LLMers Are BSing You
If you follow AI news even casually, you are constantly bombarded with headlines about how close we supposedly are to AGI, usually announced by AI CEOs competing to sound one step ahead of everyone else.
OpenAI’s Sam Altman says we are now confident we know how to build AGI. Google DeepMind’s Demis Hassabis puts it a few years out. And Anthropic’s Dario Amodei, the most aggressive of the lot, has pushed the scaling bet into outright overclaiming, arguing that powerful AI could be just one or two years away.
From Silicon Valley to Shenzhen, LLMers seem to be in a competition over who can make the wildest claim first. Bigger models will become AGI. AGI will become superintelligence. Superintelligence may wipe us out.
As a side note, the genuinely dangerous part has very little to do with some godlike machine intelligence suddenly deciding to destroy humanity, and much more to do with something far more ordinary.
Behind all this futuristic drama, though, most LLMers are making essentially the same bet: that getting from today’s AI to increasingly powerful machine intelligence, and eventually to AGI, is mainly a matter of scaling.
You know the fancy tale by now: more compute, more data, larger models, longer inference, denser GPUs and… voilà: AGI at the end of the assembly line.
There is just one problem, though…
The mathematics does not show that this assembly line leads to AGI.
Why Math Doesn’t Back Them Up
You know something is rotten in the kingdom of AI when brilliant people start waving scaling curves around as if extrapolation had suddenly become a theory of intelligence.
Sam Altman claims that model intelligence scales roughly with the logarithm of the resources used to train and run it. Well, a scaling law can tell you how the curve rises. It cannot tell you when the curve turns into a mind.
Dario Amodei’s argument is even simpler. Skip the formula, add more compute, watch LLMs acquire a broad range of cognitive capabilities, keep scaling and, somewhere up the graph, voilà: extraordinarily powerful, beyond-human systems.
At that point, the mathematics has left the room. What remains is a story about where AI might go, not a prediction of where the mathematics says it will go.
Strip away the glittering façade of GPUs, trillion-dollar infrastructure plans, benchmark curves, and promises of approaching machine intelligence, and what remains is brutally simple: the mathematics underneath does not lead to the destination they are selling, as the animation below conveniently shows.
Now let’s translate the LLMers’ grand claim into plain mathematics and see what is actually hiding underneath all the LLM hype. The recipe goes something like this: take C, the computational resources we keep pouring into the machine, and…