Some reasons not to expect a growth explosion

Many people expect global economic growth to accelerate in the future, with growth rates that are not just significantly higher than those of today, but orders of magnitude higher.

The following are some of the main reasons I do not consider a growth explosion to be the most likely future outcome.

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Most economists do not expect a growth explosion

Estimates of the future of economic growth from economists themselves generally predict a continual decline in growth rates. For instance, one “review of publicly available projections of GDP per capita over long time horizons” concluded that growth will most likely continue to decline in most countries in the coming decades. A similar report from PWC came up with similar projections.

Some accessible books that explore economic growth in the past and explain why it is reasonable to expect stagnant growth rates in the future include Robert J. Gordon’s Rise and Fall of American Growth (short version) and Tyler Cowen’s The Great Stagnation (synopsis).

It is true that there are some economists who expect growth rates to be several orders of magnitude higher in the future, but these are generally outliers. Robin Hanson suggests that such a growth explosion is likely in his book The Age of Em, which, to give some context, fellow economist Bryan Caplan calls “the single craziest claim” of the book. Caplan further writes that Hanson’s arguments for such growth expectations were “astoundingly weak”.

The point here is not that the general opinion of economists is by any means a decisive reason to reject a growth explosion (as the most likely outcome). The point is merely that it represents a significant reason to doubt an imminent growth explosion, and that it is not in fact those who doubt a rapid rise in growth rates who are the consensus-defying contrarians (and in terms of imminence, it is worth noting that even Robin Hanson does not expect a growth explosion within the next couple of decades).

Rates of innovation and progress in science have slowed down

See Bloom et al.’s Are Ideas Getting Harder to Find? and Cowen & Southwood’s Is the rate of scientific progress slowing down? A couple of graphs from the latter:

Moore’s law is coming to an end

One of the main reasons to expect a growth acceleration in the future is the promise of information technology. And economists, including Gordon and Cowen mentioned above, indeed agree that information technology has been a key driver of the growth we have seen in recent decades. But the problem is that we have strong theoretical reasons to expect the underlying trend that has been driving most progress in information technology since the 1960s — i.e. Moore’s law — will be coming to an end within the next few years.

And while it may be that other hardware paradigms will replace silicon chips as we know them, and continue the by now familiar growth in information technology, we must admit that it is quite unclear whether this will happen, especially since we are already lacking noticeably behind this trend line.

One may object that this is just a matter of hardware, and that the real growth in information technology lies in software. But a problem with this claim is that, empirically, growth in software seems largely determined by growth in hardware.

The growth of supercomputers has been slowing down for years

Developments of the performance of the 500 fastest supercomputers in the world conform well to the pattern we should expect given that we are nearing the end of Moore’s law:

The 500th fastest supercomputer in the world was on a clear exponential trajectory from the early 1990s to 2010, after which growth in performance has been steadily declining. Roughly the same holds true of both the fastest supercomputer and the sum of the 500 fastest supercomputers: a clear exponential trajectory from the early 1990s to around 2013, after which the performance has been diverging ever further from the previous trajectory, in fact so much so that the performance of the sum of the 500 fastest supercomputers is now below the performance we should expect the single fastest supercomputer to have today based on 1993-2013 extrapolation.

Many of our technologies cannot get orders of magnitude more efficient

This point is perhaps most elaborately explored in Robert J. Gordon’s book mentioned above: it seems that we have already reaped much of the low-hanging fruit in terms of technological innovation, and in some respects it is impossible to improve things much further.

Energy efficiency is an obvious example, as many of our machines and energy harvesting technologies have already reached a significant fraction of the maximally possible efficiency. For instance, electric pumps and motors tend to have around 90 percent energy efficiency, while the efficiency of the best solar panels are above 40 percent. Many of our technologies thus cannot be made orders of magnitude more efficient, and many of them can at most be marginally improved, simply because they have reached the ceiling of hard physical limits.

Three objections in brief

#1. What about the exponential growth in the compute of the largest AI training runs from 2012-2018?

This is indeed a data point in the other direction. Note, however, that this growth does not appear to have continued after 2018. Moreover, much of this growth seems to have been unsustainable. For example, DeepMind lost more than a billion dollars in 2016-2018, with the loss getting greater each year: “$154 million in 2016, $341 million in 2017, $572 million in 2018”. And the loss was apparently even greater in 2019.

#2. What about the Open Philanthropy post in which David Roodman presented a diffusion model of future growth that predicted much higher growth rates?

I think that model overlooks most of the points made above. Second, I think the following figure from Roodman’s article is a strong indication about the fit of the model, particularly how the growth rates in 1600-1970 are virtually all in the high percentiles of the model, while the growth rates in 1980-2019 are all in the low percentiles, and generally in a lower percentile as time progresses. That is a strong sign that the model does not capture our actual trajectory, and that the fit is getting worse as time progresses.

#3. We have a wager to give much more weight to high-growth scenarios.

First, I think it is questionable that scenarios with higher growth rates merit greater priority (e.g. a so-called value lock-in could also emerge in slow-growth scenarios, and it may be more feasible to influence slow-growth scenarios because they give us more time to acquire the requisite insights and resources to exert a significant and robustly positive influence). And it is less clear still that scenarios with higher growth merit much greater priority than scenarios with lower growth rates. But even if we grant that high-growth scenarios do merit greater priority, this should not change the bare epistemic credence we assign different scenarios. Our descriptive picture should not be distorted by such priority claims.