SlinkyOnStairs

> Granted, you can still rush the work, not test properly, neglect good planning and engineering.

Which is the core point of my reply and not something to just be casually handwaved, thank you very much.

The other comment got the answer already, but yes. It's a cost problem.

LLMs are designed this way so they could be trained off unstructured text, which critically can be obtained by just scraping things off the internet.

The moment you change anything about this, you incur the trillion dollar cost of needing to manually curate the training data.

There's some attempts to get around this problem with synthetic data, but they're running into problems with model collapse (Maybe severe performance degradation is worth the security tradeoff?) and the politics of AI; All major AI companies highly restrict using their systems for synthetic data & AI training, and they're too busy themselves to investigate exotic approaches.

Hence: Realistically, this is just a problem AI will have for the foreseeable future. There's no fine tuning that can fix this, nor can a new model be easily trained with these properties. The costs are just enormous right now.

Realistically, we are.

This is not some arbitrary design choice, it's the core compromise to make LLMs viable to train at all.

The point there is that it is MUCH easier to get corporate to agree to something when the cost is nebulous and being paid anyway. If you get a senior dev to clean up some tech debt, how much did that cost the company? The dev will have some multiple things at the same time, so you can't cleanly assign a number of hours, maybe multiple people are involved. It's practically just an unknowable. Practically, $0.

Anthropic will sent a concrete number bill.

"Just have a stochastic machine redo the code with no oversight"

This is an utterly terrible idea.

> so there has always been financial incentives to reduce tech debt.

Yes. In practice, this does not weigh against organisational resistance.

AI really makes it worse by adding an explicit numerical cost to doing anything.

I think you misunderstand why tech debt lingers around. It's not a capacity or capability problem.

Organisations just don't want to deal with the accountability involved with "touching cold code". Whether it's a human or "AI agent" doesn't change the "It worked in prod, you touched it, you broke it, never touch anything again" dynamic.

> Haha thought you were referring to the upsell at the start asking to subscribe to the newsletter for $70 / year.

People like you would be why I put "(titled)" in the reply.

> That's a very bold claim. Really anyone excited about generative AI dude? That's just an absurd claim, and makes it sound like he hasn't used an LLM since GPT 3.5. It's just the language is so hyperbolic and angry that it's giving me more rant vibes that really hurt the tone and damage the (many valid) claims he's trying to make.

The premise is that AI is significantly more expensive than current subscription & token fees. Within that framing, yes basically all AI users are getting conned. Tricked into redesigning their workflow around an unaffordable technology, in the hopes there will be too much sunk cost and they'll just eat a thousands-a-month fee.

> Which isn't wrong, but also Anthropic's revenue increased from $1 billion in Dec. 2024 to $47 billion May of 2026. Which of course doesn't guarantee that it will continue to grow at that scale, but it's clear that there is a strong demand for what they are creating.

"Doesn't guarantee it will continue to grow" is an understatement.

Let's take a generous assumption of the average subscription; $1000/month/seat. This will be quite a bit higher than pretty much everything but hardcore software dev, we'll re-do the math with $200 in a moment. Let's also grab Ed's $60B figure for both Anthropic/OpenAI, as it's more generous.

That's 30 million subscribers for Anthropic, 30 million for OpenAI, 60 million total.

They need to 5x. So 240 million extra subscriptions.

... Are there 240 million people left on the planet who can afford $1000/month?? (Either directly, or their employer) This kind of scaling is already hitting the limits of people on the planet. That sounds ridiculous for "240 million people" against 8 billion, but remember that $1000/month is a lot of money and a lot of jobs just do not benefit from AI. 2/3rds of employment in the US is stuff that happens in the physical world. Claude won't restock shelves, manufacture goods, construct buildings, cook food, or wipe geriatric asses.

Go again with $200/month. While this monthly fee is much more palatable, the sub-count inflates to 300 million subs needing to grow to 1.5 billion. They'd need to sell a sub to everyone in Europe and North America.

(And while there's loads of people in Africa and Asia, most of those are low income. You're not getting expensive AI subscriptions out of them or their employers either. China's obviously not gonna buy US AI, India has a GDP-per-capita of $250/month.)

> Sure, but it remains a big enough problem that human intervention and review is still necessary for any serious work across all use cases and industries.

Another important consideration: Hallucinations getting less common/severe but not (as-good-as) solved makes them worse.

LLMs used to very obviously get things wrong. And people wouldn't trust them. Now they're good enough that people blindly trust them.

Now people just directly PR AI output with little to no manual review. We even have clowns calling for the complete abolition of directly human-authored code.

Whatever gains were had in better AI code output over the past two years I lose in having to review much more thoroughly.

> He would begin somewhere–statistics on AI demand, say–and then walk the calculations carefully over to the next step–maybe revenue needed for profitability by AI companies–and you could follow the argument.

That's exactly what the first (titled) section does?

> There's no other realistic explanation except that they're making bank.

If they were, they'd never shut up about it. Yet they keep quiet about the financials.

> There's plenty of noise about banks holding large amounts of bad private credit debt.

This is still only big enough to cause funny banking collapses not actual 2008 scale financial disasters. Banks hold a lot of bad debt, but it's isolated from consumer accounts. Might not want to hold equity in SoftBank though.

> There's so much uncertainty and the combination of war, high oil prices, and uncertainty about tarriffs that the market struggles to value anything as international fear drives investment into the US and high prices confusing whether growth is growth or just inflation.

The big concern lies in what the Trump admin will do. Things could end up merely a bad recession, like the Dotcom and Telecom bubble.

Or they can attempt to keep the bubble going once it collapses, crashing interest rates, and doom the US economy.

> the big providers are charging full freight for inference.

Except they're not. Anthropic's claims of temporary profitability line up exactly with when SpaceX is giving them discounted compute, OpenAI's such a shitfest they threw the CFO off the glass cliff for daring to push back against the IPO. "Profitable on inference" is an unsubstantiated rumour.

Just look at the copilot changes. Demand switching to other providers immediately when prices rise, and there's not even certainty that the new copilot prices cover costs.

> They might not make back the money from training

This is an understatement. With all the datacenter buildout, they need trillions. For the investors get their money back and the bubble to not implode, they functionally need to unemploy everyone in the US.

If the AI dream is real, society just breaks.