Google Is Developing a New AI Chip to Make Gemini More Efficient

Alphabet is reportedly developing a new AI chip to run Gemini models more efficiently. The effort appears to be separate from the existing TPU series, targeting a design specialized for Gemini's inference and training workloads. The move to vertically integrate model architecture with silicon is spreading beyond Google as a strategy for reducing reliance on NVIDIA. Details are still limited, and we will have to wait for further information on the manufacturing process and release timing.

AMD and Microsoft Expand Their Partnership Around the "Helios" Rack-Scale Platform

AMD has announced an expansion of its strategic partnership with Microsoft. Microsoft will deploy AMD Helios, AMD's rack-scale offering that integrates GPUs and CPUs, at scale in its Azure cloud service, using it for workloads such as inference for frontier AI models. Helios is scheduled to begin shipping in the second half of 2026, with the aim of improving power efficiency and deployment speed by shifting the design unit of entire data centers to "rack scale." A clear counterweight to the NVIDIA-centric ecosystem is taking shape.

Ramp Open-Sources Its AI Model Router, While Natural Raises $30M for AI Agent Payments

The enterprise AI infrastructure layer is coming together rapidly. Ramp has released the AI model router it built in-house, reporting that selecting the optimal LLM for each task cut its internal costs by 30%. The router switches between models based on prompt complexity and latency requirements, with the goal of preventing the overuse of expensive frontier models.

Meanwhile, Natural has raised $30 million (approximately ¥4.4 billion) to build infrastructure that lets AI agents make payments autonomously. Operating in the realm of "software-driven payments" that credit card networks were never designed to handle, the company is positioning itself to directly challenge incumbent payment platforms, including Stripe. How to design trust, authentication, and cost control for the moment an AI agent executes a "purchase" is likely to become a key to agile product development in the years ahead.

Colibrì Demonstrates a 1.5TB Model, While Extreme Bonsai Quantization Hits an Accuracy Wall

Two contrasting results were reported from efforts to run ultra-large models in local environments.

While still a proof-of-concept, Colibrì has unveiled a new approach that can run a frontier-class model with 1.5TB of parameters on just 25GB of RAM. A full accuracy evaluation is still to come, but it stands as a symbolic milestone showing that inference with giant models is becoming a realistic prospect even under severe hardware constraints.

On the other hand, a benchmark posted on Reddit's r/LocalLLaMA evaluated "Bonsai-27B," quantized to the extreme at 1-bit and 2-bit, on Terminal-Bench 2.0, where Ternary-Bonsai-27B (2-bit) managed only a 7.9% score. Compared with Qwen3.5-9B (9.2%) and Qwen3.6-35B-A3B (24.3%), both of which fit within the same 8GB of VRAM, it falls short on accuracy—yet another confirmation that sheer parameter count does not translate directly into performance. Its small memory footprint and stable tool calling are commendable, but its advantage on practical workloads remains unproven.

AI Voice Phishing Reaches Human-Level Performance, According to Testing by Fred Heiding and Colleagues

In a Substack article, Fred Heiding and colleagues reported findings showing that AI voice phishing now achieves success rates nearly equal to those of human scammers, at a fraction of the cost. Thanks to improvements in voice cloning quality, targeted attacks can now be mass-produced at an unprecedented scale. As corporate countermeasures, the article points to rigorously enforcing a "never approve on voice alone" rule in internal processes and pairing it with multi-factor authentication as realistic lines of defense. Abuse of generative AI is already transforming the very economic structure of fraud.

Chugai Pharmaceutical's "10 AI Agents per Employee" Strategy, Plus the Common Traits of Japan's Successful AI Companies

New moves are also emerging in Japanese companies' practical use of AI. Chugai Pharmaceutical has set a goal of doubling its R&D output by 2030 and, as a means to that end, is rolling out an arrangement that assigns "10 AI agents per employee." The company positions AI agents as a foothold for redesigning the long, high-cost research processes unique to the pharmaceutical industry, signaling that it expects medium- to long-term returns despite large upfront costs.

Separately, an ITmedia article pointed out that companies that have succeeded in AI development in Japan—such as NTT, SoftBank, and Sakana AI—share "a certain common trait." Specifically, it cited their strategy of combining AI with existing domain strengths and their early investment in building out data infrastructure. Meanwhile, another survey shows that many manufacturers remain stuck at the proof-of-concept stage, and the gap remains wide: 65% say they "have expectations for AI," while only 16% report "clear results."