion.

What the test points you toward is the exception queue: a steady stream of non-standard cases that humans already triage, escalate, document, resolve, and audit. Take prior authorization. In Medicare Advantage alone, a recent KFF study found nearly 53 million prior authorization requests submitted in 2024, and 4.1 million were denied. Only 11.5% of those denials were appealed, and 80.7% of the appeals won. The constraint? Physician capacity. A recent AMA survey shows that physicians complete an average of 39 prior authorizations per week, which consumes roughly 13 hours. The system is designed to drain provider resources and capacity. Of the denials anyone bothered to fight, more than four in five were reversed.

Logistics has the same patterns. A shipment can technically arrive after a missed appointment or a disputed accessorial charge, but the delay still burns money. For 3PLs, for example, up to 35% of invoices are disputed. Errors, missed charges, and improper billing not only hurt profitability and increase average DSOs, but they also lead to customer churn.

Another useful indicator: glue functions. Care coordination, claims processing, freight audit, permit coordination, and procurement exceptions often live across spreadsheets, email threads, phone calls, portals, and people’s heads. The context is fragmented across multiple systems and people, and the resolution depends on judgment.

That’s what a great wedge looks like: judgment calls someone is already paid to clear, high cost to the constraint or delays, and a recurring queue.

Where the Best Enter the Queue

The best vertical AI companies don’t all look like “exception queue” companies on the surface. Some start at the queue itself: referrals, denials, permits, and billing disputes. Others start one step upstream, at the authoring event that feeds the queue. But the question that determines long-term value is the same: does the product sit where decision traces are a natural byproduct of usage?

We see two viable entry points.

1. At the exception itself.
   Tennr starts where the referral is already broken. EvenUp starts where the claim is already disputed. The exception happens often, failure or delay is expensive, and resolution requires context. Every resolved case produces a decision trace, the thing the customer trusts, forwards, reviews, audits, and acts on. It is also what lets the product learn. When resolution *is* the product, the trace comes free with every case cleared.

2. At the authoring event upstream.
   Abridge is a strong example. The clinical encounter is not always an “exception” in the operational sense, but it is always a high-context moment that must be documented. The exception queue emerges downstream when that clinical detail flows into medical coding, risk adjustment, and revenue protection. The decision trace comes initially from what’s documented, then deepens as the documentation moves through the workflow. Once you own the context, you begin to see the exceptions downstream.

Both paths work because both end up in the same place: sitting inside a high-consequence workflow, generating decision traces with every resolution. The difference is the entry point. The risk of starting at the queue is that you’re in the messiest, most integration-heavy part of the workflow. The risk of starting upstream is that you might generate context without ever reaching the queue, where it compounds. Abridge works because clinical documentation *necessarily* flows into coding and billing. An authoring tool that produces a useful document with no downstream workflow destination is just a productivity tool.

Broader platforms, Harvey, Legora, or TrunkTools, face the same question at each workflow they touch. They can be very large without looking like a single exception-queue wedge. But the defensibility question remains: does usage compound into proprietary workflow memory? Does the product learn the firm’s playbook, the client’s preferences, or category-specific nuance? Or does it remain a better interface to general-purpose intelligence? These companies need to identify queue depth *somewhere* within their breadth.

Owning the Decision Trace

As many investors in both private and public markets worry about the long-term health of the software industry, we believe the exception queue offers a compelling case for the defensibility of Vertical AI (and some Vertical Software incumbents). By executing work agentically rather than serving as a system of record, Vertical AI businesses aggregate proprietary knowledge of industry workflows. This knowledge, accumulated from customers using the platform over time, is arguably more important than simple information access: (1) it doesn’t exist in any structured format today (it mainly lives in domain experts’ heads) and (2) it is truly first-party, generated by product usage itself, mitigating any challenges around IP or ownership.

Aman Gour, CEO of FurtherAI, a startup automating insurance workflows, shares an instructive observation about the defensibility of the exception queue:

Over time, every escalation becomes a signal, every exception is feedback and every human correction shows where the runbook was incomplete. Over time, the workflow stops being a script and starts becoming the [insurance] carrier’s operating memory. This is the part the labs will find hard to reach. They will keep shipping better models and better general agents, and they should. But they do not sit inside a carrier’s production workflows long enough to learn why one account was escalated, why one risk was declined, or why an underwriter overrode the appetite guide and was right to do so.

That understanding only comes from running the workflow, in production, many thousands of times. The workflow you ship on day one is not the moat. The loop that production usage creates over time is.

Every resolved exception leaves behind a decision trace: which policy was applied, which precedent was relevant, who approved the override, what the customer accepted, how long it took, and what occurred afterward. Collect enough of these traces across cases and years, and you begin to build proprietary workflow memory, a growing record of how high-stakes decisions are made within a specific vertical and customer. Over time, workflow loops improve the product and move it closer to a true System of Intelligence.

The sequence looks like this:

The authored object is the bridge. It is the container for workflow-specific judgment. It records what evidence mattered, which rule applied, what exception was granted, what changed, and whether the resolution worked. In our Emerging Playbooks in Vertical AI essay, we described the ideal wedge as a product that creates what a human would otherwise have to create — documentation, a referral packet, a demand letter, an appeal — and hands it off to another system, as a human would. The exception queue sharpens that idea. The best wedge not only creates the authored object but also captures the decision trace—the reasoning behind it. Each trace adds to the company’s workflow memory, which is what makes the next case easier.

Two workflow loops stack from there. An across-customer loop, where patterns grow as you encounter more variants of the same problem within a vertical. And a within-customer loop, where the product learns the reasoning behind a specific customer’s decisions — the unspoken exceptions, the firm’s own decision logic that emerges only through direct interaction with the system.

As we argued in our essay on Vertical AI moats, the software vendor earns additional customer trust over time, enabling multi-product expansion. The core products continue to accrue defensibility through workflow loops, while each new product benefits from growing platform lock-in. The expansion path should follow the workflow and the decision trace.

Startup examples make this more tangible. Tennr can build referral-specific workflow memory: which providers send incomplete packets, which specialties create the most back-and-forth, which payers slow down which paths, and which missing fields predict leakage. EvenUp can build firm and case-specific workflow memory: which records change settlement posture, which injury patterns matter, which insurers respond to which arguments, and how long different case types take to resolve. For Abridge, the workflow memory surrounding the clinical notes can include specialty-specific documentation patterns, billing and coding support, care-team handoffs, and the institutional memory of how clinical cases are handled.

This is the missing middle between the authored object and the System of Intelligence, and where the moat begins to deepen. Not because the company “has data” in the abstract, but because the company builds a growing record of how high-stakes decisions are resolved within the workflow. A product that only reads data may still deliver insight. A product that resolves exceptions and captures decision traces can serve as the foundation for a System of Intelligence.

The Exception Queue Playbook

The queue is your wedge. Once you find it, the rest of the company-building question becomes more concrete. What does delay cost? What work product resolves the case? What reasoning should survive the resolution? And what does every cleared case teach the system that makes the next one easier?

The goal is not just to clear the queue faster. The goal is to turn each resolution into durable workflow memory: what evidence mattered, which rule applied, who accepted the answer, and what happened afterward. That memory is what produces the workflow loops for long-term defensibility. It is what lets the product improve, expand, and eventually become the System of Intelligence.

Here is the playbook as we see it:

1. Find the queue. The queue is usually visible before the software exists. It shows up as inboxes, spreadsheets, faxes, tickets, review comments, phone calls, aging reports, unanswered requests, and entire teams whose job is to chase the same class of exception every day. If no one owns the exception today, selling may be difficult. If an overworked team owns it with email, tribal knowledge, and manual judgment, pay attention.

2. Price the exception cost. The cost of failure or delay is how much the customer loses when the exception is handled incorrectly, late, or not handled at all. High cost is often the cleanest signal of urgency. Delay can be outright failure for certain queues. A prior auth approved too late can still fail the patient. An RFI answered too late can result in losing the bid.

3. Choose your entry point. You can start at the exception itself, where resolution is the product, or at the authoring event upstream, where you capture the context the queue needs—both work. What matters is that decision traces are a natural byproduct of usage, not a reporting afterthought.

4. Identify the authored object. The product should create something the customer already recognizes as work product: a referral packet, appeal, demand package, RFI response, permit submission, audit workpaper, coding recommendation, freight dispute file, or clinical note. This is where adoption starts, because the product does the work a human would otherwise have to do.

5. Capture the decision trace. Every resolution should preserve the reasoning: what evidence mattered, which policy applied, what precedent was relevant, who approved it, what changed, how long it took, and what happened afterward. This is cheap to design early and painful to retrofit later. If the product clears the queue but loses the judgment, it has automated labor without building memory.

6. Build the workflow loops. Every new case should make the next case easier. Not because the company has more raw data, but because the workflow memory becomes denser with useful relationships: payer behavior, reviewer preferences, jurisdiction quirks, customer policies, approval patterns, escalation paths, and outcomes. The question is whether each resolution adds to what the system knows.

7. Expand from the queue. The next product should be adjacent to the workflow memory already earned. Tennr can move from referrals into adjacent patient-access workflows. EvenUp can move from demand packages across the claim lifecycle. Abridge can move from clinical notes into coding and revenue cycle management. The expansion path follows the workflow and the decision trace.

Of course, no queue is the same, and false positives abound. High-volume, low-consequence workflows create usage without urgency. High-consequence, low-frequency workflows create consulting projects. Read-only workflows can produce insight, but often struggle to create compounding workflow memory. Broader platforms can grow large, but they still need queue depth somewhere within their breadth.

The best wedges sit where repeated resolution creates proprietary workflow memory. The best Vertical AI businesses will power a high-consequence workflow long enough to institutionalize that memory and turn it into a product that’s harder to leave every time it’s used. In the vertical application layer, selling intelligence is an ephemeral game — the real winners will own judgment.

Thanks for reading The Verticalist!

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

Dan Teran and James Gettinger are the co-founders of Gutter Capital, a New York-based fund that just closed its $75M Fund III focused on pre-seed investing. Previously, Dan founded Managed by Q — a vertical SaaS platform and B2B marketplace for office services — that he sold to WeWork for >$200M in 2019. James is a computer scientist turned professional gambler. Together, they made 100+ angel investments, launched Gutter, and most recently Elbow Grease: an accelerator that invests $300K into startups and spend 10 weeks working alongside them in Gutter’s NYC office. Tune in to hear our discussion on the changing profile of Vertical AI founders — and learn the three counter-intuitive founder signals Dan & James are leaning into.

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Today’s Episode

Most VCs will tell you they’re “founder-first” investors. In practice, that usually means they’re pedigree-first — screening for model lab resumes, repeat founders, and known technical programs as proxies for competence. The logic is understandable: at inception (indeed, often at pre-seed), there’s little else to evaluate. But Gutter Capital’s Dan Teran and James Gettinger argue the default is not just lazy — it’s empirically wrong. The biggest venture outcomes in history were built by founders who would have failed today’s pattern-matching. And AI is widening the distance between who looks fundable and who actually ships.

We’ve written about the fundability trap before — the recursive loop that sees investors pattern-match on the founder archetype most likely to raise the next round from downstream investors, making that archetype more common, reinforcing the pattern. But inherently, this behavior is retrospective. In a world changing this quickly, the founder profile best suited to win is changing too. Gutter’s entire model is a rejection of this tautological thinking in founder profiling. What emerged from our conversation are three signals Dan and James have found actually predict founder performance from Day Zero — and none of them are resume-standard.

Evidence Density Over Narrative Polish

Dan revealed that when Gutter backtested all of their Elbow Grease applications — using AI to analyze what actually predicted which founders advanced — the single strongest signal was what they call “evidence density.” Not narrative quality. Not market size. Not pedigree. Evidence density: the amount of hard, specific proof per word in a pitch.

How much hard evidence — per word in a pitch — is there was the biggest predictor of if we were actually going to pass them on to an interview.

Most founders spend their prep time perfecting the story — the narrative arc, the vision slide, the TAM chart. Gutter is saying the story matters less than the stack of receipts behind it. Specific customer names, metrics, quotes: “We reduced this from X to Y for Bob. And you can call Bob and here’s his phone number. And by the way, Bob has a cousin Jim, and Jim converted too.”

Gutter’s pro tip for founders takes it a step further: before an investor even asks for references, proactively send them three recorded customer conversations. A pitch to a prospect. A QBR with a design partner. A particularly insightful customer discovery call. The point is that it’s live, unscripted, and features a real person the investor could theoretically call. Most investors won’t. But knowing they could changes the calculus entirely.

Their experience aligns with ours here at Euclid: the strongest founders aren’t always the smoothest presenters — they’re the ones who demonstrate an uncanny aptitude for turning over every stone in their focus area. They can answer any question about their customer’s workflow in granular, unrehearsed detail. They’ve put in the work to understand the key objections and the right first ICP. Even if they aren’t from the industry themselves, they can talk the talk.

Evidence density is really a proxy for customer obsession. And as we’ve argued over and over again in our past work — and heard frequently from top vertical founders — customer obsession unlocks moats. It enables deep workflow integration, uncovers distribution advantages, powers referrals, and informs future extensibility of product and TAM. Nothing deflates interest in a deal like signs that a founder is less than completely obsessed with their vertical and customer.

A quick word from our partner on the Verticals podcast, Parafin. Recently named to the Forbes Fintech 50, Parafin has extended more than $25B in financing to their customers’ customers, powering embedded capital for platforms like DoorDash, Gusto, and Kajabi. Purpose-built for vertical platforms serving SMBs — lending, cards, and insurance without building the infrastructure yourself. Explore a custom program for your platform today.

Bad References, Good Founders

While evidence density was the most important pattern-match we dug into with Dan and James, there were several others worth noting for founders. One involved references — the cornerstone of inception-stage VC diligence.

Many treat reference calls as pass / fail. Get on the phone, ask banal questions, and if the answer is lukewarm, the deal is dead. Gutter takes a more interesting position — and it starts with asking the right question. Dan told the story of an Elbow Grease founder whose past references weren’t exceptional. But when they double-clicked with the CEO of the founder’s prior company, that “bad reference” was completely reframed:

Is the question ‘would I hire him back into the job in which he worked for me,’ or ‘would I bet on him as a founder?’ Because those are two completely different questions.

Dan speaks from experience. When he started Managed by Q at 24, he shared, his references weren’t particularly strong. He’d been a paralegal. James jokes that Dan offered him, a friend at the time, up as a reference because there just weren’t that many to choose from. Yet Homebrew led Dan’s first round pre-product because they didn’t mistake an absence of evidence for evidence of absence, as the saying goes. James — a former pro poker player turned angel — also understands the value of unconventional backgrounds better than anyone.

The broader point: at inception stage, you’re not hiring someone into a job. You’re betting on them in a role they may not have ever had. A reference that says “difficult to manage” might be the signal, not the noise. Not thriving in a large, slow organization, managerial intransigence, a desire to make the big decisions — these are all supportive of CEO potential. The question isn’t whether a founder was a good employee. It’s whether there’s evidence they will be a relentless, resourceful builder. Those are different skill sets, and conflating them is a fundamental investor mistake.

The Five-Kid Vibe Coder

There’s a prevailing narrative that AI has made the 19-year-old technical founder the apex predator — native to the tools, no legacy mental models, infinite energy. Dan pushes back: “When everyone’s telling you that the best founders are the teens, we also want to keep an open mind that that might not be true.”

Dan pointed to one of the standout founders from Elbow Grease’s first cohort — the founder of a company called Punch. Five kids, sixth on the way, based in Atlanta. His wife gave him permission to come to New York for 10 weeks. He was in the office every night and every weekend because, as Dan puts it, “his attitude was, I know this is my shot.” He ships product faster than anyone in the Gutter portfolio.

We explored the age question in No Country for Old Founders, and AI is accelerating the trend. While YC has elevated the youth profile, the numbers suggest that experience and expertise matter greatly. At Euclid, we generally find a balance between youth and experience to be the most common sweet spot. Gutter believes that AI has opened up opportunities for mid-career operators, even those outside the tech sphere or straight from industry. Domain experts who spent a decade without engineers or butting heads with them — who understood their industry’s workflows cold but couldn’t build the solution themselves — now have the tools to start independently. They’re not burning time gleaning their customer’s problems from user interviews because they lived them.

For Dan and James, it’s not that every person in every field will become a well suited startup founder. It’s that a rarefied slice of domain experts who had the tenacity and vision, but lacked a natural catalyst are now unshackled. Gutter often sees them as better positioned than any CS grad because they already have the customer relationships, the industry context, and the specific insight that makes a Vertical AI product right rather than just functional. As Dan put it, “People who have chafed at engineers their entire career now don’t really need to deal with them that much to build something that actually might be really great.”

The Takeaway for Vertical Founders

The founder profile that wins in Vertical AI is shifting, and most of the ecosystem hasn’t updated its priors. At Euclid, we still believe presentation, references, and youthful velocity are important. But we’ve always felt it’s more important for founders to be authentic — and to lean into what makes them a unique founder — than to mold themselves to trends on Hacker News or whatever platform funds choose to market around that year.

If you’re raising at inception stage: stop optimizing your pitch for narrative polish and start optimizing for evidence density. Every slide, every answer, every follow-up email should be stacked with specific names, specific numbers, and specific proof that you understand the problem better than anyone. Send customer call recordings before they’re asked for. Make the evidence so dense that investors don’t need to trust your story — they can verify it.

And don’t hide from your unconventional background — lead with it. Truly first-principles funds are increasingly embracing the “post-pedigree” founder as AI changes everything. Euclid and Gutter are aligned on one thing: domain and customer obsession are paramount. If you spent 15 years in the trades and just learned to vibe code — yes, you’re still going to have to justify why you are best suited to do everything a startup founder has to do to build a big business, and that’s no joke. But it doesn’t have to be a weakness to paper over either. With proper self-awareness, it’s a strength to build around. Most investors, like most humans, are allergic to inauthenticity. When someone leans into their reality, perceptions-be-damned — that is a rare and powerful trait.

If you’re a VC who outsources their diligence to consensus perceptions of pedigree, you might be missing out on some of the biggest contrarian winners of this AI era. At minimum, take note: a professional gambler and an exited founder just raised $75M to bet against you. And they aren’t alone.

Nikhil Basu Trivedi is the Co-Founder and General Partner of Footwork, a Seed and Series A firm with ~$400M in AUM across two funds. A 16-year venture investor and two-time Midas Seed List honoree, Nikhil has built a track record around a specific type of company — the ones that refuse to check a single box. He was an early backer of Canva, Lattice, and Frame.io, and Footwork’s current portfolio includes a strong Vertical AI lineup: Elicit (AI for scientific research), Fuse (AI-native loan origination for credit unions), and Confido (financial operations for CPG brands).

Tune in to hear why Nikhil believes some of the best Vertical AI businesses will blur the line between consumer and enterprise — and much more.

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Today’s Episode

Software venture capitalists often self-select into one of two groups: consumer and enterprise. Since firms and GPs often prefer one or the other, there’s an incentive for founders to do the same. But some of the most valuable companies of the last decade — Canva, Slack, Zoom — tapped into both user types, even if they began in one. In the AI era, we’re seeing that crossover more often and earlier. Especially in Vertical AI strategies where the end user is both the buyer’s customer and a consumer or prosumer in their own right.

Nikhil Basu Trivedi has spent 16 years betting on companies that straddle this divide. In his view, B2B vs. B2C can be a bit of a false dichotomy — and that divided perception can hold both investors and founders back from seeing the biggest long-term opportunities. In this episode of Verticals, Nikhil joins us to explore why the consumer-enterprise line is blurring, what that means for expansion and defensibility, and how to identify verticals where AI can build businesses that are category-defining across both consumer and enterprise use cases.

A quick word from our sponsor on the Verticals podcast, Parafin. Recently named to the 2026 Forbes Fintech 50, they’ve extended over $25B in financing to nearly 50K businesses on platforms like Amazon, DoorDash, and Walmart. Embedded financing programs, custom-built for vertical founders and operators like you. Click here to learn more today.

Consumer vs. Enterprise is a VC Construct

One of Nikhil’s core convictions at Footwork is that some of biggest companies in the world serve both consumers and enterprises. While he does believe in early focus, expecting founders to “pick a lane” and stick to it can be short-sighted. About three-quarters of Footwork’s 25 investments are companies that “self-identify as both consumer and enterprise in their DNA.”

The poster child is Canva. When Nikhil led the seed round in 2014, it was a consumer design tool. Today it does over $4B in ARR, with more than $500M coming from enterprise contracts (B2B seats of 25+ growing at 100% year-over-year). Nobody planned for that S-1 line item in 2014. It happened because the team built an end-user experience so good that it pulled itself into the enterprise organically.

Nikhil sees the same dynamic playing out at the model layer. Anthropic’s success versus competitive labs is widely attributed to their focus on enterprise coding agents — but that focus led to uptake in the enterprise and with consumers. Even if their total consumer market share (in the low-mid single digits) is less of a focus, the ICP of higher-end prosumers is increasingly Claude-centric. Meanwhile, OpenAI’s heavier consumer tilt, in Nikhil’s view, “has hurt them [in the enterprise],” forcing them to play Codex catch-up.

The takeaway isn’t that you need to do both on day one. Footwork — like Euclid — agrees that maniacal early focus is critical. But a CEO’s job is to continually balance two priorities: near-term execution with long-term vision. If you’re not building the team, product, and roadmap to eventually serve your most ambitious market opportunity — which very well may span enterprises and consumers — you risk capping your outcome.

AI Makes the Crossover Easier Than Ever

What’s changed is the product surface area that AI unlocks. Pre-AI B2B SaaS was reliant on UI for data input / output — a form to fill out, a dashboard to check, a record to update. The value proposition was worker acceleration: move things through the funnel, inform an analysis, serve as a system of record. LLMs have enabled many of today’s AI-native products to do the actual work — interact with users in the course of their day-to-day activities, through natural language, voice or even without active user input altogether.

Three companies from Footwork’s portfolio illustrate the pattern:

Elicit started as a self-serve AI research tool — type a question, get a systematic review of published literature grounded in 125M+ papers. Academics and researchers adopted it bottom-up. Now it’s spread into all of the top 20 pharma companies with enterprise-wide licenses. Pharma is a $1.2T revenue vertical that has produced remarkably few scaled software companies — Veeva being the notable exception. Elicit is finding purchase through the prosumer door.

It’s a strategy reminiscent of Open Evidence, which took a similar path in clinical medicine. The self-serve AI search & discovery tool for physicians may have leveraged hospital systems for early distribution, but the only way for them to achieve their incredible uptake (the majority of US doctors) was via a consumer-esque, PLG clinician-first adoption model. The company recently raised $250M at a $12B valuation. On the episode, Nikhil, Nic, and Luke all agreed one Vertical AI high-flyer that has a strong case for meriting its rich valuation — and a big part of that is precisely because of its dual enterprise-prosumer presence.

Fuse, another Footwork portco, is a different flavor of hybrid Vertical AI. It’s an AI-native loan origination system for credit unions that raised $25M in its Series A led by Footwork. In this case, the consumer element is inherent to the ICP of credit unions. Core to the value proposition is that the borrowers applying for a loan — their customer’s customer —gets a fundamentally better experience thanks to the AI-native features. Fuse gives them a shot at matching neobanks and other more digitally-inspired borrowing options, without rebuilding from scratch. AI’s form factor lends naturally to reimagining the B2B2C experience.

In short, AI has collapsed the distance between the B2B buyer and the end consumer. It’s also increased the bandwidth of user data capture, while reducing its friction — something inherently valuable to enterprises. When your product does the work instead of just recording it, the consumer-facing experience isn’t a “nice to have” — it’s the product.

The Toast Warning

If the upside of crossing over is a bigger TAM and stronger distribution, the downside of not crossing over is vulnerability. Nikhil pointed to Toast as the cautionary tale — a Vertical SaaS darling that was late to capturing the consumer side of the restaurant experience.

“I think one of the big issues for Toast,” Nikhil shared, “is having not captured the consumer side and therefore being vulnerable. They’ve not built a brand with consumers. I think much more about ordering using DoorDash than I think about Toast.”

This maps to a broader pattern we’ve written about in Navigating Vertical B2B2C: in verticals where the end consumer transacts through the platform, owning only the business-side relationship leaves the door open for someone who captures both. Embedded payments, embedded lending, consumer-facing ordering — these aren’t just revenue lines. Ultimately, businesses that serve consumers want to be where their customers are, and capturing a more highly fragmented consumer user base can be a much more difficult strategic moat to unseat.

Tune into the conversation with Nikhil Basu Trivedi, Co-Founder & GP at Footwork VC here — and subscribe on YouTube for new episodes of the Verticals pod every week!

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Brand: The Moat VCs Keep Ignoring

We also discussed how brand may be the most underrated defensive asset in Vertical AI. As we’ve discussed plenty here at The Verticalist, proprietary data, workflow loops, and network effects are the pillars of defensibility that seem to be growing only stronger post-LLMs. Nikhil’s argument is that brand — the thing people trust, the thing that occupies mental real estate — is another reason some incumbents will survive the AI transition. Even if they’re slow on agentic features.

Inverting this point: Footwork actively looks for verticals where the dominant players have terrible NPS — massive companies that nobody actually likes. His examples:

• LabCorp and Quest Diagnostics: both $20B+ market cap companies in blood testing. “Does anyone say they love Quest or LabCorp? You feel terrible going into it.” Footwork has a stealth investment going after this exact gap.

• The Farmer’s Dog: Nikhil’s prior investment, now a scaled brand in fresh pet food, built in a category where legacy brands had eroded consumer trust.

• CPG: This is one where it’s less poor NPS because of a dominant player with no incentive to change. It’s that the landscape is fragmented, with dominant players that aren’t even universally well known. Footwork backed Confido.

The implication is that, in a world where AI makes it possible to rebuild category experiences from scratch, low-NPS verticals with entrenched incumbents aren’t just underserved markets — they’re brand vacuums that can perhaps be filled faster than in past eras, thanks to the potential for faster switching cycles that AI is enabling.

Mapping the Model Lab Kill Zone

Not every Vertical AI company is equally defensible. We closed the episode with a rapid-fire exercise: are the most highly valued Vertical AI companies justified, frothy, or too early to tell? Here’s where the crew landed:

Harvey (legal) — ~$200M in ARR, $11B valuation. The 3/3 consensus was frothy. The legal vertical was one of the first to adopt AI at scale and the ACVs are large. But the product sits squarely in what LLMs already do well — document analysis, summarization, research. Competitive intensity is only increasing, from both new dedicated Vertical AI players like Legora, and most worryingly, from the model labs.

Basis (accounting) — est. ~$100M in revenue, $1.15B valuation. Our take: 1 frothy, 2 too early too tell. As Luke put it: “Selling AI into white-collar industries where somebody’s sitting in front of a computer and they’re definitely going to try it right next to Claude and ChatGPT is tougher.”

Abridge (healthcare) — $100-200M in revenue, $5.3B valuation. Our take: 1 frothy, 1 justified, 1 too early too tell. Luke finds health system incentives and the competitive threat from Epic — which we’ve covered in past episodes — too concerning. Nic was a bit more bullish, with respect to the likely data advantage the company has managed to amass so far. Interesting, this is one no one seems to think the LLM “kill zone” will expand to.

Which teams will build enduring businesses outside the kill zone? Footwork and Euclid share a similar view: deep domain knowledge built over years helps founders understand why a vertical needs an AI solution, and the industry-specific workflows and data that will make their solutions defensible over time. Vertical AI values builders who understood the customer — and perhaps the customer’s customer too — long before they pitched their first design partnership.

The Takeaway for Vertical Founders

The consumer-enterprise divide was always more of a fundraising or VC-matching construct than a company-building truth. AI is blurring the line. When your product does the actual work, the end user isn’t abstracted away behind a dashboard a consumer would never use. Nor is building unique BI features for every ICP a crippling consideration. Users are more a fundamental part of the product surface and perhaps also the long-term data or revenue opportunity.

That doesn’t mean every Vertical AI company needs a consumer angle on day one. It means founders should be “open and aware,” as Nikhil put it, that the path to a category-defining outcome may well run through both sides of the market. Build a team that can execute on multiple surfaces. Think about the consumer experience even if your first sale is enterprise. And pick verticals where the incumbent brand is weak, the market is large, and where the model companies can’t serve the pain point by lightly rebranding their core LLMs. Perhaps the convergence of the consumer-enterprise divide is just another expression of something we see as increasingly obvious: the market size for the application layer of the future is bigger than you think.

See you next week.