Here is the AI news roundup for September 22 (late night, Japan time). A startup that accelerates video generation with a software layer derived from cultured neurons has committed to a partnership with AWS. Also notable: reports that the US military moved to revise its AI-driven target decision process following a school strike in Iran, and an investigation revealing that people who train AI for OpenAI were fired for "using AI" — a lineup of stories that exposes the fraying edges of AI adoption in real-world practice. A long-form interview asking an Epoch AI researcher about a quantitative analysis of the US-China model gap is also worth your attention.

Video Generation 5× Faster, 80% Cheaper — An AI Layer Built from Rat Neurons Heads to AWS

According to The Decoder and WIRED, Biological Computing Co. has teamed up with AWS and is about to start offering a text-to-video generation model. The centerpiece is a software layer derived from actual neurons (rat brain); laid thinly on top of a base model, it is claimed to make video generation 5× faster and cut costs by 80%. The layer adds less than 0.1% in size to the base model, though which model it is built on has not been disclosed.

WIRED framed the development as "AI models made from rat brains getting closer to reality," and reported that it is a major tailwind for a field long considered peripheral — one that naturally tends toward merging with code.

What matters here is that the claim of accelerating existing models at almost zero added cost lines up squarely with video generation, where GPU cost is the single biggest challenge. That said, the base model remains undisclosed, and no independent reproduction or verification is available at this point. Whether this is more than grandiose marketing is something we will reserve judgment on until the actual offering and measurement results are published.

Epoch AI on the US-China Gap: "6–8 Months" — Evidence for RSI Is "Still Weak"

Nathan Lambert's Interconnects has published a long-form interview with JS Denain, who leads the insights team at Epoch AI. The conversation centers on the US-China gap and recursively self-improving AI (RSI) — a debate that also ties into MiMo's rise to the top spot and the fallout from the Claude leak that we reported previously.

On RSI, Denain took a cautious view of the evidence published by OpenAI and Anthropic that "AI is accelerating AI research," saying he does not consider it strong evidence of an imminent self-sustaining acceleration. He did see researchers' Codex spend, which keeps doubling month over month, as "a sign people are actually getting value" — while also cautioning that this does not automatically translate into a software intelligence explosion six months down the line.

On the US-China gap, his estimate is roughly 6–8 months based on release-date comparisons using the ECI (Epoch Capability Index). When Lambert pointed out that "Kimi K3 and GLM 5.2 look more like 2–4 months behind," Denain responded: "Depending on the measurement method, it falls within a 4–8 month range. A 2-month figure very likely reflects benchmark overfitting."

On distillation, he said his recent assessment revises its importance upward. As reasons, he cited the "Stolen Thoughts" paper, which suggested that information leakage from inside models is relatively easy, and the "Claude router" mentioned in an Anthropic threat intelligence report as a potential channel for supplying the right prompt distributions.

Perhaps the most ear-catching moment of the interview came when both said they had heard, through multiple channels, the rumor that US data vendors — having validated RL environments through collaborations with frontier labs — are reselling them to the Chinese side at roughly a tenth of the price, with a time lag. As a hypothesis explaining why the gap in capital and the gap in capability do not line up, it is a story to watch alongside the trajectory of the distillation regulation debate.

US Military Revises AI Targeting Process Following School Strike in Iran

Bloomberg reported that, following a school strike in Minab, Iran, the US military revised its processes for AI and lethal targeting. We were unable to retrieve the article body as of collection time, so details are unclear — but if procedures were rewritten after a real-world military AI operation caused human casualties, this is an extremely grave piece of news. We will be watching for follow-up reporting.

The People Training AI for OpenAI, Fired for "Using AI to Train AI"

This one is based on the headline of a 404 Media investigation. People who have handled model training for OpenAI were reportedly fired for using AI in their training work. It is a story that exposes where the "line on AI use" actually sits for those responsible for guaranteeing quality, and it has drawn a 26-point discussion on Hacker News. The collision between hands-on data quality control and generative AI looks like a challenge shared by many AI development organizations.

Stanford Swapped Students' "Race" in an Ad — the Ethics of AI Image Editing Questioned Again

The Stanford Review, a student paper at Stanford University, reported (headline-level) that the university's R&DE (Residential & Dining Enterprises) used AI to swap the race of students in photos used for advertisement production. It has drawn a 33-point discussion on Hacker News. Modifying attributes of subjects without their intent is the most sensitive territory in generative image editing, and this looks set to become a case that pushes universities and public institutions to establish usage guidelines.

"Mapping the Mind" Without a Camera — Viture's Display-Free Smart Glasses

WIRED introduced "Vonder," Viture's first display-free smart glasses. The device carries no camera; instead, it records your murmurs with a bone-conduction microphone, logging a day of thinking in an attempt to "map your mind." The camera-free design earns credit as consideration for bystander privacy, but the fact that it comes in exchange for the constant recording of your own speech points to a new trade-off for AI gadgets.

Local LLM Watch: A DeepSeek-V4.1-Compatible Fork and Early MiMo Measurements on M5 Ultra

On r/LocalLLaMA, a fork of "FreeToken," a serving engine that runs MoE models by offloading experts to host RAM/NVMe, has been released. It adds support for DeepSeek-V4.1-Flash (mHC, CSA2 sparse attention, Engram n-gram memory, and more), image input for Qwen3.8-Flash-Next, and speculative decoding via MTP draft heads — accompanied by a real-world measurement: Qwen3.8-Flash-Next (with 512 experts offloaded) running at an average of 48 tok/s (greedy, MTP disabled) on an RTX 3090×2 + EPYC setup. Particularly interesting is the report that MTP actually becomes a slowdown factor on the normal path with CUDA graphs enabled — the difficulty of tuning speculative decoding shows up in raw numbers.

In another post, early measurements were reported for running Mimo2.6-Flash on an M5 Ultra (256GB unified memory) with a patched mlx-vlm. Prompt processing averages around 1,238 tok/s, and generation averages around 49 tok/s at 32k context. Whether MTP is actually helping appears doubtful — Apple Silicon machines still have catching up to do on new models.

Elsewhere, Sebastian Raschka published analysis notes on the architecture and training of MiMo v2.6 Pro, calling it "a new open-weight best model with a simple design." In Japan, Qiita saw the publication of an article putting the Python SDK for TypeSafe AI "jev" to practical use — practical, Japanese-language knowledge around Jev keeps accumulating.