From the essay - not presented in agreement (I'm still undecided), but Dario's opinion is probably the most relevant here:

> My co-founders at Anthropic and I were among the first to document and track the “scaling laws” of AI systems—the observation that as we add more compute and training tasks, AI systems get predictably better at essentially every cognitive skill we are able to measure. Every few months, public sentiment either becomes convinced that AI is “hitting a wall” or becomes excited about some new breakthrough that will “fundamentally change the game,” but the truth is that behind the volatility and public speculation, there has been a smooth, unyielding increase in AI’s cognitive capabilities.

> We are now at the point where AI models are beginning to make progress in solving unsolved mathematical problems, and are good enough at coding that some of the strongest engineers I’ve ever met are now handing over almost all their coding to AI. Three years ago, AI struggled with elementary school arithmetic problems and was barely capable of writing a single line of code. Similar rates of improvement are occurring across biological science, finance, physics, and a variety of agentic tasks. If the exponential continues—which is not certain, but now has a decade-long track record supporting it—then it cannot possibly be more than a few years before AI is better than humans at essentially everything.

> In fact, that picture probably underestimates the likely rate of progress. Because AI is now writing much of the code at Anthropic, it is already substantially accelerating the rate of our progress in building the next generation of AI systems. This feedback loop is gathering steam month by month, and may be only 1–2 years away from a point where the current generation of AI autonomously builds the next. This loop has already started, and will accelerate rapidly in the coming months and years. Watching the last 5 years of progress from within Anthropic, and looking at how even the next few months of models are shaping up, I can feel the pace of progress, and the clock ticking down.

Which technologies have been 99% "done" for "decades?"

Bicycles? carbon fiber frames, electronic shifting, tubeless tires, disc brakes, aerodynamic research

Screwdrivers? impact drivers, torque-limiting mechanisms, ergonomic handles

Glass? gorilla glass, smart glass, low-e coatings

Tires? run-flats, self-sealing, noise reduction

Hell even social technologies improve!

How is a technology "done?"

This is a really good question.

What convinces me is this: I live in SF and have friends at various top labs, and even ignoring architecture improvements the common theme is this: any time researchers have spent time to improve understanding on some specific part of a domain (whether via SFT or RL or whatever), its always worked. Not superhuman, but measurable, repeatable improvements. In the words of sutskever, "these models.. they just wanna learn".

Inb4 all natural trends are sigmoidal or whatever, but so far, the trend is roughly linear, and we havent seen seen a trace of a plateau.

Theres the common argument that "Ghipiti 3 vs 4 was a much bigger step change" but its not if you consider the progression from much before, i.e. BERT and such, then it looks fairly linear /w a side of noise (fries).

Even if the technology doesn't get better, just imagine a world where all our processes are documented in a way that a computer can repeat them. And modifying the process requires nothing more than plain English or language.

What used to require specialized integration can now be accomplished by a generalized agent.