Every Tech Company Is Building Custom AI Chips Now. Most Will Fail.

Amazon has custom AI chips. Google has TPUs. Meta announced their own silicon. Even Salesforce is reportedly exploring custom chips.

Everyone’s trying to escape NVIDIA’s pricing. Most won’t succeed.

Custom AI chips sound great on paper: optimized for your specific workload, no NVIDIA markup, competitive advantage. Reality? Designing silicon is brutally expensive and most companies have no idea what they’re doing.

I talked to a hardware engineer who worked on a failed custom chip project. Three years, $200 million, talented team. The chip worked. It just wasn’t better than buying NVIDIA’s next generation, which arrived six months before their tape-out.

“We optimized for the wrong architecture,” he said. “By the time we fabricated, transformer models had evolved. Our chip was perfect for 2023’s AI. We shipped in 2025.”

That’s the death spiral of custom silicon: 18-24 month development cycles in an industry where models change every six months. You’re always designing for yesterday’s AI.

Google gets away with it because they have massive scale. TPUs make sense when you’re training models at Google’s volume. For everyone else? The math doesn’t work.

I ran the numbers with a chip designer. To break even on custom silicon, you need sustained demand for at least 100,000 units. At $10K per chip, that’s a billion-dollar bet. Miss the architecture trend, and your warehouse full of bespoke chips becomes expensive paperweights.

Meanwhile, NVIDIA keeps printing money. Their H100s are still backordered. The new H200? Already sold out through 2026. They’ve got customers locked in because switching costs are astronomical.

The software stack matters as much as the silicon. NVIDIA’s CUDA ecosystem took 15 years to build. Every machine learning framework is optimized for it. Every engineer knows it. Custom chips need to either replicate that ecosystem or be so much better that people tolerate different tooling.

Spoiler: they’re not that much better.

Meta’s MTIA chips are interesting. They’re not trying to beat NVIDIA at training—they’re targeting inference at scale. Different problem, potentially solvable. But even Meta admits deployment is slower than expected.

Amazon’s Trainium and Inferentia chips? Customers who tried them told me they’re “fine.” That’s not a ringing endorsement. Fine doesn’t justify rewriting your infrastructure.

The only success story is Google’s TPUs, and that’s because Google controls the entire stack. They design models specifically for TPU architecture. They own the data centers. They don’t need to sell to external customers.

Everyone else is trying to build NVIDIA competitors without NVIDIA’s advantages. It’s like trying to replicate AWS in your garage.

Here’s the dark secret: most custom chip projects aren’t about actually escaping NVIDIA. They’re about negotiating leverage. Tell NVIDIA you’re designing your own silicon, suddenly your pricing gets better.

A cloud provider executive admitted this to me. “Our custom chip project is 20% real and 80% theater. As long as NVIDIA thinks we might leave, they keep the discounts coming.”

Expensive theater—$200 million for bargaining leverage.

The sad part? There are legitimate reasons to build custom AI chips. Specific use cases where custom silicon makes sense. Edge AI devices. Specialized robotics. Ultra-low-latency trading. But those aren’t the projects getting funded.

Instead, we have a dozen half-hearted attempts to build “NVIDIA but cheaper.” They’ll burn billions collectively. Maybe one or two will survive. The rest will quietly wind down in 2027, citing “strategic reprioritization.”

NVIDIA’s moat isn’t just silicon—it’s software, timing, and ecosystem lock-in. Unless you can replicate all three, buying H100s is probably smarter than rolling your own.