Some thoughts on David Roodman’s GWP model and its relation to AI timelines
[Also posted on LW.]
I’ve been working on a report (see blog) assessing possible trajectories for Gross World Product (GWP) out to 2100. A lot of my early work focussed on analysing a paper of my colleague David Roodman. Roodman fits a growth model to longrun GWP; the model predicts a 50% probability that annual GWP growth is >= 30% by 2043.
I was thinking about whether to trust this model’s GWP forecasts, compared with the standard extrapolations that predict GWP growth of ~3% per year or less.^{[1]} I was also thinking about how the model might relate to AI timelines.
This post briefly describes some of my key takeaways, as they don’t figure prominently in the report. I explain them briefly and directly, rather than focussing on nuance or caveats.^{[2]} I expect it to be useful mostly for people who already have a rough sense for how Roodman’s model works. Many points here have already been made elsewhere.
Although for brevity I sometimes refer to “Roodman’s extrapolations”, what I really mean is the extrapolations of his univariate model once it’s been fitted to longrun GWP data. Of course, David does not literally believe these extrapolations. More generally, this post is not about David’s beliefs at all but rather about possible uses and interpretations of his model.
[Views are my own, not my employers]
Economic theory doesn’t straightforwardly support Roodman’s extrapolation over standard extrapolations
Early on in the project, I had the following rough picture in my mind (oversimplifying for readability):
Standard extrapolations use what are called ‘exogenous growth models’. These fit the post1900 data well. However, the exponential growth is put in by hand and isn’t justified by economic theory. (Exogenous growth models assume technology grows exponentially but don’t attempt to justify this assumption; the exponential growth of technology then drives exponential growth of GDP/capita.)
On the other hand, endogenous growth models can explain growth without putting in the answer by hand. They explain technological progress as resulting from economic activity (e.g. targeted R&D), and they find that exponential growth is implausible—a knifeedge case. Ignoring this knifeedge case, growth is either sub or super exponential. Roodman fits an endogenous growth model to the data and finds superexponential growth (because growth has increased over the longrun on average).
So Roodman’s model uses a better growth model (endogenous rather than exogenous). Roodman’s model also has the advantage of taking more data in account (standard economists typically don‘t use pre1900 data to inform their extrapolations).
Overall, we should put more weight on Roodman than standard extrapolation, at least over the longrun.
I no longer see things this way. My attitude is more like (again oversimplifying for readability):
Although exogenous growth models don’t justify the assumption of exponential growth of technology, semiendogenous growth models justify this claim pretty nicely._^{[3]} _These semiendogenous models can explain the post1900 exponential growth and the pre1900 superexponential growth in a pretty neat way—for example see Jones (2001).
Roodman’s model departs from these semiendogenous models primarily in that it assumes population is ‘outputbottlenecked’._^{[4]} _This assumption means that if we produced more output (e.g. food, homes), population would become larger as a result: more output → more people. This assumption hasn’t been true over the last 140 years, and doesn’t seem to be true currently: since the demographic transition in ~1880 fertility has decreased even while output per person increased. (That said, significant behaviour change or technological advance could make the assumption reasonable again, e.g. return to Malthusian conditions, human cloning, AGI)
So semiendogenous growth models are more suitable than Roodman’s model for extrapolating GWP into the future: the main difference between them is that the latter assumes population is outputbottlenecked. Both theories can explain the pre1900 data,_^{[5]} _and semiendogenous models provide a better explanation of the post1900 data.
Overall, by default I’ll trust the projections of the semiendogenous models.^{[6]} There’s one important caveat. If significant behaviour change or tech advance happens, then population may become outputbottlenecked again. In this case, I’ll trust the predictions of Roodman’s model.
Roodman’s GWP extrapolation is aggressive from an outsideview perspective
The above section gives an insideviewy reason to think Roodman’s GWP projections are aggressive. (The model assumes population is outputbottlenecked; when you remove this assumption you predict slower growth.)
I think they’re also aggressive from an outsideview perspective, based purely on recent GWP growth data.
First, the model overpredicts GWP growth over the last 60 years and overpredicts frontier GDP/capita growth over the last 120 years. (This is widely recognised, and is documented in Roodman’s original post.)
Second, its median prediction for growth in 2020 is 7%. This is after updating on the GWP data until 2019. Why is this? Roodman’s model bases its prediction on the absolute level of GWP, and doesn’t explicitly take into account the recent growth rate. Roughly speaking, it believes that “higher GWP means higher growth” based on the pre1900 data and it observes ~3% growth in the 1900s. GWP in 2020 is way higher than the average GWP in the 1900s, so the model predicts a higher value for 2020 growth than it observed throughout the 1900s.^{[7]}
Why does it matter that the model predicts 7% growth in 2020? Well, GDP growth in frontier economies has recently been more like 2% (source). That’s a difference of 1.8 doublings.^{[8]} Another 1.8 doublings gets us to 24% growth.^{[9]} In logspace, Roodman’s model thinks that we’ve already covered half the distance between 2% and 24%.^{[10]}
To put it another way, Roodman’s model falsely thinks we’ve already covered ~half the distance to TAIgrowth in logspace.
If in fact we have to travel twice as far through logspace, it will take more than twice as long according to hyperbolicy models like Roodman’s. That’s because each doubling of growth is predicted to take less long than the last. Roodman’s model thinks we’ve already covered the slowest doublings (from 2% to 4%, 4% to 7%). In its mind, all we have left are the muchquicker doublings from 7 to 14% and 14% to 24%.
How would adjusting for this change the GWP projections? Roughly speaking, it should much more than double the time until 24% growth. Double it because growth has to double twice as many times. Much more than double it because the doublings Roodman’s model omitted will take much longer than the ones it included.
I modelled this a bit more carefully, focussing on the time until we have 30% growth. Roodman’s model’s median prediction for the first year of 30% growth is 2043  ~20 years away. I tried to adjust the model in two hacky ways, each time forcing it to predict that the growth in 2020 was 2%.^{[11]} I found the median prediction for 30% growth shifts back to ~2110 or later  ~90 years away.^{[12]} The time to 30% much more than doubles.
(The average GWP growth over the last 20 years is ~3.5%. If I set the 2020 growth to 3.5% rather than 2%, the predicted date of explosive growth is delayed to ~2075, ~55 years away.)
In other words, if we adjust Roodman’s model based on what we know to be the current growth rate, its predictions become much more conservative.
To be clear, I’m not saying that these adjustments make the model ‘better’. For example, they may overadjust if the recent period is surprisingly slow.^{[13]} But I do think my adjustments are informative when considering what to actually believe from an outsideview perspective about future growth, especially in the next few decades. From an outside view perspective, I’d personally put more weight on the adjusted models than on Roodman’s original model.
(Note: there may be inside viewy reasons to think an AIdriven growth acceleration will be sooner and more sudden faster than Roodman’s model suggests; I’m putting these aside here.)
Roodman’s GWP extrapolation shouldn’t be given much weight in our AI timelines
Roodman’s model can predict how long it will take to get to a 30% annual GWP growth. Some people have thought about using this to inform timelines for transformative artificial intelligence (TAI). This rough idea is “we have pretty good outsideview reasons to think 30% growth is coming soon; TAI is the only plausible mechanism; so we should expect TAI soon”.
I don’t think this reasoning is reliable, for a few reasons (some discussed above):
The same reasoning would have led you astray over the last few decades, as the model’s predicted date of 30% growth has been increasingly delayed.
The model thinks we’re already halfway in logspace to TAIgrowth; this makes its TAI timelines aggressive.
We shouldn’t trust the predictions of Roodman’s model until we have advanced AI (or population is outputbottlenecked for another reason). So it can’t predict when advanced AI will happen.

In my mind, population being outputbottlenecked is (part of) the mechanism for superexponential growth. Roodman’s model describes how powerful this mechanism has been over human history: how quickly has it led growth to increase.^{[14]} The mechanism no longer applies, due to the demographic transition. However, advanced AI could reinstate the mechanism in a new form. So forecasting advanced AI is like forecasting when this mechanism will be in place again. But Roodman’s model forecasts growth on the assumption that the mechanism already applies; it can’t (reliably!) forecast when the mechanism will start to apply again.
Here I’m drawing on my belief that population being outputbottlenecked was an important mechanism driving historical superexponential growth, that this mechanism no longer applies, and that AI could reinstate this mechanism in a new form.

The dynamics of historical growth and a potentially future AIdriven growth explosion will be different in many ways.

Roodman’s model is fit to longrun GWP data. The dynamics increasing growth in this period are more people → more ideas → more people and a probably bunch of significant oneoff changes in institutions around the industrial revolution.^{[15]}

With AI the dynamics of increasing growth are more AIs → more ideas → more hardware/software/wealth → more and cleverer AIs >...

There’s a suggestive surface similarity there, suggesting that if the former leads to superexponential growth the latter might as well.

But the actual processes will look pretty different which could introduce huge differences in growth.
e.g. ‘How easy is it to make AI cleverer compared with humans?’, ‘How many resources does it take to sustain an AI mind compared with a human mind?’, ‘AIs can be copied’, ‘People may not want to hand over tasks to AIs’, ‘Will diminishing marginal returns to tech R&D be different for AI minds than for human minds?’.

Overall I think Roodman’s model is useful for indicating that something big could happen, that growth could dramatically accelerate, but otherwise not very informative. To the extent Roodman’s model is informative about AI timelines, I view it as aggressive for the reasons given in the bullets.
Read the report for nuance and caveats! ↩︎
In semiendogenous growth models technology improves as the result of R&D effort but there are diminishing returns—each 1% increase in technology (measured as TFP) requires more researcheryear than the last—so that you need exponential growth in researchers to sustain exponential technology growth. The justification of exponential growth is then roughly: the number of researchers has grown roughly exponentially, so we’d expect technology to have grown roughly exponentially as well. ↩︎
This assumption is made explicit in Roodman’s multivariate model. The univariate model doesn’t feature population, so is naturally understood as a purely statistical model without interpretation. However, in Roodman’s paper the univariate model is motivated theoretically as the univariate analogue of the multivariate model (in which population is outputbottlenecked). This is why I say that the univariate model “assumes population is ‘outputbottlenecked’”. Technically, you can get hyperbolic growth from the multivariate model even if population is held constant and so it is not literally true that the univariate model assumes population is outputbottlenecked. However, more extreme parameter values are needed for this to happen, and such values are in tension with the nonhyperbolic growth of the last 100 years. So in practice, if not technically, I do think of Roodman’s univariate model as assuming that population is outputbottlenecked. ↩︎
Here I’m putting aside reasonable doubts over whether their explanation of pre1900 growth is correct. ↩︎
When combined with the standard assumption that global population will stabilize, semiendogenous models imply economic growth will gradually slow down over time. They don’t imply constant exponential growth long into the future. ↩︎
I’ve oversimplified my description of the data to simplify this paragraph. In reality GWP growth increased until ~1960 and got as high as 5%, even though frontier growth stopped increasing from 1900. ↩︎
2 * 2^(1.8) = 7 ↩︎
7 * 2^(1.8) = 24 ↩︎
Why think about it in terms of logspace? Roodman’s model (ignoring the randomness) believes that “each time GWP increases by a factor f, GWP growth doubles” f depends on the data, and comes out at about 3.5 for Roodman’s data set. So in Roodman’s model, considering doublings of growth, i.e. logspace, is natural: growth doubles each time GWP increases by 3.5X. This is true of other hyperbolic models as well, e.g. Kremer (1993). ↩︎
Perhaps I should have set it to 2%, but I was using the recent GWP growth rate of 3.5%. ↩︎
The first method is my ‘growth multiplier’ explained here. Its median predicted date of explosive growth ranged from **2120 − 2140 **depending on an arbitrary choice of timescale (r in the model). See code here. The second method just reduces the instantaneous growth rate of Roodman’s model at every timestep by a constant factor ^{2}⁄_{7} (because it currently predicts 7% rather than 2%). This led to a median prediction of 2110. See code here. ↩︎
I also think Roodman’s unadjusted model is more informative about how fast we could grow if the population were as large as our economy could support (Malthusian conditions). ↩︎
Of course, Roodman’s parameters will also implicitly include other mechanisms influencing growth like the massive increase in the share of labour focussed on innovation, improvements in education, and other things. ↩︎
E.g. expansion of R&D as a share of the economy and better institutions for investing in new businesses. ↩︎
I came in with roughly the view you describe as having had early on in the project, and I found this post extremely clear in laying out the further considerations that shifted you. Thanks!
Would you be able to define “GWP” so folks like me don’t have to click one of the links or search to find this? My first reaction was “Huh? Global Warming Potentials relate to AI timelines?” :D.
Great suggestion—thanks! Have edited.
I have a lot of thoughts but not a lot of time, so apologies if this is a bit scatterbrained.
I’ve read your blog, Roodman’s blog from last year and a lot of Roodman’s report. I see this line of thinking in the following way:
Some EAs/rationalists/AI alignment groups believe that AI could be transformative because AI itself is unlike anything that has come before (I mostly share this view). Your and Roodman’s line of inquiry is to do a check on this view from an “outside view” perspective, using long term economic growth to make sure that there is more supporting the transformative AI idea than just “AI is totally different from anything before”—the inside view.
This could be particularly useful for AI timelines, and also perhaps convincing skeptics of transformative AI that this is worth considering.
The big problem then, is that economic growth over the last century+ has been at a fairly constant rate, or at least, it’s certainly not increasing.
So I completely agree with your assessment of Roodman’s model.
I actually wrote about this is a blog post earlier this year. I’m interested in the question of whether we should focus any time thinking about economic growth as a major policy outcome; if transformative AI is very close, then getting US GDP from 1.5% to 3% is kinda unimportant.
If I’m an AI skeptic, I don’t think Roodman’s model convinces me of much. It can’t really rely on GWP data of the last century because it doesn’t fit the model, so the entirety of the argument stems from GWP estimates going back 10,000 years. And the best guesses of late 20th century economists about how to measure “Gross production” in 5000 BC just seem really shaky.
So, yes, it seems really unconvincing to pull AI timelines from this data.
Also, on endogenous growth models, you don’t exactly mention it in your post, but what really jumped out at me was that you say around ~1880, people started getting richer, there wasn’t just population growth when technological progress was made. But then the next point seems very clear: there’s been tons of population growth since 1880 and yet growth rates are not 4x 1880 growth rates despite having 4x the population. The more people → more ideas thing may or may not be true, but it hasn’t translated to more growth.
So if AI is exciting because AIs could start expanding the number of “people” or agents coming up with ideas, why aren’t we seeing huge growth spurts now?
Ok final thing: I think the question of GDP measurement is a big deal here. GDP deflators determine what counts as “economic growth” compared to nominal price changes, but deflators don’t really know what to do with new products that didn’t exist. What was the “price” of an iPhone in 2000? Infinity? Could this help recover Roodman’s model? If ideas being produced end up as new products that never existed before, could that mean that GDP deflators should be “pricing” these replacements as massively cheaper, thus increasing the resulting “real” growth rate?
It certainly seems plausible to me, but I’m not sure what the practical relevance is. Would this convince people that transformative AI is a possibility? Would it give us better timelines? It seems like we’re just kinda inventing our own growth model again and then declaring that shows an “outside view” that transformative AI is a possibility. This seems unlikely to convince skeptics, but perhaps the critique of GDP calculation alone is worth broadly articulating before making any claims about AI.
This is an interesting idea. It wasn’t a focus of my work, but my loose impression is that when economists have attempted to correct for these kinds of problems the resulting adjustment isn’t nearly large enough to make Roodman’s model consistent with the recent data. Firstly, measurements of growth in the 1700s and 1800s face the same problem, so it’s far from clear that the adjustment would raise recent growth relative to old growth (which is what Roodman’s model would need). Secondly, I think that when economists have tried to measure willingness to pay for ‘free’ goods like email and social media, the willingness is not high enough to make a huge difference to GDP growth.
Thank you for this comment! I’ll make reply to different points in different comments.
The most plausible models have diminishing returns to efforts to generate new ideas. In these models, you need an exponentially growing population to sustain exponential growth. So these models aren’t surprised that growth hasn’t increased since 1880.
At the same time, these same models imply that if increasing output causes the population to increase (more output → more people), then there can be superexponential growth. This is because the population can grow superexponentially with this feedback loop.
So my overall opinion is that it’s 100% consistent to think:
The increased population of the last 100 years didn’t lead to faster growth
If AGI means that more output → more people, growth will accelerate.