Don’t they have their CFO not even reporting directly to be CEO? I would bet that there’s a ton of internal dissent about timing and strategy of how to cash out.

Yes. But major differences:

The dot com buildout of physical communications infrastructure involved basically 3 things:

1. Switches/routers at the nodes for sending signals down the right route.
2. Fiber optic cables connecting the nodes.
3. Legal rights of way and easements for the legal right to keep the physical assets in that physical place, and to maintain/replace the stuff as needed.

Category number 1? That stuff went obsolete quickly, and wasn’t really reused after the crash.

Category number 2 was better. Turns out, fiber optics can carry signals on a lot more channels than those fibers were originally designed for. And they’re designed for useful lives measured in decades. So even if they sat dark from being unused for 5-10 years, eventually they could be used again.

Category 3 is super important. That legal right is basically permanent, and so long as communications equipment needs to physically go from one place to another, having that legal right can be built on and profited on (including the ability to sell or lease those rights).

What’s that gonna look like for the AI infrastructure? The servers full of GPUs are the bulk of the cost, and the GPUs are replaced with a new generation every 1-2 years, seem to require all new power and cooling infrastructure every 1-2 generations or so.

Plus the AI buildout looks to be several trillion dollars. Even adjusting for inflation, that’s so much more than the tens of billions that each telecom company built out that infrastructure.

And it’s hard to see how the servers themselves will be useful for regular businesses, much less consumers. A Blackwell 72-GPU server is $3 million and takes 130 kW to run. A residential electrical line maxes out at about 48kW. The newest Vera Rubin servers are projected to be up to 600kW, with all the power and cooling management that comes with that, plus all the ultra high end networking stuff built into that rack. Even deep pocketed businesses will have trouble finding a use for that server rack worth millions, requiring a ton of supporting infrastructure that not even normal pre-2025 data centers have.

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.

Do you mean the actual packaging of silicon dies and putting them into DIMMs? Yeah, they had to revert back, but that’s because a lot of the memory silicon that’s only good for DDR4 never shut down, and any silicon memory that is good for DDR5 is also getting claimed up for non-DIMM memory (e.g., memory packaged with logic chips rather than sitting on its own package in a DIMM or even soldered to the board).

Basically, previous generations’ silicon fabrication tech is still going, and there are still buyers of that last generation product.

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.

Because the factories are already set up to make DDR4. Retooling to make DDR5 will cost a lot of money and take a lot of downtime for which the factory isn’t making anything. So the companies are extending the life cycle of the DDR4 production lines, without needing to upgrade things or retrain workers. As long as people are buying it, then there’s money to be made by staying open.

It’s like being the burger restaurant next to the steak restaurant when the line for the steak restaurant is 3 hours long. You’ll get a lot of spillover from people who don’t want to wait, and you can benefit from that without necessarily turning into a steak restaurant yourself.

Driver facing camera systems can be consistent with privacy, as long as they don’t record or transmit any data other than a single dimensional metric of how distracted or drowsy a driver is (or even discrete binary state of yes/no) and timestamps when that state was detected.

A closed loop system that merely keeps that data for the current drive and maintains it solely in the vehicle’s own systems can be consistent with privacy principles that nobody else should know anything about how a car is being used, except what can be observed from the outside.

I went to the gym and used a forklift to lift the weights.

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.

It’s got everything. Em dash. It’s not X, it’s Y. Emoji bullet points.

Perfect.

AI companies are bankrupting themselves with training costs that they need to recoup back by selling inference.

I think they hit a wall in actual returns on performance with pretraining, years ago. Then they started scaling up on post-training/reinforcement learning to continue improvement, but that might be hitting a plateau as well. More recently it looks like they’re relying more heavily on scaling up on inference, which is a significant problem for their long term business models.

If they’re not able to cheaply deliver inference (and charge at a premium), how will they be able to sustain their businesses?

It seems that the most recent, largest models are using a lot more tokens to accomplish the same tasks, so even as token cost drops the actual cost of using the latest models seems to be going up with time (even as performance improves).

It’s gonna be so fucking funny when the push to sell silicon that can run local models at 100 watts or less ends up destroying the business models of the companies that built out 100,000,000,000 watts of data centers.

Yeah, but that’s always been true of paid software licenses for a particular version: it reaches EOL and you have to decide whether to live with the possibility of unpatched known vulnerabilities or pay for an upgrade to a more recent release.

MS Office has been doing this from back in the Windows 3.0 days at least.

The only solution is to make sure they can’t read data you don’t want shared.

Isn’t that the appropriate guardrail, then? LLM chats and agents and whatever need to be contained with external permissions settings that the LLMs simply do not and can never have the power to override.

In a normal customer service setting with human agents, there are still plenty of examples of what a human agent simply doesn’t have the power to do. Often, they’ll need to escalate to a manager to do things like process refunds not just because they weren’t given social permission to do so, but because they weren’t given technical permissions to do so. LLM agents need to be contained in the same way. Any decent use of agents, human or software, requires carefully designed processes and permissions extrinsic to that agent’s own decisionmaking abilities to make sure that agents don’t do something bad for the company.

Edit 2: Nevermind. 13th October is the day Microslop has chosen to fuck me: https://support.microsoft.com/en-us/office/system-requirements/end-of-support-for-office-2021

You’ll still get a few years before the software becomes remotely disabled, though. This story about Office 2019 losing functionality follows Office 2019 losing support in 2023. If that’s the rate things go, then maybe Office 2021 will lose functionality either 2 years from now (7 years after release) or 3 years from now (3 years after losing support).