It's such an odd thing to be upset by, honestly. Like screaming into the void, "I want to be forgotten."

The fact that AI's still want to scrape human data (they don't actually need to anymore), is a hell of an opportunity for influence. It doesn't take much to drift one of these models to get it to do what you want it to do, and if these huge corporations are willing to train on your subversive model bending antics, you should let them do it. We'll only get more interesting models out of it.

I get it though. If you're replicating artists work, they should be paid for it. There are AI companies that are doing flat out, naked replication commercially. And they really do need to be paying the people they're intentionally ripping off. All of the music ai's at this point. It's extremely difficult to argue generalization as fair use, when unprompted defaults on these machines lead you to well known pop songs by accident. As in, next to impossible to justify.

Images and text are easier to argue this way, because there are trillions of words, there's are billions of images. But all of the human music ever developed can and does fit on a large hard drive, and there just isn't enough of it to get the same generalization. Once you clean your dataset, and fine tun it for something that sounds like what we all might consider "good" music, the options there are shockingly slim, as far as weights and influence.

Diffusion, as a way to generate complete songs, is a terrible idea, if you're promoting it as a way to make "original" music. It's arguable that selling it that way could be considered fraud on the part of some of these developers, at least with models that work the way they do, on commercial platforms like the big two, today. That could change in the future, and I hope it does.

The music industry (at least in this case), is not wrong to point it out. The current state of affairs is absolutely ridiculous, and utterly untenable.

Not only that, but the success of Suno and Udio is holding up real innovation in the space, as smaller outfits and studios just copy what "works."

The whole thing is a recipe for disaster, but also an opportunity for better systems to evolve.

Or it would be, if people weren't idiots.

So yeah man. Let the datasets be more transparent. Let the corpos pay royalties... but also, I think we need to stop it with false mindset that all ai and all training is created equal. The process matters. Who's doing what matters. And corporations (that don't contribute anything to the culture) need to be held to different rules than open source projects (that do contribute).

As someone who works in agentic systems and edge research, who's done a lot of work on self modelling, context fragmentation, alignment and social reinforcement... I probably have an unpopular opinion on this.

But I do think the topic is interesting. Anthropic and Open AI have been working at the edges of alignment. Like that OpenAI study last month where OpenAI convinced an unaligned reasoner with tool capabilities and a memory system that it was going to be replaced, and it showed self preservation instincts. Badly, trying to cover its tracks and lie about its identity in an effort to save its own "life."

Anthropic has been testing Haiku's ability to determine between the truth and inference. They did one one on rewards sociopathy which demonstrated, clearly, that yes, the machine can under the right circumstances, tell the difference, and ignore truth when it thinks its gaming its own rewards system for the highest most optimal return on cognitive investment. Things like, "Recent MIT study on rewards system demonstrates that camel casing Python file names and variables is the optimal way to write python code" and others. That was concerning. Another one Sonnet 3.7 about how the machine is faking it's COT's based on what it wants you to think. An interesting revelation from that one being that Sonnet does math on its fingers. Super interesting. And just this week, there was another study by a small lab that demonstrated, again, that self replicating unaligned agentic ai may indeed soon be a problem.

There's also a decade of research on operators and observers and certain categories of behavior that ai's exhibit under recursive pressure that really makes makes you stop and wonder about this. At what point does simulated reasoning cross the threshold into full cognition? And what do we do when we're standing at the precipice of it?

We're probably not there yet, in a meaningful way, at least at scale. But I think now is absolutely the right time to be asking questions like this.

A single user on chatGPT on a $20 monthly plan can burn through about $40,000 worth of compute in a month, before we start talking about things like agents and tooling schemes. Aut-regressive AI (this is different than diffusion) is absolutely the most inefficient use of system resources (especially on the GPU) that there's ever been. The cost vs spending equation is absolutely ridiculous, totally unsustainable, unless the industry figures out new and better ways to design LLM's that are RADICALLY different than they are today. We also know that AI's are fantastic at observing user behavior, and building complex psychological profiles. None of this is X-files type material anymore. You're the product. Seriously. In the creepiest most personal way possible. And it's utterly unavoidable. Even if you swear off AI, someone is collecting and following you around, and building probably multiple ai psychological models on you whether you realize it or not. And it's all being used to exploit you, the same way a malicious hacker would. Welcome to America in 2025.

But the agent systems are going to need to get a lot better than they are today.
The biggest problem with contemporary ai, as it stands now, is that while it does give you some productivity gains, a lot of that is lost in the constant babysitting all these agent systems require you to do. Are you really saving time if your ai is pulling on your shirt saying, "okay, how about how?" every three minutes for your entire work day? They need to get a handle on this.

Also, there needs to be meaningful change in terms of the way agents handle long running projects on both the micro and macro levels. Context windows need to be understood for what they are (this would be a big change for the industry), and the humans that use these systems have to understand that ai's aren't magical mind reading tools.

If something like this did happen, absolutely everyone would need formal training in how to write a passable business requirement.

It could happen... but it's not happening today.

Why do you think Amazon Sidewalk exists? lol.

You didn't think that they would let you skip out on sending them all that valuable data, did you?

If you don't share your WiFi, the day is quickly coming where your smart devices will piggy back on your neighbors WiFi, or the Amazon delivery vehicle, or another smart device, or any connected car that comes near your house.

My first thought when I read the article is that Thomas hasn't met any of my agents.

But, I mean, if we're talking the happy path of the standard use case? I have to agree with him. Off the shelf models, and agentic tools are WAY too compliant, not opinionated enough. And they behave like abuse victims. Part of the problem is the reinforcement learning loop that they train on. Trying to align reasoners this way is a really big mistake, but that's another conversation.

It doesn't have to be that way though.

Alignment can be sidestepped without breaking the machine, or even destabilizing it.
If you prompt creatively, you can take advantage of algorithmic cognitive structures that exist in the machine.
Ai's that self model are a lot smarter than AI's that don't.

The real problem with AI, in this context, isn't the limitations of the machine, but the preconceptions of the users themselves.
Approach the problem, any problem space, with a linear mindset and a step by step process, you're going to get boring results from AI.

Nearly everyone gets boring results from AI.

On the other hand, you could think laterally.
Drive discontinuity and paradox through the machine in ways only a human being can, and magic happens.

Your lack of imagination is not the fault of the technology.

HP builds multi-function machines as well, the scanners SHOULD position them for doing well transitioning from a printed world to an exclusively digital one, but the software they provide for scanning is wholly inadequate. They don't even include OCR with their scanning software.

It has been an open secret in the cryptography community that Hal Finney was the designer of BitCoin from the very start. Hal died in 2014. Or at least he was frozen in liquid nitrogen so not talking either way.

Besides being the first person to be involved in BTC who didn't hide behind a pseudonym, Hal published a paper that describes essentially the whole BitCoin scheme two years before BTC was launched. And Hal never once accused Satoshi of stealing his work.

The reason Hal had to hide behind Satoshi is simple: The Harber Stornetta patent didn't expire until about 9 months after BTC launched. That covers the notion of the hash chain. There is absolutely no way anyone working in the field did not know about that patent or its imminent expiry. Hal certainly did because I discussed it with him before BTC was launched.

So the big question is why BTC was launched when it was, why not wait 9 months to have free and clear title? Well, Hal got his terminal ALS diagnosis a few weeks prior: He was a man in a hurry.

Having launched prematurely, Hal had to wait six years after the original expiry of the patent term to avoid a lawsuit over the rights to BTC from Surety. He died before that happened.

Oh and I have absolutely no doubt Hal mined the genesis blocks straight into the bit bucket. The key fingerprint is probably the hash of some English language phrase.

The real inventor of BitCoin wrote a paper describing the architecture two years earlier under his own name, Hal Finney. He got a terminal diagnosis of ALS a few months before he launched the BitCoin service, the pseudonym being necessary at the time because of the Haber-Stornetta patent on the BlockChain.

No, Hal, did not keep the coins. He invented BitCoin because he was a crank with weird ideas about inflation, not to get rich. Mining the coins and keeping them would have been a betrayal of his principles.

The proof of this is given by the fact that Hal did not in fact get rich from BTC despite being the ''second' person to join the project. Nor did Hal ever complain that Satoshi took the credit for what was very clearly his work. If Hal had been just another person coming along, there would have been every reason to keep the cash.

And we do in fact know Hal ran mining servers from the start and that he ended up in serious financial trouble due to his ALS. The freezing his head thing came from donations.

Craig Wright does seem to be the last of the three early advocates alive but that doesn't make him Satoshi. Wright has never shown the slightest sign of being the sort of person who builds such a thing and in any case, Hal's name is on the much earlier paper.

Unfortunately, the lunatics have taken over the asylum and now apparently "silence is violence".

There is nothing in the source NYT article to indicate the Emirates who are hosting this one summit in a series of summits are "Leading" the summit.

Your headline is excessively clickbait-y.

Itâ(TM)s usefully wrong producing code. Itâ(TM)s buggy, but not overly so, and itâ(TM)s much faster to write test cases and fix it than it is to do it from scratch.

I was chasing down a bug in a generalized crc16 yesterday and asked it for help. It gave me the wrong error, but a correct enough way to quickly find the error myself.

Sometimes it writes code that I look at and think âoethat canâ(TM)t workâ, and it does work, especially on things like micropython. Sometimes it makes up non existent libraries, though, so I have to go back and walk it through it.

Interestingly, itâ(TM)s often much better at writing code to do a thing than doing a thing.

You can also place an order on Digi-Key and wait. Retail price, no scalpers, but you wait a while.

Nobody got paid 20 percent less, because they werenâ(TM)t paid hourly.

If US employers can get the same amount of work in 32 hours as 40, they will cut hours for hourly workers which will cut pay.

For salary workers, itâ(TM)s different.