> 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.
860 karma · joined February 21, 2026
Which is the core point of my reply and not something to just be casually handwaved, thank you very much.
> Because it wasn't about "engineers should write 1M LOC per month of product code" it was "we want to scale automated porting of code to safe languages so that 1 engineer managing 1M LOC of automated conversion can work"
These are one and the same. Whether it's ported code or not doesn't change that. The framing device also doesn't matter, because it's the exact "Oh it's our goal" shtick that executives use in the former's case.
"It's just a measure" doesn't cut it in a world where every single AI measure immediately gets turned into a target by executives greedy for efficiencies that don't exist.
EDIT:
Right, I forgot. This is HN where everyone is a galaxybrain and "Port a million lines of code per month" is a totally reasonable goal for a single individual.
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.
This is not some arbitrary design choice, it's the core compromise to make LLMs viable to train at all.
Anthropic will sent a concrete number bill.
This is an utterly terrible idea.
Yes. In practice, this does not weigh against organisational resistance.
AI really makes it worse by adding an explicit numerical cost to doing anything.
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.
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.)
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.
That's exactly what the first (titled) section does?
If they were, they'd never shut up about it. Yet they keep quiet about the financials.
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.
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.
What is being sneered at is these prototypes being put into production. What is being demanded is that additional engineering time to make sure it's actually up to scratch.
An open mind to what? To yolo deployment of dodgy code straight into production? Moving fast and breaking things?
> I’ve personally seen this workflow produce real production code, used by customers, in an extremely rapid feedback loop.
Yes. I've seen it as well. I've also seen what happens. It goes wrong.
Should the engineers building your cars, your house, all other infrastructure also "keep an open mind" to slopping up their work?
Your next words will be "I'm not working on something safety critical".
You are. Even the most basic CRUD app handling personal data of any kind of safety critical these days. Data leaks alone KILL.
I'd posit there's another layer. You have domain knowledge, certainly. But more valuable still is the wisdom to find more.
Anthropic and OpenAI can stick financial regulations in the training data all they want, but the AI systems will never learn to anticipate the future, or reach out to clients, partners, or regulators in complicated situations.
The combined incentive of cost cutting at the outsourcing firm and foolish MBAs in the west opting for the cheapest outsourcing means that the offshore does actually employ juniors, who do build up the experience to become seniors.
This is so very easily said but how else is this supposed to work, exactly?
People have to start somewhere, and McDonalds experience doesn't count for any specialized job. Fuck, the "McDonalds-tier" jobs will often turn down graduates because they'll obviously walk the moment they get something better.
If no employer is willing to take a chance on graduates, then they just can't get any job experience. "A job that will pay for a roof over one's head" really isn't that extreme an ask.
As has been said a trillion times about AI and tech before AI: Senior level staff is going to age out, it has to be replaced or the entire industry gets sent offshore.
In terms of general unemployment across fields, youth unemployment is extremely corrosive to society.
This is already visible in how anti-AI sentiment is starting to boil over and the lurch rightward in politics. If this continues to escalate, the outcome will be nightmarish. Half of them bombing datacenters, the other half cheering as ICE raids the tech workers.
In the Anthropic deal they have to be negative; Anthropic's announced higher margins during the deal.
Same thing they used to say about Lehman.
That you fail to understand why those students took such great offense to what was said at those speeches doesn't make it "anti-AI bias".
Those students reacted like that, and I used it as an example, because it's very emblematic of how tech companies and leading figures act.
People don't buy the S&P 500 because they buy the index because it spreads risk. That they won't get maximum returns is the intended risk tradeoff they want.
That people consider the S&P 500 as a vehicle for "maximum money" is precisely why it should be considered in a bubble. And why actions like the NASDAQ's fast-track exceptions are so concerning.
The moment you start making exceptions to the rules because "gotta push the stock index higher", it's game over for the entire economy.
The youth are facing an enormous employment crisis. Many have found themselves completely unemployable through no fault of their own.
And then AI leaders go around to commencement speeches to rub it in.
There's no loss of nuance, the situation has just escalated a lot.
But $12/hr is probably quite accurate. SpaceX' datacenters are horrifyingly expensive, and regular GPUs are being rented below cost in many cases.
Just the gas turbine power alone is horrific. Doubles or triples the power bill and adds a big chunk of depreciation.
If the SpaceX IPO bombs (or even merely underperforms), the expectations for the Anthropic/OpenAI IPOs collapse, and with that, everything else AI.
AI companies can't afford to let any AI company go down.
The company in question only provides cloud services, and has no access to any data.
> I just don't understand why the government won't consider funding it. It's a public infrastructure service at this point.
It has been 9 years since the last centrist ("purple") government in the Netherlands. 24 years since the last left-wing led government. Nothing more to it.
It's just decades of Neoliberal "outsource government tasks to the free market" policy. There really isn't any other reason; The Dutch government has multiple divisions which are quite good at IT. It could choose to do so at any moment, it just doesn't.
Voters just didn't care. The system worked fairly reliably. So they just kept voting for a very charismatic politician, regardless of the long term consequences.
They would have to be similar because the argument is that they are similar; "AI is like the human brain" only holds true if it actually is like the human brain, not merely a superficial resemblance.
What I'm describing in that prior comment is how a lot of people drastically simplify the resemblance in order to make it feel true; That the lack of a Jesus Christ coming down from the heavens to tell everyone they have immaterial souls that the computers don't makes the comparison more true.
> We don’t really know or understand the relationship between the raw physics and again what we consider consciousness, so it’s simply a statement of “we can’t refute that these systems exhibit something similar” because we don’t know enough to refute that
Therein lies the conflict: "You can't prove it's not conscious" is an unfalsifiable statement. You can't engage with the argument because it's proponents will always claim victory, often with their own interests at play. All concerns about "superintelligence" or the long term ethics of "when do our robots become sufficiently intelligent that they'd be slaves?" have been subsumed into the AI marketing machine. However sincere one might try to address the issue, they look like a Sam Altman stooge by association.
It's like claiming the quantum fluctuations inside a pet rock as a "consciousness", even if observed directly any measurement of random noise can still be dismissed as "nuh-uh it just takes billions of years to have a thought".
More practically with current AI systems, we can look inside them pretty well and there genuinely is nothing there. Standalone LLMs are purely feed-forward systems. Their failure modes show that they perform no meaningful thought or world modelling during inference. They're just language models.
The reasoning and agentic systems are even easier to introspect. We know how they work, we can look at the full prompts & context they operate on. There is nothing there.
This is what sets AI apart from animals, which are given the benefit of the doubt on their intelligence.
Even those comparisons need to be cautioned. The complexity of biology is enormous, and more importantly yet, it's simply not comparable. And doing so invited a bunch of bad assumptions.
An ANN could quite probably model a single in vitro neuron with reasonable accuracy. Whether that requires a hundred or a hundred million nodes isn't terribly relevant.
But the way neurons combine in vivo is completely unlike the way machine learning systems are built. Both "locally" in how neurons interface which is vastly more complex than a weighted sum of inputs, and the macro scale interactions of hormones and other chemicals.
It's not even a given that large numbers of neurons will create the emergent behaviour of human intelligence; Elephants have significantly more neurons, but they're not the triple galaxy brains writing all our science papers. Other animal intelligence similarly is only loosely correlated with brain complexity. (Heck, not to be forgotten is the other end of the scale. Plenty of microscopic life that manages shockingly complex behaviour without any dedicated neurons)
This also applies to ANNs. There's no reason to expect that stuffing enough matrix multiplications into a program will make it intelligent or turn out conscious.
Really, the history of machine learning suggests the opposite; That the big gains are primarily had in architectural changes.
In this regard, I find the talk of the "limits of AI" quite credible. LLMs have already hit the diminishing returns on their growth, and even reasoning/agentic models display failure modes that confirm they're not "thinking" in the ways that humans do.
This is not to say that we've hit the final limits of what AI in the broad sense can do, it's just that the next advancement won't be "LLM but even bigger"
1) The abstract "dictionary" version: It'd be technically correct to say that the body is a machine under the definition of "A machine is a thermodynamic system that uses power to apply forces and control movement to perform an action.".
2) But there's also the less abstract/technical: "The body is alike the complex machines we have built", and this is much less true. Especially for the brain. The "neuron" analogy in machine learning is effective, but entirely wrong; We do not fully know how even a single neuron works, nevermind any complex system made out of multiple of them.
With regard to AI, there's a lot of people extrapolating "There is no magical animating spirit, the brain is just a pile of stochastic molecules following the laws of physics" into "The brain is an inert pile of matter, computers are an inert pile of matter, ergo AI/LLMs are like the brain!"
Especially so by people who have a financial/legal interest in doing so. "AI is just like a brain, fire your employees and buy our LLM now!", "AI is just like a brain, so it's totally not copyright infringement!"