OpenAI is set to produce its first artificial intelligence chip next year in partnership with US semiconductor giant Broadcom, the Financial Times reported on Thursday, citing people familiar with the matter.
OpenAI plans to put the chip to use internally rather than make it available to external customers, the FT report said, citing one person close to the project.
OpenAI and Broadcom did not immediately respond to requests for comment after regular business hours.
OpenAI, which helped commercialise generative AI capable of producing human-like responses to queries, relies on substantial computing power to train and run its systems.
Last year, Reuters reported that OpenAI was working with Broadcom and Taiwan’s TSMC to develop its first in-house chip to power its artificial intelligence systems, while also incorporating AMD chips alongside Nvidia chips to meet the surge in infrastructure demands.
At the time, OpenAI had examined a range of options to diversify chip supply and reduce costs.
In February, Reuters reported about OpenAI pushing ahead on its plan to reduce its reliance on Nvidia, for its chip supply by developing its first generation of in-house AI silicon.
‘Deeply engaged’
The ChatGPT maker was finalising the design for its first in-house chip in the next few months and planned to send it for fabrication at TSMC, sources had told Reuters.
Broadcom CEO Hock Tan said on Thursday that the company expects AI revenue growth for fiscal 2026 to “improve significantly”, after securing more than US$10-billion in AI infrastructure orders from new customer, without naming it.
Read: Broadcom joins the trillion-dollar club
A new prospect placed a firm order last quarter, making it into a qualified customer, Tan said on an earnings call.
Tan earlier this year had hinted at four new potential customers who were “deeply engaged” with the company to create their own custom chips, in addition to its three existing large clients.
OpenAI’s move follows efforts by Google, Amazon and Meta, which have built custom chips to handle AI workloads, as demand for computing power to train and operate AI models surges. — Disha Mishra, (c) 2025 Reuters
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