There is something archaic about the way we are doing AI that I think we will look back on and laugh at.

No, I don't think AI is archaic. The way we are doing it of course isn't archaic–AI currently represents the pinnacle of human engineering. However, I strongly feel that down the line, we will look back at how AI is being done right now and laugh .

Neural networks are remarkable—but they're woefully inefficient. The sheer amount of processing power, water, and electricity to power a frontier model is truly mind-boggling. We have massive data centers to power frontier models. And while it is truly remarkable, while it is the current pinnacle of human engineering, "scaling laws" might later appear like a crutch. The way AI is being done right now, yeah, more is more—but I think the real path forward is how we can do more with less. A fundamental shift in how AI is done such that you can achieve the same (or better) intelligence on far, far less.

This idea seems laughable—but think back to supercomputers/mainframes in the 60s. The modern iPhone makes them seem like dumb behemoths. 1960s mainframes typically had around 1 megabyte (or less) of RAM. Modern iPhones have hundreds of thousands of times more memory (e.g., 6 to 8 gigabytes of RAM) and hundreds of gigabytes of flash storage. A single iPhone offers hundreds of thousands of times the processing speed and memory, consuming a tiny fraction of the power.

We are awe-struck by modern AI—but decades down the line, I think we might look at data centers the way we look at mainframes in the 60s, or even the way we look at 90s-00s PCs. The brain itself is 20-watt proof that the opportunities for efficiency may be enormous.