AI is probably similar where the Moore’s law and advancement will eventually allow people to run open models locally and bring down the cost of operation. Competiition will make it hard for all but one or two players to survive and Nvidia, OpenAI, Deepseek, etc most investments in AI by these large companies will fail to generate substantial wealth but maybe earn some sort of return or maybe not.
Then all the farmers in the midwest went broke not because they couldn't get their goods to market, but because JP Morgan's consolidated syndicates ate all their margin hauling their goods to market.
Consolidation and monopoly over your competition is always the end goal.
Surely that's only possible when you have a large barrier to entry?
What's going to be that barrier in this case - cos it turns out not to be neither training costs/hardware or secret expertise.
'Can't have your data going to China'
'Can't allow companies that do censorship aligned with foreign nations'
'This company violated our laws and used an American company's tech for their training unfairly'
And the government choosing winners.
'The government in announcing 500 billion going to these chosen winners, anyone else take the hint, give up, you won't get government contracts but will get pressure'.
Good thing nobody is making these sorts of arguments today.
I suspect the "it ain't training costs/hardware" bit is a bit exagerated since it ignores all the prior work that DeepSeek was built on top of.
But, if all else fails, there's always the tried-and-true approaches: regulatory capture, industry entrenchment, use your VC bucks to be the last one who can wait out the costs the incumbents do face before they fold, etc.
As you grow bigger, you create barriers to entry where none existed before, whether intentionally or unintentionally.
Also barriers to entry aren't the only way to get a consolidated market anyway.
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.
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.
> Similarly, business growth, per se, tells us little about value. It's true that growth often has a positive impact on value, sometimes one of spectacular proportions. But such an effect is far from certain. For example, investors have regularly poured money into the domestic airline business to finance profitless (or worse) growth. For these investors, it would have been far better if Orville had failed to get off the ground at Kitty Hawk: The more the industry has grown, the worse the disaster for owners.
Probably won't be Moore's law (which is kind of slowing down) so much as architectural improvements (both on the compute side and the model side - you could say that R1 represents an architectural improvement of efficiency on the model side).
