Of course they don’t, that’s why they’re building bunkers. Thinking it’ll slow us down, as we’ll open their bunkers like cans of tuna. A bunker only works for so long, then the survivors start hunting for them like delicious shipwrecks.

And that’s why they’re trying underhanded tactics to inflate earnings and IPO directly into the index funds, so every American’s 401K will legally have to rebalance and invest in them. They’re racing to fleece retirement funds before the bubble bursts.

Not financial advice, of course :p but people should really consider getting their stuff out and into self-directed funds or whatever it is US people do to not depend on auto-allocated funds.

Money printer go brrrrrrr

That’s simple, because the people making laws and overseeing the adherence to those laws are great buddies with those same CEOs.

So, corruption.

Though i do agree with you, there is no such thing as too big to fail. Government shouldn’t have any handouts to corporations.

Both Uber and Spotify (and AWS too) had economics of scale going for them - the more users they have, the more the infrastructure could be leveraged. This does NOT work for LLMs. More users means using more compute, more advanced tasks (like coding) uses exponential amounts of compute. A single user running a complex task can make 8 Blackwell GPUs run full tilt, and you don’t even have any guarantee that the output will be useable.

There are a few narrow areas where LLMs might be successful, like scanning for security vulnerabilities or searching large amounts of documents. The massive amount of money invested will never be recouped with these usage scenarios.

Although, most people aren’t talking about Alphafold when they’re talking about AI. They’re usually specifically referring to the generative transformer models that are currently all the rage.

I doubt anyone would care too much about a linear regression model, or multi-layer peceptron , for example.

solving something like the Erdős unit distance conjecture

Tell me you listen to media news cycle without understanding what that actually mean without telling me that.

That’s not exactly what happened, isn’t it.

Not to bring up what’s also been accomplished in cyber security

Multiple new vectors of attacks, automation of attack pipelines…

Imagine having a streaming service so bad it fails twice

I love that nobody watched anything on Yahoo! Screen except for that one season of Community

late to streaming, but practically the first subscription based system to watch movies/tv online.

First years of Netflix were the best, the product began degrading quite early on. but that was mostly companies realizing that instead of licensing their content on Netflix, they can make their own platforms.

“I need a triple A cooking game with blackjack and hookers, all written in SQL.”

“But that’s a database langu-”

“Did I stutter?”

Agreed, but hey no need to pile the the hate on Pascal, modern ones like FPC/Lazarus are pretty cool actually :)

We need this in Esperanto and the lost languages of Lutruwita.

OpenAI and Anthropic aren’t less evil than Google.

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Anthropic is doing the same too. SpaceX over here providing the shovels and pans for the modern day gold rush, sheesh.

That’s definitely costing them more than running it on their own hardware, but it doesn’t mean AI is costing them more than the AI startups. Anthropic for example is already paying SpaceX 1.25 Billion a month for compute, and has agreed to pay Google 200 billion over the next 5 years for access to Google’s compute and TPU chips.

Google’s deal with xAI specifically lets them terminate the deal with 90 days notice after the end of the year. Google is also investing heavily in building new data centers with their hardware. I’m assuming this deal means they’ve eclipsed their current TPU capacity, and are just looking for a short term bandaid until they can catch up with their new constructions.

They have big plans to build more data centers for themselves, so they definitely want more compute than the have access to right now. But even if they’re paying more to rent xai compute, they’re still paying less overall for hardware/access than their direct AI competition.

The bubble will pop, I think a lot of people are just baffled by how big it’s getting.

What people? All the credible people I read say that things fall apart Q2/Q3 2027 as debt and profit obligations are due.

The only thing that changed is now there is an energy crisis coming, so it’s possible that might force the bubble to pop sooner if all the systemic risk aligns.

Only because the hype has lasted longer than expected. Now that IPOs have been filed, the AI companies (Anthropic, OpenAI) released statements about slowing down to protect us. They’re setting the stage for lower growth. But I think you should invest every penny you have into “SpaceXMegaTwitterSuperCarAI”.

To break even on operating expenses, not even counting debt payments, depreciated capital value, or future recapitalization costs.

This is why IMO blitz scaling is dumb when your service is a commodity. I’m not any more loyal to Uber than Skip. If more investor money goes into making a cheaper meal or ride on Skip I use that. Consumers are mercenaries about that stuff.

The “blitz” part of blitz scaling assumes your customers can’t move.

An older co-worker seems to ask AI for help during work, we are blue collar. But the Owner of the company does not seem to use it whatsoever.

I ask Claude on occasion, to see if it will say something smart (it was mostly useless as fuck).

Exactly, these companies will keep leveraging more and more because they know the govt will step in and print whatever number of trillions of dollars needed to fix the accounting. Then they’ll tell us “core” inflation is only 2.8%.

You just need a better prompt, bro

I was using a set of template files designed for LLMs to review that project. It is absolutely the fault of Claude that it tools me to do something one way, then told me to try another and when I reverted it said it was the optimal approach.

Where I find it helps is in getting initial starts and as a start to code review. But in both cases they aren’t ever operating on their own and their feedback is filtered through myself or another senior dev.

Lots of anti-ai people in this thread it seems. I get it - personally I HATE the fake image generation! But I have to agree with you in terms of coding that using LLM’s correctly can offer huge benefits.

Modern harnesses are getting more and more sophisticated and your milage varies depending on how well you use them (like any complex tool). At the end of the day it’s still up to the developer to take the code and make sure it’s correct - no different from before where we used to copy code from Stack Overflow or other examples and modify them for our own use.

One thing I have to add - I honestly don’t understand why anyone would use Claude or chatgpt at their ridiculous prices when DeepSeek exists…

I have found the opposite. Codex spits back mostly useless code that is twice the length it needs to be with a bunch of unessesary stuff and Claude is the only thing I get useful output from.

ax+c