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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 house in their data centers and power their cloud and software offerings. Alphabet, the most distant of the four companies, is also reportedly in talks to sell its physical chips, called TPUs, to Meta — a move that would see it go head-to-head with leading chipmaker Nvidia (NVDA).

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

Big tech’s own component production 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 them from third parties, he wrote. Those cables are needed to connect companies’ data centers and the enterprises that use them.

The dynamic in which cloud providers are building their own components (hardware) to run their core products (software) reflects Silicon Valley’s return to vertical integration — an operating model pioneered by oil and steel tycoons in the late 19th century and adopted by IBM during the digital revolution.

IBM was one of the most successful vertically integrated companies in the 1960s, when it made the hardware components for its mainframes, or large computer systems. IBM’s strategy stemmed from the idea that making its own specialized parts would improve its end product (mainframes) and profit margins—and amid concerns over supply shortages of parts for early computers. It worked: By 1985, the company accounted for more than half of the computer industry’s market value, noted Carlisle Y. Baldwin in his book “Design Rules.”

Of course, it all fell apart after that. In the 1990s, the falling cost of semiconductor manufacturing — as well as the rise of software powerhouse Microsoft and chip leader Intel — dug into IBM’s once-serious competitive moat, and the company didn’t claim to be vertically integrated until the 2000s, Baldwin said.

Just as the rise of computers pushed IBM towards vertical integration, the popularity of AI following the launch of ChatGPT in late 2022 has put today’s cloud giants on a similar path. In particular, the hefty cost of Nvidia’s chips and their limited availability have prompted the tech giant to step up their AI chip efforts. Those custom chips are cheap and well suited for companies’ software.

Nvidia founder and CEO Jensen Huang holds a Rubin GPU and a Vera CPU at the CES tech show on January 5, 2026 in Las Vegas. (AP Photo/John Locher) · The Associated Press

“Hyperscalers … recognize that there is a serious strategic threat from a single supplier of AI compute,” said Port analyst Jay Goldberg. “And so they now have a very strong strategic reason to do their own silicon.”

Meta reportedly began testing an in-house AI chip for training models last year and recently acquired chip startup Revos to accelerate its custom semiconductor efforts. Google’s TPUs are 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 Mia 200 chip in January.

During Yahoo Finance’s recent tour of Amazon’s chip lab and nearby data center in Austin, Texas, the company showed off its latest ultraservers, a cluster of servers that includes Amazon’s next-generation in-house GPU Trainium, its CPU Graviton, and custom networking cables and switches connecting them. Amazon still sells more AI compute in remote data centers powered by Nvidia’s GPUs than in its custom accelerators, but the tech giant is increasingly highlighting the advantages of its in-house hardware.

Paul Roberts, director of technology at Amazon Web Services, told Yahoo Finance that its Trainium3 chip can provide a 60% price-performance advantage for its cloud customers for predictable workloads compared to GPUs.

“I think what we’re seeing in the market is a lot of validation of this approach [of making custom chips] — versus 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 are going to be a big deal as the AI ​​data center boom begins to feel the effects of power constraints.

But Port’s Goldberg thinks the swing toward vertical integration has reached its “very limits” and that not all Big Tech players will succeed.

“If you want to design a leading-edge chip, it’s a huge expense,” he said, “only so many companies can afford it.”

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