The AI Infrastructure Race...How the Game is Changing.

Change is afoot...

Last fall I worked with a major AI infrastructure company.

(Translation - "infrastructure" is just a fancy term that means all the hardware/computer chips needed to run AI initiatives).

𝗜𝘁 𝗼𝗽𝗲𝗻𝗲𝗱 𝘂𝗽 𝗺𝘆 𝗲𝘆𝗲𝘀 𝗕𝗜𝗚 𝗧𝗜𝗠𝗘. 👀

There's a mad dash for accessible, reliable AI compute power (chips), and the potential of a chip shortage is a real threat. 

Most come are produced by the Big Boy, Nvidia. 🎮 

But innovation is needed here. 💡

So OpenAI, father of ChatGPT, has decided to develop its own custom AI chip, which is an interesting, strategic move.

OpenAI Is Building Its Own AI Chip – Here’s Why It Matters

OpenAI is finalizing the chip’s design and plans to send it to manufacturing soon, with TSMC (the same company that makes Apple’s chips) rumored to be the manufacturer.

Building a custom chip is a major investment, with the final design phase alone costing tens of millions of dollars and taking up to six months before production begins. The risk? If the first batch of chips doesn’t perform as expected, OpenAI would have to redo the entire process.

Initially, OpenAI’s chip will have a limited role in running AI models, but it could later be used for training AI, reducing OpenAI’s dependency on Nvidia. This move follows a growing trend:

•Apple is already using Amazon’s chips to train AI and is rumored to be working on its own custom server chips.

•Meta and Microsoft are spending billions on AI infrastructure.

•DeepSeek (a Chinese startup) recently showed that powerful AI models can be trained with fewer hardware resources.

What’s Next?

If OpenAI pulls this off, it gains more control over AI training and deployment, reducing reliance on external suppliers. Plus, this could signal a broader shift in AI computing, with more companies designing custom silicon for AI workloads instead of relying on Nvidia.

One thing is clear: the AI hardware race is just getting started. 🚀