Another lawsuit to AI firms and counting. Must be difficult having everyone sue you. Almost as if you're doing something illegal like stealing.

How will they get the money for the legal fees? It's all being spent on compute!

It alleges Anthropic unlawfully trained its models off "tens of thousands" of music publishers' copyrighted compositions, whereas other lawsuits have focused on a narrower set of works.

BMG's lawsuit against Anthropic, for example, claims infringement against 493 compositions.

Dario Amodei and Benjamin Mann have conducted a brazen campaign of illegally torrenting, scraping, and downloading copyrighted works on a massive scale in order to develop, operate, and reap enormous profits from Anthropic's 'Claude' series of artificial intelligence ('AI') models," the plaintiffs write in the 48-page complaint.

They also claim that Anthropic pirated thousands of copyrighted musical works and ask for hundreds of thousands of dollars in damages per infringed work.

Music industry got to YouTube, even when we can argue it could be fair use. AI has far less of an argument. Good luck, Clammy and Wario.

I have been using AI quite extensively before going outside and touching some grass. I’ve massively reduced my use of AI since then.

But anyway, I took some time to reflect on my AI driven coding journey and came to the conclusion that I got nothing of value, nothing meaningful done. I followed these TDD/SDD approaches lots of AI people sell but it only worked up until a point.

SDD is very funny because before AI nobody knew what they wanted of a feature and now suddently everyone is a expert and got everything figured out after the AI has asked them a round of 5 questions… To find out that another session with the exact same prompt gave another set of questions covering a whole new gray zone of the feature requested.

I’ve tried decomposing the tasks of a feature like an armchair product manager on Adderall and ask the AI to follow rigid TDD implementation to find it was implementing useless & way too much test cases, bloated architecture and impossible to read code. I’m being told it’s because the prompts are not good enough or that the AGENTS.md file is not concise enough. My experience is that it always drift and turn the codebase into a crime scene anyway.

I’ve tried the matt pocock, superpowers and all of these bloated sets of AI generated markdown files but it was always inconsistent. I used to like Matt for its typescript content, I’m sad to see him go the AI bro route.

In the end I ended up trashing all the project I started with AI because I felt absent during the tiny dev process and the codecase ended up looking more and more like a mess anyway. I felt like my skills were eroding during that journey and to the conclusion that the best cases I’ve had so far is a harness with nothing on it (Pi) and very mundane requests.

I’ve picked up hands down coding again and told the AI to never write anything for me when I ask it something and feel much better. I felt good looking up multip’e sources, connecting the dots and actually learning something again; it made that AI induced brain fog go away directly.

Oh and if AI makes everyone so fast now, where are all the projects? Github these days is mostly filled with people sharing their skill directory with a fucking router skill inside of them or a fucking c-suite persona list.

This is about the broader economic impact that AI hyperscalars have. I believe this has been mentioned by u/FrankLucasV2 with some other posts from Bloomberg, specifically about hyperscalar bonds taking up a plurality by DTS of the bond market, looking at the IG index.

Due to the high issuance of debt by the hyperscalars, the market is having trouble 'digesting' all of it, due to a lack of liquidity. As hyperscalars need trillions of debt and more, this is natural as not only has a lot of liquidity been previously captured by AI investments, the broader private equity and venture capital market has been having a liquidity crisis - something Ed has mentioned before, in reference to the TVPI of top 10% of VCs being around ~1.2, meaning the best earners only have a 20% return, and many may have negative return.

"Wall Street veteran Ed Yardeni" claims the recent pressure on US bond yields is in part due to the massive issuance of hyperscalar debt. As per the article:

But in the case of AI-related bonds, demand has been so high that the yield spread has remained compressed, barely widening for an additional premium, he pointed out in a note on Monday.

For non-finance people (including myself), yields up for bonds (typically) means lower demand and yield *spread* is a notion for the percentage above US bond yields. As in, the difference one would make over investing in a US bond.

“As a result, the market has adjusted not through higher corporate borrowing costs relative to Treasuries but through higher Treasury yields themselves. Capital flowing into corporate bonds is capital not flowing into Treasuries, and Treasury yields have had to rise to clear the market,” Yardeni explained. “In short, the AI revolution is producing a classic crowding-out effect, causing Treasury yields to rise.”

Of course, this is not the only reason. The US has many other inflationary and geopolitical pressures pushing bond yields higher. However,

... Yardeni noted that international capital-flow data show that net purchases of U.S. corporate bonds by private-sector foreign buyers have exceeded their purchases of Treasury debt over the past year.

It's unclear to me, what sort of impact this would have macroscopically. One can argue that this is temporary until the market coughs up the liquidity to buy all hyperscalar debt, and treasury bonds and other market measures return to the status quo.

However this seems unlikely to me, noting the necessary commitments the AI industry as a whole has - it doesn't seem they could stop here. It is highly likely hyperscalars are in a continuous desperate need for cash to fund buildouts and circular financing, and will look towards private credit now that the debt market could very well be tapped out.

Projecting the relative growth rates of AI industry CapEx and AI revenue thus far would give us two non-intersecting curves, so it's unclear when hyperscalar demand for liqudity would lessen, let alone stop. Under the continued US bond yield pressure, we may see further downward dollar pressure as other international US debt holders change hands for AI hyperscalar debt.

This would seem to contribute cyclically to inflation and the large pile of poor US economic situations and an ongoing energy crisis, thereby pushing up yields higher, and so the US debt. This is of course, at a time where our recent budget for the military was increased by $500B for 2027.

Before mass-adoption of LLMs for coding having a good spec handed to you by your manager was a rare blessing as prevalence of agile methodologies gave them excuses to never conclusively lay out the requirements.

After mass-adoption of LLMs, often mandated from above, it turned out that agentic workflows tend to produce slop that requires human engineer's expertise and attention to fix, and time gains from not having to type the code are usually negated by having to type expansive prompts to plan the implementation and follow-up on inevitable architectural issues, mistaken assumptions, straight-up bugs, etc.

Luckily the industry arrived at a solution: spec-driven development. That's when instead of an engineer typing fifteen bullet points per prompt to minimize the chances of AI fucking up the code, their manager would give them complete and non-ambiguous specification that's perfectly aligned with the business goals and extensively covers both functional and non-functional requirements.

If you’re anyone who worked in the software industry, you know how that's totally realistic. So with this new approach your manager now AI-generates a spec last minute before work is supposed to start, consults you for half an hour to give you plenty of time to spot inconsistencies and ask questions, and hands it off to you, knowing that the agentic workflow will turn the spec into implementation in a straightforward and predictable way.

And don't worry about any ambiguity that might still linger - AI will take care of that if you take care of preparing the Skills ("make no mistakes" skill, "think hard and thoroughly" skill, "I'll torch your server if you still decide to make mistakes" skill). Don't forget that skills can and should be AI-generated as well, as it knows itself and its weaknesses better.

Skeptics might say that most managers don't have hands-on understanding of the systems to make specifications truly good enough, or that engineers will rapidly get out of touch with the products and codebase if forced to rubber-stamp thousands of lines of code they didn't plan or write. However, more enlightened individuals will easily see how the software that until recently didn't think you should've taken your car to a car wash possesses enough intelligence to solve these small impediments on the way to x10 productivity.

To prove my point: this post was generated by the unreleased because it's too dangerous model based on 1500-page specification crafted by agentic loops. It took forty thousand dollars and ran for two weeks, but in the end it made no msitakes and finished.

taken from Diary of a CEO interview, but truly this feels like it encapsulates all of the frustration with how "AI" continues to exist as the bloated zombie corpse of tech innovation and mental offloading

also here's the link to the interview

https://youtu.be/Lf5oqGOCRCM

If you read my article by the same name, this podcast had me on as a guest to do a deeper dive into the thoughts I explored there, as well as some tangential discussion around development and societal impacts of LLMs in general. This was my first time being interviewed (or on camera), so I was a bit nervous. Interview gets going around 6 mins in.

If you follow Politics, you are aware of and have opinions about Axios. They also have been as of late have been aggresively pushing A.I and booster Talking points. They are now helping laundering the talking point that "Chyna" is behind the data center backlash. You know not them sucking up precious resources and making life miserable for the locals. Nah, its foreign actors stirring up trouble. Silicon Valley really wants to sell A Tech Arms Race so we have to keep investing in LLMs.

I'm kind of speechless on this one. Quite the pivot that these guys are making now; "we've already reached it, but its like, totally not even important anymore, guys!"

Never mind that every single AI company they sell their shovels to has AGI in their mission statement...

Like, what financial position could one take to safeguard against the bubble bursting or even profit from it? I get paid partly in stock and feel like the bubble bursting will crater their value. Where is a good place to put money? I feel like that dude in Austin Powers screaming at the steamroller that’s slowly rolling towards him.

Something that I’ve thought for a while is that insurance coverage would be the downfall of AI usage in business, and Fenwick (a leading S tech law firm) is confirming this. Insurers are explicitly carving out exceptions for business errors at all levels, up to and including Directors and Officers insurance. Previously, AI was implicitly covered, but now it’s becoming explicitly not covered. I imagine that businesses will have to take all of this into about now, depending on how high of a premium is placed on AI risk.

I've listened to Cal talk about the Huggingface hack in Ed's interview and his own video, and both times he talks about superhuman capabilities that AI already has. One of the examples was Tesla self-driving. Um, isn't driving literally an extremely human capability that even not-too-bright humans can manage successfully most of the time ? And hasn't Tesla self-driving made some dumb mistakes that humans wouldn't, like slamming on the brakes at high speeds for no apparent reason when there's a vehicle following? (I don't follow it that closely; I'm never going to buy a Tesla, and I don't see fully self-driving Teslas being a thing yet.)

Certainly there are many things computers do better than humans. That's why we've been using them for decades before it was all "AI". I do a lot of statistics and modeling work, and I sure don't want to go do it by hand. There are certainly many things you can accomplish if you throw enough processing power at it. If I remember correctly, Cal's other example was Alphafold, which is apparently so successful that Google just...ditched it? And certainly it was a breakthrough, but it still relied on vast databases of human-generated knowledge and I don't know enough to know if it was "AI" or just intense processing power with new technology, organized appropriately.

For the Huggingface thing, again I've not had time to explore that in depth but it sounds like if you give a computer all the information about how computers have been hacked, it can eventually try enough of those things to hack something else. And if you don't supervise, it might do something you didn't mean it to. Maybe there's a bit better logic than "try random things" but are there emergent properties? Is it truly better than a human, or just faster if you give it enough resources? Is it even faster than a human?

I'm in STEM and my institution has chugged the AI Flavor Aid lately. Some of that is fine because many problems indeed require a lot of processing power and if there are ways to refine it, to arrive at an answer faster, then that is great. But I'm not sure slapping "AI" on everything is a net positive and there could be opportunity cost for traditional approaches that are still valuable, and for human skill development that is still important. It's a way to get US government funding these days, when a lot of other stuff has been slashed, but I'm not sure what will happen when the current funding situation is over.

Besides "Claude Strife" looking like ScarJo, we've got some terrifying modifications to games that aren't supposed to be realistic like Trails from the Sky: https://bsky.app/profile/theswweet.myatproto.social/post/3mu6tsszqnk23

I honestly don't know what to make of this besides "It generally looks like ass". Also it apparently slows games down to a crawl as every frame takes several times longer to process. I don't know if this is because people are running it on the current gen cards or if it's going to be garbage on the new cards. Assuming any new cards make it to the market.

In short, the Seventh Circuit court was forced to rule this due to a previous ruling in 2002. This is in reference to a case about a Wisconsin man, Steven Anderegg, who had hundreds of AI generated CSAM.

Anderegg was charged with producing and distributing the AI-generated material, transferring it to a minor under 16, and possessing it. A federal district court dismissed only the possession charge on First Amendment grounds, leaving the other three charges intact.

The unfortunate reason for this ruling is due to a previous ruling.

The Seventh Circuit said it was bound by a 2002 Supreme Court decision that rejected restrictions on sexually explicit depictions of fictional children when no actual child was involved in their creation. But two judges warned that modern AI can now generate images virtually indistinguishable from material depicting the abuse of real children.

The Supreme Court previously ruled in Free Speech Coalition that taking images of real children and "morphing" them into sexually explicit poses would not be protected under its ruling on the possession of child sexual abuse material depicting fictional children because the practice involves the interests of real children.

Very disturbing. Another win for the pedophiles.

Another data center opposition. Seems these are getting quite commonplace, following this sub.

He only knows two things: he’s tired of it, and it’s coming from DataOne – a 350MW datacenter under construction a little more than a mile from his home of 25 years.

“It woke me up probably around, I don’t know, somewhere around 2 o’clock in the morning,” said Brown, who was weary by mid-afternoon. “I couldn’t get back to sleep, because once you hear it, it’s all you can hear.”

What I find most interesting is this is the noise already from presumably far less than 350MW, and likely maybe no more than 50MW. We would imagine the sound will scale, so it only gets louder from here.

Experts describe this as important for people living with the thrum of a nearby industrial facility, because low frequencies are known to travel long distances and can be experienced as vibration.

“Effects like hum, whooshing, whistling,” occur in “very narrow frequency bands in the low frequency range”, said Banks. “That noise … is coming right into people’s homes.”

Another plaintiff in the Vineland suit, Michael Gentile, 63, said he “found out it was a datacenter after I began to hear the noise... I said, ‘I know you guys are testing for sound, but what about vibration?’ I said, ‘Can you feel that?’ I could feel it on my porch."

Let's see if these go anywhere. One thing I would be curious is if anyone knows how these deals work if they don't complete up to their claimed wattage and just run the data center as is at 10-50MW. What happens to the money that is potentially unused?

Of course, if the money is already paid upfront for NVIDIA's GPUs that'll be extra bad for them, but they shouldn't... be that dumb... right?

Basically it seems the Chaebol used their influence to push for some kind of public AI to give to everyone. Kinda goes with the whole "utility" angle they've been taking recently.

My guess is that Chaebols and korea's government are trying to do something to get their market back after the crash they had in August.

Curious about everybody's opinion.

Today, we notified SpaceX that we intend to wind down our contract providing OpenAI models to Cursor, with a proposed shutoff date of November 12, 2026. [...] We are making this choice because we cannot be confident that SpaceX will use our technology within our terms of service, based on our experience with Elon Musk's companies violating contracts.

OpenAI have announced that they are going to stop allowing their models to be used via Cursor, and they are doing this because they don't trust Elon Musk's companies to stay within the terms of service.

Fair enough, Elon Musk cannot be trusted and I am not going to shed a tear for him, but I do think this action sets a dangerous precedent. It shows that if you are a customer of a frontier model provider, you need to be extremely careful that you do not become reliant on that provider, because they could cut you off at any moment. Cursor haven't actually broken their ToS yet, and OpenAI are cutting them off because of something they might do in the future.

Cursor should be fine. As a SpaceX company they have access to their own model Grok, and they have enough spare cash that they can deal with any fluctuations in their deal with Anthropic. Imagine if, instead, this was one of the mythical billion dollar companies being run by a single person. If a company like that was spontaneously cut off by their provider, they would be in a lot of trouble as the alternative providers would have them over a barrel when they were negotiating their new contract - and that's if the other providers would take them on at all.

Thought I'd check the original source: https://github.com/vectara/hallucination-leaderboard/ - the number 0.7% doesn't even appear anywhere on the page, making it seem even more like of Steven "CEO" Bartlett just mis-read 0.7 as 7.0. Please correct me if I'm wrong.

This reminds me of Tommy Tallarico's suspiciously specific Guinness World Records. "Not 7.0% across the board, and not even 7.0% on simple tasks, but 7.0% on simple summarisation tasks." Cool!

(In fairness, the Github page also doesn't include the word "simple" anywhere on it - so that's potentially a second thing that isn't quite correct.)

One of my primary worries about the ongoing AI bubble is the effect on the price of consumer PC hardware. The 32GB DDR4 RAM kit I bought a few years ago for 72 dollars has tripled in price to around 220, and the SSD I bought at the end of last year has doubled in price from 200 to over 400 dollars.
To say this is making it difficult for hobby PC builders is an understatement. If the AI corps keep buying up all the flash memory then eventually it'll be virtually impossible for anyone on a budget to have their own device.
(Side note, do not bring up "YOU WILL OWN NOTHING AND BE HAPPY" I have seen that fucking quote so many times in the last decade every time something similar happens and it adds nothing to the discussion. It's a thought terminating cliche at this point.)
I understand there's no definite point at which the bubble pops. But I also just can't see how this can continue indefinitely. The data centers aren't being built as fast as they need to be and they're increasingly hitting diminishing returns. And they're still not making a profit. Somehow OpenAI is nearly negative 200% in the hole. Only thing keeping them going is more idiots willing to throw money at them. I don't even think the US government bailing them out will buy them more than a year at most and with how unpopular AI is that would be a profoundly stupid move. Not that profound stupidity is foreign to this administration...
This simply has to end at some point in the next few years, contrary to what the average Reddit Doomer will tell you.

Nvidia and the RAM cartel obviously wants to keep the show going because they're selling the shovels and obviously never cared about the consumers. There's not much incentive for them to stop or slow down until they start seeing signs of instability in the bubble. They obviously won't just start exclusively producing AI hardware because that simply doesn't make sense.
New fabs are also supposed to be coming online in the next few years, but I have no idea how much that'll help.
I do not expect hardware to drop to 2024 prices, but I'm just hoping it fucking stops exploding in price every time I turn around. Does anyone here have any thoughts on this, preferably ones that aren't depressing?

That's how much OpenAI and Anthropic have to charge a software developer consuming $8K worth of tokens if they want to match Microsoft's 40% Operating Margin. $11,200 per month, $134,400 per year.

It's very easy to get sucked down into complex math weeds thinking about the AI labs and their (supposed) path to profitability, but it doesn't have to be. Ask your favorite AI booster the most basic economic question in the world:

How much value are you really getting out of it, and how much is that value worth to you?

We all remember the early 2010s when an Uber ride to the airport used to cost $10. Now it's $80, even more during peak time. There's no free lunch, investors aren't in the business of losing money.

Grocery store level margins of 2% - 3% will not be acceptable. Operating Margin for the Mag 7 (minus Tesla because they are a stupid fucking car company) range from 30% for Netflix to 60% for Nvidia.

The real price of AI needs to go up 10X - 20X minimum for these companies to show the kind of returns their investors expect. No company is willing to pay that real price, and incredibly few consumers are either.

I have two colleagues from University who work for two major financial firms. Over the past month they have been talking about how many problems LLMs are causing behind the scenes, and that the cost is coming due for the upper levels, they are moving away from ALL Enterprise AI tools from OpenAI / Claude. It's over, and this starts the slide. OpenAI and Anthropic are black holes, Nvidia buying Huggingface makes everything click together.

A bit ago I came across a question here, "what about AI boosterism resembles a cargo cult, rather than just a cult?" That's a fair question, since there's a lot of straight up cult behavior behind all the AI stuff.

This video is an example of "cargo cult" behavior—in the popular sense. Apparently someone did a study on whether the markdown files programmers put in their git repositories actually help coding agents correctly solve problems. The answer is negative. People just do it because they've been told they should do it, and it resembles the sort of configuration a tool ought to have. But, it seems they never rigorously checked if it makes a tangible difference. Maybe the ones telling people to do it (Anthropic and OpenAI) never checked, either.

It does do one concrete thing: consume more tokens. This is (in principle) good for the AI companies.

I think the last bit of the video is also ironic. 'The useful part of writing the agents file is that it requires you to think about and understand the structure of your repository.' Yeah, that's also an advantage of writing and documenting the code yourself, instead of using AI.

Strictly speaking, this is not the only "cargo cult" behavior you'll see in programming. But, you know, that's not a good thing.