Finance and AI Timelines

post by DAL · 2025-04-16T16:55:06.957Z · LW · GW · 0 comments

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This is in part a response to AI 2027, which I think is rather vague and somewhat unrealistic in projecting the financial aspects of the AI future.  As a jumping off point, AI 2027 projects that a year from now, OpenAI’s valuation will have roughly quadrupled to $1 trillion and data center investment will have reached a half trillion dollars a year (about 10% of total US private investment), despite a piddling $26 billion in revenue.

This is a possible future — perhaps, so long as their is technological progress, investors will continue pouring money into AI in the pursuit of a speculative payday when AI is reached.  But, I think Wall Street is currently (implicitly) forecasting a different future and that this will have an important influence on events.

 

1. Wall Street expects an AI Fizzle

Wall Street valuations right now suggest a future in which AI is a valuable technology, but no more “transformative” than the search engine.

Exhibit A here is everyone’s favorite AI trade — Nvidia.  Nvidia has a $2.5 trillion market cap, which is heavily tied to AI-driven demand for the GPUs it designs but does not manufacture.  Nvidia’s high valuation represents two bets — that AI will keep driving that demand but also that AI will not itself take over chip design.  In a world with AGI (or certainly ASI), Nvidia is worthless.    Even super-intelligent AI may well need TSMC or someone else to fabricate its chips, but it clearly does not need the engineers at Nvidia to design them.

The relative valuations of AI companies also likely reflect this dynamic.  OpenAI trades at a substantial premium — with about four times the valuation of Anthropic.  Perhaps this is because OpenAI is four times more likely than Anthropic to deliver AGI, but there is no obvious reason to make that bet.  This is especially true if the route to AGI is to first developing a superhuman coder, given that Claude seems to lead the AI coding pack.

Instead, the easiest way to understand OpenAI’s relative valuation is based on its obvious strengths in branding, name recognition, and consumer adoption.  OpenAI has by far the strongest brand in the space, to the point that the ChatGPT brand has become genericized.  If the future path to profitability lies in providing a product to consumers whether on a subscription or an advertising model, then OpenAI has a very clear edge that justifies the valuation.

A simple theory explaining the valuation of OpenAI ($260 billion), Anthropic ($61.5 billion) and Ilya Sustkever’s Safe Superintelligence — which promises that its first and only product will be superintelligence ($32 billion) is that all three companies have a similar shot at achieving transformative AGI/ASI, which would presumably be worth tens or hundreds of trillions of dollars.  But, they have very dissimilar chances of becoming the next Alphabet or Meta, and so the valuation on OpenAI is mostly a bet only a 1 in 4 chance that it can turn its current gaudy user numbers into profits in line with Facebook and Instagram.  If OpenAI does not eventually move in that direction, it is likely to face an investor revolt.

2. A bear market will increase pressure on AI companies to generate profits

For now, investors are willing to tolerate AI companies burning money.  This is likely to change both as the amounts of money become large and the next time the US hits a significant recession or bear market.  This may be just around the corner with tariffs.  Even if it is not, it will happen sooner or later.

If investors begin to focus on profits and revenue, this will inevitably force the companies to invest less into racing towards AGI and more into building products that make money.  As talent and resources reorient in that direction, progress will slow.

Building a superhuman AI researcher is not a profitable activity (it only pays off via ultimate AGI) and is likely to be an especially disfavored approach in such an environment.  The money to be paid will be in automating various white collar functions, like customer service.  And this requires designing agents that are good at a very different set of skills than AI research.

3. An alternative forecast

An alternative to AI 2027, then, is a future in which over the next 12-18 months, AI companies are forced to pivot towards actually making money.   

There seem to be two routes available for that:

1. Consumer-facing: OpenAI becomes a Google search replacement, perhaps even monetizing by embedding ads in its responses.  This may be the easier route, but also suggests a limited financial incentive to keep investing in agents.
2. Enterprise-facing: This is a hard route; as Tyler Cowen has observed, there is not an AI shaped hole in most organizations.  This means spending a lot of time and effort hiring sales rep, working on integrating technologies, etc.  Agents here are important, but progress will slow down as the focus moves to designing, say, an integrated customer service chatbot rather than a superhuman AI coder.

Either route may well eventually generate the cash to fund the data centers needed to finally push to AGI but if that cash has to come from organic growth rather than VCs, it will inevitably be much slower.

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