eicker, eicker@lemmy.world

The industry keeps benchmarking against other labs instead of against user needs: If a 3.5GB model answers 95% of everyday questions well enough, the remaining few percent has to justify hundreds of gigabytes of weights, huge energy bills and constant cloud costs.

Stock prices aren’t proof of being right, but they do show investors can change their minds a lot faster than the narratives do.

Apple’s marketing deserves skepticism, but the technical argument is separate. Local inference does not eliminate giant training clusters, it mainly cuts inference costs, latency and improves privacy. Apple still uses cloud models when needed.

Cook’s biggest product might be expectation management. He rarely promises tomorrow’s miracle, which buys Apple room to ship when it suits them instead of when Wall Street gets impatient.

Apple has always been unusually willing to sacrifice short term hype for long term positioning. That does not guarantee they are right, but it is a very different bet from spending hundreds of billions assuming demand will eventually justify the buildout. If AI demand disappoints, discipline suddenly looks a lot more valuable than scale.

About 4+ maxed M4 Studios, I guess. But that‘s not the point: in 80%+ of cases, people won’t need that kind of AI model to solve their problems.

Raw capability is only one metric: A local model probably will not beat the best cloud model any time soon, but it does not need to. If it handles 80 to 90% of everyday tasks instantly, privately and at near zero marginal cost, that is a huge win. Reserve the cloud for the genuinely hard requests, not every prompt.

Hehe… 😈

Absolutely. Looking forward to seeing the next generation of Macs.

The decentralised operation of LLMs would also be significantly simpler and cheaper for the use of decentralised renewable energy sources.

I’m waiting for the next generation of Mac Mini and Mac Studio.

The present: Open Weight AI, such as Kimi’s, is already almost exactly as good as ClosedAI from Anthropic and »OpenAI«.

It would seem so. On the other hand, it is puzzling that they did not also allocate the necessary resources to the development of LLMs. 🤷

The interesting part is not whether Apple wins the biggest model race, but whether it changes the economics: If enough AI runs locally, every token avoided is cloud capacity nobody has to build. That is a very different business model from selling ever more cloud compute.

It’s easy to underestimate how different these facilities are: Traditional data centres are built to run millions of separate workloads efficiently. AI data centres are engineered to make tens of thousands of GPUs act like one giant supercomputer. The building may look similar, but the power, cooling, networking and economics are almost an entirely different industry.

The market is fragmented across the Member States, each of which conducts its own research. That is precisely what needs to change. Hopefully, this approach will work.

This is a significant step for Europe’s AI ambitions. Access to large scale compute has become one of the biggest barriers to building competitive AI models. If executed well, these gigafactories could give European startups, researchers, and companies the infrastructure they need to innovate at home instead of relying almost entirely on US or Chinese providers.

This is exactly how the DSA is supposed to work: Once a platform reaches sufficient scale and societal impact, it faces higher transparency and accountability requirements regardless of whether it’s social media, gaming, or AI. If anything, it would be more surprising if major AI platforms weren’t eventually included.