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Notes -
AI 2040: Plan A
The AI 2027 authors published a follow-up. Scott Alexander also wrote a separate blogpost and although not in the author list contributed.
It's a very speculative and optimistic timeline of AI's future evolution. It presents five ways or "plans" the US government will intervene. Unsurprisingly, the ASI-pilled authors favor strong, global regulation to ensure alignment. Summaries:
Plan A (recommended): the US makes an international treaty with China, pauses AI training (not inference, i.e. no new models but we keep using existing ones), enforces full transparency of future research, then when alignment research advances enough carefully resumes
Plan S: the US makes an international treaty with China and pauses AI training for as long as possible
Plan B: the US regulates AI at home and demands China also regulate, but doesn't negotiate with them, probably leading to a war
Plan C: the US regulates AI and ignores China, so they overtake it and reach ASI first
Plan D: the US doesn't regulate AI, we get ASI in early 2031 and it probably kills everyone
Personally, I just don't share the optimism of these guys in either direction.
I think politicians will prioritize culture war and the failing economy over AI regulation, and at most pass some executive orders suggesting companies be more careful. But I also doubt we'll have ASI that can solve the abstract problems "take over the world" or even "keep existing world leaders in power" (they're getting old and increasingly unpopular, their parties may remain in power but only if their policies significantly shift).
What I expect from AI:
Basically solve legacy code by rewriting entire codebases, applying very niche domain knowledge, and actually finding and handling edge-cases better than humans
Greatly speedup research, leading to new discoveries and inventions. Important but background things like food preservation and medicine will improve from AI-assisted discoveries. Major advancements in math and theoretical physics
Much better and cheaper education, therapy, initial medical/legal appointments, personal repairs...maybe reducing but not eliminating human jobs, because human experts will offer these services "premium"
Won't replace human artists. Some advertisements and infographics will be AI but even some will still be human. At best it will assist them in a way where the human still fully controls the output, e.g. by generating code leading to new and improved software tools to learn, practice, and create art
Used by the vast majority as a personal assistant, but doesn't replace human relations
When do people think that AI productivity gains would start showing up in broad GDP in a noticeable way? I think it will be tough to convince the mainstream to take AI risk seriously until there is tangible proof of its abilities in the real economy, so I guess hopefully whatever point of no return these guys foresee is after AI improvement drives economic growth.
I think right now the default hypothesis should be that AI use is not economically valuable – or, at a minimum, that AI output does not scale with expenses.
The reason I say this is because when Anthropic and GPT hiked their prices – which was probably aligning the value closer to their overall cost to manufacture the product – there was (apparently) an immediate and noticeable climb-down from high-volume AI use on the part of corporate America. If AI was by default economically valuable, then using more would always pay for itself. If someone sold me something that cost them $1.00 for $1.10 and I could reliably sell it for $1.20 I would be a billionaire (and so would they) and I would be a fool to ever stop buying their product. But that's not what is happening with AI.
Now that I've presented the default hypothesis, let me explain why I don't quite agree with it.
First off, token-maxxing was always going to be at least somewhat wasteful. So it's not entirely fair to judge how economically useful AI is by a period when people were literally incentivized to use it as much as possible without rewards for the costs or the product.
Secondly, I suspect (particularly for certain applications) that the "true" price point of AI will work out to be economically viable. However, the economically viable niche may be much smaller (particularly with open models in the mix) than what is necessary to sustain continued maximized R&D. I think it is fairly likely that Anthopic and OpenAI find that there isn't enough demand to cover all of their bills at some point. This does not necessarily mean that Anthropic and OpenAI die, but if the demand for something like Mythos or even Fable at their true cost is limited (and particularly if people start turning to open-source models), what will happen is that R&D will slow down once Anthropic and OpenAI have to stop burning investors money and live off of what they can make. (This would be funny since it means that the free market is better at "the AI pause" than all of the AI safety advocates in the world.)
Finally, right now the big use-case for AI is coding. And let's be real: there is only so much money out there for software. AI could be an incredible coder, the best coder in the world, but at a certain point you would stop printing money with code because there's only so much demand. Even gamers could not consume infinite video games, and if you're Uber or Zillow or whoever than shipping 5x as much code and 2x as many features doesn't actually help you earn money unless the features get you new customers...and even if every new feature AI cranked out for Uber or Zillow was optimized to them get new customers (and wasn't just a useless button that three people think is kinda cool), there is only so much money out there for houses and taxi rides.
So, TLDR, there's not infinite money out there for Anthropic and OpenAI, at least not through software. I do tend to think that light manufacturing and other physical automation are likely be a much more economically lucrative than coding (software is maybe 3% of GDP), if there's a viable path there with LLMs.
At least this is my rough, somewhat tentative model of the world – but I don't work is pretty much any of the fields mentioned above, so take my views here with a grain of salt.
I’m sure AI already has some value with the amount of time it saves researchers and developers, and this is economic value because I’m sure these groups would pay above inference costs to keep using it. My understanding is that the vast majority of big AI companies’ massive debt is from training, and even the current inference costs may be profitable, but if they’re not and most customers had to they’d pay more.
Yes, I think this is correct. Hence my prediction of a R&D slow-down once the investor money runs out.
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