Vertical AI Is Blurring Enterprise & Consumer
with Nikhil Basu Trivedi, Co-Founder & GP @ Footwork VC
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.
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.
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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!
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.
Key Moments from this Episode
00:00 — Intro
03:16 — The venture thesis behind Footwork VC
05:11 — Why the best companies win consumers and enterprises
13:05 — Should founders focus or expand faster in AI?
24:40 — Network effects, moats, and what still matters in AI
31:14 — The biggest opportunities in 2026
35:12 — Avoiding the AI kill zone
40:27 — What makes a vertical AI market worth pursuing?
42:38 — The hottest AI companies in 2026
49:17 — Final lessons for founders building in the AI era