Satya Nadella says that Microsoft has access to “all” of OpenAI's custom AI chip work with Broadcom and plans to use it to help develop its own in-house chip
www.wheresyoured.at/oai_docs/ Ed Zitron / @edzitron.com : While I do not have the full extent of its revenues, it is obvious that OpenAI's costs are dramatically higher - by several billion dollars - than previously reported. Similarly, the implied revenues are much lower than reported. — I am deeply concerned. — www.wheresyoured.at/oai_docs/ #wh... … Forums: r/technology : How high are OpenAI's compute costs? Possibly a lot higher than we thought r/BetterOffline : Newsletter: Exclusive: Here's How Much OpenAI Spends On Inference and Its Revenue Share With Microsoft
Context & Ripple Effects
Microsoft’s OpenAI relationship has recently been recast around a large equity position and a substantial Azure-services commitment, reflected in the revised investment and Azure purchasing arrangement. Access to OpenAI’s Broadcom work adds a hardware-design channel to that commercial relationship.
The move also fits Microsoft’s post-board-dispute effort to diversify AI partnerships and build internal model capabilities, as covered in its broader push beyond reliance on a single partner.
First-order effects
- Microsoft can apply insights from OpenAI’s custom-chip work with Broadcom to its own in-house AI-chip development, potentially reducing duplicated design learning.
- OpenAI’s chip program becomes a more direct input to Microsoft’s infrastructure roadmap, further linking the partners’ compute strategies.
Second-order effects
- Microsoft’s internal-chip effort gains a clearer route to tailor hardware choices around the workloads it serves through Azure and OpenAI-related services.
- The expanded technical overlap may sharpen scrutiny of how Microsoft’s OpenAI ties affect cloud competition, following FTC concerns about the investment’s potential cloud-market effects.
Third-order effects
- If similar access-sharing arrangements persist, major cloud providers may increasingly treat strategic AI partnerships as sources of proprietary hardware know-how, not only models and cloud demand.
- That would deepen the AI hardware strategy split: companies with large deployment platforms can combine external chip-development partnerships with internal silicon programs, while smaller buyers remain more dependent on merchant hardware.
The trend: AI partnerships are evolving into vertically integrated compute alliances in which model, cloud, and custom-silicon roadmaps increasingly reinforce one another.