For the curious, it was vertical integration in the railroad-oil/-coal industry which is where the money was made.
The problem for AI is the hardware is commodified and offers no natural monopoly, so there isn't really anything obvious to vertically integrate-towards-monopoly.
Aren’t we approaching a scenario where the software is commodified (or at least “good enough” software) and the hardware isn’t (NVIDIA GPUs have defined advantages)
I think the lesson of DeepSeek is 'no' -- that by software innovation (ie., dropping below CUDA to programming the GPU directly, working at 8bit, etc.) you can trivialise the hardware requirement.
However I think the reality is that there's only so much coal to be mined, as far as LLM training goes. When we're at "very dimishing returns" SoC/Apple/TSMC-CPU innovations will deliver cheap inference. We only really need a M4 Ultra with 1TB RAM to hollow-out the hardware-inference-supplier market.
Very easy to imagine a future where Apple releases a "Apple Intelligence Mac Studio" with the specs for many businesses to run arbitrary models.
I really hope that apple realizes soon there is a market for Mac Pro/Mac Studio with a RAM in the TBs for AI Workloads under $10k and a bunch of GPU cores.
>> Compute is literally being sold as a commodity today, software is not.
The marginal cost of software is zero. You need some kind of perceived advantage to get people to pay for it. This isn't hard, as most people will pay a bit for big-name vs "free". That could change as more open source apps become popular by being awesome.
Marginal cost has nothing to do with it - you can buy and sell compute like you could corn and beef at scale. You can't buy and sell software like that. In fact I'm surprised we don't have futures markets for things like compute and object storage.
The problem for AI is the hardware is commodified and offers no natural monopoly, so there isn't really anything obvious to vertically integrate-towards-monopoly.