Big Tech companies are starting to look like IBM in the 1960s

The race to dominate the growing AI market is pushing tech giants to adopt business models reminiscent of IBM (IBM) in the 1960s.

Big Tech “hyperscalers” Alphabet (GOOG, GOOGL), Meta (META), Microsoft (MSFT), and Amazon (AMZN) are all in various stages of developing their own custom AI chips to sit in their data centers and power their cloud and software offerings. Alphabet, the most distant of the four companies, is even reportedly in talks to sell its physical chips called TPUs to Meta – a move that would see it go head-to-head with major chip maker Nvidia (NVDA).

Those efforts have led Bloomberg Intelligence analysts to predict that the custom AI chip market will grow to $122 billion by 2033.

Big Tech’s production of their own components goes beyond chips: Microsoft and Amazon are actively investing in dark fiber, or currently unused fiber optic cables that are already underground, RBC Capital Markets analyst Jonathan Atkin said in a recent note to clients. Google and Meta also have their own cables but still buy from third parties, he wrote. Those cables are needed to connect the data centers of the companies and enterprises that use them.

The dynamic in which cloud providers are making their own components (hardware) to run their core products (software) shows Silicon Valley swinging back toward vertical integration — an operating model pioneered by oil and steel magnates at the end of the 19th century and adopted by IBM during the digital revolution.

IBM was one of the most successful vertically integrated companies in the sixties, when it made the hardware components for its mainframes, or large computer systems. IBM’s strategy arose from the idea that making its own specialized parts would improve its final product (mainframes) and profit margins – and amid concerns about a lack of supply of parts for early computers. It worked: In 1985, the company accounted for more than half the market value of the computer industry, Carliss Y. Baldwin noted in her book “Design Rules”.

Of course, that all fell apart later. In the 1990s, the falling costs of semiconductor production — as well as the rise of software powerhouse Microsoft and chip leader Intel — dug into IBM’s once formidable competitive moat, and the company no longer claimed to be vertically integrated by 2000, Baldwin said.

Just as the emergence of computing pushed IBM towards vertical integration, the popularization of AI since the launch of ChatGPT in late 2022 has put today’s cloud giants on a similar trajectory. In particular, the steep costs of Nvidia’s chips and their limited availability have pushed the tech giants to advance their AI chip efforts. Those custom chips are cheaper and better optimized for enterprise software.

Nvidia founder and CEO Jensen Huang holds a Rubin GPU and Vera CPU during the CES technology show on January 5, 2026, in Las Vegas. (AP Photo/John Locher) · ASSOCIATED PRESS

“The hyperscalers … recognize that there is a serious strategic danger from having a single provider of AI computing,” said Seaport analyst Jay Goldberg. “And so now they have a very strong strategic reason to make their own silicon.”

Meta reportedly began testing an in-house AI chip for training models last year and recently acquired chip startup Rivos to accelerate its custom semiconductor efforts. Google’s TPUs have become so advanced that Anthropic (ANTH.PVT), OpenAI (OPAI.PVT), and even rival Meta have signed major cloud deals with the company to access them. And after a long delay, Microsoft unveiled its next-generation Maia 200 chip in January.

During Yahoo Finance’s recent visit to Amazon’s chip lab and nearby data center in Austin, Texas, the company showed off its latest UltraServers, a group of servers that include Amazon’s latest-generation internal GPU called Trainium, its Graviton CPU, and custom networking cables and switches that connect them. Amazon still sells more AI computing in its remote data centers powered by Nvidia GPUs than in its custom accelerators, but the tech giant is increasingly emphasizing the advantages of its in-house hardware.

Amazon Web Services chief technology officer Paul Roberts told Yahoo Finance that its Trainium3 chip can deliver up to a 60% price-performance benefit for its cloud customers compared to GPUs for inference work.

“I think what we are seeing in the market is a lot of validation that this approach [of making custom chips] — versus having kind of generic GPUs — now you can have these specialized processors and accelerators that are achieving incredible energy efficiency savings,” he said.

Those kinds of energy savings will become a bigger task as the AI ​​data center boom begins to feel the effects of energy constraints.

But Seaport’s Goldberg thinks the swing toward vertical integration is reaching the “far limit,” and not all Big Tech players will succeed.

“If you want to design an advanced chip, that’s a big cost,” he said, adding that “only a lot of companies can afford that.”

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