To me, all signs point to US over investment in hardware capacity to support the "frontier" when in reality the next major breakthroughs are likely radical optimizations that end in increased consumer training and inference efficiency.

I'm not sure how that, combined with open models inching up on frontier intelligence, is compatible with these massive AI IPO valuations?

It seems like closed model AI demand has a lot of contingencies that are ultimately fragile and possibly short sighted.

The article is about a guy leaving Microsoft and the disbanding of an internal team called "AI Frontiers".

If they make more efficient models won’t people just use the big data centres to make and run even bigger more powerful models than they can currently support? Or massive swarms of agents to look at all different avenues of a problem?

Do you think humanity is just going to run out of problems that need the smartest intelligence?

If so, then yeah, increasing efficiency 100x means we hit that point much faster.

If not, then it just means we make improvements 100x faster.

Everyone wants radically more efficient AI. That doesn’t mean it’s coming any time soon. And when models are twice as efficient, guess what happens next. We try making one twice as big for the same cost.