In AI Chip Race, Nvidia’s Biggest Customers Become Competitors
In AI Chip Race, Nvidia’s Biggest Customers Become Competitors
Publish Date: 2026-05-17 00:01:00
Source Domain: www.thedailyupside.com
The list of companies creating technologies that could reduce the industry’s reliance on Nvidia might be longer than a shopping list for making a traditional mole poblano.
Among Big Tech firms, Meta rolled out four new generative AI chips in March that it said will lead to cost savings while still competing on a tech level with rivals’ GPUs. Two months earlier, Microsoft launched its Maia 200 chip, which is focused on inference (tasks such as answering queries and creating Studio Ghibli-style selfies). SpaceX, meanwhile, plans to invest between $55 billion and $119 billion in designing and manufacturing AI chips with Intel’s help.
And that’s not counting Google and Amazon; OpenAI and Anthropic, the companies that created AI’s best-known chatbots; Cerebras, which raised $5.5 billion in the year’s biggest initial public offering so far, and startups trying to break into the market.
They have a viable entry point: Now that AI has its training wheels off, the types of chips that will keep its momentum rolling are different from the ones that gave it the first push forward.
Majestic Labs Co-Founder and President Sha Rabii told The Daily Upside that AI is at a tipping point, with more work focused on inference. GPUs like Nvidia’s specialize in training AI, and companies are looking for new options that can more efficiently run AI after the models have been trained.
For Majestic Labs, the best way to make AI more efficient is to find a cost-effective way to increase memory capacity. Memory has become an expensive pain point for the AI sector, which is facing a shortage of high-bandwidth memory (HBM) needed to run the models it spent billions to create.
Making Memory
GPUs from Nvidia have incredible compute power, but relatively little memory, Rabii explained. That creates a bottleneck: “All that compute is just sitting there idle because you’re not able to feed the computational engines with the data they need to be…
