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Hello, fellow humans! News

For decades, getting engineering time was like pitching a studio executive. Your idea had to pass through product managers, sprint planning meetings, and budget committees. Each gate asked the same implicit question: "Is this worth three months and $50,000?" That friction was annoying. It was also protective.

A common story in software is that a product team uses AI coding assistants to ship a complex analytics dashboard in three days. It would have taken their engineering team three months in the old world. They were thrilled with the fast build cycle and had a great-looking feature. Two weeks later, user adoption sat at 4%. The problem was that their customers didn't want more analytics—they wanted simpler workflows. The team had built a beautiful, sophisticated answer to a question nobody was asking.

This is the AI speed trap: when building becomes nearly free, we stop asking "Should we build this?" and start asking "Why not just build it and see?" The old economic barriers that forced strategic thinking have collapsed. We need new ones.

In the pre-AI era, a bad idea carried a massive price tag. Six months of developer time meant $100,000 in fully-loaded costs, plus the opportunity cost of what else you could have built. This wasn't just expensive—it was painful. You had to defend your idea in room after room. You had to build business cases, create mock-ups, run focus groups, anything to prove the investment was justified.

Think of it like Michelangelo approaching a block of marble. The cost of quarrying, transporting, and working the stone was so high that sculptors spent weeks planning every cut. Bad execution meant months of wasted work and ruined materials. The cost structure enforced excellence.

AI has shattered this model. With coding assistants, the marginal cost of building approaches zero. A capable founder can go from idea to working prototype in an afternoon. The "vibe-coding" era has arrived—you describe what you want, and AI writes the code. No sprint planning. No technical debt discussions. No hard questions about whether this solves a real problem.

The removal of friction creates five distinct failure modes that every product leader needs to watch for.

When prototypes are cheap, the temptation is to build production-grade solutions before validating core assumptions. I've watched companies build comprehensive AI chatbots with conversation history, multi-language support, and advanced personalization—only to discover their users preferred simple FAQs. The organizational momentum behind a "complete" product makes it nearly impossible to kill, even when data screams that you should.

Product bloat used to be constrained by engineering capacity. Now every stakeholder's pet feature can get built. When every idea is buildable, "no" becomes harder to say. The result is products that collapse under their own complexity, with a graveyard of features that serve 2% of users but complicate the experience for everyone else.

AI makes technical implementation exciting again. The danger is letting these new capabilities drive strategy instead of user needs. Companies add AI features because the technology is fascinating, not because customers are asking for them. I call this "solution looking for a problem" syndrome—you've built something impressive that nobody actually wants.

"It only took a week to build" ignores that every feature must be maintained, documented, and supported forever. The code might be AI-generated, but the cognitive overhead isn't. Customer support questions are real. Bug fixes are real. Integration testing is real. Fast building doesn't mean cheap operating. You're creating what I call "zombie products"—features that nobody uses and never improve, but consume resources indefinitely.

Every hour spent building the wrong thing is an hour not spent discovering the right thing. In an era of near-infinite building capacity, the scarce resource is strategic clarity. You can build ten features in the time it used to take to build one, but if nine are wrong, you haven't gained anything except technical debt and user confusion.

Here's what makes this interesting: the same AI that makes it easy to build the wrong thing also makes it cheaper than ever to discover the right thing.

The lean startup methodology always promised rapid experimentation, but the economics never quite worked. Building five different prototypes to test five different hypotheses was prohibitively expensive. Now it's not. You can run parallel experiments that would have been impossible in the pre-AI era.

The key is replacing financial friction with disciplinary friction. Since AI removed the economic gatekeepers, you need to build your own. Before writing any code, test your assumptions with the smallest possible experiment.

The Painted Door Test: Create a button or landing page for a feature that doesn't exist. When users click, show them "We're building this—join the waitlist." The gap between clicks (interest) and email signups (intent) tells you everything about demand.

The Concierge MVP: Manually perform the service before automating it. Want to build an AI legal brief writer? Write ten briefs by hand for real clients first. If they won't pay for the outcome regardless of the method, automating it won't help.

The Smoke Test: Ask for payment before the product exists. People lie in interviews to be polite. They don't open their wallets unless the pain is real. Conversion rate to paid orders is the ultimate truth serum.

The Wizard of Oz Test: The interface looks finished, but humans power the backend. Users upload files for "AI analysis," but a human analyst reviews them and emails results. If users don't get value from the output, you haven't validated that AI can solve the problem—you've validated that nothing solves the problem.

The Broken Workflow Observation: Watch users solve the problem without your tool. Time them. Count their mistakes. If their current process isn't actually broken, your solution isn't actually needed.

These tests are now radically cheaper to build than they were five years ago. A painted door test that would have required a designer and front-end engineer can be assembled with AI tools in an hour. Use AI's speed for validation, not production.

Since the economic barriers are gone, you need intellectual barriers. Here are five methods to uncover what you don't know before you build.

The Pre-Mortem: Gather your team and say, "It's one year from now and this project failed spectacularly. Write down exactly how and why." This removes the social pressure to be optimistic and exposes the risks everyone was afraid to voice.

The Five Whys on Outliers: Don't just study average users. Study the ones who churned immediately or use your product in unexpected ways. Edge cases reveal structural weaknesses that average data hides.

Interview Your Anti-Persona: Talk to people who rejected your product or chose a competitor. Your happy customers validate your biases. Your lost prospects expose your blind spots.

The Null Hypothesis Role: Assign someone to build the case against your strategy using data. You need a dedicated critic to reveal where your logic is thin.

Shadowing with Naive Eyes: Bring in someone with zero context and watch them use your prototype while thinking out loud. You suffer from the curse of knowledge—you know how it should work. A novice instantly finds the gaps in usability that you're blind to.

AI hasn't made strategy obsolete. It's made it more critical.

When building was expensive, bad ideas killed themselves through economic friction. Now they slip through. The winners in the AI era won't be those who build fastest—they'll be those who learn fastest. They'll use AI's speed for experimentation and validation, not just production.

Next time you have a feature idea, resist the urge to immediately prompt your AI assistant to build it. Instead, ask it to design the smallest possible test to prove the feature is necessary. Build painted doors before products. Run smoke tests before engineering sprints. Let AI eliminate the cost of learning, not the discipline of thinking.

Remember: speed is only an advantage if you're running in the right direction. In an age where anyone can build anything, knowing what not to build becomes the ultimate competitive advantage.