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.

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.

These are purely paper profits.

Unfortunately, it is highly likely to take decades before Meta loses its relevance: the network effects are enormous, and the vast majority of people over 35 will probably only leave Instagram and (from the age of 60) Facebook completely in exceptional circumstances. And I don’t even want to get started on WhatsApp…

Meta can absolutely pivot to renting compute for whatever model wins, but calling that an automatic death spiral feels premature: Companies with billions of users can survive technical setbacks for years. Betting against distribution has humbled plenty of investors before.

What strikes me as particularly risky at Meta is the sheer amount of power Zuckerberg wields: ultimately, it’s the same problem as with Musk, even though Zuck has behaved much more moderately so far.

Interesting that Microsoft is effectively running a portfolio strategy instead of betting on a single lab. Anthropic is already paying off on paper while OpenAI looks much more volatile. That alone suggests the AI race is far less settled than the headlines usually imply, even for the company funding both sides. And now the Chinese Open Weight AI models are coming onto the scene too, offering almost the same quality.

Pragmatic, but revealing. If Meta truly believed Llama would stay ahead, it would not be preparing for a future where competitors power its products. It also makes business sense for Meta because it already rents out compute capacity instead of using it for its own models: supporting multiple leading models could attract more customers to its infrastructure.