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OpenAI's (and other superscalers) revenue isn't really up for debate, nor is the long term value of their mission/product. The issue (somewhat articulated in th

OpenAI's (and other superscalers) revenue isn't really up for debate, nor is the long term value of their mission/product. The issue (somewhat articulated in this article) is that the superscalers (both public and private) have generated such a massive, unprecedented amount of speculative investment as well as capital expenditure that has left the rest of the economy in the dust (aka minting a mag 7 in less than a decade) that we have a distorted view of the world. The fear of an AI bubble isn't that AI companies will fail, it's that a downturn in the AI "bubble" will lay bare the underlying bear market that their growth is occluding. What happens then? Nobody knows. Probably nothing good. Personally I think much of the stock market growth in the last few years that seems disconnected with previous trends (see parallel growth betwen gold and equities) is based on retail volume and unorthodox retail patterns (Robinhood, WSB, et. al) that I think conventional historical market analysis is completely unprepared for. At this point everything may go to the moon forever, or it may collapse completely. Either way, we live in a time of little precedence.

beeflet 1y ago
>OpenAI's (and other superscalers) revenue isn't really up for debate It isn't? What is stopping companies from building on GPT-OSS or other local models for cheaper? The AI services have no moat. I agree with your second paragraph. The boom in the AI market is occluding a general bear market.
- prescriptivist 1y ago
  > It isn't? What is stopping companies from building on GPT-OSS or other local models for cheaper? The AI services have no moat. Right now there is an efficiency/hardware moat. That's why the Stargate in Abilene and corresponding build outs in Louisiana and elsewhere are some of the most intense capex projects from the private sector ever. Hardware and electric production is the name of the game right now. This Odd Lots podcast is really fresh and relevant to this conversation: https://www.youtube.com/watch?v=xsqn2XJDcwM https://www.youtube.com/watch?v=xsqn2XJDcwM Local models, local agents, local everything and the commodification of LLMs, at least for software eng is inevitable IMO, but there is a lot of tooling that hasn't yet been built for that commodified experience yet. For companies rapidly looking to pivot to AI force multiplication, the superscalers are the answer for now. I think it's a highly inefficient approach for technical orgs, but time will create efficiency. For your joe on the street feeding data into an LLM, then I don't think any of those orgs (think your local city hall, or state DMV) are going to run local models. So there is a captured market to some degree for the the current superscalars.

beeflet 1y ago

>OpenAI's (and other superscalers) revenue isn't really up for debate It isn't? What is stopping companies from building on GPT-OSS or other local models for cheaper? The AI services have no moat. I agree with your second paragraph. The boom in the AI market is occluding a general bear market.

prescriptivist 1y ago
> It isn't? What is stopping companies from building on GPT-OSS or other local models for cheaper? The AI services have no moat. Right now there is an efficiency/hardware moat. That's why the Stargate in Abilene and corresponding build outs in Louisiana and elsewhere are some of the most intense capex projects from the private sector ever. Hardware and electric production is the name of the game right now. This Odd Lots podcast is really fresh and relevant to this conversation: https://www.youtube.com/watch?v=xsqn2XJDcwM https://www.youtube.com/watch?v=xsqn2XJDcwM Local models, local agents, local everything and the commodification of LLMs, at least for software eng is inevitable IMO, but there is a lot of tooling that hasn't yet been built for that commodified experience yet. For companies rapidly looking to pivot to AI force multiplication, the superscalers are the answer for now. I think it's a highly inefficient approach for technical orgs, but time will create efficiency. For your joe on the street feeding data into an LLM, then I don't think any of those orgs (think your local city hall, or state DMV) are going to run local models. So there is a captured market to some degree for the the current superscalars.