Nvidia is the central bank of AI

423 points by tolugenius 13 hours ago

> worth around $5.4trn

Note that the Fed has a $6.7tn balance sheet [1]. (This is a silly comparison. But still fun.)

The real comparison: Nvidia's $500+ billion of investments and commitments [2] is substantially more than any easing the Fed has done in the same time [3]. Monetarily, Nvidia is creating a lot of money in our economy.

The good news: I have seen no evidence Nvidia has borrowed against its stock or otherwise linked its equity value to these commitments. Its stock could crash without causing–as long as its cash flows continue–a credit crisis through its investments and commitments.

[1] https://www.federalreserve.gov/monetarypolicy/bst_recenttren...

[2] https://www.sec.gov/Archives/edgar/data/1045810/000104581026...

[3] https://www.federalreserve.gov/monetarypolicy/bst_recenttren...

master_crab - 11 hours ago

“as long as it’s cash flow continues” is doing a lot of optimistic heavy lifting. The whole premise of the circular financing worry is that Nvidia sits in the middle of all the guarantees made to companies like OpenAI. If any of those companies become insolvent, Nvidia is on the hook for it.
Also Nvidia isn’t really creating money. The 500B number is third party capital that already exists (BX, Apollo, etc).
- spott - 11 hours ago
  
  Yea, but they would have to become insolvent in a way that makes compute lose value.
  The reason Nvidia is comfortable making these deals is because if OpenAI can’t use the compute, someone else can.
  Granted OpenAI going insolvent likely means a drop in the value of compute…
  - kennywinker - 11 hours ago
    
    Compute has already lost value for me. Six months ago I thought you needed a 1T+ model to be useful coding. Now I am able to get by just fine with a 27b model.
    I see two factors converging to cause a collapse of this house of cards:
    1. People are realizing that what they need isn't more general intelligence, it's more specialization. A small but well tuned coding model, a small but well tuned customer service model, a small but well tuned document explorer.
    2. Specialized hardware - TPUs and NPUs - especially coming out of china. The latest GLM model was trained and runs on Huawei hardware. Nvidia is only worth so much because they are the biggest and best provider of the kind of compute needed to run llms, but the export bans mean china has a lot of incentive to topple that monopoly.
    The amount of compute we need to do the things llms do is falling rapidly, the number of people who can provide that compute is rising.
    
    vunderba - 8 hours ago
    
    > People are realizing that what they need isn't more general intelligence, it's more specialization. A small but well tuned coding model...
    It’s not quite as simple as that. Several studies have shown the opposite: models trained on more diverse knowledge tend to cross-pollinate across domains. So a more generalized model can actually perform better than a specialized one.
    That’s why you’re not seeing tons of tiny models (one for Python, one for Pascal, one for Rust, etc).
    
    kennywinker - 8 hours ago
    
    This is definitely the position of the big ai companies.
    But it doesn't match my experience. Qwen3.8 27b is clearly smarter at coding than MANY bigger models. gpt-oss-120b for example, is almost 4x the size, and performs way worse at coding tasks.
    It's clear to me that you can build small models that work well at specific tasks.
    Python vs Rust is probably too fine grained a way to build a model. Coding in general seems like a better target.
    There will always be a place for large generalist models, no doubt. But I think that place is much smaller than the big ai companies are counting on.
    
    ericd - 5 hours ago
    
    Gpt-oss—120b is like 1000 years old in AI years, whereas Qwen 3.8 27b is pretty young. What you’re seeing is that parameters aren’t apples to apples, and at a given parameter level, the new models are much, much better than the ones from a year or two ago. Like, to a comical degree.
    
    - an hour ago
    
    [deleted]
    
    MichaelZuo - 5 hours ago
    
    Wasnt this known by everyone who cared to pay attention?
    It practically became a joke about how a huge amount of the training data for GPT-4 was bottom of the barrel reddit vomit and obvious bot spam. Leading to many bizarre edge cases.
    
    kennywinker - 4 hours ago
    
    Does that not prove my point? Bigger doesn’t automatically mean better. Quality of training data, and model structure, matters as much or more than size
    
    ericd - 3 hours ago
    
    Ah sorry, I should've continued, the bigger recent models are commensurately smarter. If you really want to make the point, then you'd need to show 27b being smarter than similar vintage bigger models. And in that case, there's confounding issues like efficiency, speed due to excessive thinking maybe to make up for the smaller amount of world knowledge baked in (qwen 27b's main issue iirc), etc - they're tuned for different things.