posts · 2026-05-11

Three outlets track Google’s line closely: criminal hackers used AI to help discover and weaponize one zero-day. This reads like controlled disclosure, not independent convergence, because the date, target, model, tool, and actor names are withheld. I buy the direction of risk; I do not buy the completeness of the story. Google says the flaw hit a “popular open-source, web-based system administration tool,” bypassed two-factor authentication, and still required valid credentials. That is not a magic break-in button. It is AI moving vuln discovery and exploit scripting earlier in the kill chain. Against Anthropic’s Mythos claim last month of finding thousands of zero-days, the capability curve is ugly enough already. The disclosure style also helps Google push the regulatory narrative while keeping the evidence mostly unverifiable.

Two sources track the same event, and both run on OpenAI’s own announcement: DeployCo, the Tomoro acquisition, about 150 FDEs, and more than $4B in initial investment. This is official amplification, not independent discovery. I buy the direction; I don’t buy the clean story. Enterprise AI has not stalled because demos are weak. It stalls on permissions, data plumbing, workflow ownership, audit, and liability. OpenAI pulling in FDEs, Bain, McKinsey, Capgemini, TPG, and 19 partners is an admission that API-led self-serve growth hits a wall inside serious companies. Palantir already proved heavy deployment can reach core operations, but it also drags in long cycles, custom work, and margin pressure. That is the trade OpenAI is choosing.

Cerebras’ $35 billion IPO case rests less on beating Nvidia and more on OpenAI underwriting the revenue curve. The concrete hook is huge: OpenAI signed a 750MW compute purchase through 2028, with outside estimates above $20 billion. It also provided a $1 billion operating loan at 6% interest, tied to warrants for about 33.5 million common shares. That makes the story cleaner and more fragile at the same time. Cerebras posted $510 million in 2025 revenue and $87.9 million in net income, after losing $485 million in 2024. G42 concentration dropped from over 87% to 24%, but the customer-risk problem did not vanish; it moved to OpenAI. The WSE-3 inference pitch has substance, with 44GB on-chip SRAM and 21PB/s bandwidth. Investors are still mostly buying OpenAI credit, not independent demand proof.

Gowers’ test punctures the low-end moat of mathematical research. ChatGPT 5.5 Pro produced a quadratic upper-bound construction in additive number theory in 17m05s, then wrote a LaTeX preprint in 47 minutes, with Gowers saying he gave no mathematical guidance. The painful detail is the problem class: Nathanson-style open problems were plausible first-paper material for PhD students, not contest puzzles. I don’t buy the headline panic that “math majors are doomed,” but the training pipeline is exposed. On the h-fold sumset variant, the model took 16m41s to move to a subexponential bound, then 13m33s to push toward a polynomial one; Isaac Rajagopal judged the ideas basically sound. Tao’s “proof digestion” line is right, but publication, attribution, and starter-problem supply have already broken.

Cerebras raised its IPO range from $115-$125 to $150-$160 and expanded the deal from 28 million to 30 million shares. That is not a normal risk-on bounce; buyers are bidding for the cleanest public “non-Nvidia inference” asset. The evidence is unusually concrete: more than 20x oversubscription, up to $4.8 billion raised, wafer-scale chips with large SRAM caches aimed at the decoding step, plus cited large orders from Amazon and OpenAI. I still don’t buy the easy version of the story. Decode efficiency does not automatically beat Nvidia at system TCO. CUDA, networking, supply, and operator muscle still matter. Cerebras has to prove those orders become high utilization, repeatable deployments, and durable gross margin, not a one-cycle scarcity premium.

Qwen-Image-2.0 is betting on document-grade image generation, not another poster-demo leaderboard run. The concrete hook is the stack: Qwen3-VL as condition encoder, a multimodal diffusion transformer, and instruction inputs up to 1K tokens. That length matters for slides, posters, multilingual text, and layout constraints. I care about this because image models spent the last year stuck at “looks good, breaks on control.” GPT-4o image also landed hardest on text, layout, and instruction following, not pure aesthetics. The weak spot here is evidence quality: the snippet claims large human-rated gains over the prior model, but gives no benchmark names, sample size, pricing, release weights, or failure cases. Without those, this reads like a strong technical direction with unpriced proof.