Verizon Adds Nine Members to 6G Innovation Forum, Reports ISAC and AI Trials

Jefferies started coverage on Ambiq Micro with a Buy rating and a $90 price target, sending the semiconductor firm into the spotlight on Wednesday. Analyst Kevin Garrigan wrote that Ambiq's SPOT architecture runs standard CMOS at a 2-5x power advantage on edge devices, where available power limits AI, and said the company is moving from a niche ultra-low-power MCU supplier toward an edge AI compute platform. Garrigan noted that Ambiq's top three customers, believed to be Google, Garmin and WHOOP, made up 77.5% of sales in the second quarter, but revenue outside those top three grew 143% year-over-year while overall revenue rose 90% year-over-year. He forecast revenue rising from $72.5M in FY25 to $249M in FY28, a 51% CAGR, with non-GAAP gross margin expanding from 45.0% to 48.7%, operating margin improving from -33.2% to 0.7%, and non-GAAP EPS moving from -$2.62 to +$0.53. Ambiq holds about $367M of cash and no debt, which Garrigan said funds the roadmap through breakeven.

Silicon Labs announced at its Works With Summit 2026 a set of developer initiatives expanding its AI developer platform for IoT and edge intelligence. The Simplicity AI SDK is now in public Beta, supporting AI coding assistants including GitHub Copilot, Cursor, and Codex, with official support initially focused on Bluetooth Low Energy. The company also introduced Simplicity Design Intelligence, a growing set of capabilities starting with Hardware Intent, which uses product requirements and board schematics to guide pin, peripheral, and software configuration and catch conflicts before fabrication; an alpha release of Hardware Intent is planned for January 2027. Silicon Labs launched its open-source developer community in Beta beginning with Bluetooth LE, letting developers raise issues, propose fixes, and contribute code through GitHub. Separately, Silicon Labs is partnering with Databricks to connect its platform-agnostic edge AI and machine learning tools with the Databricks Data + AI platform, and initial Silicon Labs MLOps tools are available in Databricks today.

Elon Musk agreed with a Tesla AI engineer's argument that the automaker's early decision to develop and deploy custom inference computers across its vehicle fleet could look unprecedented as demand for artificial-intelligence compute accelerates. "Yes," Musk wrote on X on Sunday in response to Tesla engineer Yun-Ta Tsai, who said Tesla's years of iterating and scaling its own inference computers for each car sold could prove unusually important before the superintelligence era, adding that the current compute shortage is only the tip of the iceberg and that when autonomy becomes indispensable, the real shortage will follow. Tesla has spent years designing its own inference hardware to run neural networks inside vehicles rather than relying entirely on general-purpose processors, and that strategy is now advancing through AI5 and AI6, with the company saying in January that development of both custom inference chips had progressed and production was then planned for 2027 and 2028, respectively. Musk has separately said AI5 will punch far above its weight and is primarily optimized for edge computing in Robotaxi and Optimus, and during the second-quarter earnings call he said the company's planned Terafab is necessary because Tesla otherwise simply won't have enough AI chips to scale Optimus. Reuters reported in April that Tesla, SpaceX and xAI are pursuing Terafab partly because Musk expects outside suppliers cannot satisfy their long-term chip requirements, with Tesla's Austin development fab alone expected to cost roughly $3 billion.