GitHub CEO: manual coding remains key despite AI boom

Copying from another post. I’m very puzzled on why people don’t talk more about essential complexity of specifying systems any more:

In No Silver Bullet, Fred Brooks argues that the hard part of software engineering lies in essential complexity - understanding, specifying, and modeling the problem space - while accidental complexity like tool limitations is secondary. His point was that no tool or methodology would "magically" eliminate the difficulty of software development because the core challenge is conceptual, not syntactic. Fast forward to today: there's a lot of talk about AI agents replacing engineers by writing entire codebases from natural language prompts. But that seems to assume the specification problem is somehow solved or simplified. In reality, turning vague ideas into detailed, robust systems still feels like the core job of engineers.

If someone provides detailed specs and iteratively works with an AI to build software, aren’t they just using AI to eliminate accidental complexity—like how we moved from assembly to high-level languages? That doesn’t replace engineers; it boosts our productivity. If anything, it should increase opportunities by lowering the cost of iteration and scaling our impact.

So how do we reconcile this? If an agent writes a product from a prompt, that only works because someone else has already fully specified the system—implicitly or explicitly. And if we’re just using AI to replicate existing products, then we’re not solving technical problems anymore; we’re just competing on distribution or cost. That’s not an engineering disruption—it’s a business one.

What am I missing here?

I think the crux is that specification has been neglected since even before AI.

Stakeholders (client, managers) have been "vibe coding" all along. They send some vague descriptions and someone magically gives back a solution. Does the solution completely work? No one knows. It kinda works, but no one knows for sure.

Most of the time, it's actually the programmers' understanding of the domain that fills out the details (we all know what a correct form submission webpage looks like).

Now the other end has become AI, it remains to be seen whether this can be replicated.

This is spot-on, and a comment I wish I could pin for others to see.

GenAI is such a boon at present because it occasionally delivers acceptable mediocrity to PMs and Stakeholders who will accept said mediocrity because they have no real clue what they (or their customers) actually want. It’s a system of roles and output that delivers based on patterns in incomprehensively large data sets, provided to humans who cannot hope to validate that information is accurate or legitimate (and not just random patterns in a large enough data set), but passed along as gospel from digital machinegods. To a withering empire obsessed with nostalgia and whose institutions are crumbling beneath them, GenAI appears as a savior; to those confident in their position in the world order, it is merely a thin tool in a larger toolbox, to be used toward greater ends rather than middling output.

Those who understand the specification problems are in a position to capitalize off such monumental shifts, while those blindly chasing get-rich-quick schemes, grifts, and fads will be left behind.

Can you expand into the "those who understand the specification problems are in a position to capitalize off such shifts?"

Is this just business domain knowledge and good communication?