To your last point, it can’t possibly be sustainable, so it reads to me more as a short term FUD attack on American dominance in this space. It might have cost them half a billion dollars to train this model, and they’re going to make nothing off it. How many more times can they afford to do that? It’s going to get more expensive to train AI going forward, not less.
I also have a suspicion that the benchmark numbers are not real.
This happened at Google because there aren’t enough engineers to maintain all these services. This situation may not apply to Anthropic, as they can set up features to be maintained in perpetuity by Claude.
Personally, I find it difficult to competently reason about a system unless I've built my own version of that system. So if you make a practice of building your own versions of things, you end up with a more robust mental library of how stuff works. For this reason, I've never seen yak shaving as a waste of time. The yak shaving was at least 50% about loading the abstractions into my brain fully.
This seems like the obvious correct frame of mind with which to approach these tools. If it works for three hours on a task that would have taken me three work weeks, and 20% of the time it gets the task wrong, then I can just ask it to do it again with adjusted instructions. It will be much more likely to get it right the same time, and I’m still ahead of where I would have been by 14 days and 2 hours.
Over time I've found that by far the highest ROI move in a consciousness debate is to simply ask "Oh, interesting. How do you know that?" and watch everyone on all sides flounder. It's one of the few places where otherwise smart people make confident statements that they don't even realize they can't support until they're asked to try. The intuitions are so strong that they seem to swamp reason.
This has caused my own position, over time, to be a deep agnosticism about what's actually going on.
This only works up to a certain volume. The world economy requires about 38 billion barrels of oil per year. If you processed 100% of all grain, sugar crop, tuber and oilseed on Earth into liquid fuel, leaving zero for food, you'd get about 6 billion barrels of oil-equivalent in liquid fuels. Since it has to compete with food, the actual number would be much lower. It's not even close to being able to sustain our civilization.
Workflow-wise, the important distinction for me has been that I can refine a Skill by telling Claude Code to use it for related tasks until it does exactly what I want, correctly, the first time. Having a solid, iteratively perfected Skill really cuts down on subsequent iteration.
I also had a good friend who was an absolute wizard with early stablediffusion. he could make the model do things that were supposedly impossible at the time. His prompts were works of art. Now any of the commercial image models go far beyond what he could do. It's interesting to think about how there was this ephemeral art form of manipulating image models that existed for about a year.
The same could be said of prompt engineering. Gone are the days of telling the model that it is an expert software engineer with a PhD in the most relevant subtopic. These days the common wisdom is to just clearly articulate what you want it to do. Huge amounts of energy put into prompt engineering are now completely swept away by incremental model advances.
I have similar usage habits. Not only has nothing like this ever happened for me, but I don’t think it has ever deleted anything that I didn’t want to be deleted, ever. Files only get deleted if I ask for a “cleanup” or something similar.
I'm on the same page here. I have seen this sentiment about Codex suddenly being good a few times now, so I booted Codex CLI thinking-high back up after a break and asked it to look for bugs. It promptly found five bugs that didn't actually exist. It was the kind of truly impressively stupid mistake that I haven't seen Claude Code make essentially ever, and made me wonder if this isn't the sort of thing that's making people downplay the power of LLMs for agentic coding.
Perhaps surprisingly considering the current stratospheric prices of GPUs, the performance-per-dollar of compute is still rising faster than exponentially. In a handful years it will be cheap to train something as powerful as the models that cost millions to train today. Algorithmic efficiencies also stack up an make it cheaper to build and serve older models even on the same hardware.
It’s underappreciated that we would already be in a pretty absurdly wild tech trajectory just due to compute hyperabundance even without AI.