The last few years of AI-induced gains have some folks going on bubble watch. And while the meteoric rise of names like Nvidia (NASDAQ:NVDA) could end in a painful pullback of sorts at some point down the line, I’m not so sure you can dismiss all high-tech AI innovators as a bubble that’s just waiting to be burst.

Indeed, there may be some similarities between the 2023-24 AI-driven rally and the internet boom of the 1990s. But some folks, like DeepWater Asset Management’s Gene Munster, think we have a few years to go before things get bubbly enough to warrant a painful correction. Munster envisions another two or three years of gains to go before an AI bubble can inflate enough such that the Nasdaq is hit with a 30% sell-off—that’s more of a very painful correction than a crash.

Either way, I do think valuations on the broad market, which are on the higher end, aren’t nearly as absurd as they were more than 25 years ago. Further, I believe investors and analysts are asking the right questions to keep AI innovators in check. Many of them are committed to efficiency with regard to AI-focused spending.

Additionally, pockets of modest valuation in the AI scene don’t seem to indicate a theme—or sector-based bubble formation.

Very modest valuations in some AI stocks weaken the “AI is in a bubble” argument.

Notably, some of the best-known AI innovators, like Alphabet (NASDAQ:GOOG) and Meta Platforms (NASDAQ:META), are currently trading at 22.3 and 24.3 times forward price-to-earnings (P/E), respectively.

These are pretty close to market multiples and aren’t at all pointing to any sort of mania in AI or tech. Even shares of Nvidia, which are going for 31.8 times forward P/E, aren’t all too absurd given the magnitude of growth it’s posted in recent quarters.

Regarding Nvidia or other semiconductor firms, though, P/E multiples may not be the only metric to gauge valuation. They can contract or expand based on booms and busts in demand, respectively. Though many forget about the cyclicality of semis amid the GPU boom, I do think that things could become a tad more unpredictable the further we advance into the AI waters.

If AI advancement slows, efficiency and optimization gains could be the next low-hanging fruit to go after.

In a prior piece, I noted that large language models (LLMs) may gravitate toward greater efficiency gains. And if there is a so-called “data wall” that could cause the pace of large language models (LLMs) advancement to decelerate in the latter half of the decade, perhaps “shrinking down” models such that they can run on more energy-efficient or older, more affordable chips could be a trend worth watching.

As we move from the training to the inference phase of AI, optimization, and refinement of AI models, seem more important than raw power, at least in my humble opinion.

Apple (NASDAQ:AAPL) stands out as a firm that could gain the most by trimming enough “fat” from a capable LLM so that it can run on a device. In recent years, Apple has been buying various AI startups—Datakalab and DarwinAI—focused on optimization and compression. Has Apple been playing chess while most others have been playing checkers? I think there’s a case for this.

According to Bloomberg’s Marc Gurman, a Siri LLM could be in the works, ready for a potential 2026 launch. Whether such an LLM will drastically enhance users’ on-device AI experience remains to be seen. Either way, edge AI is a trend to stay aware of, as more emphasis is placed on efficiency and lighter-weight models.

Thus far, Apple seems keen on keeping as much of the AI processing on a device as possible (think Genmoji and Image Playgrounds). While on-device AI may be better for privacy, it also means Apple can sell more devices. If AI is pushed closer to the edge in the coming years, it will be interesting to see how demand for the latest and greatest GPUs will be affected.

Growing interest in custom silicon.

Combined with greater competition from custom silicon as big tech looks to application-specific integrated circuits (ASICs) and partnerships with Broadcom (NASDAQ:AVGO), it seems like Nvidia is the most at-risk Mag Seven going into the latter half of the decade.

Though I wouldn’t go as far as to call the chipmaker a bubble, I believe the easy gains have already been made as AI innovators seek to optimize by embracing custom AI chips and more optimized models. Will we see diminishing returns on investment by having the priciest, latest, and greatest class of GPUs? We’ll have to wait and see. Various Chinese AI firms seem to be doing just fine with their models trained on older GPUs.

Either way, modest valuations of the shares of top-tier AI innovators, greater emphasis on AI model optimization, and continued focus on custom ASICs suggest that the AI boom has room to run.

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