Yeah, someone else suggested a novel nootropic drug as one answer—online education is basically an alternative form of that drug that is easier to realize (or at least, it’s hard is a very different way)
ThomasJ
Karma: 138
...there are somewhere between six and ten billion people. At any given time, most of them are making mud bricks or field-stripping their AK-47s. - Neal Stephenson, Snow Crash
When we think of new technologies, we typically think of expensive, high-tech innovations, like energy production, robotics, etc. I would suggest that broader adoption of existing technologies, including social technologies, would have a bigger global impact.
For example, one technology that could dramatically impact GDP is improved managerial technology. This paper describes a study of this in India. Among the findings in the paper (or in references that it cites):
100% productivity spreads between the 10th and 90th percentile in US commodity-producing firmsA ratio of the 90th to the 10th percentiles of total factor productivity is 5.0 in Indian and 4.9 in Chinese firms
After improving management in the studied firms, “We estimate that within the first year productivity increased by 17%; based on these changes we impute that annual profitability increased by over $300,000. These better-managed firms also appeared to grow faster, with suggestive evidence that better management allowed them to delegate more and open more production plants in the three years following the start of the experiment”
FWIW, world GDP growth rates have if anything been decreasing over the last ~80 years
I don’t have any immediate ideas on long positions—the AI winter isn’t AI failing per se, right? It’s just that we stop making progress so we’re stuck where we are.
Maybe something like Doordash? They filed for an IPO recently, and if you think autonomous robots aren’t going to drive down the cost of logistics then last-mile logistics companies might be underpriced. I have much less confidence in this kind of trade though.
You can short some AI ETFs. https://etfdb.com/themes/artificial-intelligence-etfs/ has a list, although some of those are obviously miscategorized—check the holdings to see how much you agree that they’re representative.
You’re left with market risk (i.e., beta) when you do this, but if you have a diversified portfolio you’re probably okay with not putting on an additional specific hedge. That is, if you’re right and the whole market rallies (but your ETF rallies less), you’ll be okay.
If you want to be more tactical, I would look at companies that are AI-exposed and have insane P/Es. You mention Nvidia having gaming hardware, but NVDA’s PE is something like 135.92 right now, which prices in huge levels of growth. Compare 2016, when their P/E was 20-30. An AI winter would collapse the expected growth rate, leading to a corresponding drop in stock price. If you’re not convinced on NVDA, you can make a similar case for other companies whose growth narrative is driven by AI.
Finally, you should ideally have a view on when your thesis is going to play out or what the catalyst will be. Remember that during the dotcom boom/bust, “everyone” agreed that the market was nuts, but it kept going up for quite a while. And of course you should think about how to size your position and how to manage your risk while you have the position on. As the saying goes, the market can stay irrational longer than you can stay solvent.
A meeting quality score, as described in the patent referenced in this article (https://www.geekwire.com/2020/microsoft-patents-technology-score-meetings-using-body-language-facial-expressions-data/ )
Some additional ideas: There’s a large variety of “loss functions” that are used in machine learning to score the quality of solutions. There are a lot of these, but some of the most popular are below. A good overview is at https://medium.com/udacity-pytorch-challengers/a-brief-overview-of-loss-functions-in-pytorch-c0ddb78068f7
* Mean Absolute Error (a.k.a. L1 loss)
* Mean squared error
* Negative log-likelihood
* Hinge loss
* KL divergence
* BLEU loss for machine translation (https://www.wikiwand.com/en/BLEU)
There’s also a large set of “goodness of fit” measures that evaluate the quality of a model, including simple things like r^2 but also more exotic tests to do things like compare distributions. Wikipedia again has a good overview (https://www.wikiwand.com/en/Goodness_of_fit)
Microsoft TrueSkill (Multiplayer ELO-like system, https://www.wikiwand.com/en/TrueSkill)
I originally read this EA as “Evolutionary Algorithms” rather than “Effective Altruism”, which made me think of this paper on degenerate solutions to evolutionary algorithms (https://arxiv.org/pdf/1803.03453v1.pdf). One amusing example is shown in a video at https://twitter.com/jeffclune/status/973605950266331138?s=20
The Moneyball story would be a good example of this. Essentially all of sports dismissed the quantitative approach until the A’s started winning with it in 2002. Now quantitative management has spread to other sports like basketball, soccer, etc.
You could make a similar case for quantitative asset management. Pairs trading, one of the most basic kinds of quantitative trading, was discovered in the early 1980s (claims differ whether it was Paul Wilmott, Bamberger & Tartaglia at Morgan Stanley, or someone else). While the computation power to make this kind of trading easy was certainly more widely available starting in the 80s, nothing would have prevented someone from investing sooner in the research required for this style of trading. (Instead of, for instance, sending their analysts to become registered pseudoscientists)