Sure, but how likely is that?

I’m 100% positive they’re not reading my ChatGPT logs.

(I don’t use ChatGPT, and neither should anyone else.)

No, of course I’m not disputing them. It just seemed like a weird, pro-Russia take.

Lots of divisive political movements in the West really are pushed and supported by Russia. Are you disputing that?

No. We’re starting to figure out that OpenAI is both incompetent and unethical. Nothing is “escaping.” LLMs are just burning compute to accomplish tasks they’re assigned by incompetent and unethical people at the company.

If they set up their sandboxes properly, none of these hacking incidents would happen.

Let’s make one thing clear: The “extinction warning” was marketing.

LLMs generate text. That’s all they do. Don’t want them to create an army of killer robots or deadly viruses? Then don’t run them in a harness that can create robots or viruses. Problem solved.

My pleasure. The ethical problems with LLMs are my top concern, and I can only conclude that many people in my industry are too addicted to them now to internalize those problems.

I’m also in software engineering. I spent some time thinking like you, then some time trying to find a middle ground between using AI like that and doing everything myself.

Now I do it all without AI and am slowly purging all of the rituals around LLM usage from my brain. I’m trying to reclaim that space for useful knowledge in the languages I like to work with.

The reasons are numerous, some ethical, some practical, and some personal.

Ethical:

• There’s no way to use frontier LLMs without contributing to climate change. The products of Anthropic and OpenAI are built on raiding the planet’s resources.
• They’re also built on plagiarism on an unprecedented scale. Their products wouldn’t exist if it weren’t for stealing basically all copyrighted works ever created by people, and would be even less economically viable than they already are if they were forced to pay for the thievery.
• They’re addictive to use. People become dependent on them to the point of losing the ability to make decisions on their own. The non-deterministic nature of their output means people react psychologically to them the way gambling addicts react to roulette wheels, always hoping the next prompt will be the one that hits gold.
• Small, local models help, and the ones that are both open source and open weight are far superior to the rest in terms of ethics, so for anyone addicted to LLMs, I’ll recommend them as a form of methadone. But their psychological impact on their users can be just as damaging.
• The business practices of these companies is driving the largest economic bubble in history. When (not if) the bubble collapses, it will hurt millions of people. They don’t care, because in the short term, number go up.

Practical

• Model-produced code isn’t as good as anyone who’s creating it thinks it is. I’ve lost count of the number of projects I’ve seen from other developers (who think they’ve mastered the art of AI wrangling) that contain rudimentary errors - including gaping security flaws. I found the problems just by being the first person in a while to actually look at the code. No, not diffs, because when you’re churning out five thousand-line PRs, if the PR “looks right,” you’ll usually just sign off on it.
• The Supreme Court has already ruled that the output of LLMs can’t be copyrighted. So from a legal perspective, you’re risking poisoning your own projects with the output of coding agents. If someone steals that output and markets it as their own, what are you going to do? It was already derived from stolen work itself, so it’s not like it’s yours.
• Becoming dependent on businesses that have no path to profitability is a terrible idea. If you have built your workflow around Claude Code and Anthropic folds, what will you do? These companies cannot offer their services in a way that brings them sustainable profit. When the venture capital runs out, the clock starts ticking on their actual viability.

Personal

• Both of my kids are artists. I want to be able to look them in the eye and tell them I’m not using tools that are designed to make creativity obsolete and replace it with statistically generated pixels.
• I’m also a writer. I don’t want to become dependent on tools that deprived my fellow writers of their copyrights so that slop manufacturers can try to replace them with statistically generated words.
• I hate the way I feel when I’m using an LLM. The brief highs of getting the output I wanted (more or less) is offset by the impact on the way my brain works. I find myself thinking less and asking the LLM for its “opinions” more. I stop trusting my own judgment. And I start feeling addicted.

On that last point: I knew I had to stop entirely - not just limit my usage or restrict myself to local models - when I realized part of my brain really didn’t want me to. I had to have an argument with myself in order to do anything by hand, and I like coding by hand. I enjoy the puzzle-solving. AI was replacing my main source of enjoyment in my job.

And it wasn’t even doing that part well, it was just doing it faster. I am a better coder than any LLM, and that’s not a brag, because I know there are tons of developers out there better than me. Faster doesn’t equal better.

So that’s where I’m at today.

I think you’re misunderstanding what’s happening. The LG TV isn’t directly using the Windows internet connection. It’s taking advantage of Windows Update to have Windows itself install malware as a driver update for the screen.

Which I think is actually worse, because it shows that Microsoft is complicit in this nonsense.

I’m well-educated and understand the historic use of “liberal” versus today’s common usage in the United States. My only real concern is that people who are only familiar with U.S. common usage might need some explanation.

Likely. I get why people find the modern American use of “liberal” as a synonym for the progressive left annoying, but word usages change and that one has unfortunately stuck, which makes references to the “liberal media” confusing.

I’m assuming you meant “liberal” ironically? Those outlets are exactly as liberal as the right-wing conservative corporations that own them.

It’s already happening. Software instability is becoming a major problem.

Honest answer? Not much… yet. As the stupidity of “tokenmaxxing” and replacing developers with hallucinations-and-plagiarism engines really comes into focus, I expect developer hiring to ramp back up.

Meanwhile, in the real world, my company has locked down usage of Anthropic’s top-tier models, because costs were out of control. Even the low-end models are usage-capped, and developers are starting to write code by hand again.

And we’re busily setting up local models we control ourselves on our network edge. We’ll probably end up with a modest budget for frontier models (until the model providers collapse), but most of the coding will be done by local models and developers themselves.

And that seems to be the way the entire industry is going, unless Anthropic starts subsidizing tokens with investor money again.

In conclusion: Get fucked, Dario.

As I’ve mentioned elsewhere, not if by “information” you mean semantic content that a mind can process. What they have are vector fields (essentially just numbers) with statistically more or less likely relationships.

If I say, “take me out to the ballgame” to an LLM, the tokens representing the words in the next verse of the song are statistically “close” in the vector database, so it’s likely to generate them. But that doesn’t mean it actually knows the lyrics… or even has those lyrics recorded in a regular database anywhere.

That’s why they hallucinate. The model determines that the next token is something nonsensical, but it has no way of understanding that it has made a mistake. In a sense, it actually hasn’t made a mistake. It’s done exactly what it’s designed to do. It’s just that in the case of hallucinations, its output isn’t useful.

They’re information, but not the same information that was used to create them.

No, they really don’t. That’s not how they work. At least, not if the “information” you’re talking about is real semantic content that real minds can process.

Every piece of information you think an LLM has access to is actually just converted into a stream of additional tokens that are fed into the model to (hopefully usefully) modify the next tokens it predicts. That’s not the same thing as having actual access to information. Tokens are just numbers with statistically more (or less) likely relationships to each other.

I’m not trying to downplay LLMs. They’re architecturally interesting and have genuine uses. I’m just trying to head off a bit of technical inaccuracy.

The important thing to remember is that it actually has zero access to information, because that’s not how LLMs work.

At their core, they’re vector databases, and they’re trying to probabilistically come up with the next most likely token in a stream of tokens found in the DB. You can manipulate the stream by injecting text such as the content of existing files (which becomes more tokens) into the stream, but it never actually understands any of it.

That’s why hallucinations are inherently unavoidable. It’s really all just hallucinations. It’s just that you can sometimes get useful text from their hallucinations if they happen to comport with reality.

Jesus Tapdancing Christ…