AI-Native Product-Market Fit Is About Demand You Can Deliver

When does product-market fit (PMF) actually show up in AI-native companies — and what does it look like?

At Beelieve ’26, Chargebee’s Erin Gunaratna led a lively founder and investor panel that probed into those exact questions.

The panel featured Helen Hastings, Founder and CEO of Quanta; Chun Jiang, VP Products at Reforge (recently acquired by Miro); and Nikunj Kothari, Partner at FPV Ventures.

The conversation kept returning to a core truth for founders: AI-native PMF requires founders to build an operating system that can consistently deliver the outcome their products promise.

As Nikunj put it:

“Previously we would be selling software as a tool that helps improve productivity… [But] if you’re not showing a path towards how you are automating the whole workflow end-to-end, finding product market fit in this era is challenging.”

That shift, from software that assists work to products that complete work, has changed the PMF playbook for founders. Beyond interfaces, features, or models, the product is now the workflow completed, the outcome delivered, and the operating system underneath it.

The outcome is the product

Helen grounded this in how Quanta, an AI-powered bookkeeping services company, found PMF. The challenge was convincing customers that an AI-powered services model could do the work better than a traditional provider.

“We provide accounting services. So we replace the outsourced bookkeeper or bookkeeping firm that’s traditionally used by startups. And bookkeeping already had product market fit. In fact, bookkeeping had product market fit for centuries. Every company needs bookkeeping. So finding product market fit for us meant not convincing companies that they needed bookkeeping, but convincing them that our new way of doing the work is better for them and that they should take a risk on it.”

That risk is really about trust. Customers are buying confidence that the books will close, not just a tool. And once Quanta owns that outcome, the product has to absorb the messiness of real customer behavior.

“When you are selling software, you have your one deliverable, which is your product roadmap that is shared by all of your customers. When you are a services provider and on the hook for the outcome, you are on the hook for many deliverables, a deliverable per customer that all might look a little bit different and have a different messy reality that maybe you didn’t expect.”

Helen described how owning the outcome raises the stakes:

“We have to close the books at the end of the month, no matter what. No matter what weird thing they did that month, we still have to close the books… A company who decided to use the very new beta weird feature that their payroll provider just released, but it doesn’t really work yet… Or a company where an employee decided to live in four different states in one month and triggered some very weird payroll tax refunds… We did not get to say, ‘We’ll do that in Q3. We’ll put that on our roadmap.’ We needed to handle it.”

That distinction captures one of the defining characteristics of AI-native PMF: a tool can postpone complexity, but a workflow owner has to absorb it every time.

Your wedge should preserve your company’s DNA

The panel spent a lot of time on wedge strategy, discussing how AI-native companies should enter large markets without trapping themselves in a narrow product identity.

Helen offered a framing that’s especially useful for AI-native companies. Instead of asking “What’s the easiest market to enter?”, they should ask: “What must be, and remain, true about this company from day one?”.

“Our wedge was to start by selling to tiny companies, even though I ultimately knew that our main buyer would be at a bigger company. And the reason why is when I think about wedges, I think about what needs to be true in the DNA of the company from day one.”

For Quanta, that meant doing all of the accounting.

“We needed to do all of the accounting and be the full financial source of truth because that was my vision for how we’d actually be able to capture a very, very large market in the long term. And so that meant doing all of the accounting, but that would be a lot to build… if I had wanted to start by selling to bigger companies. So if I wanted to hold that constant, what are the other dials that I can turn?… That was company size and stage.”

That’s a useful way for founders to think about wedges: ask, “What do we need to keep constant so this company can become the company we intend to build?”

Nikunj described the investor version of the same problem:

“I think the best companies… are able to go both deep and horizontal at the same time. And that’s extremely challenging, especially when there’s so much competition with every vertical and every space. But I think if you talk to any enterprise today, they are looking for solutions that are able to do more, and then that can go expand [adjacently] as well.”

A wedge has to be specific enough to win, but not so narrow that the company loses its path to a large market.

Chun added another founder lesson from her experience with Monterey AI, which was acquired by Reforge: sometimes the wedge is the distribution path.

“The things I learned, if I would do it again, is probably double down on the market or distribution as a wedge instead of really focusing on the product itself. Because when I was selling the document generator, I would just try to sell to people. But if I asked more questions about, ‘Hey, what are your technical stack? What are other pain points in your workflows?’ maybe that would give me even more ideas about where I could branch to.”

Certainly, wedges need to create early revenue, as they always have, but in the AI era, the right wedge also creates learning, expansion, and market access.

Pricing changes when agents become users

Pricing emerged as a core dimension of product-market fit. 

Nikunj pointed out that per-seat pricing still dominates because buyers understand it.

“If you talk to most buyers, they’re so used to buying software on a per seat basis that that’s what they like. It’s predictable. [Your] OpenAI bill doesn’t go up by massive amounts every week or every month.”

But AI-native products are already stretching that model.

“I think slowly people are getting around to some version of [a] hybrid scenario, where it is one of these ‘yes-and’ things where you are paying per seat for humans from a platform perspective, but a lot of the workflows that are being run on top of it, a lot of the different edge cases you’re doing [are] being built as usage revenue or services revenue depending on who’s doing it.”

The deeper shift is that humans may no longer be the main users.

“Where we’re moving forward is, humans are not going to be the primary users of these platforms in the future. And from that perspective, agents are going to be. How do you price per agent? It doesn’t make any sense.”

That forces pricing to sit more closely to work completed.

“It’s end-to-end outcomes. What are the outcomes that you are trying to solve?… One easy comparison for businesses right now buying any of these agentic solutions is like, how much are you paying in payroll? And is this providing the end-to-end workflow? And then is it actually affecting your hiring plans?”

For AI-native founders, pricing is absolutely a PMF test. If the product is replacing labor, completing workflows, or changing hiring plans, a simple seat count may not capture the value. The pricing model has to answer: what work are we taking responsibility for, and how does the customer already budget for that work?

Speed comes from collapsing the distance to the customer

Chun shared how at Reforge, she found momentum by collapsing the walls between functions.

“People come to us like, ‘Hey, your team launched five products last year. How do you all do that?’ And they would think we found some secret way [to] leverage AI… but I think at end of the day, we would still attribute that speed to a lot of the human foundations that we protect, which is collapsing the role between engineering, product, design, pushing engineers to do all the customer success, pushing all the PMs to do sales.”

That kind of operating model matters more in an AI-native context because the ground shifts quickly: model capabilities and customer expectations change, competitors ship fast, and teams can’t wait for secondhand customer signals.

“Once you really force people to do these external-facing tasks, they start realizing like, okay, why are we building these features? Is this feature really worth keeping? Should we just kill the whole product?”

AI-native founders may find PMF faster when the people building the product are close enough to customers to kill things that aren’t working early.

Finding PMF and scaling it are not the same thing

Helen made an extremely sharp distinction:

“I think in the AI services world or the outcome-based world, there is a difference between finding product market fit and scaling product market fit. I really learned the difference last year… We knew that the market wanted the product. It was a fit for them. They were banging on our door, but we couldn’t reliably handle all the customers that wanted to work with us.”

That is a dangerous moment for founders. Demand feels like victory, but in an outcome-based model, saying yes too quickly can break the system.

“When you have this services layer, it becomes really easy to say yes to a prospect that maybe you can’t fully handle, maybe there are a couple things that you need to build. And that was fine in the earlier days when we were bringing on a new customer once every few weeks and the engineering team could keep up with that. But once we started bringing on many customers per week, we couldn’t keep up.”

Helen shared how Quanta made a deliberate choice to slow down.

“We had to take a pause and catch up and build more. And maybe I could have hired an army of offshore bookkeepers to do the work, but I did not feel like that was the right decision. It would dilute what made us get product market fit in the first place, our level of quality. And it would change the DNA of the company as well.”

Founders can always choose to patch delivery with people — but if the long-term advantage is automation, quality, and AI-driven workflow ownership, too much manual delivery can quietly turn a company into the thing it was supposed to replace.

Helen summarized the real test:

“It’s not just about, ‘does the customer want this thing,’ but it’s, ‘as we scale, will we be able to preserve that level of quality?’ Because traditional services companies have a really hard time doing that. And then I think the new opportunity in the AI world is not just to keep that level of quality as you scale, but can things get even higher quality as you scale?”

It’s a new bar for scalable PMF: quality should not degrade, but improve, with volume.

Margins are part of the product now

Helen closed with a founder “hot take” that may matter most for early-stage AI companies:

“The part of the founder playbook that used to work but no longer works is the belief that you don’t have to pay attention to your margins because they’re going to magically figure themselves out later on.”

That assumption belonged to a different era. Now, AI-native companies often have costs that scale with usage: model calls, compute, human review, workflow exceptions, implementation, and service delivery. Growth can look strong while margins quietly deteriorate underneath.

Helen sees this directly in the companies Quanta works with.

“I work with a lot of companies on their P&L and a lot of companies have not picked up on that yet. They are ignoring their margins. The toolkits weren’t made for early-stage companies… I’m excited to be building that tooling that helps people understand what their margins are as their costs scale with their revenue in this AI world.”

Margin visibility tells you which workflows are expensive, which customers are hard to serve, which edge cases keep pulling humans back in, and which parts of the product need more automation before you scale.

This was not the first time that “pricing needs to become a part of your product process” was said onstage at Beelieve ’26. Earlier in the day, leaders from Clay, Vercel, and Gorgias all underscored how they, too, have built margin awareness, price testing, and the ability to iterate on monetization into their workflows. 

AI-native PMF is bigger than product demand 

Across the session, the panelists shared examples that looked very different on the surface: full-stack bookkeeping, tiny-company wedges, workflow automation, hybrid pricing, agent-driven usage, customer-facing engineers, slowed onboarding, and cost visibility. But they all pointed to the same conclusion.

AI-native product-market fit is no longer contained inside the product alone. It lives across the workflow, the pricing model, the delivery system, and the operating discipline behind it.

Companies that understand those pieces together can turn early demand into something durable.

Watch the full session on demand here.

Beelieve 2026 brought together 500+ leaders shaping the future of AI-ready companies and business models in San Francisco. All sessions are available as on-demand replays. Watch here.