Yea, fair enough. His prediction was: “I think we will be there in three to six months, where AI is writing 90% of the code. And then, in 12 months, we may be in a world where AI is writing essentially all of the code”
The second one is more hedged (“may be a world”) but “essentially all the code” must translate to a very large fraction of all the value even if that last 1% or whatever is of outsize economic significance.
The original statement is:
“I think we will be there in three to six months, where AI is writing 90% of the code. And then, in 12 months, we may be in a world where AI is writing essentially all of the code”
So, as I read that he’s not hedging on 90% in 3 to 6 months, but he is hedging on “essentially all” (99% or whatever that means) in a year.
I don’t doubt they need capital. And the Nigerian prince who needs $5,000 to claim the $100 million inheritance does too. It’s the fact that he/they can’t get capital at something coming anywhere close to the claimed value that’s suspicious.
Amodei is forecasting AI that writes 90% of code in three to six months according to this recent comments. Is Anthropic really burning cash so fast that they can’t wait a quarter, demonstrate to investors that AI has essentially solved software, and then raise at 10x the valuation?
If AI executives really are as bullish as they say they are on progress, then why are they willing to raise money anywhere in the ballpark of current valuations?
Dario Amodei suggested the other day that AI will take over all or nearly all coding working within months. Given that software is a multi-trillion dollar industry, how can you possibly square that statement with agreeing to raise money at a valuation for Anthropic in the mere tens of billions? And that’s setting aside any other value whatsoever for AI.
The whole thing sort of reminds me of the Nigerian prince scam (i.e., the Nigerian prince is coming into an inheritance of tens of millions of dollars but desperately needs a few thousand bucks to claim it, and will cut you in for incredible profit as a result) just scaled up a few orders of magnitude. Anthropic/OpenAI are on the cusp of technologies worth many trillions of dollars, but they’re so desperate for a couple billion bucks to get there that they’ll sell off big equity stakes at valuations that do not remotely reflect that supposedly certain future value.
It’s worth thinking through what today’s DeepSeek-induced, trillion dollar-plus drop in AI related stocks means.
There are two basic explanations for DeepSeek’s success training models with a lot less compute:
Imitation is Easy: DeepSeek is substantially just re-treading the same ground as the other players. They’re probably training on O1 outputs, etc. DeepSeek proves that it’s easy to match breakthroughs, but not to generate them. Further advances will still require tons of compute.
DeepSeek is really clever: Facing compute constraints, DeepSeek engineers were forced to find a better way to do work and they did. That clever will likely translate into forward progress, and there’s no reason it would be limited to imitation.
If #1 is true, then I think it implies that we’re headed towards a big slowdown in AI progress. The whole economic value proposition for building models just changed. If your frontier model can be imitated at a tiny fraction of the cost after a few months, what good is it? Why would VCs invest money in your training runs?
If #2 is true, then we may be headed towards incredibly rapid AI progress, and the odds of recursively self-improving AI are much higher. If what you really need to build better models is tons and tons of compute, then AI can’t speed itself up much. If what you need is just lots of cleverness, then it’s much easier to imagine a fast takeoff.
#1 is likely better for alignment in that it will slow things down from the current frenetic pace (the possible downside is that if you can imitate a cutting edge model cheaply and easily then hostile actors may deliberately build misaligned models).
#1 also seems to have big implications for government/legal involvement in AI. If the private sector loses interest in funding models that can be easily imitated, then further progress will tend to rely on either: government investment (as in basic science) or aggressive IP law that allows commercialization of progress by preventing imitators (as we do in drug development). Either of those means a much bigger role for the public sector.
So, I certainly wouldn’t expect the AI companies to capture all the value; you’re right that competition drives the profits down. But, I also don’t think it’s reasonable to expect profits to get competed down to zero. Innovations in IT are generally pretty easy to replicate, technically speaking, but tech companies operate at remarkably high margins. Even at the moment, your various LLMs are similar but are not exact substitutes for one another, which gives each some market power.