Beyond Product-Market Fit (PMF)
An Operating Framework for Startups
Introduction
For many years, the tech sector has regarded product-market fit (PMF)
as a key gauge for measuring the success or failure of early stage companies, and many startups become obsessed with it. While important, PMF remains a high-level concept rather than a practical framework for navigating the unique challenges of AI-native companies. In today’s AI-driven environment, PMF is too abstract to guide the day-to-day decisions that shape a startup’s success or failure.
This deep dive document (download the PDF) outlines five critical “fits” that AI startup founders must actively manage—each representing a distinct challenge that can derail even the most promising venture. These insights come founders of AI-native companies across healthcare, nonprofit, creative, and brand marketing. Their experience reveals patterns that transcend individual markets with practical guidance for entrepreneurs operating in a rapidly evolving AI landscape.
The five critical fits are:
Founder-Market Fit. Do you possess the domain expertise necessary to disrupt your chosen vertical?
Customer Segment to AI Precision Fit. Is your AI accurate enough for this customer segment’s risk tolerance?
Decision Maker to Budget Fit. Are the people championing your solution within the prospect organization the same people who control the budget?
Core Value to Commoditization Fit. Can you build defensible value in a world where tech evolves weekly?
Founder-Market Fit. Do you possess the domain expertise necessary to disrupt your chosen vertical?
A panel of founders & CEOs discuss the challenges of PMF in the Age of AI
These fits are interconnected, yet each demands focused attention. Together, they provide a more granular and actionable framework than PMF alone can offer. As one founder noted, product-market fit isn’t a destination you reach one morning — it’s an evolving target in a market that never stops moving. Understanding these helps founders stay ahead of that movement.
Founder-Market Fit
Why Domain Expertise Matters More Than Ever
In the age of democratized AI, when every startup has access to the same large language models (LLMs) and engineering talent, founder-market fit has become a crucial differentiator.
Ideally, founders possess deep, intuitive knowledge of the workflows, pain points, power structures, and unwritten rules that govern a specific market. Especially for B2B companies, a lack of domain expertise creates severe disadvantages.
Founders who don't understand their industry will struggle to identify which problems are worth solving, which competitors pose genuine threats, and which partners can accelerate growth. They'll miss nuances of compliance and misunderstand the organizational dynamics influencing purchasing decisions.
The Learning Curve Challenge
Domain expertise provides more than knowledge — it provides relentless curiosity. Kian Alavi of Mazlo, a startup offering AI-powered financial management for nonprofits, explains:
“That doesn't necessarily give me an advantage to win, but it gives me a deep curiosity that just will not leave you alone.”
Kian Alavi, CEO Mazlo
Building Empathy Through Experience
Understanding customer workflows at a visceral level enables authentic conversations that unlock insights competitors miss. This empathy allows founders to ask the right questions, recognize patterns in feedback, and iterate toward solutions that genuinely fit the market.
Navigating the Competitive Landscape
Founder-market fit means understanding who your real competitors are — not just obvious ones, but incumbents who might give away your core feature, adjacent players who could pivot into your space, and potential partners who could amplify your reach.
The Financial Reality Check
Deep domain knowledge extends to understanding your target market's economic structure. Founders with domain expertise can model their business more accurately and avoid costly miscalculations about unit economics, sales cycles, and growth trajectories.
Key Takeaway
Founder–market fit keeps you from building great tech on top of bad economics. Elegant AI won't save you if the business model collapses the moment it hits a real customer.
Customer Segment to AI Precision Fit
Understanding Accuracy Requirements
Accuracy isn’t one-size-fits-all. Some customers need perfection, others need speed. Startups get into trouble when they assume the same benchmark works across segments.
Complete Automation Overhype
Customer expectations have shifted from “streamline my work” to “eliminate my work,” pressuring startups to deliver automation that may not yet be feasible. As Mazlo’s Alavi notes, LLMs aren’t fully reliable, especially for critical tasks like finance or IRS filings for nonprofits, where even a 2% error can cause failure and serious consequences.
Segmentation Based on Risk Tolerance
Different customer segments within the same industry can have wildly different appetites for AI-related risk. In healthcare, early-stage researchers may eagerly adopt AI tools, while clinicians require near-perfect accuracy.
"We only have to be wrong once and we're done," explains Erwin Estigarribia of Headlamp Health. "There are different levels of appetite for risk. 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 to damage the relationship."
Kian Alavi, CEO Mazlo Explains why AI accuracy matters for non profits when managing and moving money around.
Managing Customer Expectations
Customers don't care about technical details—they want to know whether your solution will make their work easier and deliver reliable results.
"They don't care about what an LLM means or hallucination or who Sam Altman is or any of that kind of stuff," Alavi notes. "They're like, can you finally make my work easier?"
Key Takeaway
Estigarribia points out that AI breaks in specific, predictable ways. First-principles thinking helps you see those risks
clearly — like how LLM-based therapy augmentation has, in some cases, led to psychosis. Pairing technical understanding with domain expertise is what lets founders decide where AI belongs and where it’s dangerous to use.
This technical understanding, combined with domain expertise (founder-market fit), enables founders to make informed decisions about where AI can safely and effectively be deployed versus where it introduces unacceptable risk.
Decision Maker to Budget Fit
The Champion vs. The Buyer
One of the most common pitfalls in B2B sales is confusing enthusiasm for purchasing power.
"A product champion may be passionate about your solution, but if that champion doesn't control the budget or have authority to purchase, all that enthusiasm translates to exactly zero revenue."
The challenge intensifies in today's enterprise environment, where organizations have created innovation teams specifically tasked with exploring emerging technologies. These teams are designed to get excited about cutting-edge solutions, but they often sit far from the CFO and mainline budgets.
David Wiener of Rembrand — a startup using AI for product placement in video and advertising — 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. And so, you get this false signal on product market fit."
Spot False Signals -Multithread the Organization
Successful B2B selling requires "multithreading" — simultaneously managing relationships with multiple stakeholders who each have different priorities, perspectives, and levels of authority.
"Whoever's out on the front and talking to folks, you need to be figuring out who is the buyer," Mazlo's Alavi explains. "How are they related to each other and how am I sending them messages that show them the value that they need so that they can start having dialogue internally?
"You don't want one person to come in at the end and just kill the deal, and you don't want one champion that doesn't have any real power."
Kian Alavi, CEO Mazlo
YouTube video with David Wiener on “Are you building the right product for the right segment and customer user?”
Collapsing User and Approver Into One
The most elegant solution is designing your product and go-to-market strategy so that the user and the budget holder are the same. Wiener's company achieved this by pivoting to target media managers with budget authority who could act independently. When possible, targeting stakeholders who both experience pain and control the purse strings dramatically shortens sales cycles.
Key Takeaway
Early-stage founders win by embracing the chaos. You can’t spreadsheet your way into understanding an org chart. You have to talk to people, map the politics, and run scrappy experiments. Or as Alavi says: “There’s an art here, and you have to hustle your ass off to figure it out.”
That hustle includes blunt questions about budget control, approval processes, and hidden blockers. It also means testing your assumptions about who actually has the power to say yes.
This means asking direct questions about budget authority, understanding approval processes, and identifying potential blockers before investing months in a deal that can’t close. It means designing experiments to test assumptions about who holds power and who influences decisions.
Core Value to Commoditization Fit
The Rapid Pace of Technology Commoditization
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.
"What took months to build can be replicated in weeks by competitors with access to the same foundational technologies."
Building AI wrappers around existing workflows, while potentially useful, 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 going to last very long, even in healthcare, which has longer product life cycles."
The Platform Dependency Risk
Building solutions on platforms controlled by other companies introduces existential risk. Platforms can change their terms, deprecate APIs, or simply cut out intermediaries to capture more value themselves.
"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. And I've learned that the hard way."
Video of Erwin Estigarribia talking about protecting what you’re building.
Data as the Defensible Moat
When features commoditize, proprietary data becomes the key source of sustainable advantage. Unique, curated, and analyzed data grows more valuable over time and creates real barriers to entry.
Mazlo exemplifies this by becoming a system of record: tracking every transaction, adding compliance, and collecting all related data, making them the trusted source of truth for customers.
Video of Kian Alavi, Mazlo sharing how speed is critical to finding your moat.
Speed as Temporary Advantage
While data provides long-term defensibility, speed offers critical early-stage advantages. Rapid iteration allows startups to establish market position before larger competitors respond. Speed buys time to build sustainable advantages, but alone it doesn’t secure long-term success.
Network Effects Still Matter
Traditional strategies remain relevant: building two-sided networks that create value for suppliers and customers still generates strong network effects, now enhanced by data advantages and fast execution for truly defensible positions.
Key Takeaway
Perhaps most importantly, founders must accept that commoditization isn’t a one-time threat to address — it’s an ongoing reality. Technology will continue to evolve,
open-source alternatives will emerge, and competitors will copy successful features.
The question isn’t whether commoditization will happen, but how quickly you can stay ahead of it. This mindset shift — from finding a defensible position to continuously creating new value — represents perhaps the most fundamental adaptation required in the AI era.
Pricing Model to Value Fit
Aligning Price with Perceived Value
The final critical fit involves ensuring that your pricing structure matches the value customers perceive and are willing to pay for. This sounds straightforward in theory but proves remarkably complex in practice, particularly when serving multiple customer segments with different financial capabilities and value expectations.
The challenge intensifies when building products that customers genuinely love but struggle to afford. India Lossman of Boombox.io articulates this painful reality: "We built a product that our customers love because we listen to them. We knew who the decision makers were — specific roles for musicians like producers and audio engineers. They control the technology, they make recommendations to their collaborators. But budget was a problem. They're like, give us the world, but they struggle to pay for it."
Segmentation and Pricing Strategy
Successful pricing strategies often involve creating tiered offerings that serve multiple segments while capturing appropriate value from each. 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. They have discretionary cash. So, we just took the product that we'd already made and said, what features can we use? How can we re-skin it, make it fun and increase the audience?"
This expansion strategy — taking proven product capabilities and repositioning them for segments with better payment capacity — offers
a path forward when initial target markets prove economically challenging.
India Lossman, Cofounder of Boombox.io, shares difficulties around pricing.
Subscription Models and Retention
The choice between monthly and annual subscriptions involves trade-offs between acquisition friction and retention rates. Monthly subscriptions reduce barriers to initial signup but increase churn risk. "We had to switch from monthly subscriptions to annual," Lossman explains. "Our customers didn't like the idea. Sometimes you have to do something that might seem counterproductive, but it's the right decision to make."
Key Takeaway
Pricing model fit intersects with every other fit discussed in this paper. It depends on founder-market understanding (knowing what customers can and will pay), customer segment to AI precision fit (premium accuracy commands premium pricing), decision maker to budget fit (pricing must make sense to whoever controls the budget), and core value to commoditization fit (defensible value supports sustainable pricing power).
Omar Tawakol observed: “I’m going to tie together a few things you guys said because I do think there are some playbooks for pricing here. You figure out fast where there’s traction and then that creates data uniquely in your platform, which you can then train on to build defensibility.” In other words, pricing strategy both influences and is influenced by every other strategic decision.
Conclusion
Product-market fit, while important, obscures more than it reveals. The concept’s broad appeal comes from its simplicity, but that same simplicity makes it inadequate for guiding the specific decisions AI startup founders face. The five fits outlined provide a more granular and actionable framework:
Founder-market
Customer segment to AI precision
Decision maker to budget
Core value to commoditization
Core value to commoditization
Each fit represents a distinct challenge that can independently determine whether a startup succeeds or fails, regardless of how well the other fits are managed. What emerges from these insights is a picture of AI entrepreneurship that demands both depth and agility. Founders need deep domain expertise to understand their markets, technical sophistication to match AI capabilities with customer needs, sales acumen to navigate complex buying organizations, strategic thinking to build defensible value, and financial discipline to create sustainable business models.
These fits are not sequential steps but concurrent challenges that must be managed simultaneously. A misstep in any dimension can derail progress in others. Yet founders who actively manage all five fits position themselves to build companies that not only survive the AI gold rush but establish lasting competitive positions.
"The most important insight may be the simplest: there are no shortcuts."
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 everyone involved.