The sigmoids won't save you

> If you used the same architecture as GPT2 today you're in for a bad time training a new frontier model. It's only because we have dozens of breakthroughs

What exactly are these dozens of breakthroughs? Most frontier models architectures today still look very much like GPT2 at their core. There were various of improvements like instructgpt, finetuning techniques, efficiency improvements with kv caches, faster attention, lora, better tokenizers, etc. Most of these are for making things run faster. The biggest differentiator has probably been data curation and post-training data and the ability to fit more into the model. But I think we had few breakthroughs that would fall into the category of different technologies.

We are multiple orders of magnitude away from Landauer limits - so next big thing in matmul could be photonic multipliers - there’s a bunch of them coming up in the next 3? years. So that’s a 2-4 order of magnitude improvement. Sigmoid?

So, this is not quite right: Alexander contributed to the report, but his personal opinion is more like the mid-2030s[1]. Freddie feels like this is him backing down from the original statement, but in fact he said this at the time the report was published, and in fact pointed out a graf below the quote that Freddie claims does tie him to 2027:

> Do we really think things will move this fast? Sort of no - between the beginning of the project last summer and the present, Daniel’s median for the intelligence explosion shifted from 2027 to 2028. We keep the scenario centered around 2027 because it’s still his modal prediction (and because it would be annoying to change). Other members of the team (including me) have medians later in the 2020s or early 2030s, and also think automation will progress more slowly. So maybe think of this as a vision of what an 80th percentile fast scenario looks like - not our precise median, but also not something we feel safe ruling out. [2]

I don't think this changes your observation that he is "personally invested" (i.e. believes this trendline will continue), but I'm pretty sure when AGI doesn't appear in 2027, many people will believe that this invalidates the arguments being made here (or in the report). The actual report was intended to give a feel for what a near-future "disaster" AGI scenario, and settled on a date to give that some concrete immediacy. The collective review that gave that as a possible, but not inevitable date is still ongoing (they originally pushed their best estimate out a bit further, but now they think, judging by the goals that are being hit, their scenario was a little too conservative). [3]

[1] https://freddiedeboer.substack.com/p/im-offering-scott-alexa... [2] https://www.astralcodexten.com/p/introducing-ai-2027 [3] https://blog.aifutures.org/p/grading-ai-2027s-2025-predictio...

Reinforcement learning has become a huge portion of compute used during training runs [1] and synthetic data is letting us get lots more mileage out of the existing data. Additionally, there is lots of new, high quality data being created and collected each day. I think the "running out of data" thing was pretty poorly reported by mainstream media.

[1]. https://www.dwarkesh.com/p/dario-amodei-2

This feels like a really verbose way of saying "things have been growing fast for a while so they should continue to grow as fast for just as long", and then he places the burden on people to prove him wrong. Um, no, the burden of proof is shared, "this will just keep going" requires just as much proof as "this is going to level off" if you're just looking at trend lines.

It's better to look at the underlying factors. Money sources are drying up, nobody is making a profit outside of nVidia, most blackwell GPU's are likely not even installed yet and will probably be 2 generations behind when they finally are being used, data centers are hitting all sorts of obstacles getting built and powered and they're getting built slowly, most AI researchers seem to think that LLMs are a dead end, the newer models seem to be getting more expensive and sometimes worse, or even potentially are showing signs of model collapse (goblins..), the supposed productivity gains are not materializing.. AI has worse public sentiment than congress.. I could keep going. Some obscure "law" seems to pale in comparison to the hard evidence that the status quo is utterly unsustainable and none of these companies seem to have a realistic plan other than trying to become too big to fail essentially.

I like some of this guy's writing on other topics, but to me this is a prime example of what happens when you get public "intellectuals" talking about subjects far outside of their area of expertise. It's not as bad as Richard Dawkins latest fall into psychosis but it's basically the same phenonmenon.