I give it one to two more years before open source models have fully caught up. Products are commodities and models are commodities too. GPUs cores are still hard to get for inference at scale right now. They need a platform with lock in but unsure what that would look like and why it wouldn't be based on open source models.

I think this "Mythos" situation, whether real or hype, points to the endgame here. Eventually, when you have a model powerful enough to have big consequences in the world, you stop worrying about selling it to consumers and start either a) using it to rule the world or b) watch as it gets nationalized. If you have a machine powerful enough to automate everything, why sell access to it when you could just...be all things to all people? Use the god machine yourself to take over more and more of the economy?

They are a commodity - but also cyber weapons. Warmongering nations are now in an arms race to have the best AI so they can have superior cyber weapons, intelligence capabilities. But they don't want to pick just one lab, they want multiple AI defense contractors to compete over contracts.

As the US sold weapons to many nations in the past, so will China, the US, France, etc sell AI cyber capability to other nations. Likely every modern nation will need some datacenter to host a cluster of the preferred vendor, as nobody's going to trust the US or China with their security.

the prospect that any of those big players will be able to pay back 100s of billions with profit on top sounds fantastical to me

it will be interesting to see it unfold

None of them have any moat, OpenAI already lost the lead [1] and no one is "winning". It is just a race to the bottom as they burn through GPUs that won't even last that long.

[1] https://x.com/kenshii_ai/status/2046111873909891151/photo/2

Everyone using Claude code on a personal subscription is default opted in to getting their data trained on. Private troves of data like are seen to potentially end up in a winner take all scenario. More data, better models, attracts more users, results in more exclusive data (what Altman calls the data flywheel).

$30B ARR says otherwise.

Please, some of us are long NVIDIA...let us cope in peace. :-)

Here is the thing nobody wants to say out loud or they are too dumb to realize. AI is intelligence, and intelligence has almost never been the binding constraint on productivity.

So you will get no productivity increase from the AI bubble. Yes, you read that correctly.

The test is simple, if raw brainpower were the bottleneck, you could 10x any company by hiring 200 PhDs. In practice you get 200 brilliant people writing unread memos, refactoring things that worked, and forming a committee to rename the committee. Smart has always been cheaper and more abundant than the discourse pretends.

Every real productivity revolution came from somewhere else like energy (steam, electricity), capital stock (machines that do the physical work), or coordination (railroads, shipping containers, the assembly line, the internet).

None of these raised the average IQ of the workforce, they changed what a given worker could move, reach, or coordinate with. Solow old line basically still holds. The output per worker grows when you give the worker better tools and infrastructure, not better neurons.

Meanwhile the actual bottlenecks in a modern firm are regulatory approval, legacy systems, procurement cycles, customer adoption, internal politics, and physical supply chains that don't care how clever your email was. A smart brains intern at every desk produces more artifacts, not more throughput, and in a lot of organizations, more artifacts is actively negative ROI.

Jevons does not save you either, cheaper cognition mostly means more slide decks, not more GDP.

So the setup is that models are commoditizing on one side, and on the other side a product whose core value add (more intelligence, faster) is aimed at a constraint that was never really binding. This of course a rough combo for a trillion dollar capex supercycle.

Fun for the trade, while it lasts, but there is no thesis. Just dont tell CNBC and short NVDA on time ,-)

>I mostly see their products as commodity at this point, with strong open source contenders.

> Eventually it will become hard to justify the premium on these models.

On the contrary, the model is the moat.

The model represents embodied capital expenditure in the form of training. Training is not free, and it is not a commodity, it is heavily influence by curation.

Eventually the ever-increasing training expense will reduce the competition to 2-3 participants running cutting edge inference. Nobody else will be able to afford the chips, watts, and warehouse. It's a physics problem - not a lack of will.

If you're a retail user, and a lower-tier model is suitable for your work, you'll have commodity LLM's to help you. Deprecated models running on tired silicon. Corporate surveillance and ad-injection.

But if you're working on high-stakes problems in real time, you're going to want the best money can buy, so you'll concentrate your spend on the cutting-edge products, open API's, a suite of performance monitoring tools and on-the-fly engineering support. And since the cutting edge is highly sought after, it's a seller's market. The cutting edge products buoyed by institutional spend will pull away from the pack. Their performance will far exceed what you're using, because your work isn't important. Hockey stick curve. Haves and Have-Nots.

The economic reality is predetermined by today's physical constraints - paradigm shifting breakthroughs in quantum computing and superconductors could change the calculus but, like atomic fusion power, don't count on it being soon.