Why is it a problem that the Chinese labs are just distilling down Anthropic’s models? Aren’t Anthropic’s models not just distilling down other people’s work?
Feels like Anthropic crying do as I say not as I do.
It ain't gonna happen. At work I have a dropdown menu in vscode with a dozen models to use interchangeably. They're all essentially commodities and will compete on price and squash almost all profit margin.
That's not their business model. They won't win on price, but they won't compete on price. Their business model is making the current state of the art.
If I'm a business and I need something done today, and bc Anthropic has the best model, there's a 99.9 chance it will be completed successfully for $1000. And using Deepseek there's a 70% chance it will, for $10 - you or me will go for the $10. Big businesses don't. Bc 1000 per task is nothing to them.
Yes, large corporations frequently pay orders of magnitude more for slightly better software. That's why Oracle produces the best stuff on the planet.
The real issue is that Deepseek has a 99.7% chance. So I can run it 10 times until it works and still pay 1/10 the money.
Actually opposite occurs. Big businesses are ok with a mediocre but cheaper result. Very few are willing to pay such cost. Just look at tech wages and the distributions
Except businesses are going the opposite direction here. The lack of stickiness makes the “premium” argument hard to play. Oracle won because swapping databases is a giant PITA. Swapping models requires almost no effort for most uses. And because of that enterprises are all building model marketplaces where providers have to compete on price performance.
Most folks I know can choose from any of the big labs or open weight models and they get billed internally for tokens against their budget. There’s little incentive to no switch to the lower cost closers.
This setup is a nightmare scenario for the big labs trying to execute the traditional enterprise sales plays. Those only work if your product is sticky and AI models are one of the least sticky things in the history of tech.
Big business doesn't pay more for better, but the do pay more for predictability, support and targeted outcomes. They will happily trade a chance at 100% better results for 10% less chance of unplanned outcomes
The business I work for is absolutely sensitive to 10 vs 1000, depending on the task. And it's a multi-billion dollar business. 1000/task may not be much on it's own, but there are a lot of tasks.
cmiles8 · · focus · HN ↗
Feels like Anthropic crying do as I say not as I do.
jorblumesea · · focus · HN ↗
it's not complex. there's hundreds of billions of investor dollars counting on vendor lock in and walled gardens
2OEH8eoCRo0 · · focus · HN ↗
dpweb · · focus · HN ↗
If I'm a business and I need something done today, and bc Anthropic has the best model, there's a 99.9 chance it will be completed successfully for $1000. And using Deepseek there's a 70% chance it will, for $10 - you or me will go for the $10. Big businesses don't. Bc 1000 per task is nothing to them.
HWR_14 · · focus · HN ↗
The real issue is that Deepseek has a 99.7% chance. So I can run it 10 times until it works and still pay 1/10 the money.
bushbaba · · focus · HN ↗
thadt · · focus · HN ↗
Big businesses might pay $1000 vs $60 for certain tasks, but that won't work out well at scale.
cmiles8 · · focus · HN ↗
Most folks I know can choose from any of the big labs or open weight models and they get billed internally for tokens against their budget. There’s little incentive to no switch to the lower cost closers.
This setup is a nightmare scenario for the big labs trying to execute the traditional enterprise sales plays. Those only work if your product is sticky and AI models are one of the least sticky things in the history of tech.
andrew_lettuce · · focus · HN ↗
rootusrootus · · focus · HN ↗
Also, is it really 99.9% vs 70%, or 99.9% vs 99%?
teaearlgraycold · · focus · HN ↗
jorblumesea · · focus · HN ↗
why should sammie or darigold have the keys to the kingdom?
teaearlgraycold · · focus · HN ↗