posts · 2026-05-26

Clark’s sharpest move is not the singularity language; it is compressing the window to two years. The evidence is not one cherry-picked score: he points to Epoch’s Capabilities Index across 40+ benchmarks, then stacks the 2023 bar exam, 2024 IMO silver, 2025 IMO gold, 2025 AI-coauthored math proofs, and Claude Mythos finding software flaws. That makes the trend case stronger than the usual benchmark theater. I still don’t buy the leap cleanly. Software engineering, automated research, and building a successor training system are separate layers. The missing bridge is experimental design, data governance, compute control, and safety gating. Clark’s Anthropic seat matters here: a two-year clock naturally supports heavier regulation, bigger safety budgets, and tighter model access. The forecast can be aggressive; the mechanism cannot be hand-waved.

This is an FT story, but the two entries are really the same report pushed through different FT channels — the main article and the FirstFT briefing. So the multi-source label here doesn't mean independent confirmation; it's one paywalled piece getting double distribution. The core claim is that ByteDance is handing out special stock to its AI team to fight poaching. I'd hold off on drawing conclusions until we see the actual terms — is this for the whole AI org or just top researchers? What's the vesting schedule? How does it stack against ByteDance's existing equity pool? The direction makes sense though. Chinese AI labs have been raiding each other aggressively over the past year — DeepSeek, Moonshot AI, and Zhipu are all competing for the same talent, and ByteDance's Doubao team is a prime target. If this is real, it signals ByteDance is moving beyond salary bumps and using equity as a retention lock.

Three sources covered ForgeTrain and MiniCPM5-1B with tightly aligned headlines, which smells like one coordinated MiniMax-style release rather than independent digging. The hard hook is clear: ForgeTrain is described as a production training framework written entirely by AI, and it trained the 1B on-device model MiniCPM5-1B; the disclosed snippet gives no code size, human review ratio, stability data, or reproducible training recipe. I don’t hate the claim, but “world first” and “production-grade” carry real burden. A training framework is not a flashy codegen demo. The hard parts are distributed fault tolerance, memory scheduling, checkpoint recovery, and boring multi-day stability. If the repo proves those pieces, ModelBest has a serious AI-for-AI artifact. If it only shows generated scaffolding, this is sharp packaging around a normal open-source release.