AI speeds up coding, but product strategy becomes the real bottleneck

The author opens with a personal statement about motivation: the drive to work on problems that matter, with people who understand the domain, and in a direction the author can articulate and believe in. This sets the stage for the article's core claim: that in the era of AI-assisted development, the product function — the ability to decide what to build and why — has become the true constraint on company success.

The author argues that the assumption underlying much current thinking is wrong. If execution has become faster, the bottleneck has shifted. Companies are still grappling with how to convert AI investment into real productivity gains, and many leaders attribute the payoff to engineers writing code faster. The author calls this a misdirection. The real win, according to the piece, requires product to make more and better decisions about what to build. The author observes this confusion directly: product managers and even customer support staff are now opening pull requests. While not inherently wrong, this represents a misallocation of product's time, because "every hour product spends shipping is an hour it doesn't spend on the one thing only it can do."

The economics of feature survival are key to the author's argument. Experimentation is cheap now; prototypes can be discarded. But features that are kept get baked into the product. Rollback is technically possible but rarely free. Users adopt new concepts, integrate them into habits, and sometimes forget older ones. Each step compounds within a domain, making the product progressively harder to understand and navigate. The decision to keep something, not the act of building it, is what shapes the product — and that decision remains expensive even when building is cheap. Done carelessly, this erodes user trust that the product has a coherent destination.

The author then lays out a vision of good product work: teams that become domain experts and share that expertise with engineers; teams that spend most time in a loop of collecting feedback, processing it, and producing clean problem statements; teams that brainstorm and carefully design high-level solutions; teams that partner with engineering to ensure shared language and domain modeling; and teams that communicate their plans across the organization. AI can accelerate information gathering and pattern discovery, but it cannot replace judgment about which problems are worth solving or what the product should become. That decision is the work — and it is the bottleneck.

The author concludes with a call to each function. To engineers: take ownership of what you build and the time you spend. Your experience deserves a good plan. Defend the quality and simplicity of your product. To product managers: your engineers can ship faster than ever. Make sure you wield that power for users' advantage. Now more than ever is the time for your deepest work, not your broadest.

The piece is framed as a reflection on how the economics of software development have shifted in the age of LLMs. The author's central observation is that speed of execution, which was once a scarce resource, is no longer the limiting factor. Paradoxically, this abundance of execution velocity creates a new and more costly bottleneck: the quality and clarity of product direction. The author notes seeing product managers and customer support staff writing code directly — a sign, in their view, that teams are confusing "being faster" with "being better."

What makes this argument distinctive is the author's framing of product decisions as irreversible or nearly so. Once a feature is shipped, users adopt it into their mental models and workflows; rollback requires not just code removal but also user re-education. Each feature that survives compounds the product's complexity. This cost structure — where reversal is nearly as expensive as forward motion — means that the decision to keep something is qualitatively more important than the ability to build it cheaply.

The author's prescription is a return to disciplined product methodology: deep expertise in domain, systematic feedback collection, and clean problem definition. AI is acknowledged as a tool that can accelerate information gathering and pattern recognition, but not as a substitute for judgment about what problems matter and what direction serves users best. The piece implicitly assumes that this kind of strategic clarity is what separates successful AI products from the noise of fast-shipped but ultimately directionless features.

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