The challenges are not the ones you know from other frontier models. Physical AI has to be reliable to a degree chatbots never are, and it only pays for itself if it can handle a range of real tasks rather than one demo. Both come from real-world information specific to your business, which is another way of saying your IP.
That information is your business. You can hand it to another company and watch the value land with your competitors, or you can control it yourself. Own it and the options stay yours: build the advantage, capture the full worth of your own expertise, or sell it to a competitor later if that is the better trade.
Robots and sensors get cheaper and more capable every year, and everyone can buy the same ones.
Simulated data and anything already sitting on the internet are available to your competitors on the same terms.
Foundation models are converging. A dozen of them will get you to the same place.
What does not become a commodity is the ability to create value from what only you know.
When a business builds a factory it plans for the future. The same goes for product design, electrical systems, floor space, transportation. All of it is laid out to drive and capture value years ahead of when it is needed.
Right now there is a narrow window to do the same for robotic AI. The most important elements can be put in place cheaply today, far more cheaply than they can be bought later. That only holds if you start now, and if you know what to lay down.
The hardware finally works and the models generalize. What is scarce now is data. Collection gets cheaper every quarter, but the value of a dataset you own only compounds, so waiting costs more than starting.
Demos get you to 95 percent. Production demands 99.99 percent. Those last decimal places do not come from a better architecture. They come from data covering the cases nobody films: rare failures, bad lighting, worn tools, floors that are actually dirty. None of that is on the internet. Somebody has to go and collect it.
Foundation vision models give you breadth. Your process needs depth. What works is a large backbone with your own task data layered on top. The generic model gets you moving. The specific data is what competitors cannot copy.
Outsource Physical AI and you hand a stranger your operating practices, then eventually your margin. The alternative is to control it. Take one of the dozen cheap models that already get you to 95 percent, boost it with your own data, and own the value chain while your secrets stay in the building. If it works, the capability compounds in-house. If it does not, the dataset and the model you built are still worth millions to someone else.