741–750 of 822 posts
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It's a shame HN refuses to seriously engage with the topic of AI safety. Either you think model intelligence will continue to improve or you don't. If you think it won't continue to improve, sure, open models are great. If you think it will continue to improve, then we are all fucked if models continue to be open on release.
Fucked how? The models capacity is great for defense too.
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More and more I find myself trying to stop Opus from doing something stupid, and at every turn I need to tell it to stop overcomplicating things. I think the models are being optimized for wealth extraction from users and companies, instead of solving problems. I don't know why Opus would try to create an entire library when I told it specifically to do something simple that would take 2-3 lines of Python.
> I think the models are being optimized for wealth extraction from users and companies, instead of solving problems. I don't think so. Expect that in a market with high vendor lock-in but that's not the case here. The market is extremely competitive and switching cost are near zero. Anthropic can't afford to pull shit like this and sacrifice quality.
Plus there's subjective stuff even for coding, people learning how to deal with it. Even on HN you can already see cloude/codex camps each strongly convinced that one is better than the other.
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It’s all about timing. This is tech bubble 2.0, Dotcom Boogaloo. If you’re able to flip it quickly, you’ll have generational wealth. If not, you could be holding a lot of worthless paper.
Yes. But is your impression that this is the strategy of people like Amodei? My impression is that it isn't, that they are actually true believers, and not just trying to hit the timing right and flip it.
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Some napkin math -- total global labor compensation is about 50% of the GDP, which puts it in the USD 50 - 60 Trillion range: https://ourworldindata.org/grapher/labor-share-of-gdp This source claims that knowledge workers alone (probably because they are paid much more) account for 35 - 50 Trillion of that: https://github.com/danielmiessler/Substrate/blob/main/Data/K... If LLMs can boost their productivity even by an…
I’d also point out that LLM inference revenue already totals more than 100B annually based on publicly reported numbers. Almost none of that is replacing knowledge workers. Almost all is increasing their productivity. So empirically what you describe is already happening to a nontrivial degree.
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why are you listening to these idiots who have every incentive to spin the story as much as possible FCFF = EBIT(1-t)-Reinvestment I dont care about your gross profit - this kind of cash profit determines the value of operating assets.
Well yeah obviously they have to stop reinvesting more than they make at some point to become profitable. To be clear, I think what Dario was saying was that if you consider each model training + deployment as a company , meaning all expenses and taxes, it was still profitable. Whether he's lying is another question, but seems unlikely.
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More and more I find myself trying to stop Opus from doing something stupid, and at every turn I need to tell it to stop overcomplicating things. I think the models are being optimized for wealth extraction from users and companies, instead of solving problems. I don't know why Opus would try to create an entire library when I told it specifically to do something simple that would take 2-3 lines of Python.
My experience with Opus in the last weeks is the opposite. I have the feeling Opus got smarter since they released and blocked Fable. Maybe they got more compute available since a) they finished Training Mythos/Fable and b) couldn't provide inference for it?
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> Completely different results as LLM is non-deterministic. You'd need to produce this like 20 times by each model and then do 2x20x20 cross comparisons by both models and ultimately distill the 2x20x20 comparison results into two reports of how they differ. In this non deterministic computing future, everything else is voodoo, feelings and "vibes".
I would expect a model's result each time to be of a similar quality to the other times. There's something wrong if it does a way better or worse job, at the same problem, sometimes. It's possible, but I haven't heard anyone saying that they do.
People use LLMs to do vulnerability scanning by throwing them repeatedly at a codebase. Depending on the run they return with nothing, with a false positive, with a true vulnerability. These are very different destinations when faced with the same problem, sometimes.
Since GPT2, people have been throwing a ton of crap at the wall just to pick out one nugget that's uncharacteristically more solid than the others. Honestly? It's not just possible—it's core to how they operate. And it always has been.
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More and more I find myself trying to stop Opus from doing something stupid, and at every turn I need to tell it to stop overcomplicating things. I think the models are being optimized for wealth extraction from users and companies, instead of solving problems. I don't know why Opus would try to create an entire library when I told it specifically to do something simple that would take 2-3 lines of Python.
Yeah. Mine really likes to read excess code. I'll ask it questions like "If I move all these three ETL jobs into a subfolder will it break anything?" It'll start with giving me the simple answer but then continue on to consider another question and realize it requires reading my entire other repo that handles all of my cloud's infrastructure. And it'll proceed to read through tens of thousands of lines of terraform.
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More and more I find myself trying to stop Opus from doing something stupid, and at every turn I need to tell it to stop overcomplicating things. I think the models are being optimized for wealth extraction from users and companies, instead of solving problems. I don't know why Opus would try to create an entire library when I told it specifically to do something simple that would take 2-3 lines of Python.
It's really bad when you let opus do investigations on broken java or infrastructure stuff. It starts decompiling .jar, sometimes multiple versions of the same dependency, reading every single kubernetes/terraform file and loading all the logs and info kubectl offers.