Ai revenue or datacenter/compute revenue? There's a lot of circular financing right now and there's a pretty obvious bubble for sure but I haven't been able to figure out what exactly this guy's argument is after seeing it a few times recently. Like yeah most ai datacenter spend is those two companies - that's pretty standard monopoly (or monopsony for the cloud providers) dynamics. If you're saying some major percentage of all ai related spending is openai and anthropic spending money on compute - well that's not exactly right because Google etc are also spending money on building datacenters (that's not ai spend in this definition? WTF is ai revenue exactly?) - and that's just pretty much indicating it's a frothy market with two unprofitable companies at the center with huge cogs? We knew that already.
Feels like they want to make a clean headline grabbing argument about how "70% of all the spending is actually just these two companies" and are ending up with a really muddled headline that's just like yeah that's how monopoloies and duopolies work. When there's a lot more insidious circular complicated shenanigans going on that gets collapsed by this framing.
I think this whole premise is ignoring the point that most decently sized companies want to and eventually will be running their own LLM workloads. Currently use cases are limited by scope and imagination, and predominantly focused on cost efficiency. Once a use case becomes a top line revenue driver budgets will become essentially only limited by ROI. There are some inference workloads that are unacceptable to send to the frontier labs for privacy reasons. Most of it will come from firms who want to productionalize their own fine-tunes. In any case, the market for inference is less than 1% of what it will be in 5-10 years. This whole notion that GPUs will be sitting idle en masse is ridiculous. People will just start running GPU databases if it becomes cost efficient.
Hilarious. You can agree or disagree with Zitron but he really has no business talking about Goldman Sachs. His posts are littered with evidence he has no ability to perform the type of financial analysis he thinks he does.
You can like the character or not, but there is a trend I’m following ( heavily vested in NVIDIA, so tongue in cheek when I say this ) that might be highly align with Zitron. Looking at the moves from NVIDIA ( Groq )and AMD ( Taalas ) which are pure inference plays. I believe this shows that the impetus to train a better-bigger model might be coming to a level of maturity that might merit a serious threat to the frontier labs.
For frontier labs, their fund-train-new model play might not be as effective, and a shift of spent of compute cost moving away from training to inference might be a tell-tell sign of the LLM as we know it plateauing out as scale is just not as effective. Open models might also be placing a major pressure on meeting then revenue targets need to sustain the model, lots of customer hosting their own inference to mitigate costs.
If you only move the needle just slightly in the direction of inference, frontier labs will soon loose their alphas. Becoming just another SaaS for inference might not be as attractive unless you are Google/MSF ( IMHO ).
Should this pan out, it could be a scenario where the NeoClouds could soon loose their biggest customers, so I tend to agree with that aspect of Zitron’s view.
Ed wrote a post back in 2024 where he claimed OpenAI would fail in 2 years if they didn’t do the, seemingly impossible at that time, things like raising more money than any company before etc.
And OpenAI did all of that and is still alive today. Would be good to know this context because if you are out there boldly making doomer predictions month after month then you should also rate your previous ones.
Because one day Ed will be right and he’ll go around and take a victory lap while ignoring he has basically not been right before.
Im not sure what this phrase even means. Does it mean that "70% of the revenue made by companies selling tokens comes from openAI and anthropic"? If so, how does it follow that "the AI industry doesn't exist"? For every other industry, the "size of the industry" is given by the total revenue, not by the percentage of the top 2.
These people don't know what they're talking about.
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[ 0.30 ms ] story [ 11.3 ms ] threadFeels like they want to make a clean headline grabbing argument about how "70% of all the spending is actually just these two companies" and are ending up with a really muddled headline that's just like yeah that's how monopoloies and duopolies work. When there's a lot more insidious circular complicated shenanigans going on that gets collapsed by this framing.
Hilarious. You can agree or disagree with Zitron but he really has no business talking about Goldman Sachs. His posts are littered with evidence he has no ability to perform the type of financial analysis he thinks he does.
For frontier labs, their fund-train-new model play might not be as effective, and a shift of spent of compute cost moving away from training to inference might be a tell-tell sign of the LLM as we know it plateauing out as scale is just not as effective. Open models might also be placing a major pressure on meeting then revenue targets need to sustain the model, lots of customer hosting their own inference to mitigate costs.
If you only move the needle just slightly in the direction of inference, frontier labs will soon loose their alphas. Becoming just another SaaS for inference might not be as attractive unless you are Google/MSF ( IMHO ).
Should this pan out, it could be a scenario where the NeoClouds could soon loose their biggest customers, so I tend to agree with that aspect of Zitron’s view.
Thoughts?
And OpenAI did all of that and is still alive today. Would be good to know this context because if you are out there boldly making doomer predictions month after month then you should also rate your previous ones.
Because one day Ed will be right and he’ll go around and take a victory lap while ignoring he has basically not been right before.
These people don't know what they're talking about.