The Lean Startup Is No Longer Enough
When AI builds faster than you can learn, what matters most?
In the early 2010s, building software came with real constraints. Developer salaries hovered around six figures in the US, and engineering time was a startup’s most expensive resource. Writing production-ready code wasn’t just time-consuming, it was financially risky.
And beyond code, the infrastructure itself was heavy. You had to set up servers, manage deployments, and build your stack from scratch. Speed wasn’t just a nice-to-have. It was survival.
Around that time, cloud platforms like AWS and Heroku began reshaping the landscape:
No need to buy and set up your own servers → With AWS, you can launch virtual servers in just a few clicks.
No need to manage your own data center → Platforms like Heroku handle that for you.
This shift lowered the entry barrier for product development, but the process of writing and maintaining production-ready code still consumed significant time and money. It wasn’t yet a world of instant iteration.
The Lean Startup philosophy emerged in response to these conditions. Its core idea — Build, Measure, Learn — offered a way to reduce waste in a resource-constrained environment. Instead of betting months on a full product, founders were encouraged to test assumptions early. A MVP didn’t need to be perfect; it just needed to be real enough to generate feedback. Learning faster meant surviving longer.
The types of questions founders were trained to ask reflected the technical and economic pressures of the time:
‘‘Why am I building this?’’,
‘‘Who is it for?’,
‘‘What’s the smallest version I can test before committing resources I might not get back?’
That mindset led to iconic early tactics. Dropbox, for example, validated demand using a product demo video before writing a single line of code for the actual tool. Because building the real thing was too expensive. The goal wasn’t to impress users with functionality: it was to find out whether the problem even mattered enough to solve.
Lean worked not just because it made sense in theory but because it fit the constraints of its time. When code was expensive, learning fast became your best defense.
When Building Is No Longer the Bottleneck
But today, building software looks nothing like it did in 2010. With the rise of generative AI, tasks that once required weeks of engineering effort can now be completed in hours, sometimes minutes.
Early-stage teams are already taking advantage of this. Tools like GitHub Copilot, Replit’s Ghostwriter and Vercel AI SDKs let founders deploy and document code in a fraction of the time.
This shift is already visible in the numbers
As Jared Friedman, General Partner of YC, mentioned recently on their YouTube, a quarter of the W25 startup batch have 95% of their codebases generated by AI:
“…It’s not like we funded a bunch of non-technical founders. Every one of these people is highly technical, completely capable of building their own products from scratch. A year ago, they would have built their product from scratch, but now 95% of it is built by an AI,” he said.
We’re not talking about no-coders. We’re talking about people who can code, opting not to -> because it works.
The same pattern shows up even at scale: Duolingo, a public edtech company, announced that it had launched 148 new courses in a single week, all built with AI-generated content. For comparison, it had taken the company over a decade to create its first 100 courses. The cost of iteration is collapsing, and the implications are hard to ignore.
Mark Zuckerberg, on the other hand, speaking about Meta’s internal AI coding agents, recently predicted:
“…I’d guess that within 12 to 18 months, most of the code going into these efforts will be written by AI. And I don’t mean autocomplete. I mean better-than-average-engineer quality.”
He wasn’t talking about passive tools. He described systems that reason, run tests, find bugs, and write better code than most engineers:
It’s a new substrate for product creation:
“Build” is no longer the bottleneck.
“Measure” might still be.
“Learn” definitely is.
The edge moves from execution to insight. So the question shifts too. Not just “How fast can I test this idea?”, but “What’s worth testing when the cost of testing is near-zero?”. The Lean Startup isn’t irrelevant but it’s optimized for a world that’s disappearing. It still teaches speed. But today, speed is free. What matters now is what to test, not just how fast to test it.
Who Wins Then?
When execution becomes easy, strategy becomes everything.
AI has made it trivial to ship features but not to get them adopted. Distribution, not development, is now the battlefield.
Startups used to win by launching. You showed up with a new idea, and that was often enough to stand out. Now, everyone shows up. Everyone launches. The feed scrolls on.
So the edge moved. Not to who builds, but to who lands. Successful founders think in loops, surfaces, and behaviors. They ask, “Where will this live? What will carry it forward?”
That’s why many That’s why many teams today start from distribution, not functionality.
BeReal’s daily notification isn’t just a feature; it’s a daily ritual. A trigger loop.
Luma disguised growth as calendar utility; every event created became a distribution node.
Arc didn’t just redesign the browser; it designed status, and turned users into proud promoters.
But this isn’t just a change in go-to-market thinking. It reflects a deeper shift in how software gets made.
As Erman Taylan argues in his newsletter #Soft Commitment, building software today looks nothing like it did a decade ago. Drawing on thinkers like Fletcher Richman, he suggests that even the software development cycle itself — SDLC — has morphed into something new.
In an AI-native world, building looks more like this:
1️Prioritize
2️ Ship the fastest testable version, even a clickable prototype
3️ Measure real user signals
4️ Iterate or kill, then repeat
Code isn’t the bottleneck anymore. Time-to-feedback is.
And the tools aren’t the only thing that’s changed. So has the terrain.
The internet has shifted from open discovery to gated ecosystems.
In 2010s, launching meant posting to Hacker News or Reddit. Today, discovery happens inside TikTok, Instagram, YouTube, the App Store, Discord, Shopify.
Products don’t just compete on value anymore. They compete on where they appear, how they integrate, and how quickly they trigger behavior.
That means products today don’t just compete on value. They compete on:
Context (Where do they appear?)
Integration (How do they plug into workflows or ecosystems?)
Triggerability (How easily do they form habits or spread?)
Sure, the fundamentals still apply. You still need to talk to users. You still need to know your ICP.
But Lean and Agile? They’re no longer the strategy. They’re the starting line.
Bonus: The Rise of Solo Founders
According to Carta’s 2025 Founder Ownership Report, 35% of all new startups in 2024 had a solo founder. That’s up from 29% in 2023, and just 17% in 2017, a sharp and steady increase over the past seven years, drawn from anonymized data across more than 45,000 U.S. startups.
There’s clearly something happening. But what should we make of it?
One explanation is purely logistical: with AI tools accelerating everything from prototyping to testing to shipping, the minimum viable team size may have shrunk. Tasks that once required three or four people — design, frontend, backend, growth — can now be bootstrapped by one highly focused individual. The cost of starting alone has gone down.
But does that mean it’s better to go solo?
This trend might shows correlation, not causation. Some of the most resilient, durable startups are still built by teams — ones that combine different ways of thinking, challenge each other’s blind spots, and make better decisions under stress. Founding alone might be faster, but that doesn’t mean it’s smarter or more sustainable.
So how should we read this data?
It might simply reflect a growing willingness to begin alone, not a new formula for long-term success. It could signal changing attitudes about early-stage risk, or growing trust in tool-driven leverage. Or maybe, it’s a temporary consequence of a world where the cost of trying has collapsed.
Whatever it is, it’s not a verdict. But it’s a question worth holding on to.
What’s really changing here, and what still holds true?