Most startups quickly become focused on a business achievement metric that has been around for some time: product-market fit (PMF). While important, PMF functions more as a high-level concept or set of steps than a practical framework for navigating the unique challenges facing AI-native companies. Here we break down five critical “fits” that AI startup founders must actively manage—each representing a distinct challenge that if not achieved, can derail even the most promising ventures.
These insights emerged from a panel discussion at the super{summit} 2025 event among founders who have built AI-native companies across healthcare, nonprofit, creator tools, and brand marketing. Their collective experience reveals patterns that offer practical guidance for entrepreneurs navigating the rapidly evolving landscape of company building.
1. Founder-Market Fit
In an era when every startup has access to the same LLMs and global talent pool, founder-market fit has become a crucial differentiator. This isn’t simply about having worked in an industry—it’s about possessing deep, intuitive knowledge of the workflows, pain points, and unwritten rules that govern a specific market. For B2B companies in particular, lacking domain expertise creates a significant disadvantage. Founders who don’t truly understand their target industry use-cases will struggle to identify which problems are worth solving, which competitors pose genuine threats, and which partners could accelerate growth. In addition, industry leaders will generally trust the view of an insider when it comes to consequential disruptions over the opinion of an outsider. Both founders may know how to disrupt, but only the insider has a realistic understanding of the consequences of the disruption and how to mitigate them. Because of that, they will have access to accelerated deal-making that an outsider would find harder to get access to.
Domain expertise provides more than knowledge—it provides relentless curiosity. As Kian Alavi of Mazlo, a startup offering financial management solutions to non-profits, explains: “I actually spent 14 years in the back office of a nonprofit dealing with pain. I think it gives me a few advantages: just a deep curiosity that just will not leave you alone.”
This curiosity becomes the engine that drives rapid learning and adaptation. Understanding customer workflows at a visceral level enables founders to conduct authentic conversations that unlock insights competitors miss. “What matters is that you put in the time to come up the learning curve and learn those problems and have that empathy and compassion,” Alavi notes.
2. Customer Segment to AI Precision Fit
One of the most nuanced challenges facing AI startups is matching the precision capabilities of their technology to the accuracy requirements of specific customer needs. This isn’t a binary question of whether AI is accurate enough—different customer segments have vastly different tolerance levels for degrees of error. For example, early voice-to-text systems were used to make it easier to get a transcript of a meeting to help sales professionals understand whether a pitch was proceeding correctly. They didn’t have to be 100% accurate. As the accuracy of the AI increased it became possible to get fully accurate summaries of a meeting if you had missed it. The gap between those use cases was about 5 years, so understanding when the AI is good enough for a particular customer segment is a very consequential issue.
Customer expectations around AI have shifted dramatically. As Alavi observes, the promise has evolved from “can you streamline my work?” to “can you eliminate my work?” This creates pressure to deliver automation that may not yet be technically feasible, especially in high-stakes domains. “When we’re talking about money movement and compliance and finance and IRS filings, you can’t screw up 2% of money movement. For a nonprofit, they’ll fail their filing and then they’re out, kaput, gone!”
Different customer segments within the same industry can have wildly different risk appetites. Erwin Estigarribia of Headlamp Health, a startup focused on improved mental health outcomes, explains: “We only have to be wrong once and we’re done. An early stage researcher is more willing to take a risk and look at data that they haven’t had access to in the past. But when you’re interacting with a patient through our application or a clinician, you only have to get that wrong once.”
A critical aspect involves setting appropriate expectations. Customers don’t care about the technical details of LLMs or hallucination rates. “They don’t care about what an LLM means or who Sam Altman is,” Alavi notes. “They’re like, can you finally make my work easier?”
3. Decision Maker to Budget Fit
One of the most common pitfalls in B2B sales is confusing enthusiasm for purchasing power. A product champion within an organization may be passionate about your solution but lack budget authority or decision-making power. The challenge intensifies in today’s enterprise environment, where innovation teams are designed to explore emerging technologies but often sit far from the CFO and mainline budgets. As David Wiener of Rembrand, a company that provides brand marketers the ability to do AI-native product placement in ads, cautions: “The more there’s an innovation team, the further they are from the CFO. You get these innovation teams excited about what you’re doing and you never exit innovation land.”
Successful B2B selling requires “multithreading”—simultaneously managing relationships with multiple stakeholders who each have different priorities and authority levels. “You need to figure out who the buyer is,” Alavi explains. “How are they related to each other and how am I sending them messages that show them the value they need? You don’t want one champion that doesn’t have any real power.”
Even when founders think they’ve mapped all key decision makers, surprises often emerge late in the sales cycle. The ability to respond quickly to late-stage objections can make or break a deal. The most elegant solution is designing your product and go-to-market strategy so that the user and the budget holder are the same person, eliminating entire layers of sales complexity.
4. Core Value to Commoditization Fit
In the AI era, features that seem defensible today can become commoditized overnight. The speed at which technology evolves means that competitive moats erode faster than ever before. Building AI wrappers around existing workflows represents low-hanging fruit that incumbents can easily replicate and often give away for free.
“Very low tech AI-based wrappers on workflows are going to be commoditized quicker than you can blink,” Estigarribia warns. “Having that as part of your value proposition is not gonna last very long, even in healthcare, which has longer product life cycles.”
Building solutions on platforms controlled by other companies introduces existential risk. “I get really paranoid about building solutions on other people’s platforms because they control your destiny,” Estigarribia explains. “Companies can merge, cut you out, all kinds of things.”
If features commoditize quickly, what remains defensible? The consensus points to proprietary data as a primary source of sustainable competitive advantage. In particular, proprietary data sets allow you to train models for tasks or workflows or accuracy profiles that your competitors can’t touch. That way, even when an algorithm gets open sourced, you can have a large unique way of training that algorithm that others can’t replicate. Alavi describes how Mazlo became a system of record: “We took hold of the transaction, every dollar in every dollar out, we handle it, we track it, we’re collecting all the data on it. And so now we are their source of truth.”
5. Pricing Model to Value Fit
The final critical fit involves ensuring that your pricing structure matches the value customers perceive and are willing to pay for. This proves remarkably complex when serving multiple constituencies with different financial capabilities and value expectations. For example, smaller customers may prefer a pay-as-you-go usage based model, while larger enterprises may be more interested in capping the growth of spend. Furthermore, investors with older models may prefer SaaS as a pricing model, while more recent investors in AI companies are used to token based usage models.
You can’t choose a price that will please all constituencies, nor should you. The prudent builder will prioritize matching pricing to growth in your target customers by aligning to what those customers value. Once you nail that, it is easier to educate investors on why customer growth is important than it is to convince customers that they should prioritize a pricing model pleasing to investors.
The challenge intensifies when building products that customers love but struggle to afford. India Lossman of Boombox, a music collaboration platform, explains: “We built a product that our customers love. But budget was a problem. They’re like, give us the world, but we don’t wanna pay for it.”
Successful pricing strategies often involve creating tiered offerings that serve multiple segments. Lossman describes Boombox’s adaptation: “We’ve expanded our target audience and now we’re going after not just working musicians, but hobbyists as well using an annual subscription model. These are folks that have jobs that make music for fun, and they have discretionary cash.”
Takeaways
Product-market fit, while important, obscures more than it reveals. The five fits outlined in this article—founder-market, customer segment to AI precision, decision maker to budget, core value to commoditization, and pricing model to value—provide a more granular and actionable framework. These fits are not necessarily sequential steps but concurrent challenges that must be managed simultaneously. The democratization of AI technology has lowered barriers to building products, but it has raised barriers to building sustainable businesses. Success requires doing the hard work of understanding customers, choosing appropriate technology, navigating organizational politics, creating defensible value, and structuring economics that work for all involved.
