beautifully put, AI is changing the way we make software but the way we measure productivity is still in our hands. L'chaim!

So what has actually shipped? I'm already using much many more AI-coded projects in my daily life than I was a few months ago.

I agree with the main point but this misses something crucial:

> why wouldn’t you use it to deliver more value to your customers, faster? That should show up as MAU, conversion, revenue

Most roadmaps are full of garbage and would be better off being deleted. You get very few truly useful new features in a year.

To paraphrase ESR: the value to your customers is in them being able to know that can rely on your product still operating next year, not in those 20 new features.

Or to think about it another way, maybe block will be better off with fewer developers, but only if they produce sufficiently FEWER features so that they’re forced to prioritize.

Confusing skeptic and sceptic will never not be funny to me (edit: I now live in shame)

When will performance or lacks of bugs become a metric again?

Many mid size to large companies are hilariously inefficient and the executives have no idea how work actually gets done. This means you can (in theory) fire a decent percentage of your workforce without affecting your output. You can then claim they’ve been replaced by AI without anyone ever challenging that assertion. I’m not making a pro or anti AI claim here, just saying that you won’t know whether you were wrong about it until/unless things start to go really badly.

It’s worth looking at sectors where LLM code generation hasn’t been very visible, such as certification-accredited flight-control, braking, train-control, medical, or nuclear-control source code involving real-time embedded operating systems. This sector relies on assurance: deterministic scheduling requirements, detailed commit traceability, tool qualification, configuration management, independent verification, etc.

Since this is an area where failure can lead not to Instagram accounts getting hacked, but planes falling out of the sky and nuclear reactors spewing radioactive elements, it’s worth a close look. Some of the most visible companies in this sector include: QNX, Wind River, SYSGO, Lynx, Green Hills, Siemens Embedded, etc. None of them seem to have much if any adoption of LLMs for source code generation based on public statements.

Research in this area agrees with this view:

“In this paper, I have conducted a comparative analysis of the C++ code generated by popular LLMs including: OpenAI ChatGPT, Google Gemini, DeepSeek, Meta AI, and Microsoft Copilot for compliance with MISRA C++. The study revealed that none of the evaluated LLMs generated MISRA-compliant code despite clear prompts, with DeepSeek showing the fewest violations and Meta AI the most.”

https://arxiv.org/abs/2506.23535

I don't get how if productivity is barely moving, how a decrease in comprehension will improve anything.

The kloc fallacy never actually disappeared. Project and engineering managers got wise to the fact that it was only loosely correlated with shipping features, and stopped emphasizing it. Most everyone else has carried on silently believing it without really thinking about it. And of course engineers themselves have always believed it. How many times have you heard some guy talk about how he wrote 10kloc over the weekend as a brag?

So, how the comapny will be evaluating the students on what basis?