But regardless, compute will get to a point where human level intelligence close to as efficient as we are. You could argue it already is today, when you factor in the resources that the average person in the west already uses in terms of their overall impact on the planet.
I can just as well describe the future evolution of the internal combustion engine and claim it will get more and more efficient and eventually we will be able to burn oil so efficiently that our personal vehicles can fly through the atmosphere at twice the speed of sound.
There is limitations to digital computers just as there are limitations to internal combustion engines. Our brains are not digital computers. When we use our brains we don’t just do a bunch of linear algebra.
This is a silly comparison. There is a certain quantity of energy stored in oil, so we know what peak efficiency looks like. We don't actually know what amount of energy is required to solve certain problems. We quite literally have models with quite a bit of capability that can run locally on a phone today, right alongside Stockfish, for example.
And this is to say nothing of work happening now on new hardware approaches, such as Normal Computing's work on thermodynamic matrix math: https://www.normalcomputing.com/blog/a-first-demonstration-o...
That said, this feels like a strange tangent: I'm not sure it's that important that the models be as energy efficient as a human brain. We don't avoid cars because they're less energy efficient than our legs. ;)
This matters because unlike cars LLMs are only doing stuff we can already do using our brains, just several orders of magnitudes less efficiently. Cars can at least take us distances we would never be able to using our muscles. In comparison, if I need to compile CPython into a WASM binary I can simply download a library that does it, or copy paste code in a few seconds, for a million billionth of the energy it takes an LLM to do the same. Except when I download the library or copy-paste the code I (hopefully) attribute the original author and give them credit for their work.
I'm suggesting that while LLMs are bounded by physical reality, that you actually don't know what that bound is. Just a few years ago we would have thought it a fantasy to have a conversational model run on a phone.
Even if you could compute it now, that would still be tied to current architectures. With appropriate incentives, we'll continue developing hardware to make these models more efficient to execute. It's very likely that you'll be able to run a Fable caliber coding model on your phone in the next five years.
>This matters because unlike cars LLMs are only doing stuff we can already do using our brains, just several orders of magnitudes less efficiently. Cars can at least take us distances we would never be able to using our muscles.
But that's not largely true of cars. The majority of trips are five miles or less and could easily be replaced with a bicycle. While I might personally use a bicycle, the majority choose a car to save a bit of time and effort.
So, please continue to enjoy your car, and I will continue to enjoy ready access to an LLM for a variety of other tasks. My inference energy costs are almost certainly less than your vehicle usage. ;)