It’s going to be an exciting week, and it starts with our all-new iPad mini. Thinner and lighter than ever, with an OLED display, water and dust resistance, better audio, powered by A20 Pro… it’s the biggest update ever to iPad mini!

While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected. Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions. Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent. But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models? We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem. Read the full report: cohere.com/blog/concerning-b…

% of Anthropic compute & infra spend dedicated towards training+R&D / inference, per S-1: 2023: 94% / 6% 2024: 70% / 30% H1 2025: 56% / 44% H2 2025: 65% / 35% H1 2026: 73% / 27% inference share peaked H1 2025 if inference efficiencies continue to compound faster than training efficiencies, not hard to imagine a world where, despite continued inference growth, the terminal state of compute demand is that it is ~ all dedicated to training believe this is much more likely than the consensus view that it is ends up being ~ all inference (though both will likely be important demand sources for some time)

“If all you care about is the journey, then you will never reach your destination.”

In the current macro environment, raising capital to fund AI compute buildout has become increasingly challenging. This is true even for large established players—Meta acknowledged as much in its latest 10-Q amid rising commitments and cash-flow pressure—and far more so for the smaller so-called “neoclouds.” This is occurring against a backdrop of rising compute rental rates across the entire term structure. Relentless demand for AI, visible in sharply higher prices, is precisely why so many are still racing to add capacity. Institutional-grade, liquid compute futures based on transparent and trustworthy benchmarks are therefore both important and useful for the further development of AI in America. Neoclouds that execute well and deliver reliable performance can lock in future revenues, reduce risk, and secure cheaper financing as a result. Silicon Data publishes daily market-implied forward rate curves for B200, H100, and A100. Once futures trading goes live, we will also have tradable futures curves. Current markets are displaying near-term backwardation (declining rates) and longer-term contango (rising rates).

“AI compute is the new oil—and needs a new market.” — @TheEconomist Cool to see @computeexchange featured as the marketplaces building that future! We are building a neutral, independent marketplace where buyers and sellers can discover prices, access verified supply, and transact through marketplaces, RFQs, and auctions. The compute market is coming. 🚀 economist.com/finance-and-ec…

How much is my $rcmt going to go down if the AI trade blows up?