eicker

Google isn’t getting special treatment here because it’s Google: Waymo isn’t importing finished consumer cars, it’s importing a vehicle platform, removing the original electronics, and integrating its own autonomous system. That’s much closer to sourcing components than selling Chinese EVs directly to the public, so it’s not really an apples to apples comparison.

Unpopular opinion: Google’s approach actually makes sense here. The value isn’t the rolling metal box, it’s the autonomous driving stack. If Zeekr can build a high quality EV platform more cheaply, why reinvent it? Buy the best chassis, replace the electronics you don’t trust, and focus engineering effort where your competitive advantage actually is.

Two research efforts gave AI the same open problem. Their papers were submitted three hours apart ... raising new questions about independent discovery and scientific credit.

Alibaba’s AI model Qwen3.8-Max made widely accessible ahead of open-weights release: Flagship 2.4 trillion parameter model highlights native multimodal capabilities and ... agentic tasks.

It would be surprising if they didn’t release Astra. And yes, we’re living in crazy times.

The impressive part isn’t that an AI produced a proof, it’s that Lean lets everyone verify it. The frustrating part is the model stays closed. Science advances fastest when others can reproduce both the result and the method, not just inspect the finished homework.

We’re reaching a point where the interesting part isn’t just whether an AI found the proof: it’s whether anyone outside the company can reproduce the result. Publishing Lean proofs is great. Keeping the model closed means the process stays a black box.

OpenAI Says Astra Solved 10 Open Math Problems With Lean Proofs: The proof files are public, but the new model is still private.

Apple is known for reselling and recycling.

Subscriptions were supposed to replace cable, then software, then heated seats, now phones. Funny how every innovation somehow ends with paying forever. If your business model needs me renting hardware I already carry everywhere, maybe the product isn’t improving fast enough to justify buying it?

Should you still buy your next smartphone — or subscribe to it instead? Apple, Samsung … are betting that leasing, subscriptions, and guaranteed buyback programs can make upgrading more attractive.

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.