111–120 of 128 posts
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In a way in US it is - _IF_ ppl were rational economic agents and free market allocation worked student loans should reflect on the wages too.
Luckily we're not, because then your tip to a waiter/waitress would be dependent on their student loans remaining, especially considering how many expensive liberal arts majors struggle to find a sufficient career.
Analogous statement: Evian use 1.25 million litres of water per employee per year. When can we expect other non-bottled-water corporations to rise to this level of water usage?
Why do authors spend so much time coming up with a book when they can literally pick one up for free at the library?
Even if the current generation of frontier models becomes 10x cheaper, companies will still end up spending much more per employee than they do today. Lower prices will not reduce AI spend. They will simply increase usage. There is no real ceiling on how much companies can delegate to AI. The only limit is the floor where spend too little, and you simply stop being competitive.
My company recently got a ton of AI credits on Linear and they are testing out this feature where whenever an issue is created in linear, it triggers an AI agent to automatically fix the issue. The idea is that when the dev gets to it, he will just check the preview-URL or run unit tests and rubber-stamp the PR. It is unlikely this kind of agentic workflow will ever get cheaper. Agents get stuck in doom-loops quite o…
This seems like a very '500kB is all you'll ever need' thing to say. This exact scenario has seen a precipitous decline in cost and increase in reliability over the past year.
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Luckily we're not, because then your tip to a waiter/waitress would be dependent on their student loans remaining, especially considering how many expensive liberal arts majors struggle to find a sufficient career.
That's not how free markets work
A.I suffers from the last-mile problem. It can do 90% of the work in 20 minutes but then the remaining 10% ends up taking 20 million hours to actually finish. It frustrating to the point that I sometimes want to throw the whole thing out and start from scratch.
Some people would argue that this is the best way to use AI as it exists today. Generate a POC and then if that POC makes sense, then rebuild it from scratch by hand. Maybe you can still use AI for a bit of boilerplate generation, but you should write all of the business logic and verify it by hand. Personally, I'm starting to lean more and more towards this approach. Though, I have to admit, for a well defined bug t…
Who are these people? Where did they argue that?
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That’s true, there will always be demand for ultra-intelligent assistants, especially if they surpass what humans can achieve at similar cost. For the other 90%, the average frontier model will be good enough.
I don't disagree with you, but it's important to pay attention to where the money is. Cheap non frontier models is something that Anthropic and open AI could do too, but who's willing to pay a premium for using them? It will be like competing to sell rice, lots of demand at Rock bottom margins.
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Why can't we pass on the costs of OpenAI and Anthropic's training back to OpenAI and Anthropic? Bandwidth isn't free, and all my life I've been told that piracy is theft.
Because the money that OpenAI and Anthropic have to pay those costs with comes from the users who pay for the service. There is no "passing the cost" of anything. The consumer always pays all costs.
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> Compare AI costs per-engineer-salary-dollar, because more expensive engineers probably need more expensive AI. Let's see how this works out in the long run. For a historical analog, more expensive engineers don't use more expensive computers (by and large).
Which is probably a backwards anti-pattern companies have built. Your most expensive engineer's time is most valuable, so if you give them standard issue which is half the speed, you are throttling the value you can get from your engineer. Not to mention the mental drain of your cursor barely being able to move due to all the bloated virtual networking systemization. It would seem to make sense to give more valuable…
(Well, for this to make sense, you need good computers to be cheap, but the returns to diminish quickly after that. Which used to be the case until fairly recently: single core performance was pretty much capped, and after your whole compile is parallelised adding more cores to a machine didn't really help your best engineer either. Similarly for RAM.)