Those exploratory "side quests" in industry are how we got the transistor, the liquid crystal display, Shannon's information theory, the laser and practical photovoltaic cells, among many other foundational technologies upon which we rely today.

> and corporate labs pick it up once it reaches the point where the basics show commercial promise.

How does that work when the research team moved elsewhere? Re-hire talent? That takes time - the 1 thing you don't have in a fast-moving field like AI. Post sums it up nicely:

"Do the math—how much is it worth to halve training and inference cost given that the industry has investments approaching a trillion dollars?"

How much is these researchers' salaries, compared to what MSFT spends on compute? Chances are it's peanuts in comparison.

> CS is a little bit of an exception because the capital cost of basic research isn’t, proportionally, as high, and because the promise-to-product path is often short enough that having done the basic work in house can be a meaningful advantage in getting to market first (though, see Google on much of modern AI for a counterexample)

...And because they have monopoly money. Basically, research labs are usually vanity spending for corporations with too much money. Xerox parc was funded by a monopoly. Bell labs was funded by a monopoly. Google funds its moonshots with its monopoly.

Remind me again where the transformer comes from?

> it happens in academia and corporate labs pick it up once it reaches the point where the basics show commercial promise.

Ah, well, good thing we've almost entirely defunded academic science.

It seems to me that everyone is in "always exploit" mode.

Seems weird when it's become apparent that LLMs aren't really special sauce outside of just having the compute.

The mainstream is wrong and in a lot of ways hazardous. He had every right to say he wasn't willing to sign is name on the wrong technology when he new better. When you hire smart people you listen to them. Elsewise you get what you deserve.

It's not a distraction if the research pans out and then they become the market leader for the next several years or more.

LeCun wasn't wrong. He simply had higher standards for results and accountability that LLMs are, by design, incapable of reaching.

Zuckerberg wanted to placate his shareholders with a shiny new thing now.

would've stopped Meta from delivering a polished product like Muse sooner.

Muse is not polished, unless usability and programming standards have fallen so far in the LLM vibe coding era that a pre-alpha state is now considered polished.