It's hardly an inevitability though (nothing is... and analogues to the industrial revolution are iffy at best, we haven't ever had an attempted replacement for intelligence itself before).

Society is doing this at an unprecedented cost and it's clear a large portion of the population is uneasy with it. Whether society in the US, Europe, and Asia will continue to allow such investment at the expense of everything else remains to be seen.

The practice of writing code, or programming, in recent years has really fallen into two buckets:

The vast majority of folks are given a task, they write code to complete that task, and the task completion then counts towards some objective (eg; a new feature, product or fixing a bug). Perjoratively, they've been known as "ticket takers".

A much smaller group have instead worked in the other direction-- identifying where improvements can be made to a product, piece of infrastructure, or pain point and transformed that into tasks that can then be solved via code.

How much of a role you play in that strategy and formulation has been the real differentiator. Not so much what you know. While these are correlated, they're very different.

At a high level, it's been the difference between "developer" and "engineer" but the reality is the titles have become somewhat meaningless in recent years where many "engineers" are just doing the same CRUD tasks over and over.

The reason this matters is that at some point, you can only abstract so far... the requirements for what to build have to come from somewhere. At the most extreme case, there's only the CEO and a company that's nothing but AI agents. In the least extreme case (today) each line worker could manage 1 or more LLMs/agents.

It's not entirely clear to me or frankly a large portion of those in the industry that we're suddenly on pace for one outcome vs another. But I do think that software isn't particularly unique here other than it was an initial starting point for LLMs to deliver value. All white collar work is at risk including CEOs.

And if that happens it would be outlandish to think a utopia emerges... the opposite is far more likely.

The problem is the valuations assume astronomical growth... that is likely impossible for all of them to simultaneously achieve. Which means something's got to give.

They're not and it's not clear why you seem to believe that. The immense capex for buildouts, training costs, etc. are not rolled into inference costs. Moreover, companies are already rapidly starting to re-evaluate token spend.

Honestly... not that dramatically. Each release is much more marginal. And quoted official benchmarks doesn't translate very well into the real world.

4.7 regressed hard in some ways. But a compounding factor too is that the claude code harness seems to nerf the model after a few months. Probably to reduce token use.

So far 4.8 seems less verbose but we'll see in practice what it translates into meaningfully.

To get a quality, lasting, result you're ultimately having to carefully study everything otherwise you end up quickly accumulating cognitive debt and the speedup soon shrinks as you're constantly having to revisit the initial approaches.

Increasingly, we're also seeing the moat shrink somewhat. Frontier models are converging in performance (and I bet even Mythos will get matched) and harnesses are improving too across the board (OpenCode and Codex for example).

I get why they're trying to do that (a perception of a moat bloats the IPO price) but I have little faith there's any real moat at all (especially as competitors are still flush with cash).

No matter what, common people are quickly losing agency in that discussion.

This feels a bit like a strawman. How do you assess it to be bad software without being an engineer yourself? What constitutes successful for you?

If anything, AI tools have revealed that a lot of people have hubris about building software. With non-engineers believing they're creating successful work without realizing it's a facade of a solution that's a ticking time bomb.