The debate over slowing AI development is shifting from industry statements into the political arena. According to information collected on September 13, US President Trump rejected slowdown requests from the CEOs of Anthropic, OpenAI, and xAI, while former President Obama reportedly urged Democrats to come up with a "clear plan" for AI safety. On the local LLM front, reports keep coming in of people running Qwen 3.8 27B at practical speeds on a single RTX 3090.
Trump Rejects Slowdown Requests; Obama Demands a "Clear Plan"
The political response to the call by major CEOs to slow AI development, which we covered last time, has started to move.
According to Yahoo News, President Trump rejected the slowdown request from the CEOs of Anthropic, OpenAI, and xAI. The report was also picked up on Hacker News, where it sparked discussion.
Around the same time, former President Obama urged the Democratic Party to position AI as one of its "central issues" and to have a "very clear plan" for addressing the technology's economic impact and safety concerns (TechCrunch). While both parties have begun invoking "AI safety," they remain split between slowing development and prioritizing competition.
Anthropic CEO Dario Amodei also spoke to CNBC about the AI development slowdown he advocates, calling China "the hardest dilemma." Behind this lies the concern that a slowdown by US companies alone would mean little unless China follows suit.
The safety debate is shifting from industry self-regulation into legislation and diplomacy. Going forward, US–China relations and the design of domestic regulation are likely to become the focus.
Altman Pitched "AI-Powered Grid Defense" to Electric Utilities
A September 10 report from Politico revealed that Sam Altman had proposed "AI-powered grid defense" to electric utilities. With the explosive power demand of AI data centers becoming a pressing issue, he appears to have been pitching the idea of using AI to defend the power grid in return. Our collected data includes only headline-level information, so we will have to wait for further reporting on the details — but as an attempt to flip AI's weakness (its power demand) into a value proposition (its defensive capability), it is worth watching.
Practical Local LLM Use Draws Near on a Single RTX 3090
Last time, we covered running local agents on a 3× RTX 3090 setup with 64GB VRAM. This time, the reports we gathered address the question of whether a single card can be practical.
On Reddit's r/LocalLLaMA, a user who migrated from a MacBook M5 Pro (48GB) to an RTX 3090-equipped Linux machine reported an average of 100 tokens/sec with Qwen 3.8 27B (vLLM + Q4 quantization). On the Mac, llama.cpp (Unsloth Q6 + MTP) delivered only around 20 tokens/sec. The user also remarked that they are "finally close to letting go of their Claude subscription," serving the model through an OpenAI-compatible endpoint and using it from a desktop app. While tool-call failures happen more often, the assessment is that Qwen 3.8 notices the failures and can recover on its own.
In Japan, a hands-on article published on Zenn connected Qwen3.8-27B (quantized build with MTP) on a single RTX 3090 to Claude Code and had it develop a Three.js shogi app. Beyond one-off generation speed, it evaluated whether the setup could carry through builds, rule tests, and browser operations, and it summarizes practical settings such as batch size, KV cache, and inference effort.
In addition, a recipe was shared for running INT4-quantized Qwen3.8 27B with a 144K context (FP8 KV cache) using vLLM on a 24GB RTX 3090 (WSL2 environment). Prompt processing runs at roughly 1,000 tokens/sec for 10K-token inputs, and generation averages around 38 tokens/sec. Compared with llama.cpp's 25–30 tokens/sec, the configuration raises the baseline across the board, including long-context workloads.
The assumption that "local LLMs are slow" is being overturned even on a single, easily obtainable secondhand GPU. What sets this round apart is the accumulation of real-world measurements for coding-agent use cases.
Attendee Reports from AGNTCon + MCPCon Japan 2026 Published
Attendee reports have been published for AGNTCon + MCPCon Japan 2026, held on September 10–11 at Bellesalle Shibuya Garden in Tokyo. The authors describe getting a firsthand feel for a world where using AI agents is simply the norm and connecting them via MCP has gone mainstream. Many talks covered saving context, carrying memory across sessions, and ways of thinking about safety — suggesting that the practical challenges of agent implementation are shifting toward context management and governance.
Design Industry Bankruptcies Up 166.6% Year over Year
A survey by Tokyo Shoko Research (TSR) found that design industry bankruptcies reached 40 in the first half of 2026 (January–June), up 166.6% year over year to the highest level in 14 years (via a Qiita commentary). While the spread of generative AI is cited as a background factor, the commentary also points out that "generative AI adoption is not the only reason." As an example of AI-driven replacement starting to show up in concrete statistics, this could become a yardstick for gauging spillover into other industries.
Summary
The slowdown debate has moved into the realm of politics and diplomacy, and its endgame is still nowhere in sight. Meanwhile, in the field, practical know-how for using AI autonomously — from single-GPU local operation to agent context management — is steadily accumulating.