rdedev

> their return on capex multiple, their CEO said they make a six-fold profit on their compute capex with 10 month recuperation

I am not familiar with chineese model companies as much as I am with US based ones so I don't have much to say beyond that the CEO is incentivced to pump up those numbers.

> By limiting the number of tokens you use per month? per week, per hour? And by limiting the inference time compute dedicated to each turn in each session.

If this was so simple I don't know why GitHub copilot went to token based billing at my company.

> This does not need to be solved, but rather only quantified. Innovation is needed to be able to reasonably bound this variance for a reasonable subset of tasks

It's much better to make a business case for them after finding this bound right? Currently I can't use copilot for anything serious since I cannot predict how many credits one request is going to consume.

Your point about non frontier tasks using less tokens makes sense. As you said, let's see if it holds up

These companies have spent billions of investor dollars and they will need to recoup that cost soon. And then show year over year growth on top of that.

Unless they can massively scale down training and inference cost or implement AGI I don't know what their plan is. Just provide a subsidized plan for the next 10 or 20 years? Their costs are directly proportional to the amount of tokens the LLM produces. How is a monthly subscription plan supposed to account for such costs?

Remember that 20$ is a subsidized rate openai is currently willing to provide. Once that goes down you think these guys would be willing to pay token based billing charges ?

That could just be selection bias. How about all the brilliant people we don't know about cause their theories did not match experimental data? Doesn't matter if their reasoning quality is top notch

It also presupposed the fact that everyone can articulate what their political and moral stances are

Opus 5 really loves explaining concepts using a lot of jargon as well. Often ive had to ask Claude to explain its explanation

Maybe ASI can do the things you claim. Like figuring out why we have an appendix just by looking at our DNA.

Till then I don't think current crop of LLM will ever touch that capability. I'll believe it when I see it

They are already being trained in biology right now? What he is saying is that they are being trained on what we know about biology currently. AI is going to hit that limit. And there is no way for the AI to gain more knowledge without actually doing lab experiments

Even if AI gets super smart, it will run into the limits of what we know about biology. Someone will have to do lab experiments to provide more knowledge to the AI. This is different from say building a computer virus or hacking since the AI can do all of these on it's own

If you want to dismiss him for being hyperbolic or under estimating the usefulness of AI go for it.

If you are making the case that he is full of shit, please provide some actual evidence of his main thesis that AI companies are not going about this in any sustainable way

It could have worked if they weren't leveraged to their necks on spending. All based on the assumption that they can make something like 10x or higher in a few years.

Another point against your gym analogy. When people don't have that much disposable income why would they subscribe to something they semi regularly use? And if it becomes more expensive why would they not cancel it?

> The solution is to gut your product org, replace management with leading engineers, and hire some of your most fanatic users. They don't need to do anything other than give their opinions, and use the product every day. Put them in a room with your top engineers, and the software will mysteriously get better.

This will just lead to massive selection bias. If all you care about are power users go for it. Otherwise you end up with a complicated system that is going to put off new users.

You can even have different power users who likes different parts of the software. Now it's totally possible to end up with extensive subsystems that don't really gel with each other

My anecdote for this is the the recentish redesign of Musescore. Tantacrul, the ux designer/product manager for Musescore has an hour long video on how he redesigned the interface and UX of the software. He is also a musician so you can call him a power users if you want but that was not the users he had in mind when redesigning the UX

https://youtu.be/Qct6LKbneKQ

My codebase had a dataset with a bunch of SMILES strings and the word Malaria. Fable did not want to touch that codebase

So I guess the new opus will not run on my drug discovery project. It's just a binary classifier to screen for new malaria drugs. Fable completely have up on that codebase citing bio security concerns. Seems like this domain will go unsupported by Antropic

My favorite is the history of chili pepper. It's something so fundamental to indian cuisine that I assumed chillies were native to India for a long time. His video shows the true history of how that spice travelled from the Americas to all over the globe

Putting things into perspective is not minimising the problem. We literally have to do this to prioritise where our efforts can be useful.

Your argument makes sense if ai datacenters were using something close to like alfafa farming but the difference between them is soo massive it does not make sense.

Reducing pollution is a much better problem to fight for

There is a thing called political power. The local baker may be the most vile racist out there but he cannot affect my life. The CEO bank rolling a political party can

The group includes Netflix, the most technically capable streaming company. It's sad that companies will only go down kicking and screaming even for the mildest of regulations

I'm pretty sure paris looked looked nothing like now 100 years ago. Some other buildings had to come down to put up that art noveau building. I'm pretty sure someone would have been complaining about how that changes the character of the city they grew up in.

To directly answer your question about where to draw the line a simple heuristic is to leave public buildings alone. This can even include a privately owned shop that is accessible to the public. They are part of the cities culture. Private homes in the other hand shouldn't be protected.

Reminded me of this quote: the problem with machine learning is that it's the machine that does the learning

I'm pretty sure the LLM will get fed up and start writing an RPC

Also > An API arbitration aberratically assigned at authorized access, abridged and annotated, analytically assuring absolute assurance

Cool that you wrote all the words starting with "a" but I don't understand what you mean

> It’s becoming cheaper and easier every day to start a company that will disrupt the established players and bring down prices.

The underlying assumption here is that there is always something established players won't try to buy out new companies or use their existing capital to screw over others

> The winners in the end will be consumers, and the losers will be the big AI companies

Right now that is not the case. Look at the PC industry. I worry for the autonomy of a consumer in the future. It's probably going to be something like here is your rental thin client PC with agents on a monthly rental plan. What's that? you want to build a game with your own gpu? No no. A consumer grade gpu does not make sense in this day and age. Just ask the agent to build your game. We need the gpu compute for better things

I guess this is the reason why the sunscreens from haruharu are now suddenly available in amazon

Due to "politics" my teams main responsibility went from developing ai agents to just testing out a chat it developed by another team.

When you spend 8 hours a day doing mind numbing tasks, tasks that won't help you land another job and is constantly under stress of being fired, its bad for your mental health

It's hard to imagine if you have not experienced it. The air would still be hot even after the sun sets in some parts of India. Usually when wind blows over you you feel cool. With hot air it's like a blow dryer in your face. Just thermal energy being dumped on you making you feel even worse

This is why I really like karapathy's idea of llms having spiky intelligence.

We would assume that if tasks A and B are closely related. Mastery in A would mean mastery in B but that doesn't always work with an LLM

I tried running fable on this ML model I've been building. It's basically a binary classifier to predict activity of a compound for a certain assay.

Fable detected that it's something to do with biochemistry and switched over to opus. Huh

The example I had was of Ramanujam. "It was revealed to me in a dream"

There is also the possibility that an LLM judge would be happy with some code that looks like LLM generated code. But a maintainer for a specific project might not merge it for stylistic reasons

> The main idea is we provide documents to the LLM and it asks lot of questions which clear ambiguity and possible misconceptions the LLM might have

This kind of works but the difficulty is that you have to be very explicit about everything. It was mentioned in a spec document that a particular excel file is treated as a source of truth throughout the whole company and it is treated as an append only database. The agent still decided to add a check to see if a previous row was modified. It pushed back on its decision when asked why it decided to do so. "What if someone entered it wrong and had to correct it"? Valid question but it's not my teams responsibility to check for it

This check makes sense from a traditional development view point and that's why the agent did it. I would say it's good practice too but it's beyond the scope of the project it was working on. If what you are doing is beyond the norm you have to watch out for things like this