Believe the Checkbook
December 22, 2025
Everyone’s heard the line: “AI will write all the code; engineering as you know it is finished.”Boards repeat it. CFOs love it. Some CTOs quietly use it to justify hiring freezes and stalled promotion paths.
The Bun acquisition blows a hole in that story.
Here’s a team whose project was open source, whose most active contributor was an AI agent, whose code Anthropic legally could have copied overnight. No negotiations. No equity. No retention packages.Anthropic still fought competitors for the right to buy that group.
Publicly, AI companies talk like engineering is being automated away. Privately, they deploy millions of dollars to acquire engineers who already work with AI at full tilt. That contradiction is not a PR mistake. It is a signal.
One thing I’ve heard repeatedly over the last year or two, when people are critical of code generation using large language models, is something along the lines of: “But writing the code is not the bottom line when it comes to software engineering.” And there’s some validity to that. The question is: well what is the bottleneck? People might say testing. People might say architectural decisions. Quality assurance. All those are clearly choke points in delivering software. But here Robert Greiner observes that “The bottleneck isn’t code production, it is judgment.”
I certainly think there’s something to this, but I think sometimes what we do is we stop with an observation like that or the observation that the code generation is not the bottleneck. I think it’s really important here is to think through the next steps and the consequences. So if judgement is the bottleneck, not code generation, then what are the implications for engineering leaders, which Robert Griner explores here? For software engineers themselves, whether junior, mid-career, or senior? For companies and organisations, and more broadly?
And is this true only of code or is it true of other outputs of generative AI?
My working hypothesis would be that it is, and so organisations and individuals should be developing and encouraging the development of judgement, what some people might call taste. Because it’s that discernment, that judgement, that taste which is certainly valuable in software development, but I think in other fields will become increasingly valuable, because the models will be able to, are already able to, generate a lot of code, a lot of copy, a lot of images, a lot of legal advice.
A key question will be “what is the value of any particular generation from a model?”
That’s where expertise comes in, that’s where taste comes in, that’s where discernment and judgement come in. So develop those, continue to develop those. What has long differentiated a person in terms of capability, in many respects, is not the ability to recite vast bodies of knowledge; it is the ability to know among all the vast knowledge what is the appropriate knowledge to deploy in a particular situation.
