From the AI news of September 18, here's a roundup centered on a day when major companies responded one after another to the debate over the pace of AI development. Amazon made clear that it does not see this as "a choice between progress and safety," while Palantir CEO Karp argued for "personal responsibility." Meanwhile, Waymo announced a 2028 expansion into Singapore, as development and business growth continue to move steadily forward. In the open-source world, China Telecom released a 29B MoE model trained entirely on Ascend NPUs, and an AMD-optimized fork of llama.cpp also drew attention.
Companies Weigh In on the Slowdown Debate — Amazon: "When It's Ready," Palantir: "Personal Responsibility"
As a movement grows among major lab executives calling for slower development following a string of safety incidents, Amazon has for the first time taken a clear position. A company spokesperson commented: "We don't see this as a choice between progress and safety. Models should be released only after rigorous testing and robust safeguards, once they are ready and safe to use," also acknowledging that continued safety breaches would pose risks to the entire industry.
In response, Palantir CEO Alex Karp said in a CNBC interview that "the first line of defense is developers themselves taking responsibility for their own actions," emphasizing self-responsibility on the part of companies rather than government regulation. As Anthropic CEO Dario Amodei and others call for an industry-wide slowdown, the debate over regulation and where responsibility lies is beginning to split into sharply defined camps.
At the same time, Bloomberg has analyzed how these "existential fears" are increasingly shaping the AI debate itself. While some view recent incidents as signs of AI slipping beyond human control, others counter that such sensational rhetoric is actually crowding out opportunities to hold companies accountable for real-world impacts — a divide that remains stark even among experts.
Waymo to Launch Paid Robotaxi Service in Singapore in 2028
Waymo, a subsidiary of Alphabet, has revealed plans to launch a paid robotaxi service in Singapore in 2028. The company will deploy a fleet of Jaguar I-PACE electric vehicles in the country within the next few months, and starting next year, trained specialists will manually drive them to learn and map Singapore's roads and monsoon weather patterns. For a company continuing to expand both domestically and internationally, the Southeast Asian market looks set to become a new milestone.
US-China Summit: AI Rivalry Set to Be a Major Agenda Item
At next week's meeting between US President Trump and President Xi Jinping, AI, the quagmire of the trade war, and the yuan are expected to be major topics. The focus will be on whether the two countries can make concessions or set "guardrails" in the core areas of their competition, with markets reportedly sending mixed signals at this point.
China Telecom's "Xing4.0-29B-A4B" — A 29B MoE Trained Entirely on Ascend NPUs
"Xing4.0-29B-A4B," a next-generation model developed by China Telecom's AI technology subsidiary, has made it onto Hugging Face's trending list. It uses a MoE architecture with 29B total parameters and 4B active parameters per token, and natively supports a context length of 256K tokens (extendable to 512K). What makes it notable is the training environment: it is reportedly the first model of this scale to be trained entirely on the MindSpore framework over Ascend 910C clusters. Through multi-layered optimizations such as fine-grained MoE communication optimization and selective recomputation, the company says training throughput was improved by roughly 96% over the out-of-the-box baseline.
On benchmarks, it scores 75.0% on SWE-bench Verified (versus 53.0% for the comparison model Gemma4-26B-A4B) and 57.5% on Terminal-Bench 2.1, results that show an edge on agentic tasks. In addition to inference with vLLM, SGLang, and KTransformers, it also offers adaptations for agent frameworks such as OpenCode and Claude Code, and prominently positions itself as an "agent-oriented architecture" (mHC + MLA + MTP).
An RDNA3-Optimized Fork of llama.cpp — 82 Tokens/sec on Dual Radeon 7900 XTX
In the local LLM community, an optimized fork of llama.cpp tailored for dual AMD Radeon 7900 XTX setups is making waves. According to a report posted on Reddit's r/LocalLLaMA, the decode speed of a Q8-quantized Qwen 3.8 model improved roughly threefold, from about 28 tokens/sec with standard llama.cpp plus Vulkan to about 82 tokens/sec. It is drawing attention as proof that practical speeds are achievable on consumer AMD GPUs, and the comment section is brimming with expectations of further improvements to come.
Paper Pickup: What Actually Works in Coding Agent "Harness Design"
From Hugging Face's Daily Papers, we'd like to highlight an empirical study that systematically examines the design of the "harness" underpinning coding agents — the framework for planning, tool use, and context management. Most evaluations compare finished agent systems against each other, and therefore cannot isolate which components contribute how much. In this study, the researchers built a modular harness with a fixed execution loop, and evaluated 4 models across 176 experimental configurations — varying planning, tool interfaces, and context management — on benchmarks including SWE-Bench Verified. As a first attempt to give quantitative shape to "which ingredients work, and when," it looks set to become a useful reference for agent design.