China Sends a Message, Opus 4.8, & More

TLDR;

Welcome back! Today, we tackle a broad list of topics.

From your weekly ratio of AI solving maths problems, Huawei’s new chip breakthrough, Uber sounding the token alarm, a trillion-dollar company going up by 19% on a single day, and Opus 4.8, among other interesting news.

Enjoy!

THEWHITEBOX

Google’s AlphaProof Nexus Solves 9 Erdős Problems

As published on arXiv, a Google DeepMind-led paper introduces AlphaProof Nexus, a framework that uses large language models and the Lean proof assistant to search for formally verified mathematical proofs.

The authors report that their strongest agent solved 9 of 353 open Erdős problems, including two questions that had been open for 56 years, at an inference cost of “a few hundred dollars” per problem. It also proved 44 of 492 OEIS conjectures after autoformalization and manual review.

The idea is that AlphaProof Nexus takes a Lean theorem with missing proof steps, lets LLM-based prover subagents revise proof sketches, and checks progress through Lean. In layman’s terms, it lets an agent “guess and verify” different approaches to solving maths problems.

To be clear, Google’s proof-solver, Alphaproof, has existed for a while now. The difference is that Nexus gives Alphaproof as a tool to another AI agent.

But why does this work so amazingly well?

TheWhiteBox’s takeaway:

The key is the use of the Lean proof assistant and compiler that can automatically verify correctness. This is a perfect counterbalance to an LLM’s biggest problem: hallucinations, as well as helping it improve under automatic verification.

Remember that in AI, we can only learn what can be measured. AIs excel at those areas where verification is simple; areas where discerning a model’s response quality is simple or even automatic, as is the case in maths.

Lean enables the AI to engage in a learning loop in which it can try new ways of solving problems and receive automatic feedback, making learning a matter of computation.

As I always say, there’s a reason we have amazing coding agents while AIs are terrible at writing. It’s not magic; it’s one task that is easily verifiable versus one that is very hard (what is great writing? It’s highly subjective).

But to me, the highlight here is the costs. They took only a couple of hundred dollars per solved problem. That is great news because it means frontier prices on hard problems are falling.

On the flip side, AI getting better at verifiable domains is not surprising; it’s maths, a matter of optimizing against a known and measurable objective. There’s zero reason to believe an AI can’t optimize against such problems.

But finding a way to train AIs on non-verifiable domains? Well, that’s still a mystery to this day.

HARDWARE

Huawei’s New Chip Breakthrough

Huawei has published a paper that is making quite the noise. The claim is that it has found a new way to scale chips despite US export controls: LogicFolding.

The problem China faces is simple. Advanced chips have historically improved by shrinking transistors, which lets companies pack…

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