Meta Might Use Google TPUs in 2027. Should Nvidia Investors Panic?

Quick Read

• Meta (META) is in talks with Alphabet to invest billions in Google TPUs for data centers by 2027.

• Nvidia maintains dominance in AI chips through its CUDA ecosystem.

• Potential approval for Nvidia to sell GPUs in China remains a key near-term catalyst for the stock.

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Meta Platforms (NASDAQ:META) caused some ripples in the tech scene when it was reported that Mark Zuckerberg’s tech titan is in discussions with Alphabet (NASDAQ:GOOG) to invest billions of dollars in Google TPUs (Tensor Processing Units) for its data centers by 2027. Undoubtedly, it should come as no surprise that some tech titans are looking to diversify their AI chip supply beyond Nvidia (NASDAQ:NVDA) and Advanced Micro Devices (NASDAQ:AMD).

If you’re going to pour significant funds into advancing the effort, I think diversification is key to ensuring the right supply of chips in an era when we could continue to see GPUs fly off the shelves.

Given the hoarding action we’ve seen across the tech scene with DRAMs (Dynamic Random Access Memory), perhaps it’s not too far-fetched to learn of a firm backing up the truck on as much AI hardware as possible, including from other sources beyond the great Nvidia.

Google-Meta talks are not good news for Nvidia. But are the fears overblown?

Although I understand why Nvidia shareholders would be concerned over a potential Meta-Google deal, I do think that we’re still quite a while (perhaps many years) away from transitioning from GPUs to ASICs. In any case, it might be more about what the next big trend is for AI chips, and that’s why we saw Broadcom (NASDAQ:AVGO) enjoy a massive gain when the Meta-Google talk news broke.

Either way, Google TPUs might be getting more attention these days, but let’s not forget that there are a slew of other firms, including the Mag Seven players, that are working to create their own ASIC AI chips as well.

Whether we’re talking about TPUs or Meta’s MTAI (Meta Training and Inference Accelerator), there’s a lot of big money being invested to wean off those critical Nvidia GPUs, which remain critical to the future of the AI race. Despite the efforts, Nvidia has remained the dominant powerhouse in AI chips, and it’s likely to stay that way over the foreseeable future.

Though Google TPUs or any other in-house AI ASIC chip innovations made across the tech scene are definitely worth keeping tabs on, I don’t think panic is ever a good move for any investor. Of course, there’s the potential for Google TPUs and other ASICs to eat away at the market share of the likes of Nvidia. But we also have to remember that Nvidia isn’t going to be sitting around as competitors come for its big slice of the pie.

Reasons Nvidia stock might not be worth panic-selling right here

Through its CUDA ecosystem, Nvidia has quite a sizeable economic moat. It’s not just the GPUs that are the lone stars of the show over at Nvidia. With numerous AI-optimized libraries that play well with its own GPUs, the transition away from GPUs won’t be a seamless one to make. Either way, Nvidia investors should take time to think things through, especially as shares run the risk of falling into a bear market (a 20% fall from peak levels).

Though ASIC disruption is a longer-term threat, I think there are a few timelier catalysts that might just cause sellers of Nvidia shares to regret running to the hills while shares go for just $177 per share. Notably, what happens if Nvidia is given the green light to sell its GPUs in China?

Given how U.S.-China relations have been going, this remains a number-one bull point that could make it tough to sell on weakness. The announcement of such a green light, I think, could pave the way for a massive move that might take Nvidia shares back to new highs.

The bottom line

Combined with a healthy backlog, there’s plenty of reason to stick with Nvidia amid its slump. At the same time, there are a lot of points to sell as well.

Undoubtedly, there’s the Michael Burry short, which I’ve remarked on in prior pieces, as well as the potential impact if an AI bubble were to suddenly pop sometime soon. So, in short, panicking is never a good idea. Evaluating the new slate of information and revisiting your thesis is the way to go before making a move.

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