The Nonlinear Library EA - AI stocks could crash. And this could have implications for AI safety. by Benjamin Todd
May 9, 2024
06:17
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: AI stocks could crash. And this could have implications for AI safety., published by Benjamin Todd on May 9, 2024 on The Effective Altruism Forum.
Just as the 2022 crypto crash had many downstream effects for effective altruism, so could a future crash in AI stocks have several negative (though hopefully less severe) effects on AI safety.
Why might AI stocks crash?
The most obvious reason AI stocks might crash is that stocks often crash.
Nvidia's price fell 60% just in 2022, along with other AI companies. It also fell more than 50% in 2020 at the start of the COVID outbreak, and in 2018. So, we should expect there's a good chance it falls 50% again in the coming years.
Nvidia's volatility is about 60%, which means - even assuming efficient markets - it has about a 15% chance of falling more than 50% in a year.1
And more speculatively, booms and busts seem more likely for stocks that have gone up a ton, and when new technologies are being introduced.
That's what we saw with the introduction of the internet and the dot com bubble, as well as with crypto.2
(Here are two attempts to construct economic models for why. This phenomenon also seems related to the existence of momentum in financial prices, as well as bubbles in general.)
Further, as I argued, current spending on AI chips requires revenues from AI software to reach hundreds of billions within a couple of years, and (at current trends) approach a trillion by 2030. There's plenty of scope to not hit that trajectory, which could cause a sell off.
Note the question isn't just whether the current and next generation of AI models are useful (they definitely are), but rather:
Are they so useful their value can be measured in the trillions?
Do they have a viable business model that lets them capture enough of that value?
Will they get there fast enough relative to market expectations?
My own take is that the market is still underpricing the long term impact of AI (which is why I about half my equity exposure is in AI companies, especially chip makers), and I also think it's quite plausible that AI software will be generating more than a trillion dollars of revenue by 2030.
But it also seems like there's a good chance that short-term deployment isn't this fast, and the market gets disappointed on the way. If AI revenues merely failed to double in a year, that could be enough to prompt a sell off.
I think this could happen even if capabilities keep advancing (e.g. maybe because real world deployment is slow), though a slow down in AI capabilities and new "AI winter" would also most likely to cause a crash.
A crash could also be caused by a broader economic recession, rise in interest rates, or anything that causes investors to become more risk-averse - like a crash elsewhere in the market or geopolitical issue.
The end of stock bubbles often have no obvious trigger. At some point, the stock of buyers gets depleted, prices start to move down, and that causes others to sell, and so on.
Why does this matter?
A crash in AI stocks could cause a modest lengthening of AI timelines, by reducing investment capital. For example, startups that aren't yet generating revenue could find it hard to raise from VCs and fail.
A crash in AI stocks (depending on its cause) might also tell us that market expectations for the near-term deployment of AI have declined.
This means it's important to take the possibility of a crash into account when forecasting AI, and in particular to be cautious about extrapolating growth rates in investment from the last year or so indefinitely forward.
Perhaps more importantly, just like the 2022 crypto crash, an AI crash could have implications for people working on AI safety.
First, the wealth of many donors to AI safety is pretty correlated with AI stocks. For instance as far as I can tell Good Ventures sti...
Just as the 2022 crypto crash had many downstream effects for effective altruism, so could a future crash in AI stocks have several negative (though hopefully less severe) effects on AI safety.
Why might AI stocks crash?
The most obvious reason AI stocks might crash is that stocks often crash.
Nvidia's price fell 60% just in 2022, along with other AI companies. It also fell more than 50% in 2020 at the start of the COVID outbreak, and in 2018. So, we should expect there's a good chance it falls 50% again in the coming years.
Nvidia's volatility is about 60%, which means - even assuming efficient markets - it has about a 15% chance of falling more than 50% in a year.1
And more speculatively, booms and busts seem more likely for stocks that have gone up a ton, and when new technologies are being introduced.
That's what we saw with the introduction of the internet and the dot com bubble, as well as with crypto.2
(Here are two attempts to construct economic models for why. This phenomenon also seems related to the existence of momentum in financial prices, as well as bubbles in general.)
Further, as I argued, current spending on AI chips requires revenues from AI software to reach hundreds of billions within a couple of years, and (at current trends) approach a trillion by 2030. There's plenty of scope to not hit that trajectory, which could cause a sell off.
Note the question isn't just whether the current and next generation of AI models are useful (they definitely are), but rather:
Are they so useful their value can be measured in the trillions?
Do they have a viable business model that lets them capture enough of that value?
Will they get there fast enough relative to market expectations?
My own take is that the market is still underpricing the long term impact of AI (which is why I about half my equity exposure is in AI companies, especially chip makers), and I also think it's quite plausible that AI software will be generating more than a trillion dollars of revenue by 2030.
But it also seems like there's a good chance that short-term deployment isn't this fast, and the market gets disappointed on the way. If AI revenues merely failed to double in a year, that could be enough to prompt a sell off.
I think this could happen even if capabilities keep advancing (e.g. maybe because real world deployment is slow), though a slow down in AI capabilities and new "AI winter" would also most likely to cause a crash.
A crash could also be caused by a broader economic recession, rise in interest rates, or anything that causes investors to become more risk-averse - like a crash elsewhere in the market or geopolitical issue.
The end of stock bubbles often have no obvious trigger. At some point, the stock of buyers gets depleted, prices start to move down, and that causes others to sell, and so on.
Why does this matter?
A crash in AI stocks could cause a modest lengthening of AI timelines, by reducing investment capital. For example, startups that aren't yet generating revenue could find it hard to raise from VCs and fail.
A crash in AI stocks (depending on its cause) might also tell us that market expectations for the near-term deployment of AI have declined.
This means it's important to take the possibility of a crash into account when forecasting AI, and in particular to be cautious about extrapolating growth rates in investment from the last year or so indefinitely forward.
Perhaps more importantly, just like the 2022 crypto crash, an AI crash could have implications for people working on AI safety.
First, the wealth of many donors to AI safety is pretty correlated with AI stocks. For instance as far as I can tell Good Ventures sti...