This is exactly what model-based systems engineering tools like SysML v2 are designed for. Model-based systems engineering aims to generate _all_ engineering artifacts from a formal model.
Imagine it -- security policies, infrastructure, etc. all codified in a formal model.
- Push-button generation of ISO-27001 documentation.
- Push-button generation of Terraform.
- Push-button generation of SpiceDB policies.
- ...
There is _a lot_ of missing technology, but this is critically important because it will help us ensure regulatory compliance at far greater speeds in fields like nuclear and automotive. And it enables automated reasoning over the models, to make sure you're actually doing what you set out to do.
Came here to say pretty much this. Hardware seems more valuable than a model.
I think AI could be commoditized. Look at DeepSeek stealing OpenAI's model. Look at the competitive performance between Claude, ChatGPT, Grok, and Gemini. Look at open weight models, like Llama.
Commoditized AI need used via a device. The post argues that other devices, like watches or smart glasses, could be better posed to use AI. But...your point stands. Given Apple's success with hardware, I wouldn't bet against them making competitive wearables.
Hardware is hard. It's expensive to get wrong. It seems like a hardware company would be better positioned to build hardware than an AI company. Especially when you can steal the AI company's model.
Supply chains, battery optimization, etc. are all hard-won battles. But AI companies have had their models stolen in months.
If OpenAI really believed models would remain differentiated then why venture into hardware at all?
Of course the selection of data is arbitrary -- but Rich gives us a definition, which he makes abundantly clear and uses consistently. All definitions can be considered arbitrary. He's not making any claim that we have all the relevant bits of data or that we can be sure what the data really means or represents.
But we can expound on this problem in general. In any experiment where we gather data, how can we be sure we have collected a sufficient quantity to justify conclusions (and even if we are using statistical methods that our underlying assumptions are indeed consistent with reality) and that we have accrued all the necessary components? What you're really getting at is an __epistemological__ problem.
My school of thought is that the only way to proceed is to do our best with the data we have. We'll make mistakes, but that's better than the alternative (not extrapolating on data at all.)
Imagine it -- security policies, infrastructure, etc. all codified in a formal model.
- Push-button generation of ISO-27001 documentation.
- Push-button generation of Terraform.
- Push-button generation of SpiceDB policies.
- ...
There is _a lot_ of missing technology, but this is critically important because it will help us ensure regulatory compliance at far greater speeds in fields like nuclear and automotive. And it enables automated reasoning over the models, to make sure you're actually doing what you set out to do.