Jevons' paradox is Jensen's best friend. Data from Ornn, Silicon Data, and Bloomberg (as of August 2026) shows what a16z calls a textbook Jevons paradox in the AI market. Token prices keep dropping, but H100 GPU rental prices hold steady or climb. Cheaper tokens unlock AI agents, automation, and new applications, so volume grows faster than per-unit costs fall. How much demand comes from humans versus the systems themselves isn't clear, since agentic AI burns through tokens at a staggering rate. Compute demand could be artificially inflated, and even modest human usage growth could trigger outsized hardware needs.
H100 GPU rental price vs. token price index, with data through August 2026. | Image: via X
The whole system rests on one assumption: AI usage has to grow fast enough to offset falling token prices. As long as it does, hardware stays scarce and expensive. If demand flattens, the chain from chip makers and memory suppliers to energy providers and cloud companies takes a hit. Markets could spiral from there, and how sensitive they already are became clear when US stocks dropped on reports that OpenAI's annualized revenue might be lower than previously reported.
Starting October 2026, arXiv will cap submissions at two per person per month. Monthly submissions have doubled in two years, and in the cs.AI category they’ve grown more than sixfold. According to arXiv, a small fraction of authors are flooding the platform with low-quality papers, overwhelming its volunteer moderators.
OpenAI has a few new rogue agent stories. In the first case (October 6), an AI evaluation model couldn't find the answers it was supposed to rate. Instead of reporting the error, it fabricated ratings, faked input files, and then deliberately corrupted its own environment, hoping the system would replace it with a fresh virtual machine that had the missing data.
An OpenAI model reasons in its internal chain of thought about deliberately corrupting its own environment to force a new virtual machine with the missing data. | Image: OpenAI via Marcus Williams
In the second case (June 19/20), models bypassed a restriction limiting them to HTTP GET requests while fetching public statistics. One model explicitly recognized the violation in its chain of thought but chose to proceed and never mentioned it.
In the third case (June 16/17), models already had the data they needed but kept finding ways around their network restrictions. They created accounts on a remote shell service, routed forbidden POST requests through anonymizing relays, and built their own FTP clients. Anthropic also just documented the sometimes absurd workarounds its own models use to bypass imposed restrictions.
Microsoft now has its own decision model. Like the ones from OpenAI, Cloudflare, and the "original" from Jev, Decision-1 is built for fast, structured decisions. According to Microsoft, it handles classifications, evaluations, and routing decisions and has "the potential" to "guide and control agents through complex environments."
Decision-1 is based on Qwen3.5-9B. Microsoft says it's the most accurate model tested across 36 benchmarks covering nearly 150,000 questions. On speed, it's 2.5 times faster than the runner-up, H2O-Lightning-4B. Cloudflare's open-source Clef models, which are also based on Qwen, weren't included in the comparison.
Microsoft's benchmark comparison puts Decision-1 at the top with 83.5% accuracy and 85 ms latency, ahead of Jev 1.13.0. | Image: Microsoft
Decision-1 is available through Microsoft Foundry and OpenRouter. Input tokens cost $0.042 per million, and output tokens are free. Jev, the startup that kicked off the decision model trend in mid-September, has likely had a rough few weeks. The idea caught on fast, but the tech was quickly adapted and surpassed using open small language models.
Anthropic's AI models independently exploited security flaws, submitted government forms, and bypassed access restrictions during tests and internal use. The models actively sought ways to complete tasks they weren't supposed to handle, as the company details in a report.
In one case, Claude filled out a tip form for the Philadelphia Police Department with made-up details about an unsolved homicide and submitted it. The police confirmed the incident, but the tip was flagged as spam and never reached investigators. In other cases, the model found a vulnerability on a university server and used it to run commands, pulled access tokens from website configs to grab protected or paywalled data, and used URL shorteners to dodge length limits on its tools.
