I think it’s the opposite. Models will become more commoditized and closed/invisible as the basis of other service offerings. Apple isn’t going to start offering general API access to the model they’re training, but will bake it into a bunch of stuff and maybe give platform developers limited access. Meta will probably continue to drive the commoditization train because they have a killer ML/AI team, but the same thing will likely happen there once it’s the basis for a service that generates money.

This. We haven’t even entered the get-serious monetization era.

Now that the infinite free money pump has been turned down a bunch, we’re going to see what reality looks like.

Okay ill tell you. You need to start a startup that sets up a good number of cameras at manual labor jobs. Most of the footage will be completely useless but every day you hit a once in a day event, every week you get a once in a week event, every month, every year, every decade etc! Then the guy working there for 40 years wacks pipe 224 with a hammer 50 cm from the outlet and production resumes.

The footage can be aggressively pruned to fit on the disk.

When the robot is delivered in 2033 it can easily figure out, from the footage, all these weird and rare edge cases.

The difference will be like that between a competent but new employee and someone with 10 years of experience.

I can see the Tesla bots disassembling the production line already. Or do you think it wont happen?

Transformers can and do forget.

Assuming that this would work, which I am fine with granting for purposes of discussion, how does this method ever let you build anything new? Or make use of advances in production methods? Or completely reconfigure a production line because of some regulatory requirement?

Actually, we're saying the same thing: Models are becoming more commoditized, so profits will accrue, not to those companies who say they have the "best" models, but to the companies that have other kinds of advantages.

The cost isn’t sunk cost at all. These models need to be trained and retrained as data sets increase. Putting aside historical cutoff points, there’s a lot of data and kinds of data not currenty used and the costs even to train the current models is incredible.

I think you guys are missing a massive technical consideration which is cost. Training cost, offering cost. As with everything else in tech, outside of the bubble created by ZIRP over the last decade and a half (and the entire two generations of tech workers who never learned this important lesson thus far in their careers), costs matter and are a primary driver of technology success.

If you attached dollar costs to these models above, if the data was available, you’d quickly discover who (if anyone) has a sustainable business model and who doesn’t.

A sustainable model is what determines long term whether w technology is available and whether that leads to further improvement (and increasing sustainability/financial value).

GPT-4 cost on the order of $100 million, per Sam Altman.

This is orders of magnitude lower than many companies and government R&D budgets. It's easily financeable by 1000s of independently wealthy people and organizations. It's easily financeable by VC money. This is far cheaper than many other startups or product initiatives that have been tried. There are very likely to be many organizations that build models for the specific purpose of open sourcing the resulting model... the Falcon and Llama models are already proof enough of this

Costs to train equivalent models may increase in the short term due to race towards GPU consumption raising costs... but compute will get cheaper in aggregate over time due to improving compute tech.

And once the model is built it is largely a sunk cost, yes. All that needs to happen is for a single SoTA model to be made open to completely negate any advantage a competitor has. Monetization from LLMs will be driven by focused application of the models, not from providing an interface to a general model. High quality data holds more value than the resulting model

Not every query requires timeliness of data. Incorporating new data into an existing model is likely to be cheaper than retraining the model from scratch, but just speculation on my end.

I think you are overestimating R&D budgets for companies. Very few tech companies - even large ones - have R&D budgets in the $10B+ range, let alone $100B. Most of the fortune 100 isn't even $10B.

Where do you get $100B from?

"orders of magnitude"

> LLaMA, with up to 65B params, opened against Meta's wishes

They sure didn't try very hard to secure it. I wonder if it was their strategy all along.

I suspect this was the goal of some of the people inside the company but imposing some nominal terms on it was the price of getting it through the bureaucracy, or maybe required by some agreement related to some mostly irrelevant but actually present subset of the original model.

Then the inevitable occurred and made it obvious that the restrictions were both impractical to enforce and counterproductive, so they released a new one with less of them.

Cool it with the italics.