Interesting. I guess a key question is whether another wave of capabilities (e.g. gpt-5, agent models) comes in soon or not.
Benjamin_Todd
Karma: 8,265
Agree it’s most likely already in the price.
Though I’d stand behind the idea that markets are least efficient when it comes to big booms and busts involving large asset classes (in contrast to relative pricing within a liquid asset class), which makes me less inclined to simply accept market prices in these cases.
You could look for investments that do neutral-to-well in a TAI world, but have low-to-negative correlation to AI stocks in the short term. That could reduce overall portfolio risk but without worsening returns if AI does well.
This seems quite hard, but the best ideas I’ve seen so far are:
The cluster of resources companies, electricity producers, commodities, land. There’s reason to think these could do quite well during a TAI transition, but in the short term they do well when inflation rises, which tends to be bad for AI stocks. (And they were effective hedges in the most recent draw down and in 2022.) Some of them also look quite cheap at the moment. However, in a recession, they will fall at the same time as AI stocks.
Short long-dated government bonds or AI company credit. In the short term helps to hedge out the interest rate and inflation exposure in AI companies, and should also do well in the long term if an AI boom increases interest rates. Credit spreads are narrow so you’re not paying much for the hedge. However, if there’s a recession, these will also do badly.
Index shorts (especially focused on old economy stocks). This could reduce overall market risk, and AI stocks will most likely fall at the same time as other stocks. If you buy long dated put options there’s some reason to think AI will increase volatility, so you might also benefit a little there. However, on net it might be desirable to have high market exposure / this trade most likely loses money.
Long-short multi-asset trend-following. This is an active strategy (so you might be skeptical that it works) but tends to do well during macro regime changes / big market crashes / high volatility, which will likely be times when AI stocks are doing badly. But for the same reasons it could also do well during an AI boom.
However, all of these have important downsides and someone would need to put billions of dollars behind them to have much impact on the overall portfolio.
(Also this is not investment advice and these ideas are likely to lose a lot of money in many scenarios.)
I should have maybe added that several people mentioned “people who can practically get stuff done” is still a big bottleneck..
My impression is that of EA resources focused on catastrophic risk, 60%+ are now focused on AI safety, or issues downstream of AI (e.g. even the biorisk people are pretty focused on the AI/Bio intersection).
AI has also seem dramatic changes to the landscape / situation in the last ~2 years, and my update was focused on how things have changed recently.
So for both reasons most of the updates that seemed salient to me concerned AI in some way.That said, I’m especially interested in AI myself, so I focused more on questions there. It would be ideal to hear from more bio people.
I also briefly mention nuclear security, where I think the main update is the point about lack of funding.
AI stocks could crash. And that could have implications for AI safety
Hi Wayne,
Those are good comments!
On the timing of the profits, my first estimate is for how far profits will need to eventually rise.
To estimate the year-by-year figures, I just assume revenues grow at the 5yr average rate of ~35% and check that’s roughly in line with analyst expectations. That’s a further extrapolation, but I found it helpful to get a sense of a specific plausible scenario.
(I also think that if Nvidia revenue looked to be under <20% p.a. the next few quarters, the stock would sell off, though that’s just a judgement call.)
On the discount rate, my initial estimate is for the increase in earnings for Nvidia relative to other companies (which allows us to roughly factor out the average market discount rate) and assuming that Nvidia is roughly as risky as other companies.
In the appendix I discuss how if Nvidia is riskier than other companies it could change the estimate. Using Nvidia’s beta as an estimate of the riskiness doesn’t seem to result in a big change to the bottom line.
I agree analyst expectations are a worse guide than market prices, which is why I tried to focus on market prices wherever possible.
The GPU lifespan figures come in when going from GPU spending to software revenues. (They’re not used for Nvidia’s valuation.)
If $100bn is spent on GPUs this year, then you can amortise that cost over the GPU’s lifespan.
A 4 year lifespan would mean data centre companies need to earn at least $25bn of revenues per year for the next 4 years to cover those capital costs. (And then more to pay for the other hardware and electricity they need, as well as profit.)
On consumer value, I was unsure whether to just focus on revenues or make this extra leap. The reason I was interested in it is I wanted to get a more intuitive sense of the scale of the economic value AI software would need to create, in terms that are closer to GDP, or % of work tasks automated, or consumer surplus.
Consumer value isn’t a standard term, but if you subtract the cost of the AI software from it, you get consumer surplus (max WTP—price). Arguably the consumer surplus increase will be equal to the GDP increase. However, I got different advice on how to calculate the GDP increase, so I left it at consumer value.
I agree looking at more historical case studies of new technologies being introduced would be interesting. Thanks for the links!
I mean “enter the top of the funnel”.
For example, if you advertise an event as being about it, more people will show up to the event. Or more people might sign up to a newsletter.
(We don’t yet know how this translates into more intense forms of engagement.)
It’s fair that I only added “(but not more)” to the forum version – it’s not in the original article which was framed more like a lower bound. Though, I stand by “not more” in the sense that the market isn’t expecting it to be *way* more, as you’d get in an intelligence explosion or automation of most of the economy. Anyway I edited it a bit.
I’m not taking revenue to be equivalent to value. I define value as max consumer willingness to pay, which is closely related to consumer surplus.
I agree risk also comes into it – it’s not a risk-neutral expected value (I discuss that in the final section of the OP).
Interesting suggestion that the Big 5 are riskier than Nvidia. I think that’s not how the market sees it – the big 5 have lower price & earnings volatility and lower beta. Historically chips have been very cyclical. The market also seems to think there’s a significant chance Nvidia loses market share to TPUs or AMD. I think the main reason Nvidia has a higher PE ratio is due its earnings growth.
I agree all these factors go into it (e.g. I discuss how it’s not the same as the mean expectation in the appendix of the main post, and also the point about AI changing interest rates).
It’s possible I should hedge more in the title of the post. That said, I think the broad conclusion actually holds up to plausible variation in many of these parameters.
For instance, margin is definitely a huge variable, but Nvidia’s margin is already very high. More likely the margin falls, and that means the size of the chip market needs to be even bigger than the estimate.
I meant from the EA catastrophic risk community, sorry not clarify.
Can you elaborate? The stock price tells us about the NPV of future profits, not revenue. However, if we use make an assumption about margin, that tells us something about future expected revenues.
I’m also not claiming to prove the claim. More that current market prices seem consistent with a scenario like this, and this scenario seems plausible for other reasons (though they could also be consistent with other scenarios).
I basically say this in the first sentence of the original post. I’ve edited the intro on the forum to make it clearer.
Perhaps you could say which additional assumptions in the original post you disagree with?
Ah makes sense! :)
Not sure I follow. Current market expectations for AI is that AI WON’T automate a large fraction of jobs. Rather just that it’ll produce value a few trillion dollars, which is only couple of percent of world GDP.
But even if AI does automate a lot of work, that doesn’t mean everyone is suddenly poor. If AI is valuable, then GDP will grow, which means there’s on net more money around to pay for AI software.
The market plausibly expects AI software to create trillions of dollars of value by 2027
Updates on the EA catastrophic risk landscape
This is great. I’m so glad this analysis has finally been done!
One quick idea: should ‘speed-ups’ be renamed ‘accelerations’? I think I’d find that clearer personally, and would help to disambiguate it from earlier uses of ‘speed-up’ (e.g. in Nick’s thesis).
As one practical upshot of this, I helped 80k make a round of updates to their online articles in light of FTX. See more.
I want to be clear it’s not obvious to me OP is making a mistake. I’d lean towards guessing AI safety and GCBRs are still more pressing than nuclear security. OP also have capacity constraints (which make it e.g. less attractive to pursue smaller grants in areas they’re not already covering, since it uses up time that could have been used to make even larger grants elsewhere). Seems like a good fit for some medium-sized donors who want to specialise in this area.