In the aggregate, I agree, but in tech things are pretty loose outside of California.

I started doing "ML" ~ 20 years ago building classifiers people would laugh at today and even at the time barely impressed people when they were 95% correct.

I moved into NLP and built NERs that missed 2-10% of named entities per document routinely. Best of breed approaches and models rarely fared better.

Learned the cornerstones in school for ML; linear regression, ANNs, traditional RL, image classifiers, A* bots, etc, most of which got baked into transformers later on.

Then the transformers went from interesting novelty to useful. I couldn't build a useful one locally, but the toys versions were still fun to play with.

Then the novelty LLM went from useful to generally applicable. Then they became a silver bullet.

I still can't build one locally. I can distill or build or fine tune if you give me some rented GPUs. But to call this ML is very much a stretch.

I still use the traditional ML a lot, but mostly for evals and analysis.

I get being naturally bummed by this but I can't justify feeling anything but vaguely nostalgic about it. Someone with a $20 subscription can mog anything I can build with the skills I picked up.

If someone hands you a silver bullet you'd be a fool to decline it and spend your time hand casting a crude piece of brass. If the difference between 95% and 99% means you know how to aim or oil the gun, that's the world we live in.

Building a good RAG pipeline or prompt optimization or LLM consensus is dumb stuff that produces a better result than anything I could do from my 2010 ML/AI textbooks. I don't lack the knowledge or capacity to compete, I lack the compute.

That's the job now for 99% of companies.

You can argue that it's unlikely the for-profit conservatorship of the non-profit is incompatible with that goal, but legally that becomes very much grey area.

But he'll also likely be shaving equity here and there along the way to hedge this bet.

Specifically because it's not a natural market. There are people who secure a 2-year, consequence-free term to impact U.S. law, at the behest of people with money.

Lobbying is special interests dictating decisions that often are not financially, morally, or otherwise ideal/beneficial to the other party (the United States and its people). This wouldn't fly at any corporation or business because there would be direct impacts on the bottom line or reputation of the company.

Sometimes just having the time/compute to explore the available space with known knowledge is enough to produce something unique.

I think one thing that's lost in all of the LLM tooling is that it's LLM-or-nothing and people have lost knowledge of other ML approaches that actually work just fine, like entity recognition.

I understand it's easier to just throw every problem at an LLM but there are things where off-the-shelf ML/NLP products work just as well without the latency or expense.

The risk of getting in early on crypto is you lose a little money. The risk of not is missing out on money. You can't simply replay that later, the way that you could invest the time to catch up on how git works.

You kind of have to go on "feels" for a lot of this.

That's important. Cloudwall isn't really saying they have some secret sauce here, but it's noteworthy who they nabbed.