The Cult of Product–Market Fit: A Guide to the Holy Grail for Startups
A dry, mildly academic roast of product–market fit—how it became startup religion, why the binary myth misleads, and what AI is doing to the altar.
In startup lore, product–market fit (PMF) has achieved an almost mythological status. It is spoken of in reverential tones as the decisive moment a scrappy venture transforms into the Next Big Thing, a near-supernatural state where the product “just works” and customers miraculously multiply. Marc Andreessen famously defined product–market fit in 2007 as “being in a good market with a product that can satisfy that market.”[1] Ever since, this deceptively simple concept has been enshrined as foundational scripture in Silicon Valley’s playbook – a “holy grail” that founders are told to obsess over, chase at all costs, and recognize by instinct when (or if) it finally arrives.
The term itself is now ubiquitous. Barely a pitch deck or founder AMA goes by without invoking “PMF” as either an achieved milestone or the elusive goal du jour. It features in startup coaching sessions, investor memos, and endless Twitter threads with an almost religious fervor. In fact, one might think product–market fit were a divine state of grace bestowed upon the worthy startup – a binary moment of salvation when suddenly “the customers are buying the product just as fast as you can make it,” money is “piling up in your company checking account,” and “reporters are calling” to crown you the next unicorn[2]. By this narrative, before PMF lies darkness and despair; after PMF, salvation and scale.
But how did this concept rise to such prominence, and at what cost to clear thinking? In the following essay, we trace the history and mythology of product–market fit – from its origins in Silicon Valley scripture through its canonization by venture capitalists and startup gurus like Andreessen and Paul Graham, to its fetishization in pitch theater and founder lore. Along the way, we’ll satirically examine how PMF became the deus ex machina of startup success, often treated as an almost magical inflection point that absolves all sins. We’ll poke at the sometimes absurd lengths founders go to “find” or gamify it, and how it serves as a convenient narrative device in fundraising. Finally, we’ll explore how the latest craze – generative AI – is changing the rules of the game, perhaps even hallucinating markets into existence or shifting what PMF means altogether in the age of ChatGPT and Midjourney.
Prepare for a dryly humorous, scholarly-ish journey through the Cult of PMF. We’ll mix academic analysis with a healthy dose of cynicism and irony – footnotes and all – to illuminate how a once-obscure concept became the guiding star (and security blanket) of the startup world. Welcome to the church of product–market fit; please take a complimentary VC-branded hymnbook on your way in[^1].
[^1]: Free “Make Something People Want” stickers available at the door, courtesy of Y Combinator.
Origins: The Gospel of Market Fit
Every grand mythology has a creation story, and for product–market fit that genesis can be pinpointed to a single, highly influential blog post. On June 25, 2007, Marc Andreessen – then a freshly minted serial entrepreneur, now the archetypal venture capitalist – published an essay titled “The Only Thing That Matters.” In it, Andreessen crystallized a concept that Andy Rachleff of Benchmark Capital had been preaching internally: namely, that out of the trinity of startup factors (team, product, market), it was market that reigned supreme[3][4]. Success flowed not from having the smartest team or the slickest product, but from landing in a fertile market hungry for what you offered. If the market was big and ravenous, it would “pull the product out of the startup” – dragging even a half-baked product and a mediocre team to success, as long as they filled a real demand[5]. Conversely, “when a great team meets a lousy market, market wins,” Andreessen argued, and “markets that don’t exist don’t care how smart you are.”[6][7]
This was a striking inversion of the usual Silicon Valley hero worship of brilliant founders and ingenious products. Andreessen’s epistle proclaimed that even an average product, made by merely competent folks, could triumph if it fit a real market need, whereas the best people with the flashiest tech would flounder in a market that didn’t want them[8][9]. To drive home the point, he codified Rachleff’s Law of Startup Success: “The #1 company-killer is lack of market.”[6] In other words, thou shalt not build a product nobody wants. The logical conclusion? Focus everything on achieving that hallowed alignment between what you offer and what the market desperately needs – or perish.
Thus the term product/market fit was born (or at least popularized – Silicon Valley historians may debate earlier usages, but 2007 was its coming-out party). Andreessen offered a clear definition that would be quoted for years to come: “Product/market fit means being in a good market with a product that can satisfy that market.”[10][11] A bit tautological, perhaps (a fit means you fit), but its very simplicity made it sticky. More memorable was Andreessen’s vivid description of how PMF feels in action. He wrote that when you don’t have product–market fit, “the customers aren’t quite getting value… word of mouth isn’t spreading… deals never close.” But when you do have product–market fit – oh boy – “the customers are buying the product just as fast as you can make it… Money from customers is piling up… You’re hiring [staff] as fast as you can… Reporters are calling… You start getting entrepreneur-of-the-year awards… Investment bankers are staking out your house. You could eat free for a year at Buck’s.”[12][13] In short, you’ll know it when you see it. It’s the startup equivalent of nirvana: a state of undeniable momentum where demand far outstrips supply, and success seems all but assured.
With this one quasi-prophetic blog post, Andreessen planted the seed of a new startup gospel. The idea of product–market fit gave a name to that crucial early turning point in a startup’s life, around which so many founder stories pivot. Silicon Valley now had a succinct answer when asked the secret to startup success: “It’s all about product–market fit.” The concept struck a chord because it framed the chaotic, unpredictable process of starting a company in almost binary terms – before PMF and after PMF – making the journey sound more manageable (or at least, giving founders a singular target to chase in the fog). As Andreessen put it, “the life of any startup can be divided into two parts: before product/market fit (BPMF) and after product/market fit (APMF).”[14] Before PMF, a startup is like an alchemist mixing concoctions in search of gold; after PMF, one has the philosopher’s stone and can do no wrong (or so the legend goes).
Andreessen didn’t just coin a term – he issued a directive. “When you are BPMF, focus obsessively on getting to product/market fit,” he commanded. “Do whatever is required to get to product/market fit… tell customers no when you don’t want to, tell them yes when you don’t want to, raise that fourth round of highly dilutive venture capital – whatever is required.”[15][16] In other words, nothing is more important in the early days than finding that fit. You can (and should) ignore all the niceties of running a polished business – the org charts, the scalable processes, even, apparently, basic HR hygiene – if it distracts from the singular mission of nailing PMF. Andreessen drove this home with a cheeky (and oft-quoted) contrast: A startup that achieves PMF can screw up practically everything else along the way – “channel model, pipeline strategy, marketing plan, press relations, even the CEO sleeping with the venture capitalist” – and still succeed[17]. But a venture that never finds PMF will impeccably manage all aspects of its business and still go off a cliff[18]. In other words, product–market fit covers a multitude of sins; lack of it is the one sin for which there is no redemption.
By the end of 2007, the term product–market fit had entered the Silicon Valley lexicon as a kind of ultimate answer to the startup riddle. It was the prime mover in the success equation – “the only thing that matters”, as Andreessen boldly declared[1]. Entrepreneurs and investors alike began echoing this new article of faith. Paul Graham, co-founder of the famed Y Combinator accelerator, had already been preaching a similar mantra in simpler words – “Make something people want,” the unofficial slogan of YC. Graham would later describe product–market fit as the moment when “you’ve made something that people want” so definitively that growth becomes a given[19]. (Indeed, YC’s early success stories like Airbnb and Dropbox were often cited as exemplars of chasing user love before scaling – we’ll revisit those tales soon.) Venture investors, for their part, were quick studies: the smarter VCs realized that coaching their startups toward PMF (or at least recognizing when one had it) was key to unlocking value.
Thus, in the late 2000s, the gospel of market fit took hold. Like any good gospel, it reduced complexity to an enticingly clear principle: get the product right for the market, and thou shalt be saved (and fabulously enriched). Andreessen had unintentionally set himself up as a sort of modern Moses on the tech Mount Sinai, delivering the first commandment of startup success. Over the next decade, this commandment would be codified, analyzed, reinterpreted, and zealously practiced by the faithful – sometimes to productive effect, other times to almost comic excess.
[^2]: One wonders if Andreessen’s vivid scenario of success – “you could eat free for a year at Buck’s” (the legendary VC-favored diner in Woodside) – is the tech bubble equivalent of “land flowing with milk and honey.” Every era has its promised land.
Canonization in Silicon Valley Scripture
If Andreessen’s 2007 post was the genesis of the PMF concept, the 2010s were its evangelical explosion. The idea of product–market fit was eagerly canonized by incubators, venture capital firms, and the burgeoning cottage industry of startup literature. What began as one man’s theory swiftly became capital-T Truth in startup circles – a fundamental tenet to be taught, tested, and tirelessly pursued.
Y Combinator (YC), the startup accelerator co-founded by Paul Graham, played a crucial role in enshrining PMF as holy writ. YC had always told founders to focus on building something people deeply want – “ramen-profitable” businesses sustained by genuine user love. Graham’s essays like “Do Things that Don’t Scale” hammered this point home, using examples like Airbnb. In Airbnb’s early days (circa 2009), struggling to grow, Graham advised its founders to personally meet and delight their first users – literally photographing hosts’ apartments themselves – to ensure those users absolutely loved the service[20][21]. This unscalable hustle helped Airbnb discover tweaks that made their product dramatically more appealing. It was an object lesson in finding product–market fit: better to have 100 users who adore you than 10,000 who are lukewarm. Graham often emphasized that it’s fine – even expected – to start with a tiny market (just Harvard students for Facebook, design bloggers for Pinterest, etc.) as long as those users passionately embrace the product[22][23]. From that beachhead of love, you could expand outward. Implicit in this was the PMF doctrine: first nail a tight product–audience fit, then scale up the volume. By the early 2010s, YC’s gospel to its hundreds of new startups each year was effectively: find product–market fit, then hit the gas. The term PMF itself became common YC parlance – even if Graham himself preferred plain English (“make something people want”), the concept was the same. Many YC alum recall that the constant refrain from partners was to iterate until PMF, even if it meant multiple pivots or abandoning early ideas. The accelerator’s internal library and startup school materials soon featured entire sections on product–market fit as a milestone (lectures, Q&As, war stories of how X or Y startup “finally found PMF”). In short, YC helped formalize PMF as the primary goal of any seed-stage startup’s life.
Around the same time, The Lean Startup movement provided the methodology to chase this goal. Eric Ries’s influential 2011 book “The Lean Startup” (and Steve Blank’s teachings before that) treated startup building as an exercise in hypothesis testing and iterating toward product–market fit. Although Ries didn’t invent the term, he helped canonize it by embedding it in a broader framework: startups exist to validate a business model through continuous experimentation, and the ultimate validation is achieving product–market fit. Concepts like the Minimum Viable Product (MVP) and the pivot were popularized as tools to reach PMF faster. If your initial product iteration didn’t resonate with the market, you were to “pivot” – i.e. change some element of product or target market – and try again, repeating the cycle until something caught fire (or you ran out of money). The Lean Startup Co. training materials explicitly call product–market fit a “critical concept” in the methodology, “coined by Marc Andreessen in 2007” and essential to understand[24]. They even offered playbooks for “achieving product–market fit” step-by-step, as if following a recipe[25][26]. PMF was no longer just a concept; it was now a formal milestone with its own pyramid diagrams and checklists in the entrepreneur’s toolkit.
Venture capital firms, too, baked PMF into their investing philosophies. Many VC pitch templates and accelerator demo day scripts evolved to include an almost ritualistic acknowledgment of the startup’s PMF status. Founders would proclaim something like, “We’re on the verge of product–market fit, and with your investment we’ll get there and scale,” or (even better) “We achieved product–market fit in Q4, as evidenced by our user retention and revenue growth – now we’re raising our Series A to accelerate.” To declare one had (or was close to) product–market fit became a badge of honor – an all-clear signal to investors that it was safe to pour in fuel. Venture blogs reinforced this thinking: Andreessen Horowitz (a16z) frequently referenced PMF as the key inflection point in startup trajectories, and others like First Round Capital’s magazine ran detailed case studies on “finding product–market fit” (for example, the much-shared account of how Superhuman methodically engineered its way to PMF, which we’ll revisit shortly).
It reached a point where even MBA programs and corporate innovation labs co-opted the term. No longer confined to hacker slang, “product–market fit” was discussed in Harvard Business School case studies and McKinsey consultants’ slide decks. The idea had ascended into the pantheon of business jargon. Tech conference panels incessantly debated how to reach PMF faster, how to know if you had it, whether one could lose it, etc. The concept solidified into doctrine: startups must find product–market fit before worrying about scaling up, and smart investors must wait for signs of PMF (or at least strong “early indicators”) before writing the big checks. To use a religious analogy, PMF became the baptism a startup needed before it could be accepted into the congregation of viable businesses.
Of course, with canonization came a bit of calcification. As more and more people parroted the importance of product–market fit, the term sometimes lost clarity. It became a buzzword invoked to mean “we think people like our product” even when evidence was scant. But as a narrative, it was powerful. It gave founders and funders a common focal point and vocabulary. It’s telling that traditional business literature started to adjust its focus as well: Whereas old-school management guides concerned themselves with optimizing mature businesses (assumed after achieving a fit), the new startup literature drilled into the messy pre-PMF phase. In fact, entrepreneur Andrew Chen noted that a lot of classic advice is “focused on life after product/market fit… A lot of startup stuff is focused on the initial phases… What happens when… it’s not quite working yet?”[27]. That in-between – the search for PMF – is the essential startup struggle. Silicon Valley, ever fond of turning difficulties into romantic legends, labeled that struggle the “Trough of Sorrow.” A widely shared Y Combinator sketch called the Startup Curve illustrates a founder’s journey: initial excitement and a spike of publicity (the TechCrunch bump), followed by a plummeting depression (the trough) when growth stalls and doubt creeps in, then – if one perseveres – the slow climb through iterations (the “wiggles of false hope”) until finally breaching the “Promised Land: Product–Market Fit.”[28][29]. Yes, the Promised Land – they actually call it that. Few metaphors could better underscore how sacred and story-like this concept had become. PMF was the heroic destination of the startup quest. It was Zion. It was El Dorado. It was that point in the graph where the line inflects up and to the right forever, and all the years wandering in the wilderness are justified.
The infamous “Startup Curve,” originally drawn at Y Combinator, charts the founder’s journey through the Trough of Sorrow toward the Promised Land of product–market fit[28][29]. This diagram has achieved canonical status in startup circles, reinforcing the near-religious narrative of PMF as a moment of salvation.
By 2015 or so, product–market fit had been well and truly canonized. The phrase appeared in countless Quora answers, Medium articles, and panel discussions. It was no longer a mere concept – it was Startup Dogma 101. And like any dogma, it invited both devout adherence and a bit of critical re-examination, which we’ll explore next. But one thing is clear: product–market fit had completed its journey from a niche idea to a core part of the Silicon Valley belief system. Founders were now openly fetishizing the pursuit of PMF, sometimes to a fault – treating it as the be-all end-all milestone that would magically solve their problems. As we’ll see, that obsession was not without irony, and not without skeptics.
[^3]: It’s worth noting that Steve Blank, often called the father of Lean Startup, described a startup as an “organization formed to search for a repeatable and scalable business model.” In plainer terms: search for product–market fit. You could say PMF is just a rebranding of “a business model that actually works.” But “product–market fit” has a nicer ring – almost like two puzzle pieces clicking perfectly together. Who doesn’t love a good fit?
The Holy Grail Syndrome: PMF as Startup Salvation
With product–market fit firmly ensconced as startup scripture, an unintended side effect emerged: PMF obsession. Founders began to revere PMF not just as a useful concept, but as a near-mystical event – the singular moment of deliverance when their startup would be saved from oblivion. This often came with a very binary, absolutist interpretation: either you had product–market fit (in which case, rejoice, for eternal hypergrowth is now yours), or you didn’t (in which case, nothing about your business was truly working). Such black-and-white thinking can be dangerously simplistic, yet it became common folklore to treat PMF as a binary threshold. Consider the oft-repeated adage attributed to Paul Graham: “You know you have product–market fit when you’re not asking whether you have product–market fit.” In other words, if you have to wonder, you ain’t got it. The assumption is that when it hits, you’ll feel it in your bones – just as Andreessen described with his euphoric laundry list of obvious signs[12]. This framing – pre-PMF = purgatory, post-PMF = paradise – led many founders to develop what we might call Holy Grail Syndrome.
Symptoms of Holy Grail Syndrome include: treating finding PMF as the sole meaning of your startup’s life, fantasizing about the “big bang” moment when suddenly everything clicks, and perhaps neglecting other important gradual improvements because they don’t immediately flip the PMF switch from off to on. Founders talk about “searching for product–market fit” in the same tone knights of yore spoke about the quest for the Grail – a journey of trials and tribulations that will ultimately reward the pure of heart. It’s no coincidence that startup culture borrowed the term “epiphany” (Steve Blank’s Four Steps to the Epiphany is literally about the moment of finding a workable model). The narrative arc is almost religious: suffering, doubt, perseverance, then revelation and triumph. Once you “find product–market fit,” all shall be added unto you – growth, fame, fortune.
This almost binary view of PMF as a moment of salvation has been both inspiring and misleading. On one hand, it motivates teams to keep pushing through the Trough of Sorrow, assuring them that all it takes is one breakthrough and the rocket will finally ignite. As one startup pundit consoled, “Getting to product/market fit is hard, and even though you feel like you’re uniquely failing, you’re actually not – every startup has to go through this… it’s just part of the struggle to being successful.”[29][30] In other words: hang in there, most companies wander the desert for 12–24 months or more, but if you persist, you might reach the promised land. This kind of talk can steel founders’ resolve in tough times.
On the other hand, the fetishizing of PMF as an on/off state can encourage magical thinking. It’s easy to believe that once PMF arrives, everything becomes easy – your product grows itself, investors throw money at you, and you can basically walk on water. Andreessen himself propagated a bit of this magic: “Whenever you see a successful startup, you see one that has reached product/market fit… conversely, well-run startups that never find it go off a cliff.”[31] He went so far as to say that in “almost every case” of a successful startup, the cause was actually product–market fit – “because, really, what else could it possibly be?”[32]. That rhetorical question sounds almost comically zealous – as if no other factor (not timing, not strategy, not luck) could possibly play a role. This absolutism is part of the myth. It positions PMF as a panacea, a silver bullet, the one true cause of victory.
Reality is often messier. In truth, even post-PMF startups can and do fail if they bungle execution or get blindsided by a market shift. And plenty of “successful” companies never experience a single lightning-strike moment of fit; instead, they gradually inch their way into viability. The nuanced view – increasingly voiced by experienced founders and investors – is that product–market fit is a spectrum, not a binary[33][34]. You can have a weak or partial fit (a subset of passionate users, but not broad appeal), a moderate fit, or a strong fit that deepens over time. Bessemer Venture Partners recently articulated this: “PMF isn’t binary; it’s a spectrum… There is no elusive ‘eureka’ moment where you can rest easy that you’ve achieved it perfectly. Rather, [think of] how your product meets dynamic customer needs – it develops over time and becomes a stronger signal as you grow.”[35][33] They describe light signal PMF (a handful of early users love it, but inconsistent retention), moderate signal (pockets of traction in a segment), and strong signal PMF (high retention, word-of-mouth kicking in, customers pulling the product faster than you can supply)[34]. In other words, you don’t go to sleep one night clueless and wake up the next morning with a perfectly tuned money machine. You might notice some signs of life, then a bit more, and only in hindsight realize you crossed some threshold of sustainable demand.
Critics of PMF orthodoxy point out that viewing it as a singular milestone can cause founders to misattribute success or failure. As Andreessen noted, successful teams often retrospectively credit all sorts of irrelevant factors for their win, when “in almost every case, the cause was actually product/market fit.”[32] But is that always true, or is it a form of survivor bias? Conversely, founders may chalk up their struggles purely to “we haven’t found PMF yet,” when perhaps their strategy or team dynamics or unit economics are also problematic. The binary PMF narrative provides a convenient storyline (“we just need to find PMF!”) that glosses over other complexities. It can also lead to a check-the-box mentality, where teams prematurely declare victory after hitting one spike of traction. For instance, a startup might see a sudden uptick in users one month and high-five that they’ve attained PMF, only to discover in subsequent months that those users didn’t stick around – whoops, false positive. In the AI startup boom, this has become especially pertinent: “Wildly fast early traction does not always translate into sustainable [revenues]… AI experimentation budgets are high, but the threat of churn looms large.”[35] The hype around AI tools (where a cool demo can attract millions of curious users overnight) means many founders think they have PMF because growth is exploding, but it might just be a short-lived novelty effect. As one venture capitalist put it in 2025, “in the AI era, intuition can mislead and traditional signals of PMF might be false positives.”[36] The users could be kicking the tires and leaving just as quickly.
Even outside AI, seasoned entrepreneurs like Rand Fishkin have argued that product–market fit as a concept can be “broken.” Fishkin observed that the startup ecosystem’s near-religious “obsession with product market fit” often masks deeper issues. In a 2020 critique, he noted that founders fixated on the mythical PMF moment sometimes ignore the many incremental tactics that actually drive traction – like refining pricing, positioning, and branding[37]. “Magical belief in a ‘product–market fit inflection point’ shifts all the weight onto the product itself and limits creative ideas around what might solve the company’s issues,” Fishkin wrote[37]. The very simplicity that made PMF an appealing idea can become a mental trap, encouraging teams to view progress as a simple toggle (off -> on) instead of a continuous process of tuning and improvement[38][39]. Fishkin essentially asks: is there any value in thinking about PMF as a true/false binary? Or does this framing actually hinder founders from doing the unsexy work that builds a business?[38] His answer: “to me, it’s a clear no… The only way fit-vs.-no-fit is helpful is in its simplicity. And by now, we should know better than to trust ‘simple’ over the complex reality.”[40] In other words, chasing a single yes/no milestone can be counterproductive, as it might lead you to neglect the very tweaks (in marketing strategy, customer service, etc.) that could improve your product’s appeal.
All this is to say, the PMF-as-salvation narrative has its downsides. It can border on a cargo cult: just find this one magic thing and boom! – paradise. In satire, one might picture founders performing ritualistic “product–market fit dances” or wearing T-shirts emblazoned “Have you found PMF?” as they burn cash in offering to the Market Gods. At times, startup Twitter does resemble a theological debate, with folks arguing whether PMF is permanent or fleeting, whether one can be “pregnant with PMF” (almost there) or if you either have it or you don’t. Some zealots insist PMF must come before scaling; others tell tales of companies that scaled first and retrofitted PMF later (a heretical notion!). The orthodoxy still holds: better to suffer in the wilderness until true PMF is attained than to indulge in the false idol of premature scaling. There’s even data supporting this: studies of startup postmortems have found that premature scaling (spending big on growth before nailing PMF) is a leading cause of failure, essentially because it’s like pouring gasoline on a pile of wet logs – a wasteful conflagration that yields no sustained fire.
In the end, one can’t fully blame founders for treating product–market fit as the end-all be-all – the industry drumbeat is loud. Investors often demand that binary narrative (“tell us if you have PMF or not”). And indeed, crossing some threshold of market traction is incredibly significant. But as we’ll explore next, this dynamic has led to founders essentially gamifying the search for PMF – turning it into something of a sport or a sloganeering exercise. If the whole world is telling you that nothing matters except getting that golden stamp of “Market Fit Achieved,” it’s only natural to focus single-mindedly on it. Perhaps too single-mindedly, at times.
[^4]: An ironic footnote: Andreessen’s original post promised future essays answering questions like “What actually makes a product ‘fit’ a market? How do you know when to change strategy? When do you swap out team members?”[41]. He teased that all would be revealed in later chapters of the gospel. Those follow-ups never quite materialized in full. One might say the rest is left as an exercise for the founder. And so the faithful pore over the one scripture we have, reading between the lines.
The Gamification of the Quest for PMF
If product–market fit is the holy grail, then startups have devised all manner of treasure maps, compasses, and even cheat codes to obtain it. In the last decade, the pursuit of PMF has itself become fetishized and gamified – founders set out not just to build a great product, but explicitly to “win” at the product–market fit game. This often involves quantifying the unquantifiable: creating metrics and KPIs to tell you (and your investors) whether you’re getting warmer or colder in the search for fit. And like any game, it can encourage some quirky strategies and possibly perverse behaviors.
One of the most famous “PMF gamification” approaches came from the email app startup Superhuman. Its founder, Rahul Vohra, found himself in the classic bind circa 2017: lots of hype, lots of work, but an uneasy feeling that they hadn’t truly hit product–market fit yet, even though they had users in beta. How to get there systematically? Vohra turned to an idea from early Dropbox advisor Sean Ellis, who had proposed a survey-driven metric for PMF. Ellis had discovered a magic number: If at least 40% of your surveyed users say they would be “very disappointed” without your product, then you likely have product–market fit. Companies that struggled to grow almost always fell below that 40% very-disappointed threshold, whereas those that took off tended to exceed it[42]. In other words, if fewer than 4 in 10 of your users really care about your product, you haven’t yet made something people truly want; but once you cross about 40%, you’re onto something.
This “40% rule” became a kind of high score target for growth hackers. It transformed PMF from a fuzzy feeling into a concrete checkbox: Hit 40% on the Sean Ellis test = Achieved PMF (you may now proceed to Scale Level). Superhuman embraced this wholeheartedly. They built an “engine” to find product–market fit by surveying users, calculating the disappointed percentage, and then iteratively tweaking the product to raise that percentage[43][44]. Vohra recounts how they segmented users to find the most enthusiastic cohort, then doubled down on features for that cohort to boost the overall score. They would re-run the survey after product changes to see if the “very disappointed” metric moved up. In effect, they were playing a game of PMF Pac-Man – gobbling pellets of user satisfaction to push their score past 40%. At one point, by prioritizing a subset of power-users and focusing only on their feedback, Superhuman managed to bump their PMF score by 10%, inching closer to that coveted 40% mark[45][46]. The team made this number their North Star: “The percent of users who answered 'very disappointed' quickly became our most important number… we tracked it on a dashboard”, Vohra says (one imagines a big “PMF-o-Meter” in their office)[47]. They ultimately did surpass 40%, declared victory on finding product–market fit, and then confidently launched and scaled the company.
The Superhuman story, widely publicized via First Round Review, further gamified the startup world’s view of PMF. It suggested that product–market fit is something you can measure and optimize deliberately – like tuning an engine – rather than simply a Eureka moment bestowed by fate. Founders ate this up. Suddenly every other seed-stage team was running similar surveys: “How would you feel if you could no longer use our product? Very disappointed, somewhat disappointed, not disappointed?” and hoping to see that magic 40% “very disappointed” figure. It gave them a concrete goal, a sense of progress (“we’re at 25%, we need to get to 40%!”), and – perhaps most importantly – a sexy slide for the pitch deck: “Our PMF score is 45%, indicating strong product–market fit!” Investors, who love metrics, could nod sagely at that. Thus, the ambiguous quest for PMF got a scoreboard.
Of course, like any single metric, the 40% rule can be gamed or misinterpreted. Clever founders might, for instance, only survey their most active users (ignoring the silent majority who churned) to inflate the percentage. Or they might frame the question in ways that bias towards “very disappointed.” More fundamentally, chasing a numeric target can distract from why users love or don’t love the product. A high “very disappointed” score is a lagging indicator of doing a lot of things right; it doesn’t tell you what specifically to fix or build. Nonetheless, the gamification mindset has firmly taken hold. It appeals to engineers and MBAs alike: if PMF is the endgame, let’s instrument it, measure it, AB test it, and push it to 100% (why stop at 40?).
Beyond surveys, startups try to reduce PMF to various proxy metrics. Retention rate is a big one – if users keep coming back (say, X% 30-day retention), that’s evidence of PMF. Or net promoter score (NPS) – if your users enthusiastically recommend you to others, you likely have fit. Or simply organic growth vs. paid – if a large chunk of your new users are coming through word-of-mouth, not costly ads, it signals genuine demand (Sam Altman characterized PMF as when “users spontaneously tell other people to use your product.”[19]). It’s common to hear a founder claim, “We knew we had PMF when our retention cohort line went flat and referrals became our #1 acquisition channel,” essentially translating the warm-and-fuzzy concept into metrics investors can sink their teeth into.
This metric fixation can veer into PMF theater, though. Consider the early-stage startup that hasn’t actually launched a product yet, but in fundraising slides they include a hypothetical graph of future user growth with a big red dot labeled “PMF here!” (Yes, I have seen pitch decks where founders literally mark a point in time when they plan to achieve product–market fit, as if scheduling a software release.) It’s almost tongue-in-cheek – everyone knows PMF can’t be reliably predicted or timed, but the narrative convention demands an Act II climax in the startup story, and PMF fits the bill. So we get founders earnestly talking about product–market fit as if it were a level in a video game: “We pivoted twice and finally unlocked PMF 2 months ago, and now we’re raising funds to exploit that.” Investors might play along, asking things like, “What makes you confident you have PMF?” prompting the founder to produce a litany of stats: “Our DAUs doubled and we’re 20% week-over-week on revenue since mid-quarter – see this curve? That’s the inflection!” Everyone nods in approval, and the term sheet gets closer.
To be fair, having some concrete signals of PMF is important in fundraising. As one VC at Bessemer noted, “The sooner you can demonstrate early signs of strong product–market fit, the better [for fundraising]… Show me a real customer and how delighted they are… extremely strong product–market fit is the pattern investors are really sensitive to.”[48] He explained that a “bottoms-up pitch” – telling the story of a specific customer who had a problem, used your product, and is now in love with it – beats a top-down TAM (total addressable market) story any day[49]. Investors basically want to see the glimmer of PMF in how users adore your product. One colorful metaphor from an investor: “Don’t mess with scaling or pricing until you have radical product–market fit – that feeling that the fish are jumping in the boat.”[50] In other words, when customers are practically wriggling into your net begging for the product, that’s when you know you’re onto something. (We can add “fish jumping into boat” to the long list of vivid PMF descriptions, alongside “hair on fire” need, “flying off the shelves,” etc. The language around PMF is nothing if not evocative.)
Founders hear these metaphors and may over-interpret them. Some practically role-play their startup as if fish were leaping aboard, even when they’ve just coaxed a few trout. There’s a bit of fake-it-till-you-make-it involved: if you act like you have product–market fit, perhaps people (customers, investors, press) will believe you do, and then it becomes a self-fulfilling reality. This can lead to premature victory laps. A company might announce “we have strong product–market fit” in a press release or blog after securing a few big clients or hitting a milestone, in part to signal strength externally. It’s almost as if PMF is a certification to be earned – like ISO 9000 but for startups – and once you claim it, you wear it as marketing. Cynics might chuckle at startups boasting “we achieved product–market fit!” the way one might say “we achieved profitability” – the former is arguably more subjective. But in the performative world of startup PR, any clear narrative is useful.
Meanwhile, inside the company, the gamified quest can have cultural effects. Teams under pressure to find PMF may engage in a flurry of experiments – which is good – but could also develop a fetish for pivots. Pivoting (changing your product or target market) was once seen as a last resort; in the PMF chase era, some founders wear pivots as badges of honor, as if each pivot is an extra life in a video game. There’s even a dark joke in startup land: “We pivoted ourselves to death.” That is, in trying so many things in search of PMF, you might lose any coherent vision or burn out your team. Gamification can make you chase short-term wins (like bumping a metric) while missing long-term value. For example, you might remove a complicated but innovative feature to improve onboarding conversion (hitting a metric), only to find you’ve gutted what made the product truly special; users sign up more easily but don’t stick around – oops, no PMF after all.
In the most literal sense, some entrepreneurs have tried to game the concept of market demand itself – e.g., using clever hacks to simulate interest. One notorious tactic is the fake door test: put up a landing page for a product that doesn’t exist to see if people click “Buy” (then say “coming soon”). This can gauge demand before building anything. It’s a lean technique, but taken to extremes, one can “conjure” a false semblance of product–market fit by aggressive growth hacking: spending wildly on ads to drive user signups, offering freebies to boost engagement, artificially inflating user numbers to impress investors – all smoke and mirrors if those users don’t become real, loyal customers. In the era of AI tools, one could even imagine founders using bots to pose as active users or generating AI-written testimonials praising the product, to give the illusion of market traction. (We haven’t quite seen a scandal of a startup training an AI to mimic user engagement to fool VCs, but hey, anything for PMF, right?)
At its most absurd, the fetish for PMF leads to vanity metrics and self-deception. Founders might cherry-pick data that tells the PMF story while ignoring conflicting signals. For instance, trumpeting a high growth rate but glossing over that it was just a one-week spike from a PR mention; or highlighting a small cohort’s perfect retention while the overall user base quietly churns. All is fair in love and war – and in the quest for product–market fit, it seems. The game must be won, or at least appear winnable.
To put a finer point on it: product–market fit, the concept, was intended as a compass; but many have turned it into a scoreboard. The compass is invaluable for direction – keep tweaking until customers truly love your product. The scoreboard mentality, however, can make startups chase numeric milestones and declare “Game Over, we won!” prematurely. Some veterans now encourage a return to viewing PMF less as an event and more as a continuous process. As one VC advised AI startups, “think of PMF as a moving target – especially with AI, what users consider ‘good enough’ is changing all the time”[51]. In other words, you might hit PMF today, but if you stand still, you could lose it next quarter because user expectations evolved or new competitors emerged. The “game” resets in each market cycle.
But where’s the fun in that for a true believer? Half the allure of PMF in startup mythology is that it’s a heroic milestone – the boss level where you slay the dragon and get the treasure. Continuous process doesn’t make for as good a legend. So the culture, tongue in cheek, continues to treat it a bit like finding Willy Wonka’s golden ticket. In the next section, we’ll see how this plays out in the narratives founders craft for pitches and the theater of fundraising, where PMF has become a central plot device.
[^5]: Fun fact: The origin of the 40% rule comes from Sean Ellis’s work circa 2010. It was based on surveying users of Dropbox, LogMeIn and other early freemium hits. One wonders, had the figure come out to 37% or 53%, would startups still treat it as gospel? Perhaps “exceeding 37% very disappointed” would have been the magic. It goes to show how a single number, once canonized, becomes a talisman.
Pitch Decks and Fundraising Theater: PMF as Narrative Device
In the theater of startup fundraising, every great story needs a turning point. For modern startups, product–market fit serves as the ultimate plot twist – the moment the protagonist (the startup team) transforms from underdogs to unstoppable heroes. Savvy founders weave PMF into their company’s narrative, and woe to the team that shows up to a VC meeting without addressing it. Over the years, PMF has become the narrative device in pitch decks, demo day presentations, and VC meetings – often wielded to dramatic effect.
Picture a typical pitch deck slide titled “Traction & Product–Market Fit.” It might show a timeline: early product launch, a flat line of modest growth (the struggles of BPMF – Before Product–Market Fit), then a highlighted point in time labeled “Achieved PMF” (sometimes with a little starburst or icon for emphasis), after which the curve turns upward exponentially. The founder stands in front of this slide and delivers the rehearsed line: “After months of iteration, in July we hit product–market fit – since then, growth has been explosive.” It’s the startup equivalent of “And then I found the magic lamp.” The audience of investors perks up. This is the part of the movie they came to see – the inflection point that justifies a big investment to ride the wave.
Even when not so explicitly drawn, the PMF narrative lurks in many founder pitches. At Y Combinator Demo Days, for example, founders have mere minutes to excite investors. They often begin by framing a big problem, describe their product solution, then present evidence that “it’s working”. That evidence is essentially a proxy for PMF: maybe it’s growth numbers (“10,000 users in 3 months, growing 25% week over week”), or engagement stats (“users spend 2 hours a day in our app, indicating we’ve nailed the experience”), or revenue traction if applicable. While they may not say “we have product–market fit” explicitly (some do), the implication is we’re on that trajectory. If you listen for it, many pitches boil down to: “Here’s our problem/solution. We launched. After some iterations, we’re now seeing strong uptake – users love it. It’s the beginning of product–market fit. Thus, your money will help us scale a proven thing.” The phrase “proven thing” is key – investors want to feel the core value prop is proven (again, essentially PMF), so their capital mainly goes to gasoline for the fire, not to fiddling around trying to spark a flame.
In fact, venture investors often categorize startups by pre-PMF vs. post-PMF to guide their investment thesis. Early seed investors accept they’re betting on teams still searching for PMF; Series A investors, in today’s market, often want to see clear PMF or at least “early PMF signals” before cutting a check. By Series B and beyond, not having PMF is usually a deal-breaker. So founders tailor their storytelling accordingly. Seed-stage founders pitch vision (“we will find PMF by doing X, Y, Z”), but Series A founders are under pressure to demonstrate that PMF is already in hand or imminent. This can lead to some theatrical embellishment. It’s not lying, per se, but there is an art in making nascent traction look like incontrovertible evidence of fit. For example, a startup might have one niche user segment that’s super engaged, while other segments are lukewarm. In fundraising, the founder will highlight the passionate niche as proof of PMF (“these users absolutely love us!”) and downplay the rest, implying they’ll conquer those later. They present the niche traction as a microcosm of broad PMF. This is a bit of a gamble – sometimes that niche is all they’ll ever get – but many a startup has raised money on the back of a fervent subgroup of users portrayed as the harbinger of mainstream success.
There’s also an inside joke that every founder claims they are either “pre-PMF” or “post-PMF,” but never “mid-PMF.” Because “mid” would imply uncertainty, and uncertainty doesn’t get funded. So you either humbly admit you’re still searching (which is expected at the seed stage), or you confidently claim you’ve found it. By the time of a Series A pitch, few will say “we’re not sure if we have PMF yet.” Instead, they’ll say, “we have clear indications of PMF – for example, our customer referrals have doubled and churn is extremely low,” or “we can’t onboard customers fast enough; demand is outpacing our capacity.” These are textbook signs of product–market fit (customers banging on the door) that VCs love to hear[2]. Founders essentially perform the role of PMF evangelists for their own company: they are trying to convince investors that the hardest part is over, the nut has been cracked, and now it’s about execution and scale (which, conveniently, is where venture dollars go to work).
Investors themselves often prod for the PMF story in meetings: “Tell us about your product–market fit – who are your users, how much do they love it? Have you hit an inflection point?” If a founder is too coy or realistic (“well, we’re doing okay but we’re still iterating”), it might raise concerns. The unwritten expectation is that by the time you’re seriously pitching institutional investors, you should either have PMF or have a compelling plan to get there soon. Otherwise, the VCs might kindly suggest you come back later. It’s almost like needing to show your badge at the door of the club: “PMF, huh? You on the list?”
This dynamic encourages what we might dub “PMF Theater.” Founders may present things more rosily than they truly are, and investors, for their part, may suspend a bit of disbelief if they like the team/space – until proven otherwise. It’s not uncommon for a company to raise a robust round on the premise that they have product–market fit, only for everyone to realize a year later that they actually didn’t; the growth fizzles, and tough conversations ensue. At that point, founders might pivot (with investors’ blessing) and essentially reboot the PMF search, except now with a lot more money burned. This scenario is basically the dreaded false positive PMF. It’s the equivalent of a false dawn – you thought the sun was rising, but it was just a particularly bright meteor. False positives can be lethal because they lure startups into scaling prematurely (hiring, spending) on the assumption that the foundation is solid when it isn’t.
To avoid false positives, some VCs have developed their own internal definitions or tests for PMF. For instance, an enterprise-focused investor might say, “Product–market fit, to us, means at least 10 paying customers who renew and reference each other.” A consumer app investor might look for “30%+ 90-day retention with organic growth.” These are heuristics, but they inform how stringently the investor grills the founder’s story. If a founder says “we have PMF” but the metrics don’t match the VC’s mental model, they’ll push back. The savvy founder anticipates this and comes armed with data to support the claim: cohort charts, user testimonials, growth curves – essentially an evidentiary case for PMF. It has the flavor of a courtroom drama: “Exhibit A: our retention curve flattening, Your Honor. Exhibit B: inbound enterprise requests tripling quarter over quarter.” The goal is to leave no doubt that “the fish are jumping in the boat,” as mentioned earlier[50]. And if the fish are indeed flopping on deck, the VC will want to invest before the whole school is caught by someone else.
Amusingly, PMF-speak even shows up in public company narratives now, albeit in code. CEOs of newly IPO’d tech firms on earnings calls will talk about expanding into new markets or launching new products and say things like, “We’re seeing good initial uptake, but it’s early – we’re focused on refining the product–customer fit.” They might not say “product–market fit” outright (too jargony for Wall Street), but the concept is sneaking in. Essentially, even big companies acknowledge that new initiatives need to find PMF. This is a late-stage echo of the early-stage concept, showing just how far the terminology has penetrated.
Let’s also acknowledge the comedic aspect: PMF as theater can be quite literal at times. I’ve attended pitch competitions where founders perform mini-skits or use humorous analogies to convey how much customers love their product (“Our users love us more than caffeine – one told us our app is the first thing she checks every morning before coffee!”). Others might bring a prop – say, a stack of printed fan mail from users – slapping it on the table to dramatize the love they’re receiving. It’s all part of selling the feeling of PMF. In the most exaggerated cases, founders have made grandiose statements like, “If we turned off our service, there’d be riots in the streets.” (Unless you’re Twitter in its heyday, that’s probably hyperbole.) But you get the idea: in pitch theater, confidence is king, and declaring one’s product indispensable is an act of confidence.
Meanwhile, the investors play their role: the inquisitors and the kingmakers. They often relish asking the tough question in front of an audience: “Yes, but do you really have product–market fit yet?” – to see how the founder handles it. The best founders respond with humility and evidence: “Great question. Here’s how we think about PMF: we know we’re not done but in our core use case we see 50% of users coming back daily and writing us love letters. For example, [insert anecdote]. We believe that’s a strong signal of PMF in that beachhead market, and our plan is to expand it.” This kind of answer shows both the recognition of PMF as a journey and enough proof that the journey’s well underway.
If all goes well, the investors buy in – literally. They fund the startup, often using the language of PMF themselves in the press release: “We invested in XYZ Startup because we saw clear evidence of product–market fit and an opportunity to help them scale their solution to a much larger market,” says the VC quote. It’s part of the ritual. The investors might even advise the startup publicly or privately to “double down now that you have PMF” – meaning hire more, spend more on customer acquisition, step on the gas. That’s the desired end to the story: with PMF achieved (or convincingly performed), the startup can transition from search mode to growth mode. Curtain falls on Act I (the search), and Act II (the scaling) begins.
Of course, behind the scenes, everyone knows reality can diverge from the script. Sometimes Act II reveals that Act I’s climax was a fluke. But in the collective psyche of startup land, product–market fit remains the climactic plot point – a moment of catharsis where chaos turns to order. It’s no wonder founders and investors alike cling to it as a narrative device; humans understand stories, and PMF provides a ready-made story of struggle and redemption that resonates. Even if it’s oversimplified, it brings coherence to the madness of startups. And as any good dramaturge knows, what the story lacks in accuracy, it can make up for in emotional truth. The emotional truth here is that building something people truly want is the hardest part – so when a founder tells a tale of how they did exactly that, everyone wants to believe.
[^6]: One can imagine a satirical “Startup Pitch Bingo” card where “product–market fit” is the free center square. Other squares: “huge TAM,” “network effects,” “passionate users,” “hockey-stick growth.” It’s a cliché because it’s expected. If a founder didn’t mention product–market fit at all, investors would probably start asking themselves if the founder understands what really matters.
AI and the New Quest for PMF 2.0
As if the traditional startup game weren’t challenging enough, the rise of AI-powered products in recent years has added a whole new twist to the search for product–market fit. The emergence of large language models (ChatGPT and friends), generative AI like Midjourney, and other AI-as-a-platform phenomena have simultaneously accelerated some aspects of finding PMF and complicated others. In short, AI is changing the rules of engagement – perhaps even the definition of product–market fit itself.
On the one hand, AI tools have made it easier than ever to build and iterate on products quickly. Got an idea for a new app? In 2025, you can plug into GPT-4 or another API, generate a decent prototype or design with minimal coding (maybe just prompt engineering), and spin up a landing page in a day. Need to understand user feedback? You can have an AI summarize thousands of survey responses or support chats overnight, yielding insights that might have taken a human team weeks. In theory, this means startups can iterate toward PMF at warp speed – testing hypotheses, tweaking features, personalizing user experiences – all thanks to AI assistance. As OpenAI’s product lead (perhaps unsurprisingly) described it, “It’s easier [in the AI era] because AI can help you iterate faster, understand users better, and build more personalized solutions… You can prototype in days, not months.”[52][53]. An optimistic founder might think, Great, we’ll use ChatGPT to brainstorm 100 product variations, Midjourney to design them, and quickly find which one users latch onto. The quest for PMF becomes a high-speed race car instead of a plodding caravan.
However, the AI coin has a flip side. The very power of AI to accelerate development also raises the bar of user expectations – and introduces new uncertainties. Users now expect a lot more “magic” in their products. When consumer-grade AI can compose music, write code, and hold human-like conversations, the novelty bar is sky-high. An AI product that merely works is not enough; people want it to dazzle. As one AI-focused author put it, “user expectations have skyrocketed. Users compare every AI product to ChatGPT… The bar for ‘good enough’ has never been higher.”[54][51]. That creates a paradox: AI makes it easier to build product; but harder to impress users. In practical terms, a startup might quickly code an AI-driven tool that does something cool, and get a bunch of initial users due to the hype. But those users, spoiled by exposure to top-tier AI, might shrug and churn unless the product continuously improves or delivers near-magical accuracy. So the startup thinks it hit PMF because it got 100k signups in a week (thanks, TechCrunch and Product Hunt!). But a month later, 90k of those users vanish because the product didn’t live up to inflated expectations. Was PMF achieved? Or was it a mirage? The AI era is full of such head fakes.
Another challenge: AI can enable products to do things users didn’t even know they wanted, or conversely, encourage founders to solve “problems” that aren’t actually real. In classical startup doctrine, you identify an existing pain point and build a solution. But AI is weird – it can conjure entirely new capabilities, creating a chicken-and-egg scenario for PMF. As Miqdad Jaffer (OpenAI’s product lead) noted, “AI products often solve problems users didn't know they had — or create new workflows they never imagined possible. Your initial problem hypothesis might be wrong, not because the market was misunderstood, but because AI unlocked a more valuable use case.”[55][56]. This means a startup could start building X, only to discover via user behavior that the real killer feature is Y (something tangential, enabled by the AI). The path to PMF might zigzag more unpredictably as a result. Traditional PMF frameworks assume a somewhat stable target need; AI may cause the target to move or multiply as the technology suggests novel applications. Founders have to be extra attuned to emergent use cases – effectively letting the market reveal itself, since AI’s capabilities might outpace what the market initially thought it wanted.
Moreover, AI startups often operate in nascent markets where usage patterns are not yet established. In 2023–2024 we saw a Cambrian explosion of AI tools: AI writing assistants, AI image generators, AI coding copilots, etc. Millions tried them, but how many stuck as regular users paying for a service? Some did (e.g., many professionals adopted GitHub’s Copilot for coding), but others remained experimental or one-off novelties. This introduces a new PMF question: is the market real or just a hype mirage? AI can “hallucinate” – not just fake facts in chatbots, but in a sense, hallucinate markets. By that I mean, the hype around AI can create a temporary swell of interest that looks like a market wave, but it might evaporate when the novelty wears off. Startups can misinterpret this swell as genuine product–market fit. A cynical example: an AI startup launches an app that writes personalized Shakespearean sonnets. It goes viral on TikTok; teens everywhere download it and generate sonnets about their pets. Usage skyrockets for two weeks (call the VC, we have PMF!). But then everyone moves on to the next trend. The market for AI pet sonnets wasn’t real; it was a meme. The founders essentially hallucinated product–market fit – the underlying need was shallow.
We saw some of this with the wave of GPT-based chat apps. Many had explosive user numbers early on. But sustaining engagement and monetization has proved tricky for all but the truly value-delivering ones. The upshot: finding sustainable PMF in AI may require filtering out hype-driven signals from genuine signals. Bessemer Venture Partners cautioned in mid-2025 that “initial positive reception is [often] a light signal of PMF. Novelty isn’t the same as value. If users don’t integrate your product into their daily workflows, you don’t have PMF yet.”[57]. In other words, usage != true adoption. The old wisdom holds: a product that is merely interesting is not enough; it must be indispensable to a core group of users.
Yet, ironically, the AI boom has also led to a kind of PMF panic among founders: given how fast the field is evolving, the fear is that if you don’t lock in your market fit quickly, someone else (or a bigger competitor) with a better model will swoop in and steal the market. There’s a sense of a ticking clock. That may cause startups to cut corners in the PMF process. For instance, instead of carefully honing one use case, an AI startup might try to grab as many users as possible with a half-baked general solution, hoping to land-grab a market before it’s fully proven. This can result in those large but shallow user bases we discussed. It’s almost a reversal of the lean startup mantra – more of a blitzkrieg approach: get big while the window is open, figure out sustained fit later. Some companies have succeeded this way (you could argue that early Uber or Twitter scaled with growth even as they figured out long-term retention on the fly). But it’s high risk, and many such attempts flame out.
Meanwhile, incumbents and platform providers (like OpenAI itself, or the Googles and Microsofts) are constantly improving the baseline AI tech, which means the goalposts for user delight keep moving. If today your AI writing tool is slightly better than average, tomorrow the average gets better thanks to a new model release. So you can lose PMF not because you changed, but because the environment changed around you. This dynamic forces AI startups to continue iterating on “fit” even after they thought they had it – a never-ending chase. The AI PMF Paradox, as Jaffer called it, is that “achieving PMF in the AI era is both easier and harder than ever before.”[52] It’s easier to get started, harder to stay ahead.
Interestingly, some have suggested that AI may redefine what we consider product–market fit at a conceptual level. For example, if an AI product is constantly learning and adapting to each user (personalization at scale), then PMF might not be a one-time state but an ongoing relationship. Each user could conceivably have a different “fit” curve, as the AI tailors itself. The company’s job becomes ensuring the AI keeps aligning to users as their needs evolve – more like balancing on a surfboard than standing on solid ground.
Another facet: startups are using AI not just in their products but in how they search for PMF. We already touched on using AI to analyze feedback or generate prototypes. There are even attempts to create AI agents that test business ideas autonomously. For instance, one founder on Reddit claimed “I built an AI agent that gives instant answers about product–market fit & demand, pricing & positioning feedback…”[58]. The idea is to feed an AI some data or assumptions and have it predict or validate where PMF might lie. While promising in theory, this veers toward relying on AI to solve what is fundamentally a human puzzle: what do people want and value? AI can assist, but it can also hallucinate convincingly – imagine an overeager AI market research tool telling you, “Yes, people definitely want a smart refrigerator that quotes Shakespeare,” when in reality, no, they really don’t. Blindly following AI-generated “insights” about market fit could lead founders down some strange rabbit holes.
In summary, the emergence of AI is shifting the PMF landscape in several ways:
· Speed: Faster prototyping and iteration means potentially finding (or thinking you found) PMF faster. The whole lifecycle compresses.
· Hype vs Reality: Harder to discern true user love amid hype-driven spikes. Novelty complicates the signal.
· Moving Targets: AI capabilities and user expectations evolve rapidly, so PMF may be more fluid and temporal. “Fit” today might not be “fit” tomorrow if the market’s expectations change.
· Expanded Definition: Possibly thinking of PMF not as a single state but as a continuum that an AI product must continuously maintain across different users and contexts.
· Tools for the Quest: Founders using AI to aid the search, which can help uncover insights but also potentially mislead if not careful (garbage in, garbage out applies to AI too).
Will the term product–market fit itself change? Perhaps not immediately; it’s ingrained. But we already see language like “AI-market fit” or “model-market fit” being tossed around in AI circles, to emphasize that sometimes the bottleneck is whether the AI model is capable enough for the market need. One could facetiously imagine future founders in 2030 saying, “We’ve achieved model-market fit – the model can do what users want – but now we need full product–market fit (including UX, distribution, etc.).” The jargon may split hairs, but it underscores that as technology layers increase, the concept of “fit” might get more multidimensional.
In a satirical sense, one might say: In the old days, founders prayed to the deity of PMF for deliverance; now they also have a pet AI oracle whispering guidance – though the oracle sometimes lies. Founders in 2025 find themselves performing a juggling act: appeasing the traditional gods of Product and Market with a great fit offering, while also harnessing the dragon of AI without getting burned. They might still be on the hero’s journey to PMF, but now the journey has side quests, time warps, and trickster guides.
At least one comforting truth remains amidst the AI upheaval: ultimately, humans still decide if a product has market fit. If you make something (AI-laden or not) that truly solves people’s problems or delights them beyond others, you’ve got it. No amount of AI hype can replace the basic validation that a group of people ardently want what you provide and will pay (or stick around) for it. In that sense, the essence of product–market fit remains timeless, even as the tools and contexts evolve. It’s still about that almost ineffable connection between a creation and a need.
[^7]: Perhaps we need a new term: “product–market fluid.” Because in AI, fit might be less like snapping a puzzle piece in place and more like staying in sync with a moving puzzle. But “PMF” has a nicer ring than “PM fluency,” so scrap that. We’ll keep the old acronym and just argue about what it means.
Conclusion: The Myth Endures (with a Wink and a Nudge)
Surveying the saga of product–market fit – from its canonization by Silicon Valley luminaries to its elevation as a quasi-religious quest, and now its evolution in the AI era – one is struck by how much weight this once-humble term has been made to carry. In the mythology of startups, PMF is the kingmaker, the binary switch, the holy grail, the promised land. It’s a narrative device, a strategic objective, a psychological crutch, and a yardstick all in one. Few concepts in business have enjoyed such cultural hype while being so elusive in practice.
It’s easy to poke fun at the almost cult-like reverence: the founders intoning “PMF” like a mantra, the investors acting as high priests discerning true fit from false idols, the cottage industry of blog posts and frameworks claiming to teach the one true path to PMF enlightenment. Our tone here has been satirical, and deservedly so – there’s ample irony in how a concept meant to simplify thinking has spawned endless complexity and fetishism. Founders have indeed gamified and fetishized the search, at times confusing the appearance of fit with the substance. We have hacky surveys and vanity metrics trying to bottle the lightning. We have theatrical pitch rituals to convince ourselves and others that yes, we have it – we are chosen. And we have the perpetual risk of mistaking noise for signal, especially in these hype-laden times.
Yet, strip away the buzzwords and hyperbole, and the core insight remains refreshingly sane: make something people really want (and ideally, something lots of people want). That’s product–market fit in a nutshell, and who can argue against it? Marc Andreessen’s forceful proclamation that “the only thing that matters” is getting to PMF[1] was, in a sense, a plea for focus. Don’t get lost in the sauce of secondary concerns – remember that without a product that genuinely satisfies a market, nothing else you do will save you. On that count, the mythology aligns with reality. If our satire has a sympathetic edge, it’s because as much as we lampoon the excesses, we recognize the kernel of truth at the center of the cult.
Perhaps the healthiest way to view product–market fit is as a useful myth. It’s a myth in that it simplifies a continuum into a binary and tells a story of salvation that real life seldom adheres to perfectly. But it’s useful because it gives startups a lodestar and a common language. Myths can inspire and galvanize, as long as one doesn’t take them too literally. The savvy founder uses PMF as a guiding concept – a North Star to navigate by – without expecting a literal epiphany or finality. They know it’s more like a milestone on a longer journey than the finish line of a sprint. Conversely, the savvy investor looks for evidence of PMF while understanding it’s not a magic on/off switch – more like a dimmer that can brighten or fade with context.
In the end, the term endures because it captures something real and important, even if our collective handling of it is ripe for satire. It’s hard to imagine Silicon Valley discourse without “product–market fit” – what would we say instead? “Making stuff people want at scale”? Too clunky. So PMF will stay enshrined in our slide decks and startup playbooks. We’ll continue to chase it, to crow about it when we think we have it, to worry when we don’t, and to retrospectively ascribe our victories and defeats to it (sometimes correctly, sometimes oversimplifyingly so). And as new waves of technology come (be it AI, AR, Web3, or whatever buzzword du jour), we’ll adjust the mythology slightly – but likely still talk about PMF, because fundamentally, we’re still asking: did we make something people care about?
One can almost picture future anthropologists studying early 21st-century startup culture and noting: “They believed in a concept called product–market fit, a pivotal harmony between creation and commerce. Their legends spoke of those who achieved it and prospered, and those who never did and vanished. It was both doctrine and delusion, guiding them through uncertainty.” Not a bad summary, really.
So here’s to product–market fit: the concept, the legend, the meme. May founders find it (in reality, not just in PowerPoint), may investors recognize its shades of gray, and may we all keep a slightly cynical smile as we utter the words. Because as serious as the startup game can be, it’s always healthy to remember that some of our most sacred ideas can sound a bit absurd when said out loud. Product… Market… Fit. Say it enough times, it starts to sound like a weird mantra – which, in the church of tech, it kind of is.
And as any good satirist would, we’ll end with a final wink: Product–market fit is dead; long live product–market fit! In the ever-evolving kingdom of startups, the king is constantly proclaimed dead and reborn, but he still rules. So long as humans have products to build and markets to appease, we’ll be chasing that mythical fit – sometimes stumbling, sometimes soaring, and always swapping stories about how we got there or why we haven’t. It’s our foundational myth, and we’re sticking with it, for better or worse[40][32]. After all, what else could it possibly be?
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