Microsoft / Copilot

Microsoft is pushing a data center expansion to add 26 GW of compute, tripling its current capacity. The figure far exceeds previously disclosed plans, signaling a massive bet that AI inference and training demand will keep surging. The post doesn't spell out a timeline, locations, or budget, but 26 GW alone is larger than many countries' total grid capacity—power and cooling will be the hard constraints.

The two newspapers claim ChatGPT and Copilot trained on their journalism without permission, calling generative AI a 'snake eating its own tail' that could destroy the outlets producing the content. The Seattle Times case stands out because Microsoft and OpenAI previously funded some of its journalism projects. Microsoft says it's surprised but open to talks.

Greg Brockman reposted Satya Nadella's tweet saying GPT-6 Astra is now running on Azure and early customers are already using it. Nadella linked a Microsoft Foundry blog post calling Astra a frontier model for work scenarios. The post doesn't disclose performance numbers, pricing, or specific customer names—I'd hold off until more details land.

Satya Nadella posted that GPT-6 Astra is already running on Azure for early customers. The model is available through Microsoft Foundry, with details on the Azure blog. The post doesn't disclose pricing, benchmarks, or specific customer names—only the launch and distribution channel are confirmed so far.

The Trump administration filed a statement of interest supporting OpenAI's fair-use defense. The NYT sued OpenAI and Microsoft in December 2023, seeking billions in damages over training on its articles. The post doesn't detail the administration's full legal reasoning beyond opposing a narrow reading of fair use.

Microsoft Paint's local AI generation hits the cloud twice: first for prompt review and issuing a serial number plus watermark ID, then again to sign the output with a C2PA credential. Reverse engineering shows watermark injection is a hard gate—failure aborts the image. All six major vendors keep governance in the cloud even when inference runs locally. Regulations only require detectability, not per-user traceability; the extra step is vendors building their own risk controls. Three interfaces reveal the real posture: does the prompt leave the device, who issues the identifier, and how long are records kept. Microsoft has not disclosed retention periods.

Bill Gates warned in a NYT interview and a nearly 6,000-word essay that the AI industry is privately alarmed but publicly downplays severe threats to jobs and human life because trillions of dollars are at stake. He cited three tech moments that truly amazed him: the 1980 graphical user interface, OpenAI's pre-ChatGPT demo in 2022, and Anthropic's Claude Code this year. He called AI's impact on employment 'completely, absolutely, totally different' from past disruptions and said mass unemployment is inevitable without intervention. His proposals include a 'token tax' to raise the cost of replacing humans, 'Human Reserved' job categories like caregiving, and mandatory reviews for AI systems that could design bioweapons. Gates acknowledged his flawed-messenger status after the Epstein scandal and Microsoft antitrust case, but said he will raise AI risks alongside global health in every conversation with world leaders.

Bill Gates posted a long essay on his blog about AI's social impact. He supports slowing AI but doubts it's sustainable. The fresh part: two concrete policy ideas. First, a robot tax—companies replacing workers with robots wouldn't get immediate full write-offs, and the revenue would fund retraining and safety nets. Second, 'Human Reserved' jobs—barring AI from tasks like delivering a terminal diagnosis, or protecting roles held by older workers who can't easily switch careers. The post doesn't specify tax rates, timelines, or legislative paths.

Gates warns the world is not remotely ready for AI's impact and 'we are not preparing for it.' Once a staunch optimist, he now aims to reclaim a central role in shaping AI globally. The post only shows the essay's opening; his proposed solutions aren't detailed in the snippet.

OpenAI published the first measured results for Jalapeño, its custom inference chip. On the InferenceX benchmark running GPT‑OSS 120B, it delivered higher peak throughput per kilowatt and lower token latency than the commercial systems compared, with strong results on DeepSeek R1 and Kimi K2 as well. The post frames this as a working first-party silicon path that gives OpenAI direct control over serving economics. It also details a multi-supplier compute portfolio—Microsoft, NVIDIA, AWS, AMD, Broadcom, Cerebras, CoreWeave, Oracle, SB Energy, SoftBank—and a self-built data center in Georgia called Project Camellia. The core argument: co-designed hardware and software lower the cost of useful intelligence, which expands usage, funds further R&D, and creates a compounding advantage.

Latent Space traces how every component of the ML pipeline has flipped from human-made to model-made since 2022. The reward signal went synthetic first with InstructGPT's reward model, then Phi's textbook-quality synthetic pretraining data, followed by Alpaca-style distillation where a frontier model acts as teacher. Meta's self-rewarding models automated curriculum design in 2024, and Karpathy's autoresearch loop ran 700 overnight experiments in 2026, cutting GPT-2 training time from 2.02 to 1.80 hours. The latest step is Z.ai's GLM-5.3 synthesizing entire RL environments. The author frames this as '10% worse, but 100x cheaper and 10,000x faster human simulation.'

GitHub Copilot passed 20M users; M365 Copilot's paid penetration is ~3.3%, with only 20–30% of purchased seats active weekly. The split isn't about tech: GitHub's agent drives more commits and CI minutes, so usage revenue rises with output. An M365 agent that automates reports and invoice checks would let enterprises cut E5 seats at $57/user/month—Microsoft's price sheet only offers per-seat add-ons, never outcome-based pricing. Google shut down standalone browser agent Project Mariner and folded its pieces into Search and Chrome to protect ad exposure. Meta put revenue-generating agents on ad-free WhatsApp; personal agents remain free tests. Amazon's incentives align best, but Alexa+ was delayed two years and saw low voluntary use after a forced rollout. Adobe's subscription pivot slashed net profit 65% and took 18 months to lock in recurring revenue—today's giants haven't yet chosen to take that hit. The litmus test: the better this agent works, does the company make more money, or less?

Microsoft CEO Mustafa Suleyman announced the first in-house reasoning model, MAI-Thinking-1, now available on Microsoft Foundry. The model was built from scratch. The post does not disclose parameter count, benchmarks, pricing, or technical details.

Ben Thompson draws a direct line from Nvidia's current position to the 1873 railroad bond collapse. He traces how Jay Cooke funded the Northern Pacific Railway through retail bonds—12% commission, $200 in stock per $1,000 bond sold—until credit tightened in September 1873, triggering a multi-year depression. Liaquat Ahamed's new book '1873' converts the era's $500M annual railway bonds to roughly $600B today, matching projected 2026 Big Tech AI investment. Microsoft CEO Satya Nadella cited the book on the latest earnings call. The post notes Microsoft is the only hyperscaler still ramping spend, but the paywall cuts off the rest of the analysis—no specific verdict on Nvidia's risk is disclosed.

FT flags that Microsoft, Amazon, and Google have racked up huge off-balance-sheet purchase commitments for AI infrastructure. Microsoft's obligations alone exceed $300bn, over 6x its reported debt. These don't hit the balance sheet but lock in future payments. If AI returns disappoint, the hidden leverage hits earnings directly. The post doesn't detail default clauses, but the market is pricing capex without much attention to these commitments.

Agent Plugins 1.0.0 is an open, vendor-neutral spec that packages Agent Skills and MCP servers into a portable directory. Google joins Amazon, Cursor, Microsoft, OpenAI, and Vercel as a core maintainer. The format is deliberately minimal: plugin.json declares only a name and schema, skills live in skills/, and MCP servers go in mcp.json with explicit transport types. v1 intentionally omits install mechanisms, permission models, and sandboxing—those are left to each client. The post also notes that a single skill or single MCP server doesn't need a plugin; the format earns its keep when components must travel together.

Microsoft's latest filing breaks out the OpenAI relationship for the first time: roughly 70% of its AI revenue comes from OpenAI. Most of the $24.1B is cloud bills for training and running ChatGPT on Microsoft data centers, plus model development costs and a cut of OpenAI's own sales, all consolidated by Microsoft. Microsoft has also invested $11.9B into OpenAI.

Microsoft's latest filing breaks out AI revenue: Azure AI services hit a ~$43B annual run rate, and $35B of that comes from reselling OpenAI's APIs—over 80%. The Copilot family (M365, GitHub, Dynamics, Security) together reached ~$18B annualized, though the post doesn't split them by product. The picture is clear: Microsoft's AI business today is mostly an OpenAI reseller, and its own Copilot products haven't yet become a second pillar.

SpaceXAI's first quarterly filing shows $18.37b total capex, $15.83b of it on AI infrastructure, well above the $13.2b consensus. That AI spend alone is nearly 40% of Microsoft's total capex, and the sequential dollar increase matched the other hyperscalers. The difference is funding: Microsoft's operating cash flow covers 155% of capex, Meta 106%, SpaceXAI just 12%. Both equity and credit markets have repriced it—SPCX closed at $108 vs a $135 IPO price, and every tranche of the $25b June bond trades below par, with the 2056 notes at 90 cents on the dollar.

Microsoft researchers debunk eight common GenAI claims with internal data: devs spend only ~14% of time coding, so AI code-gen touches a small slice of the job and can push pressure downstream. Measuring impact by AI-generated lines of code was statistically invalidated a decade ago, yet some companies still report it. The piece also covers trust, learning cost, and enterprise constraints that slow real adoption—useful as a discussion starter for engineering leads.