My employer is counting token usage, so explaining my project between tokens isn’t necessarily a bad thing. I am clearly a more productive engineer because of it \end{sarcasm}
Uber’s situation was different, though. The reason Uber were bleeding money is because they purposefully made all their rides cheap to undercut the taxi businesses. People used Uber because it was cheaper than renting a taxi.
Now you can’t really find taxis anywhere, even at airports it’s a lot more difficult than it used to be.
Once the taxi business was disrupted enough, Uber’s pricing skyrocketed and customers had basically no other options for competition on pricing.
OpenAI basically created a new market. There is no AI chatbot incumbent to disrupt and swallow.
Uber/Lyft takeover had little to do with price (though, yes, they were cheaper) and everything to do with reliability and overall quality of service. Even though ride sharing industry lost money in subsidy arms race and side bets it was fundamentally sound in major metros since early on (similar to how Amazon was fundamentally sound from early on, despite not recognizing profit for a long time). Popular "analyses" kept equating Uber/Lyft with firms losing money on every sale with no path to fix it but the demand was always there as riders had already left taxis and transit on reliability and convenience grounds.
Some humans will need to interpret the thinking and apply it somewhere and take some responsibility for those decisions. If you think AI can do all that end to end it’s a different question but we’re nowhere near that right now.
Definitely, I’m not saying that AI can entirely replace humans. But AI is definitely replacing parts of many jobs. If AI companies raise their rates to be profitable, and it turns out that paying for profitable AI is not worth it vs paying for humans, that might be a sticky situation.
There will always be a competitor that can undercut the inference market. There is no "moat" given that you can self host decently capable LLM agents like Qwen3.6 on not super expensive hardware, like an AMD R9700, and still get competitive speeds to most cloud interfaces.
If you can self host it that easily, any Joe can scale it out much like shared web hosting, and shared web hosting or even dedicated rented boxes has always been cheaper than the big cloud providers.
I don't think OpenAI or Anthropic can reasonable compete in the long term if they can't achieve "AGI", and they won't, no matter what shareholders desire.
Actually the point is total cost wise outside of subsidy it is not cheaper than humans. the bigger problem is as the parent said open AI created a market. It is selling a commodity service with investor funds. There is no moat. your second sentence soon you won't be able to find human thinkers is on its face absurd, assuming the human race continues. Thinking is the human ecological niche.
For now, businesses are getting addicted to cheap tokens. As the screws get turned, business will debate whether they should spend budget on humans or tokens. What's further devastating is that humans are also becoming addicted to cheap tokens. Much human output is nowadays a token slopfest. People are becoming dumber too. So the real business question will be spending budget on token monkeys or tokens.
Which doesn't work the same way at all. With taxis, making them unprofitable leads to a long-lasting lack of taxis. When lots of jobs are lost, it actually becomes easier to hire someone with the right experience.
It might work very much the same. Discourage a cohort of CS grads into following another career path. Give businesses enough time to fully commit to “agentic workflows” such that they don’t have the expertise for in-house engineering anymore. Completely spaghettify every code base such that only AI would be willing and able to implement new features in it. Let customers lower their expectations of quality to meet what AI can product. By the time they crank up the token price, it may be hard or impossible for businesses just to switch back to human engineers.
It depends on how long you can keep those people un- or underemployed. I think engineers are rapidly bleeding experience even while being employed if all they do is prompting.
But when lots of jobs are lost, consumer spending is lost, and it becomes harder to sustain a business (whether B2C or B2B) and afford to hire someone...
If you knew about how much man power it takes to maintain, evaluate and improve agentic workflows, I don’t think you would write such a thing. In this context, AI is a jobs program for permanent employment.
Japan too. never thought I'd see it here but a taxi driver took the long way after a work drinking party. I guess he thought we were too drunk to notice. Well my boss sure did and lost his mind at the guy.
Likely the continued existence of taxis are keeping Uber's prices in check in the Australian market.
Uber will be running an optimisation model and be charging the maximum market can sustain, with additional goals such as eliminating competition and not being shut down by regulators.
My family and I have gone back to using car services for rides to the airport b/c "Uber XL" seems to include a WIDE variety of vehicles in terms of size and cleanliness.
A car service is about the same cost, the car looks brand new and clean and the driver is helpful.
Uber's situation is exactly the same. OpenAI is offering inference for a bunch of industries at prices that make it more competitive than hiring humans to do the same work.
If the break-even price to actually provide the service wasn't actually economic compared to humans, would there be nearly as much of a market? That's the real question. OpenAI is basically betting that they can live long enough that AI systems get built around them, which creates enough of a lock-in that they still have customers when prices increase by a lot.
I think you underestimate the price by a few orders of magnitude where it makes sense to pay a model instead of a human. If someone earning 200,000 a year gets replaced by paying 500 a day to Anthropic or OpenAI their employer comes out ahead.
There's likely always going to be value in limiting the number of $200k+ SWEs you have to pay. But that's not the interesting case.
What about the $10k/year offshored employees that are getting replaced by AI call centers? If that were the break even, then once you close down the whole building and develop the systems to not need them, then how much would inference costs have to go up before all that gets unwound and handed back to humans? It's more than you think - there's real margin there.
The uber situation was even more insidious than that. It wasn't like college students were calling cabs to go to bars in 2013. Uber created a market. It was essentially a mind virus. Gee now I can go to this place all for $7. Chum the water, establish the new pattern of living that people won't ever back away from, then twist the knife and raise prices knowing they won't revert back to whatever Old Way now long forgotten or not even engaged with by the upcoming generation.
If your comment is intended to convey sympathy on these workers, I think you're going to have a difficult time finding folks that align with you.
If your comment is intended to remind folks that these workers can simply resign of their own free will to find meaningful and dignified work at a different employer, I think you're going to have an easy time finding folks that align with you.
> The task is to place four black queens and one black bishop on the chessboard so that there is no square not under their attack
> In other words, after arranging the five black pieces, it must be impossible to place the white king anywhere without it being in checkmate.
These two sentences mean very different things in the normal rules of chess. And if you replace the word “checkmate” with the word “check” in the second sentence it still doesn’t mean the same thing as the first sentence.
The first sentence implies that all the pieces must be defended.
Edit: Eh, I guess it depends on how you view the word “attack” since all the pieces are the same colour.
You can saturate the entire power budget on pretty much all GPUs just by moving data in and out of HBM. There is no compute needed at all to do this, and bandwidth bound workloads are extremely common in the scientific computing space.
If only AI safety research had a mechanism this clear. "We have proof that building the machine will kill everybody, so get to work making a provably safe version."
Except that you have the logic backwards. It's an argument that something ("safe" general purpose AI) can't exist rather than that it has to.
People want AI to be able to do every good thing but no bad thing, which is impossible twice. First because false positives and false negatives trade against each other, so a general purpose AI which can do anything approximating all the good things is going to have the bias leaning heavily towards being able to do things in general and therefore being able to do many things that are bad. And second because "good" and "bad" aren't things that anybody can agree on and then some people will demand that it must do X while others demand that it not do X (e.g. "help the rebels win the war"), which means someone is inherently going to be unsatisfied and it's not a thing that can be sensibly regarded as everyone working towards a common goal.
Only that doesn't work either, because what people want is for themselves to have it but not their opponents, and you not building it while your opponents do is the opposite of that.
It's like calling for a general halt to the production of military equipment. How do you expect that to actually happen?
The first one is a difficult balance but not really impossible. The second is basically utilitarianism: Of course you can't maximize all wishes because they often contradict each other, but there can be a reasonable trade-off. Some tradeoffs are clearly better than others.
> The first one is a difficult balance but not really impossible.
It's a direct trade off. If you want it to do more "good" things you make it able to do more "bad" things.
> Of course you can't maximize all wishes because they often contradict each other, but there can be a reasonable trade-off. Some tradeoffs are clearly better than others.
The easy tradeoffs are the ones nobody disputes and everybody is already trying to do. There is no lobby for having it hallucinate more or give you ingredients that will combine to make poison when you ask for a tasty recipe.
But the algorithm still isn't practical on existing quantum computers, or ones that are going to be around any time soon, so there's no reason not to publish in full.
> See, some of the most reputable people in quantum hardware and quantum error-correction—people whose judgment I trust more than my own on those topics—are now telling me that a fault-tolerant quantum computer able to break deployed cryptosystems ought to be possible by around 2029.
Evidence that a hard problem is solvable, and information on solution characteristics, are a big help to others.
Even non-disclosure is just science-neutral, not anti-science.
Partial disclosures are common where disclosures involve risky things, or where a problem was solved as part of an economic concern. But there are non-conflicting opportunities to partially inform others.
That's the whole point. And it's not "build on their work", it's "question their work", because so far every time someone's announced some magic quantum thing it's been followed up shortly afterwards by people poking holes on it, a famous recent example being the "quantum computer" that was replaced by /dev/random and it produced the same results. So the magic here isn't the quantum, it's coming up with a way to publish a claim in a way that it can't be refuted.
There are plenty of smart people in the "AI community" already who know it. Smugly commenting does not replace actual work. If you have real insight and can make something perform better, I guarantee you that many people will listen (I don't mean twitter influencers but the actual field). If you don't know any serious researcher in AI, I have my doubts that you have any insight to offer.
It doesn’t work per-song. Songs have multiple chords, some even with alterations. If you tune an E so that it is perfectly a major third above C, then that E won’t be a perfect fifth above an A note. The Am chord has the notes A, C and E, so Am has notes that all belong to C major.
Additionally, some songs even change keys, which makes “per-song” not enough of a constraint.
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