211–220 of 227 posts
What's the likelihood that universities eventually become open model providers?
Zero, but update priors when you see campus football stadiums replaced with datacenters and gas turbines
Earlier quoted context omitted.
Universities are struggling to prevent their students using AI, because it makes both learning and evaluation extremely difficult.
This. It's extremely frustrating, AI can be a 24/7 tutor, but too many students use it to do their work instead. We have to really rethink how and what we teach, and how we evaluate. Scoring (non-handwritten) homework is pointless, even contra-productive (because it incentivizes cheating, even for the students who don't want to, just to not be outscored by the cheaters). Hand-written homework means the students at le…
The uncomfortable implication is that "AI sovereignty" may end up being less about training your own GPT-class model and more about securing compute, energy, datacenter security and contractual access
People pay for cars and trucks (same cost scale) globally as they’re profit-generators for every user (as far as giving them access to jobs).
This is the same.
Earlier quoted context omitted.
And even if there weren’t any jarring errors, and rest assured there’s about a billion of them, there’s no appeal to this. It’s all context free short unassociated clips of pretty faces dancing on a beach. And? There’s no narrative, there’s now sense of reality, it’s just a sense of here’s a million pixels of colours that have proven to go well with each other, it’s _slop_. It’s been years and the only place AI has c…
How elitist of you to belittle 95% of all creators who are no better than that.
Earlier quoted context omitted.
Everyone is expecting them to invade Taiwan, but why not merely extort Taiwan?
You mean by contributing to RAMpocalypse the mainland incentives the west to build own fabs, making Taiwan expendable for us someday?
Earlier quoted context omitted.
It would be awful if running models locally became the primary way of using LLMs. On dedicated servers sharing GPUs across requests, energy usage and environmental impact is way lower overall than if everyone and their mother suddenly needs beefy GPUs. It’s the equivalent of everyone commuting alone in their own car instead of a train picking up hundreds at once.
So instead, we are building data centers for capacity we aren't sure exists... Data centers that are orders of magnitude more resource intensive than anything than came before. Hell there is one planned for Utah that I saw would consume 2x the power of the states current usage, which would there by triple a single states power usage overnight. Tell me how that is somehow more efficient?
So the question is rather what the most efficient way to serve AI is - locally or from the cloud, so: data centres.
I think we'll know this is true when deepseek becomes illegal in the US. I tried to sign up for deepseek API access directly from the company out of gratitude for the open source contribution (deepseek.com) but payments are blocked by US government rules.
The point of open weight models is not having to rely on specific providers for them.
Earlier quoted context omitted.
> There is no secret sauce the US labs have that the Chinese ones don't, or won't have soon enough. Over last year it seems that the only thing US labs are ahead is money spent. At least half of technical innovations if not more came from Chinese labs and was published openly.
Broad and deep capital markets are a real competitive moat for the USA. No other country or economic bloc can quickly deploy huge amounts of capital to new opportunities nearly as fast. China can work around that to an extent with a command economy that focuses resources on national strategic priorities but it's slower and less effective over the long term.
Pure spending power doesn’t give you the edge in tech. Creativity, and innovating under constraints leads to success.
Physics and economics will drive cost. Current token pricing is based on unsustainable investment and energy cost. However, this is more of an optimization problem than an inherent show stopper. Token cost will inevitably come down over time. But this could take a while before it catches up with demand. Manufacturing will step up to provide cheaper GPUs. Etc. There will be some consolidation but the whole thing will…
Those billions in AI datacenter infrastructure will eventually be repurposed to run smart models like Mythos, not ChatGPT or even Opus/Sonnet. That future "revenue model" is quite robust to any foreseeable competition from on-prem FLOSS inference. It's a natural fit to the actual capabilities of large datacenter-scale compute.
Earlier quoted context omitted.
Broad and deep capital markets are a real competitive moat for the USA. No other country or economic bloc can quickly deploy huge amounts of capital to new opportunities nearly as fast. China can work around that to an extent with a command economy that focuses resources on national strategic priorities but it's slower and less effective over the long term.
Actually this will end up being the greatest disadvantage. Pure spending power doesn’t give you the edge in tech. Creativity, and innovating under constraints leads to success.