The most important part of the AI boom might be the one investors still aren’t paying enough attention to.
For the last two years, nearly all the focus has been on the “brain” side of the AI story, mainly models, chips and data centers.
But this week’s chart shows where the AI story is heading.
Because once intelligence gets good enough, the next step is pretty obvious.
It has to do something in the real world.
Where Intelligence Meets Labor
Coming out of CES, I said the timeline for robots had moved up.
What stood out there wasn’t just how fluid the machines looked. It was how close they suddenly felt to deployment.
Then we saw China showcase humanoid robots on one of the biggest live television broadcasts in the world.
Again, what mattered wasn’t the spectacle. It was the fact that China was confident enough in its robots to let them perform in public, in real time, with no margin for error..
That confidence is starting to show up in the U.S. too.
And that brings me to today’s chart.
It’s about the entire system forming around robots. And it helps explain what it actually takes to move AI out of the data center and into the real world.
Source: https://x.com/StockSavvyShay/status/2030279815568245134
You can see how layered this buildout already is.
At the top, you have the names everyone knows, like Microsoft, Google and Meta. These are companies training the robot’s AI “brains.”
Right below these hyperscalers are the simulation platforms. That’s where robots are trained to operate before they ever touch a real environment.
They’re learning how to walk, lift, balance and recover thousands of times over. So when they show up in a warehouse, it’s not their first attempt. It’s closer to their ten-thousandth.
Then you get into the hardware that actually runs robots. This requires on-device compute, the kind that can process inputs and respond instantly.
This layer is what enables a robot to adjust its grip while carrying something or correct itself mid-motion without stopping. Because hesitation in the physical world usually leads to failure.
Below that is the layer that actually ties everything together. It’s where real-world inputs get processed.
This requires sensors collecting data, software turning it into movement and systems making sure machines can work together without getting in each other’s way.
For a long time, these pieces existed, but they didn’t quite sync up.
You could have something that moved well, or something that processed information well. But getting them to work together reliably was a challenge.
That’s changed.
Today, these systems are starting to operate as a single loop, able to sense, process information and act in real time.
Right now, robots are getting tested in factories, warehouses and logistics operations across the globe.
These are environments where AI has to deal with constantly changing conditions. Places where objects aren’t perfectly placed and tasks don’t often repeat the same way twice.
Once AI can handle those conditions, it becomes useful in the physical world.
And from there, the path forward is pretty clear.
When machines can do physical work at a lower cost than humans, adoption will happen quickly.
And it won’t stay confined to one industry. It’ll happen anywhere the work is physical, repetitive and harder to staff.
That’s a large share of the labor market. Roughly a third of jobs involve physical, repetitive work. And in many industrial and emerging economies, that share could be even higher.
As much as 50%.
Here’s My Take
This week’s chart shows where the AI story is going next.
The first phase was about building intelligence. The next phase is about putting that intelligence to work.
That’s where the bigger economic impact will be felt.
But as you can see, this rollout won’t be driven by one company.
It’s Convergence X in action, with multiple technologies coming together to bring AI into the physical world.
And I believe that’s where the next set of tech winners will come from.
Regards,
Ian KingChief Strategist, Banyan Hill Publishing
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