How AI will fail

[LukeTbk]

Something like that. That really depends on what cloud AI will let you do. Not only is cloud AI limiting what I want it to do,

Not sure how limiting it is, Coreweave does not really know what their clients are doing with their compute with quite good encryption, that why even secret obsessed big pharma research create and use custom models on them. Cloud AI is a large sector that goes from using ChatGPT to running your own model on coreweave computers with a lot of controls.

There is different level of cloud AI.

Still, all of those combined will likely be under $100B/year in revenue.

ads targeting and content suggestion alone has been way bigger than this for a while now and the biggest cash cow for AI, the added value of AI to ads for the big player is about 150-200 billions a year market and should still be the biggest revenues source in 2026, unless anthropic ridiculous growth rate is maintained all year long.

Openai-Anthropic combined revenues are about at 75 billions ARR right now, a lot of it is programming.
Self driving crossed the 10 billions a year in 2024 or early 2025 for standard passenger and around the same for industrial ($20 billions..., expected to be around 35 billions in totals in 2026)

I don't know what it would cost to let claude lose on a big personal repo that would burn tokens like jet fuel, but i know it's more than I want to spend.

running something that the equivalent of what run fast enough on personal computers would not be claude cost too, it is better to be fair (for both speed and cost) to compare similar capable affair, deepseek v4 flash, a 284billions model using a 1M context window would be quite expensive to run at home and would be hard without really cheap electricity to beat the $0.10-$0.20 per millions token price you can get

 

[stuffmonger]

ads targeting and content suggestion

Targeting is a simple match for simple things like user location. It doesn't use neural nets at all.
Content suggestion is not something consumers pay for. It's a part of a bigger package that's ad supported. Short video is simple reinforcement learning based mostly on video duration. It's not generative.

Openai-Anthropic combined revenues are about at 75 billions ARR

The cost is higher than the revenue for those 2, and my list was for profitable applications only.
Claude Code already started pushing smaller limits, and making users angry.
If they stop subsidizing, the revenue will deflate.
Google gave me $2K in tokens last year, so I used them all, then stopped.
Some companies used tokens as a metric kinda like lines of code in the past, so tokens were wasted to satisfy this KPI.

Self driving crossed the 10 billions

Same story. Waymo uses humans to intervene, which is very costly. They don't disclose the intervention rate, so we can't tell how much they're losing.
If there's a floor for how low the intervention rate can be, Waymo and Zoox will be shut down like Cruise.
It makes no sense to pay humans to babysit a car when they can be driving it instead.

 

[LukeTbk]

Targeting is a simple match for simple things like user location. It doesn't use neural nets at all.

And the historic of all users action, scrolling and what not., it is custom to the user.

And you have meta advantage+/advanced google affair that are generative, they generate hundreds on alternative ads, test them and go with the tops one according to target profiles.

Content suggestion is not something consumers pay for.

I do not imagine they directly pay for it but almost... (if we of course mean by customer here the ads buyers), that said Google has now 325 millions paid subs accross service a lot from youtube premium, not sure how big it would be without content suggestion that hook them.

The cost is higher than the revenue for those 2, and my list was for profitable applications only.

Anthropic just had a positive ebidta quarter, https://www.wsj.com/tech/ai/mind-bl...rofitable-quarter-7edbf2f4?eafs_enabled=false and it depend what we mean by cost higher than revenues and subsidizing, anthropic mostly stopped subsidizing (as you said limits and what you can do for free being replaced more and more by api that charge by usage).

But they have 70-80% margin on the inference itself, they will probably go back to burning a bit of money everything included and being profitable overall last quarter the result of having an hard time buying enough compute for training, but the the new thing, their revenues exploded so much they have an hard time spending it all, as much as they want to do so.

Same story. Waymo uses humans to intervene, which is very costly.

growing fast, profitability is something they can see already, but Tesla FSD, many chinese one do not use paid humans intervention but push it to the users, John deer let the farmers intervens and do not do it themselve, more predictable fleet system for the walmart has really low intervention cost

 

[1.1.2.3.5...]

Wait, what are you claiming is done for autonomous tractors? (JD isn't unique to autonomous tractors).

Most precision ag applications are not FSD. But most major brands are advertising fully autonomous machines within ~5 years. Well see what actually happens.

Open question is not around capability but around liability. If you use a human in the loop, it just reduces the liability exposure.

 

[LukeTbk]

Wait, what are you claiming is done for autonomous tractors? (JD isn't unique to autonomous tractors).

That they let the farmer interven with their phone when there is an issue, instead of having remote john deer employee doing it a la waymo, is that false ? i.e. a lot of semi automous system use the clients as the person in charge, they do not pay employees to do so, so those FSD fees have giant raw margin once R&D-training are amortized, waymo is a bit more of an execption in that field, but their ability to charge more for rides than human driven service show that profitability should be quite possible.

 

[1.1.2.3.5...]

That they let the farmer interven with their phone when there is an issue, instead of having remote john deer employee doing it a la waymo, is that false ? i.e. a lot of semi automous system use the clients as the person in charge, they do not pay employees to do so, so those FSD fees have giant raw margin once R&D-training are amortized, waymo is a bit more of an execption in that field, but their ability to charge more for rides than human driven service show that profitability should be quite possible.

edit: edit: edit:
https://www.businesswire.com/news/h...utonomous-Tractors-in-American-Sugar-Industry

Things are changing so fast these days it's really hard to know what's happening across different businesses. So basically ignore my comments above. I just don't know anything for sure.

 

[stuffmonger]

Tesla's taxi has a human sitting in the passenger seat. Yeah, much self-driving, such wow. Camera only systems suffer from phantom braking, and won't be as reliable as LIDAR. Zoox and Waymo are the only ones who have a chance of succeeding.

Autonomous is well defined. Fully autonomous means level 5. Conditions are still debatable. E.g. weather, area restrictions.

Farming robots are usually programmed to go in a straight line, then turn by waypoint. This isn't reliable enough as 1.1.2.3.5... said, evidenced by the job listings for remote operators/supervisors.
Since a tractor could be millions of $, farmers need a solid proof that the system is at least as reliable as a human driver. There is no such proof.

 

[LukeTbk]

edit: edit: edit:
https://www.businesswire.com/news/h...utonomous-Tractors-in-American-Sugar-Industry

Things are changing so fast these days it's really hard to know what's happening across different businesses. So basically ignore my comments above. I just don't know anything for sure.

yup, John Deere is not the one doing it, farmer (or here the farm group getting together to do it, centralizing it a bit for their group member) is taken care of human intervention. For small independant farm, it get be the family with their phones.

 

[LukeTbk]

Tesla's taxi has a human sitting in the passenger seat. Yeah, much self-driving, such wow.

Tesla used to charge $6000 for FSD upfront, now $99 a month and the human intervention cost is past to the buyer.

Chinese self driving in some section, as an human but that can work or sleep, we are talking about revenues here, not if it is level 4 or 5, the fact they can already charge good money at lower level is a good sign for them, not a bad one.

 

[1.1.2.3.5...]

Being in the industry you would think I would know a lot more, but I am not customer facing and everything changes so quickly I don't want to make any specific claims. But I will say, devils in the details here.

 

[DukenukemX]

Not sure how limiting it is, Coreweave does not really know what their clients are doing with their compute with quite good encryption, that why even secret obsessed big pharma research create and use custom models on them. Cloud AI is a large sector that goes from using ChatGPT to running your own model on coreweave computers with a lot of controls.

One example is when I ask Grok to make images of middle-eastern leaders having a missile up their rears and it told me it can't do that Dave. Which shows me who exactly is running the show when you can't criticize them through basic images. From a pure art standpoint, I'd rather have a LLM any day of the week. Then there's the misinformation which Google and Gemini are really proactive about it. It's gotten so bad that I get better results with Bing. Then there's special interest groups who are trying to use AI to change peoples perspective of them. Not that I find any real use having AI answer me questions that it can't do, but if I wanted J.A.R.V.I.S. then I'd run it on a RPI. A very powerful RPI.

Gotta love that she thinks people trust AI. Isn't that cute.

 

[LukeTbk]

One example is when I ask Grok to make images of middle-eastern leaders having a missile up their rears and it told me it can't do that Dave.

that 2 different things, there is no LLM (that I can think of) that you can run at home but not on the cloud and I can easily imagine a local Apple AI with many of those limitations. Cloud vs local is just where the model is ran, not which is it.

 

[1.1.2.3.5...]

that 2 different things, there is no LLM (that I can think of) that you can run at home but not on the cloud and I can easily imagine a local Apple AI with many of those limitations. Cloud vs local is just where the model is ran, not which is it.

Well sure. But there are specific cases where the cloud doesn't work as well imho. For example I have an extremely large corpus of ebooks on technical things that I was to use as part of a rag setup. I could maybe figure that out, but much easier and cheaper to do that locally.

Additionally I want a smaller voice to text model that runs constantly and sends prompts to my larger llm. So i can talk with the system while it's working on things.

Technically all doable over tcp and iot clients, but not ideal.

 

[LukeTbk]

Well sure. But there are specific cases where the cloud doesn't work as well imho. For example I have an extremely large corpus of ebooks on technical things that I was to use as part of a rag setup. I could maybe figure that out, but much easier and cheaper to do that locally.

yup or dlss is better to be next to the data, cars use a lot of locals (they have a lot of video data and spotty connexion) robots will has well.... for ebook it would be relatively easy to upload them on your AWS or others virtual machine, static data is easy to make local to the model in the cloud, it is more realtime continous flow of local data where local become more interesting... has for much cheaper it could depends on the electricity cost and upfront investment for the hardware, it could be hard to beat deepseekv4 pricing.

But often will LLM you have text (very small data) relative to the compute to be made, which made cloud an easy to be used.

 

[1.1.2.3.5...]

yup or dlss is better to be next to the data, cars use a lot of locals (they have a lot of video data and spotty connexion) robots will has well.... for ebook it would be relatively easy to upload them on your AWS or others virtual machine, static data is easy to make local to the model in the cloud, it is more realtime continous flow of local data where local become more interesting... has for much cheaper it could depends on the electricity cost and upfront investment for the hardware, it could be hard to beat deepseekv4 pricing.

But often will LLM you have text (very small data) relative to the compute to be made, which made cloud an easy to be used.

These are books with a copyright that should not be in the parameters of LLMs, but we know they are to some extent.

But even if the model has that knowledge parameterized, it cannot cite the book. I want to be able ask my model something about say category theory and give me an exact citation. If LLMs are doing that without author's permissions, that's a really big problem.