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.
I’m pretty skeptical local models can hold a candle to the cloud-based ones, particularly the ones Apple trains.
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.
also this is just the beginning. give it 5-10 years and it may be more like 99%.
Most people don’t need deep agentic ai on their phones, they just need quick answers to questions, to add events to their calendars, answer emails, and remember things about their lives. These local llms actually perform better than the cloud ones for these tasks
He can see into the future.
Future #1: The lifetime of the pure AI companies is limited.
Future #2: Local LLM’s are trending, because “token” prices will go up like crazy.
The present: Open Weight AI, such as Kimi’s, is already almost exactly as good as ClosedAI from Anthropic and »OpenAI«.
Yeah “See into the future”. They’ve done the business analysis and determined the outcomes you gave. Apple has the confidence as a company to hold back even if the market wants them to do something and know they can hold firm through the irrationality of the market and come out the other side.
One has to wonder how companies like Google and Microsoft will fare when the financial engineering blows up in their faces. I’m guessing they think the government will bail them out.
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.
This isn’t seeing into the future. This is just taking a look at the present.
Present #1: AI datacenters cost a fuck ton
Present #2: No customer is willing to pay the costs of AI unless they sell at a loss
It seems to have been a plan for a long time, given their huge shift to unified memory architectures across most of their hardware.
They’re pretty much the only vendor where you can cost-effectively deploy a foundational LLM locally.
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. 🤷
Looks like an interesting approach
Absolutely. Looking forward to seeing the next generation of Macs.
And this will have huge ramifications for my field, energy, because a substantial portion of compute power usage will move out of data centers and into the edge (your iPhone)
The decentralised operation of LLMs would also be significantly simpler and cheaper for the use of decentralised renewable energy sources.
Tim Cook for all his faults is always been great at politics and this is an extension of that.
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.
I am practicing that strategy now. I recently upgraded to an iPad Pro M5 (the day price increases were announced, jumped on a deal immediately), upgraded several shortcuts with Apple Intelligence, and am refining them to run entirely on-device instead of in PCC (not using ChatGPT at all).
I’m waiting for the next generation of Mac Mini and Mac Studio.
