We’ve spent decades optimizing for code quality. Code reviews, static analysis, comprehensive test coverage, and meticulous architecture reviews—all in service of writing perfect code. But here’s the uncomfortable truth: in an AI-first development world, code quality isn’t the bottleneck anymore. The code itself has become cheap.

Think about Photoshop. For the vast majority of users, the free or low-cost versions handle 90% of what they need. If you need pixel-perfect precision for professional work, you pay a premium. But for everyone else, good enough is actually good enough. The same principle applies to AI-generated code.

The Quality Threshold Has Shifted

AI-generated code quality has reached a point where it’s comparable to—and often better than—human-written code for most use cases. This isn’t about perfection. It’s about recognizing that the quality bar for most code doesn’t need to be perfect. Just like Tesla’s Full Self-Driving doesn’t need to be flawless to be better than human drivers in most scenarios, AI-generated code doesn’t need to be perfect to be better than what most teams produce under time pressure.

And this is just the beginning. AI-generated code will continue to improve in quality, speed, and cost. The models get better with each release. Generation gets faster. Costs continue to drop. What’s good enough today will be even better tomorrow, making the economics even more compelling.

The implications are profound. When achieving good code quality is no longer the limiting factor, what becomes the bottleneck? Build times and release processes. These are the real slow downs, along with human gates in the development workflow.

The Real Bottlenecks

While we’ve been obsessing over code quality, the actual friction points have shifted. Build times that stretch into minutes or hours. Release processes that require multiple approvals and manual steps. Human gates where engineers wait for reviews, approvals, or decisions. These are the constraints that actually limit velocity.

When code generation takes seconds but builds take minutes, the math doesn’t work. I’ve seen teams generate a complete API endpoint in 30 seconds, only to wait 12 minutes for the build to complete. When you can generate a feature in an hour but wait three days for it to ship through approval processes, you’re optimizing the wrong thing. The bottleneck isn’t writing code—it’s getting code into production.

This shift requires rethinking our entire development workflow. If code is cheap, we should be generating more of it, testing it faster, and shipping it more frequently. But our processes are still optimized for the era when writing code was expensive.

Sampling Code Like Raw Materials

If you want to keep humans in the loop while optimizing for speed, you can take samples of generated code for review. This mirrors how quality control works in manufacturing. When buying mass quantities of raw materials, you don’t inspect every single item. You sample. You trust the process for the majority and apply scrutiny where it matters most.

By default, use cross-agent checks. Have one AI agent generate code and another review it. This catches most issues without human intervention. Then sample the output—review a percentage of the generated code, focusing on critical paths and high-risk areas. For example, a team generating 50 API endpoints might have AI agents cross-check all of them, then have humans review 10%—the 5 most critical endpoints that handle payment processing or user authentication. Most code can be rewritten from scratch if needed, so the cost of a mistake is lower than the cost of reviewing everything.

This approach requires a fundamental shift in how we think about code review. Instead of reviewing everything, we review strategically. We trust the process for routine code and apply human judgment where it adds the most value.

The New Economics of Code

When code is cheap, the economics of software development change. The cost of generating code drops dramatically. The cost of testing and deploying code becomes the constraint. The value shifts from writing code to understanding what code to write, when to write it, and how to integrate it into larger systems.

This doesn’t mean code quality doesn’t matter. It means we need to be strategic about where we invest in quality. Not every line of code needs the same level of scrutiny. Not every feature needs the same level of perfection. We can afford to be more experimental, to try more things, and to learn faster.

The teams that recognize this shift and adapt their processes accordingly will move faster and learn more. They’ll generate more code, test more ideas, and ship more frequently. They’ll treat code as a means to an end, not an end in itself.

What This Means for Engineering Teams

This shift has practical implications for how we structure teams and processes. If code is cheap, we should be generating more of it. If build times are the bottleneck, we should be optimizing builds. If release processes are slow, we should be automating them.

It also means rethinking how we measure engineering productivity. Lines of code written is even less meaningful than it used to be. Time to ship, frequency of deployment, and ability to experiment become more important metrics. Instead of tracking “500 lines of code written this week,” track “3 features shipped, 12 experiments run, average time from idea to production: 2 days.” The teams that measure what actually matters—impact and velocity—adapt faster to the new economics of code.

Most importantly, it means recognizing that the value of engineers isn’t in their ability to write code—it’s in their ability to solve problems, make good decisions, and build systems that matter. Code is just the output. The thinking, the judgment, and the system design are what actually matter.

The Future of Code

Code is becoming a commodity. This isn’t a threat to engineers—it’s an opportunity. When code is cheap, we can focus on what’s actually valuable: understanding problems, designing solutions, and building systems that solve real problems.

The engineers who thrive in this world aren’t the ones who write the most code or the most perfect code. They’re the ones who know what to build, why to build it, and how to build it sustainably. They’re the ones who can work effectively with AI agents to explore solution spaces and make good decisions.

Code is cheap. Judgment is expensive. And that’s where the real value lies.