• All these predictions are taken from Forbes/Rob Toews' "10 AI Predictions For 2026".

• For the 2025 predictions you can find them here, and their resolution here.

• You can find all the markets under the tag [2026 Forbes AI predictions].

• Note that I will resolve to whatever Forbes/Rob Toews say in their resolution article for 2026's predictions, even if I or others disagree with his decision.

• I might bet in this market, as I have no power over the resolution.

Description of this prediction from the article:

Today, nearly all of the world’s AI organizations, devices and products are powered by chips from a remarkably small number of companies: Nvidia, Google, AMD, Amazon, a handful of others.

A few technology behemoths like Tesla and Apple are large and sophisticated enough that they design their own chips purpose-built for their needs.

But for everyone else, the way the world works today is that, regardless of the details of your AI product or workload, you find an existing chip from this short list of chipmakers and you make it work for your needs.

What if it didn’t have to be this way?

What if it were possible for every company to design and deploy its own custom chips, optimally suited for the particular products and use cases that that company is pursuing? Tradeoffs between energy efficiency, compute power, cost, form factor and more could all be optimized for each particular application.

AI is on the cusp of invading the physical world. It will soon be embedded in millions of robots, autonomous vehicles, smart glasses, smart necklaces, home appliances, drones, brain-computer interfaces and more. The ideal chip for a humanoid robot is very different from the ideal chip for a pair of smart glasses. Enormous performance, cost and efficiency gains could be unlocked across the economy if it were feasible to more precisely tailor chips to the use cases to which they are applied.

This will not happen overnight. Transitioning to a world of ubiquitous customized silicon will take many years. But 2026 will be the year that the power of this idea becomes evident and that companies begin planning in earnest for it.

Among the first movers will be the large AI labs, for whom purpose-built chips will become one more part of the technology landscape on which to innovate and compete in order to continue advancing the frontier of AI models. A few months ago, OpenAI announced a partnership with Broadcom to develop its own in-house chips. It is not crazy to imagine that, eventually, OpenAI will develop a new purpose-built chip for every new AI model generation that it trains, with the two co-optimized for one another.

But the big AI labs will just be the start. Robotics companies, consumer hardware companies, autonomous vehicle companies, BCI companies and beyond will increasingly begin planning to design their own AI chips.

One important driver of this trend will be the fact that cutting-edge reinforcement learning systems will make it possible to automate more and more of the chip design process, dramatically reducing the time and cost to develop a custom chip and therefore making it a viable option for many more companies. Today, designing a new chip can take two to three years. Imagine if that could be reduced to two to three weeks. Ricursive Intelligence, a buzzy new startup that came out of stealth earlier this month, is tackling this exact challenge.