When Building Becomes Free: The Post-Scarcity Product Paradox

We're racing toward a fascinating inflection point in software development. AI coding agents are getting better, faster, and cheaper at an exponential rate. Companies like Cursor are hitting $200M ARR, Devin's worth billions, and every week there's a new agent that can build entire applications from a prompt.

But here's the thing that keeps me up at night: what happens when building software becomes essentially free?

The naive answer is that every company magically completes their roadmap and becomes wildly successful. Every feature request gets built, every bug gets fixed, and engineering backlogs disappear into the ether.

If only it were that simple.

The Experiment Bottleneck

Product development isn't really about building things. It's about running experiments.

We make our most educated guess about what drives value for users, build it, and see if it worked. Today's development process, with its natural constraints, actually protects us from our worst impulses. When engineering resources are limited and building takes weeks or months, we're forced to be selective. We debate, we prioritize, we kill bad ideas before they consume resources. The friction in the system acts as a natural filter.

Here's the uncomfortable truth: our product backlog is just a ranked list of our best guesses, and most of those guesses are wrong. We're spared from ourselves by only ever working on the top 1-5% of the backlog. That "brilliant" idea sitting at position #47? It never sees the light of day, and that's probably a good thing. The constraint of building speed has historically saved us from pursuing every half-baked hypothesis that sounds good in a meeting.

When building time approaches zero, we don't get infinite success. We get infinite opportunities to be wrong, faster.

There's a tempting narrative in agile circles that every experiment can be built, tested, and iterated on in a single sprint. This works beautifully for certain types of features, but the reality is that not all learning happens on sprint boundaries. Some experiments need to marinate. Customer behavior changes take time to manifest, seasonal patterns need seasons to observe, and genuine workflow adaptations don't reveal themselves in two weeks. When you can build a feature in an hour, but it takes three months to know if it actually solved the problem, you've created a dangerous gap. You're either making decisions on incomplete data or you're stacking experiments on top of each other, unable to isolate what's actually working.

The challenge compounds when you're forced to define experiments at lightning speed. With much less time to gather data from previous experiments, you lose the natural reflection periods that used to happen while engineering was building. Those three weeks of development time weren't just about writing code; they were about watching the metrics from your last release, talking to customers about their experience, and refining your hypothesis for the next round. When that buffer disappears, you're making rapid-fire decisions with decreasing signal quality.

This creates three new bottlenecks that completely reshape how product teams need to operate.

1. The Human Bandwidth Bottleneck

You can only test so many things at once before users get lost and overwhelmed. Your tools and products are there to help people do a job, and if your tool changes every day, asks for feedback constantly on something new, you're going to burn out your users. They didn't sign up to be perpetual lab rats.

I've seen this play out already with companies that got too aggressive with A/B testing. When every interaction becomes an experiment, users lose trust in the product's stability. They can't build muscle memory, they can't train their teams, and they start looking for alternatives that feel more solid.

The constraint isn't our ability to build or even to test. It's the user's capacity to adapt and provide meaningful signal. Companies that respect this cognitive load limit will paradoxically move faster by moving more deliberately.

2. The Great Convergence

When everyone can max out the accelerator, things converge quickly. We're already seeing AI-coded products take over industries with much smaller and more dollar-efficient teams. But when everyone has access to that capability, we'll see features and products converge in record time.

That killer feature you spent months perfecting? Your competitor's AI agent will build it in an afternoon after seeing your launch tweet. The moats we've relied on – execution speed, technical complexity, engineering talent – they're evaporating.

Product differentiators can no longer be something that's buildable. They'll need to be exclusive access to data, regulatory capture, network effects, or other resources that can't be replicated with code. The new moats become ungenerative: things that no amount of AI coding agents can recreate.

3. The Intuition Premium

Here's the ultimate irony: as every team becomes an AI demigod, intuition and insight become the most valuable traits for team leaders.

Picking what NOT to build becomes the core competency. Choosing directions that have promise but little supporting data or evidence becomes how innovation happens. Not too different from today, but it will happen at a much faster rate.

When you can build anything instantly, the most valuable skill becomes knowing what not to build. The leaders who thrive won't be the ones with the best data or the fastest deployment pipelines. They'll be the ones who can look at a sparse signal and say "I think there's something here." They'll need the confidence to pursue non-obvious directions that the convergence forces haven't discovered yet.

The New Product Playbook

So what does product management look like in this brave new world?

Experiment Design Over Execution: Product managers shift from managing timelines to designing meaningful experiments. The question isn't "can we build this?" but "what will we learn from this?" Teams will need to develop new frameworks for experiment validity that account for the loss of natural pacing.

Deliberate Pacing: Just because you can ship hourly doesn't mean you should. Successful teams will create artificial constraints, giving users time to absorb changes and provide real feedback. This isn't about being slow; it's about being intentional with your learning cycles.

Position-Based Strategy: Instead of competing on features, companies will compete on unique data access, regulatory positions, or network effects. The strategy conversation shifts from "what should we build?" to "what unique position do we hold?"

Conviction-Driven Development: With infinite building capacity, the scarcest resource becomes conviction. Teams need leaders who can make bold bets on incomplete information, who can resist the urge to build everything just because they can.

The Bottom Line

In a world of infinite efficiency, the bottleneck isn't engineering talent or capital. It's human cognitive bandwidth, strategic positioning, and the courage to build something that doesn't yet make sense to anyone else – including your AI agents.

The companies that win won't be the ones who build the most or the fastest. They'll be the ones who understand that just because you can build everything doesn't mean you should. They'll be the ones who can find signal in the noise, who can create stability in the chaos, and who can maintain conviction when everyone else is converging.

Welcome to the post-scarcity product paradox. The constraints have changed, but the game is more interesting than ever.

What do you think? As AI agents reshape software development, what new bottlenecks are you seeing emerge? How is your team preparing for a world where building is essentially free?