Did AI Kill the Lean Startup?
Of course not. AI should supercharge core Lean Startup principles, which remain fundamental to building successful startups.
Recently, Garry Tan (Y Combinator) and Nathan Covey (Founder) said that Lean Startup was killed by AI.
Their argument? AI makes it so easy to build stuff that you should just go build. Follow your curiosity. Live on the edge of the future. You’ll bump into something useful eventually.
That’s nonsense.
Yes, AI makes it easier and cheaper to build. You can spin up landing pages, agents, and even entire apps overnight. But faster doesn’t mean better. Speed without direction is just chaos (not “focused chaos” 😏).
Eric Ries, the author of The Lean Startup, never said “don’t build”. The core Lean principle is literally: Build–Measure–Learn. It’s about building quickly in order to learn (not build quickly to fail fast, btw). The faster you go through the loop, the better your odds of landing on something that actually matters. AI is an accelerant to Lean Startup, not its replacement.
The anti-Lean sentiment coming from YC and elsewhere is reacting to a straw man: that Lean is slow, focused on too much validation, and allergic to building. But Lean Startup was never about analysis paralysis. It was never about “ask 100 people what they want and then you’re set.” It was always about de-risking the riskiest assumptions—as quickly and cheaply as possible. And then iterating (over and over).
That’s the whole game. Not guessing. Not blindly launching. Not betting your time (and runway) on unvalidated hunches.
Let’s break down the real Lean Startup principles, and why AI doesn’t kill them. It supercharges them.
1. Start with the riskiest assumptions
Lean Startup emphasizes starting with the riskiest assumption first. Nine times out of ten, that’s desirability. Will anyone even want this? Will they care enough to switch from what they’re doing now?
AI doesn’t change that. If anything, it gives us a better tool to test it. Now you can build a landing page, wire up a chatbot or agent, and simulate the core value prop in a weekend. That’s good, because you can now launch something lightweight to test desirability in the real world, although you don’t have to build a complete product. Prototypes are perfectly suited for testing value propositions and desirability and they can be created quickly with AI tools.
There is a trap though. Once founders ship something—even something small—they tend to fall in love with it. They over-invest emotionally. It’s no longer an experiment, it’s their startup. That makes it hard to walk away, even when the signal is weak.
To YC’s credit, they push hard on growth metrics. They want to see real traction. If things aren’t moving, they push teams to pivot. True validation only comes when you launch something and see how the market reacts. You can’t interview your way to victory. We’re 100% aligned on that. Unfortunately, founders build and launch without real discipline and get caught in the hype.
Lean Startup said “validate before building” not just because building used to be slower and more expensive, it’s because launching clouds your objectivity. It becomes harder to see your work clearly once it’s out in the world. Use AI to launch faster and test desirability. But don’t confuse building fast with being right.
You can run the experiment. Just don’t forget it is an experiment.
2. Validated learning matters more than raw output
AI makes building easier. But are you learning anything when you launch?
Most AI startups today are obsessed with building and launching. Very few are actually measuring learning. They’re not talking to users, not watching behavior, not asking why users churn after day two.
Here’s the real danger: without validated learning, pivoting becomes much harder.
I define a pivot as a shift in one aspect of your startup’s focus, based on validated learning. Not guessing. Not flailing. Not jumping to the next shiny idea. Pivoting is strategic. It’s directional. It requires clarity about what’s working and what’s not.
If you’re not testing your riskiest assumptions properly—if you’re not focused on learning—you don’t pivot. You just restart. You throw out idea #1 and spin up idea #2, without ever knowing whether #1 had potential. Or you spin up five ideas simultaneously because you can, but don’t have a good enough framework for judging which is best (except for early growth signs, which aren’t always enough). Did you test things properly? Did you give it enough time? Probably not.
In a rush for hyper-growth (which AI absolutely fuels), founders jump from idea to idea until something “sticks.” When it does, they might not even understand why. That’s not a repeatable process. It’s luck. You can’t scale luck.
3. Customer understanding is non-negotiable
Skipping customer understanding = almost certain death. ☠️
In 2007, I built Standout Jobs in a market (HR/recruitment) that I didn’t understand well enough. The tech worked. The product was slick. But the market didn’t buy. In my post-mortem, I realized, “The lack of market understanding ultimately meant that we couldn’t match the right product to the right market at the right price.”
Founders today are doing the same thing all over again, this time with AI.
They spot a clunky workflow in an old school industry and think, “AI can fix that!” Maybe it can. But unless you understand why it’s clunky in the first place—regulations, incentives, integrations, legacy behaviors—you’re solving symptoms, not root causes.
That’s why deep customer understanding is everything. It’s not optional.
Mike Krieger, Anthrophic’s Chief Product Officer (and ex-Instagram co-founder) says it well:
Don't just know the company you're selling to, but know the person you're selling to at the company.
AI can help navigate customer understanding. Use it to simulate early customer interviews with “synthetic users” that behave and respond like your target audience. It’s a fast, low-cost way to pressure-test your assumptions, spark insights and prep your thinking before getting in front of real people.
But let’s be clear: synthetic users are not a replacement for actual users. They don’t have unpredictable emotions. They don’t give confusing, frustrating feedback. They don’t reveal buying behavior. They don’t ghost you on follow-up.
Synthetic interviews are a great starting point, but if you stop there, you’re flying blind. You still need to talk to real people. You still need to feel their pain, understand their motivations, and decode the irrational stuff that makes or breaks product adoption.
4. Distribution is still a monster
Distribution is getting harder, not easier. When 1,000 AI startups all launch the same idea with the same landing page template and the same OpenAI wrapper, nobody stands out.
Pete Flint from NFX recommends going into very narrow niches (which I love).
There are two major issues AI startups are facing:
False positives on early traction
High churn (which may be “invisible” early on)
Many AI startups see early traction, but that momentum may be a mirage. Those “positive signals” may be false positives. The mainstream buzz around AI is driving people (including those working in old school businesses and big enterprises) to try AI tools quickly. Bosses are telling employees, “use AI!” So they do, even if they don’t understand what’s happening or if it’s helping.
That leads to tons of signups, early adoption and even revenue. But AI tools can still disappoint in the same way that plenty of software did before. People get distracted by other shiny objects and jump from one solution to the next. Suddenly those early growth signals aren’t real enough to justify raising millions of dollars and growing at all costs.
We’re now seeing high churn become a real problem for new AI products. Users try a new AI tool, come back once or twice, then move on. People are discussing this more openly: enterprises are buying fast, startups are hitting big MRR and ARR numbers quickly, and then churn kicks in because the tools don’t create enough value, switching costs are low, etc.
Dirk Sahlmer’s post is spot on:
Your AI startup hit $2M ARR in weeks?
Cool... but let's talk about the elephant in the room:
10-15%+ Monthly Revenue Churn.
This is the reality I've seen across many of the AI SaaS companies I spoke to in 2024.
Here's what's happening right now:
63% of global IT leaders fear being left behind if their organizations don't use AI
US companies spent an average of 650,000 dollars on AI last year for this reason
Fear of missing out (FOMO) is driving rapid, sometimes hasty AI adoption
The result?
Explosive early growth: Check.
Product-market fit: Let's wait and see.
Jason Lemkin from SaaStr describes “stealth AI churn” which may be a precursor to serious problems:
Traditional churn is binary: customer pays or doesn’t pay. Stealth churn is analog: customers pay the same for now — but use you 40% less. They’re still showing up in your retention cohorts, still generating ARR, still appearing in your success metrics.
But the underlying consumption patterns tell a different story:
Session frequency drops as AI tools handle quick tasks
Feature adoption stalls because alternatives exist for advanced use cases
Time-to-value shrinks but so does total time spent
Expansion revenue evaporates as customers find they need less, not more
The scary part? Your NPS might actually go up. Customers love using you for what you’re genuinely best at, while AI handles the rest. They’re happier with a smaller slice of their workflow.
Even grassroots startup founders see it coming. One post on r/startups warns:
“A lot of these companies look like they’re winning right now but that early traction doesn’t mean long-term success… the real test is whether people keep using the product after the novelty wears off.”
Churn matters. You can launch fast with AI, but fast churn still kills growth and breaks distribution.
Don’t get me wrong, growth is good. But growth without retention is noise. Distribution isn’t about getting people to try your product. It’s about getting them to stay.
5. The MVP isn’t dead. The bar has just gone up.
There’s a growing chorus saying “MVPs are dead.” Some now want MLPs (Minimum Lovable Products), others prefer SLCs (Simple Lovable Complete). My suggestion? Ignore the terminology (although I remain steadfast in my belief that the term MVP still works and is completely valid!)
At the end of the day, your job is to solve a real problem, create actual value, and then solve for distribution. That’s it. You don’t need the perfect label. You need traction with meaning behind it.
The purpose of an MVP is to get to problem–solution fit; that critical stage where you’ve validated there’s a real problem by building a solution that creates value.
AI makes it easy to build and ship. This should make it easier to build “complete MVPs” that create actual value (instead of shit MVPs that are so thin no one cares). It also leads to over-building. Founders love features. Without proper customer insights or validation, founders can just keep building and building and building. Bloated software was never the answer to winning.
I’m blown away by the capabilities of no-code and AI coding tools to help bring someone’s vision to life. I love it. Being familiar and comfortable with these tools is a must. Tools that make it easier to build products are huge unlocks for tons of founders (especially non-technical ones). But don’t be delusional. Just because you can build something, doesn’t mean you should!
Don’t worship the acronym (MVP, SLC, MLP, etc.) Worship the outcome: clarity on problem–solution fit, confidence in the value you’re delivering, and enough user insight to scale with intention.
6. Measuring the right things still matters
Validated learning isn’t a gut feeling. It’s not “this feels promising” or “our demo signups are growing.” It’s rooted in innovation accounting, one of the original Lean Startup principles that gets far less attention than it deserves.
The idea is simple: define a baseline, run experiments that drive learning, and use the results to show true progress. In a world of AI-powered speed, this gets complicated. It’s so easy to launch multiple products and chase “winning” without knowing how to methodically and systematically get there.
If you’re not measuring the right things, you’re just accelerating your way into a wall.
I’m biased, but this is where Lean Analytics is helpful.
For starters, you need to figure out what stage you’re at. We identified 5 stages that every startup goes through: Empathy, Stickiness, Virality (Growth), Revenue and Scale.
Going through the stages in order increases your odds of success, because you’re building a logical foundation:
Empathy: Prove people care about the problem
Stickiness: Prove people will adopt the solution
Virality (Growth): Prove you can acquire more customers in a repeatable way (beyond early adopters)
Revenue: Prove the economics of your business
Scale: Grow!
At each stage there are key metrics you measure (which are also dependent on the type of business you run).
The emergence of AI doesn’t change this. AI should accelerate your experiments (which is a good thing!), but it also makes it easier to get lost in noise:
Signups ≠ retained users
Chat interactions ≠ engaged customers
Early ARR ≠ product-market fit
I’ve seen founders stare at dashboards that are lighting up—all green—and still feel like things aren’t working. That’s because they’re not tracking learning. They’re tracking growth surface area, not progress toward value.
Use AI to build faster, test more, reach further, but ground yourself in metrics that matter. Ask:
Did we de-risk a core assumption?
Did we learn something new about the user or the problem?
Can we draw a clear line between this metric and a better outcome for the user?
If you’re not tying your growth to real user value, you’re not building a business. You’re building noise.
So no, Lean Startup isn’t dead
What’s dead is the lazy version of Lean that people caricature to feel better about skipping customer work.
What’s alive and thriving is the opportunity to use AI to supercharge the Lean cycle.
Build faster? ✅
Run experiments more cheaply? ✅
Get prototypes in front of users instantly? ✅
Skip customer insight? ❌
Ignore distribution? ❌
Assume people want your product because it’s cool? ❌
In the past it may have taken you a week to run 1 experiment. Today you can run 10 in the same time. That’s powerful, but only if you do it properly, stay hypothesis focused, prioritize what matters and remain intellectually honest about the results.
Does AI increase the odds of success?
AI is spawning an explosion of new startups. That’s a good thing. Entrepreneurship is one of the only real solutions to the world’s biggest problems. We need more founders!
AI is a tool to speed up startup creation, which is good (if done properly)
AI-first startups (where AI is core to the solution) will solve huge problems that previously could not be solved as effectively (but only if customer validation is at the core)
Net-net, I’m bullish on AI. But not if we throw away the fundamentals. If you do that, you’re still going to fail, regardless of how committed you are to using AI.
Failure comes because of:
Weak customer insight
No real distribution plan
Shallow industry understanding
Obsessing over building, not validating
A leaky funnel with no plan to plug it
It’s 2025 and we’re still making the same mistakes. The scary part? We’re doing it faster. That’s not progress. That’s just speed.
If you’re building with AI, awesome. Use it. Push the boundaries. But don’t fool yourself into thinking that validation is a relic of the past.
Solve real problems. For real people. In ways they actually want. Honestly, this may be the only legitimate moat left (true customer insight).
Do that, and you’ll stand out from the AI noise.
Otherwise, you’re just another product (or collection of features) in the infinite scroll of “launches” no one asked for (but they’ll test things out any way, maybe even pay and then churn out…)
Ben , this is one of the excellent analysis that I have seen in near time. Great job!!
Great insights! So true