But that begs the question: What happens to all those older-generation chips?

The AI stock boom has lost a lot of momentum in recent weeks due, in part, to worries that so-called hyperscalers aren’t correctly accounting for the depreciation in the hoard of chips they’ve purchased to power chatbots.

Michael Burry—the investor of Big Short fame who famously predicted the 2008 housing collapse—sounded the alarm last month when he warned AI-era profits are built on “one of the most common frauds in the modern era,” namely stretching the depreciation schedule. He estimated Big Tech will understate depreciation by $176 billion between 2026 and 2028.

But according to a note last week from Alpine Macro, chip depreciation fears are overstated for three reasons.

First, analysts pointed out software advances that accompany next-generation chips can also level up older-generation processors. For example, software can improve the performance of Nvidia’s five-year-old A100 chip by two to three times compared to its initial version.

Second, Alpine said the need for older chips remains strong amid rising demand for inference, meaning when a chatbot responds to queries. In fact, inference demand will significantly outpace demand for AI training in the coming years.

“For inference, the latest hardware helps but is often not essential, so chip quantity can substitute for cutting-edge quality,” analysts wrote, adding Google is still running seven- to eight-year-old TPUs at full utilization.

Third, China continues to demonstrate “insatiable” demand for AI chips as its supply “lags the U.S. by several generations in quality and severalfold in quantity.” And even though Beijing has banned some U.S. chips, the black market will continue to serve China’s shortfalls.

Meanwhile, not all chips used in AI belong to hyperscalers. Even graphics processors contained in everyday gaming consoles could work.

A note last week from Yardeni Research pointed to “distributed AI,” which draws on unused chips in homes, crypto-mining servers, offices, universities, and data centers to act as global virtual networks.

While distributed AI can be slower than a cluster of chips housed in the same data center, its network architecture can be more resilient if a computer or a group of them fails, Yardeni added.

“Though we are unable to ascertain how many GPUs were being linked in this manner, Distributed AI is certainly an interesting area worth watching, particularly given that billions are being spent to build new, large data centers,” the note said.

But when COVID hit soon after, he realized he may have made a mistake.

“That didn’t work because there were no more cafés,” he told students at Stanford University’s School of Engineering centennial celebration last week, Business Insider reported.

The transition from president of Google parent company Alphabet to a 40-something retiree ended up not being as smooth as he imagined, and soon after he said he was “spiraling” and “kind of not being sharp” as he stepped away from busy corporate life.

Therefore, when Google began allowing small numbers of employees back into the office, Brin tagged along and put his efforts into what would become Google’s AI model, Gemini. Despite being the world’s fourth-richest man with a net worth of $247 billion, retirement wasn’t for him, he said.

“To be able to have that technical creative outlet, I think that’s very rewarding,” Brin said. “If I’d stayed retired, I think that would’ve been a big mistake.”

By 2023, Brin was back to work in a big way, visiting the company’s office three to four times a week, the Wall Street Journalreported, working with researchers and holding weekly discussions with Google employees about new AI research. He also reportedly had a hand in some personnel decisions, like hiring. 

Skip forward to 2025 and Brin’s plans for a peaceful retirement of quiet study are out the window. In February, he made waves for an internal memo in which, despite Google’s three-day in-office policy, he recommended Google employees go into the company’s Mountain View, Calif. offices at least every weekday, and that 60 hours a week was the “sweet spot” of productivity.

Brin’s newfound efforts at work may have been necessary as OpenAI’s release of ChatGPT in 2022 caught the tech giant off guard, after it had led the field of AI research with DeepMind and Google Brain for years.

To be sure, Google for its part has been rising in the AI race. Analysts raved last month about Gemini 3, the company’s latest update to its LLM, and Google’s stock is up about 8% since its release. Meanwhile, OpenAI earlier this month declared a “code red,” its highest alert level, to improve ChatGPT. 

Brin added in the talk at Stanford that Google has an advantage in the AI arms race precisely because of the foundation it laid over years through its neural network research, its custom AI chips, and its data center infrastructure.

“Very few have that scale,” he said.