501–510 of 538 posts
When "open source" means freeware, it's like saying "we want free copies". What we should be saying is: We want a public, community-ran project that does pretraining and training collectively. This means working on a training corpus in public and somehow coordinating the training work. This is a complete change of what the term means, It's like how people conflate piracy with theft. Two different things, use differen…
Another potential player could be Apple if they open sourced a frontier model, and make back some of the capex on hardware. Imagine AIserve hardware with continued expansion of MacMini’s and Mac Studios.
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I do believe that if OpenAI and others release an open-weight model that is better or on par with their frontier variants, it might ruin their primary business model. That is, of course, unless they develop their own hardware specifically to run this open model. But, that does ruin the point of open models.
When/if gains slow down, I can definitely see branching out into hardware to sell for on-prem inference once the models can be etched into the silicon with hard wired weight chips. I'd guess maybe at least 5+ years away from that though.
This, and distributed LLM inference. We are at a point where no single person can setup a rig to run a SOTA model, it is just too expensive. So we must build and adopt frameworks that allow individuals to share resources to run SOTA models in a distributed manner. That way they will also be non-censorable by governments. Also The only way to prevent that one entity weaponizes it, is by giving EVERYONE access to it.
I wonder if there is way local small LLMs can complement each other in away that the sum-total yields a much more performant LLM
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Yes but not violations of the laws of physics. You need extremely fast communications, memory bandwidth, etc; you cannot get that with distributed training. You're up against the speed of light and the interconnect that powers the internet. You will always have horrifically slow latency compared to if you pack the servers together in the same place with specialized networking.
Agree about the physics; disagree about the larger point. I am not questioning that servers packed together may achieve an optimal result in how we are currently doing things, but, and this is my point, what if we didn't. This is entirely the wrong question to ask. The question to ask is: how it could be adapted to distributed training.
I think the most important problem is that you have to marshall enough compute to be meaningful, and that is going to be more and more difficult as frontier compute requirements grow.
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Agree about the physics; disagree about the larger point. I am not questioning that servers packed together may achieve an optimal result in how we are currently doing things, but, and this is my point, what if we didn't. This is entirely the wrong question to ask. The question to ask is: how it could be adapted to distributed training.
You know what I'm surprised to find out this is far more feasible than I assumed; DiLoCo + INTELLECT models demonstrate how feasible decentralized training is already, that is very surprising to me that you can get that far with so much less communication bandwidth. Not only that, but that distributed training is _more_ feasible as you scale since compute needed scales as the square of parameter count but communicati…
But, and it is not a small but, there is no money in it. In fact, big orgs are bound to lose money should something like that succeed.
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As I replied to a child comment - this is a nice idea that just isn't tenable in reality. AI hardware isn't just hilariously faster than consumer GPUs, it's also hilariously more power-efficient and has hilariously better connectivity. Every one of these dimensions kills the idea. The far, FAR superior power efficiency means that even if you did harness every public GPU or GPU-like device on earth, you'd end up consu…
> It would be better for governments to buy and own their own datacenters, maybe as a coalition, and dedicate their operation to the public good. I believe that is what we actually have to do. 100% agree. The US government basically has to nationalize AI and capture an outsize portion of the revenue from it in order to fix the economy, as the combination of debt burden and interest rate pressure from de-dollarization…
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>> The US government basically has to nationalize AI and capture an outsize portion of the revenue from it Currently AI has generated no profit. And as it sits, is a non viable business. I refuse to include the sellers of shovels as AI revenue. If the companies buying the shovels are still losing money, then the tool supplier fortunes have nothing to do with the economics of the AI application layer, who is losing mo…
It's the most naive opinion that keeps getting shoveled around. You have a product that is viewed as essential by businesses, with revenue growing by 10x a year and geopolitical ramifications that have continued to rear their heads and your opinion is "this is all an unprofitable shill". It is extraordinary to me that people really believe this. Whether or not labs run at a loss today is absolutely irrelevant. There…
Businesses also bought dot com infrastructure, telecom fiber, crypto platforms, metaverse tools, and overbuilt SaaS. The question is whether the AI application layer can charge more than its full cost and the costs are inference, infrastructure, depreciation, R&D, customer acquisition, support, compliance, security, and error remediation.
The numbers so far do not inspire confidence. OpenAI reportedly did $4.3B in revenue in the first half of 2025 while burning $2.5B, and Microsoft said OpenAI related losses reduced its own quarterly net income by $3.1B. An MIT 2025 enterprise AI study found $30 to 40B spent on GenAI with 95% of organizations seeing zero return.
One of the core technical reason is that hallucination destroy enterprise economics. If SAP hallucinated 2% of invoices, or Oracle returned fake rows 2% of the time, nobody would call that early stage friction. They would call it unusable for core operations.
In legal AI, even specialized tools have been measured hallucinating 30% of the time. The problem is that as AI gets better it is confidently, plausibly wrong. That forces humans to verify it.
So the cost does not disappear. It moves from doing the work to checking the work. AI coding has the same issue. If an autopilot got you there faster but one flight in ten became unstable unless the pilot constantly supervised it, that is not productivity.
For the bull case to work, the usage must explode, the quality must improve, prices must fall, reliability must rise, legal risk must shrink, and margins must expand and all this at once. I would say that instead of a business model, this is five miracles stacked on top of each other.
Everybody who understands the technical problems is proposing a government fix. There is another option, foundations / NGOs could do this. Of course, openai has shown how quickly that can pivot into something completely different.
IIRC, there once was a foundation created to create AI that is open for all. What happened to it?