No, the article is saying that it is why these robots were popular. Because unlike a human delivery person, there was no tip expected for the robots.

Trade secrets necessarily have to be analyzed under the protections of contract law.

Something can only be a trade secret if the purported owner of that proprietary information protects the confidentiality of that information, including through contractual restrictions. That’s why I’m talking about contracts when asking whether trade secret protections apply.

I just pulled up the ChatGPT terms of use

Who’s talking about ChatGPT or OpenAI?

I just pulled up the Anthropic commercial API terms, since that’s the situation covered by the original article (big corporation using Anthropic’s paid API):

Use Restrictions. Customer may not and must not attempt to (a) access the Services to build a competing product or service, including to train competing AI models except as expressly approved by Anthropic; (b) reverse engineer or duplicate the Services; or © support any third party’s attempt at any of the conduct restricted in this sentence.

Ok, so it’s a contract that purports to prohibit pretty much this kind of model weight extraction, and I’m saying that Anthropic probably considers the model weights to be trade secrets.

Are you under the impression that trade secret protection only happens when the contract says the words “trade secret”?

Or, analogously, consider customer lists. Having a contract that says “don’t copy my customer lists even if I sometimes disclose a single customer at a time when we partner together on projects” is probably enough to adequately maintain trade secret protection over those customer lists, even if individual customers are sometimes disclosed under a contract.

I’m just stating what I believe the law is, not what it should be, or even claiming that what the law is today is good. I’m just saying everyone should be aware that the law is quite protective of big corporations and their proprietary secrets. I still think this qualifies as a trade secret that they’ve protected with their own contracts.

Ok, do these countries also make a contract not to distill LLMs void, as well?

Can you name a country where signing up for a paid account to an online service, and using the service and paying the invoice that comes in, doesn’t form a legally binding contract between the customer and the vendor?

Sharing trade secrets under the terms of a contract that dictates how one can use the information still retains trade secret protections.

Without a contract: intentional disclosure to the person who receives it generally destroys the trade secret status of the information, because the “owner” of the information didn’t do a good job trying to protect it.

With a contract: intentional disclosure to a person under the terms of the contract makes the contract’s own protections of the information relevant, and misuse of the information by the recipient can get them sued under the contract. Plus, the information itself probably retains trade secret protection so that even if that person gives the information to a third party who can’t be sued under a contract they never agreed to, there are still rights to protect that trade secret as property.

I’d be shocked if any paid API use isn’t under a robust, enforceable contract. The only question is whether the contract language itself effectively prohibits distillation.

Yeah, it wouldn’t be copyright. It might be trade secrets, though. And trade secrets can be made out of public data, but arranged in a way that gives competitive advantage (for example, customer lists themselves might be trade secrets, even if each entry is a publicly available set of name/contact information/job title/company).

The actual process of creating semiconductors is basically:

1. Etch a stencil that has the pattern you want.
2. Place the stencil over a piece of silicon.
3. Bombard the silicon and stencil with radiation so that the chemical properties of the silicon change exactly under that stencil.
4. Repeat the process with multiple other stencils, so that the resulting silicon has basically shapes of wires and logic gates that can perform different functions with the electricity running through those shapes.

In recent years, step 3 has gotten so complicated, based on needing to create radiation of exactly a particular wavelength of extreme ultraviolet light focused exactly on the silicon (and the mask/stencil above it), because that wavelength allows for the smallest possible features on the silicon. So they take purified tin, melt the tin into molten liquid, and ejecting the molten tin in a liquid jet downward into a vacuum at exactly the right speed to where it forms into droplets of the exact size for the machine (about 50 μm), then blasts each droplet, mid-fall, with a 1.6kW laser that heats it up so hot that it vaporizes and ionizes into plasma at the exact position where a system of highly polished and precisely positioned mirrors focuses the UV radiation evenly onto the silicon surface.

Oh, and the machine makes one tin droplet every 1/50,000 of a second, so in any given second it ionizes 50,000 droplets in the stream.

The machine costs something like $300 million, and requires full time experts to make sure that it’s working correctly.

Everything else in the fabrication facility is similarly complicated, which is why a fab represents something like $30 billion in total costs over its lifetime.

At 15: I better get an email address that reflects my interests and personality!

At 25: Ok ok my name is the only permanent thing about me so it should be based on my name.

At 35: why am I revealing this much personal information in my email address? I should just have a random jumble of letters.

“I outsourced the copywriting to the lowest bidder, who happened to be in Poland”

At least they moved onto year-based versioning. That was probably the best part about the 26/Tahoe release.

It’s called decoding and encoding.

But the big data centers doing all the video processing for the big video services (including both permanent videos from a library and things like live streaming) are encoding the videos with settings that require less computational power to decode. The idea is to be able to let even old budget smartphones still be able to display the video with very low power requirements on the client device. There’s no universe where consumers decoding digital video will be a high-power computational task.

Restaurants have sharp knives in the kitchen, but generally serve food that requires only minimal cutting effort from the table knives set out with the rest of the table settings. Dining will always be easier than cooking, by a margin that makes the difficulty of dining not worth mentioning, so it would be bizarre to criticize a knife as being only good for cooking and eating food, when plenty of dining tableware knives out there would be insufficient for kitchen work.

You’ve made the mistake of lumping decoding and encoding together based on the algorithmic/mathematical similarity of those tasks, when everyone else is more inclined to discuss the very different end user use cases of those computing needs.

I don’t think government funding can actually offset the crash in consumer and business demand being insufficient to cover the cost of the most expensive models on the most expensive GPUs. But if you look through my comment history I’ve made the comparison to supersonic flight, because I genuinely believe there’s a possibility that governments fund the expensive branch of this technology for their own military or surveillance or law enforcement purposes without the benefits necessarily actually spilling out into normal commercial applications.

We’ve hit the point where training a model (both pre training and post training) isn’t the expensive part, and the expensive part is actual inference, which makes it hard to scale the most expensive models to where it’s useful for a lot of people. So it might be that the companies and governments that can afford to operate an expensive model might be the only ones to do it. And they’ll be able to, without necessarily the public being able to have access to the same tech.

Plenty of examples of companies spending more than they earn for decades. Before OpenAI and Anthropic, though, nobody has ever needed to raise more than $100 billion from investors before turning a profit, though. The scale is immense, enough to where it affects the liquidity of the investors that have funded their rise.

The business model should be that with economies of scale they could provide compute much cheaper than average consumer can buy to run locally.

That business model assumes that the huge cloud models will always maintain a gap worth paying for, compared to the local models. I’m just not convinced that the average consumer will need cloud models for summarizing their emails or the news of the day.

And for actual costs of their data centers, there literally aren’t enough humans in the world where $20/month AI spending per person will help them break even. They’ll need to sell big accounts (many businesses spending billions per year) in order to break even.

There’s just no way to pay for the cost of these services, though.

When someone constructs a 100 MW data center (now considered a smaller one for new construction), that’s about $2 billion in total costs to outfit the whole operation. And then once it’s on, we’re talking something like $10-20 million/month in electricity alone, and a few million in other costs. How many $20 subscriptions do you need to sell just to break even with your operating expenses? How many $100/month subscriptions do you need to sell to make a dent on your interest payments on the construction? Will there be a market for $1000/month subscriptions from millions of customers? If not, how’s this all going to be paid for?

Once you get into things with useful generation and large context windows, or things like video generation, suddenly you need one or more $10,000+ pieces of hardware to run it.

A Blackwell server with 72 GPUs costs about $3 million, plus requires 130 kW of power (about 3 residential homes’ max rated power through a residential 200A circuit box, for about $600-$1000/day in electricity cost).

You’re gonna need to sell a lot of $20/month subscriptions to get that paid for, assuming that the server is good for 5 years. If it’s only good for 3 years, the economics are basically impossible.

But you’re seeing a screenshot of an unmatched order that no driver has claimed yet. I’m saying that unless an actual match is accepted, that’s not really evidence that people in a place don’t tip well, just that some people don’t get their orders filled.

If you never give less than $5, then any order you’re involved in will involve at least a $5 tip. That may not be representative of the orders you’re not involved with.

I think the user decides how much to tip in advance, and the app conveys that information to potential matches. Orders with low tips tend to sit there unclaimed, because no driver wants to bother with that

I’m not sure if Uber does it that way, but Doordash does.

I remember reading about a case a few years ago where a warehouse couldn’t figure out which of its workers was just periodically taking shits in random corners of the warehouse. I think I’m starting to understand a different angle to that story, though.