These are some notes for a panel discussion this afternoon – they are sketches, much more work needed on this and I need to also figure out if this is quite right, and don’t feel it is, not yet.
Notes on macroeconomics and AI
The mental models we have when we try to assess the effects of artificial intelligence on the economy matters. There are several different mental models in circulation, and it is useful to try to sketch them out.
First, let’s look at AI-as-additional-humans. In this model AI is simply the addition of more humans to the economy, in some respect. If these new “humans” offer labor at a lower cost, this will affect the economy much as the influx of more labor into the economy would overall.
Second, let’s look at AI-as-cheaper-prediction. This perspective (from Agrawal et al 2020) suggests that AI is a way to reduce uncertainty in the economy. Ultimately, if this is true the endpoint may well be a planned economy in which the elimination of prediction cost means that we can coordinate much more effectively, but we could also imagine that there are boundary horizons where prediction reaches a lowest possible cost, and can go no further.
Third, we can think about AI as driving automation overall — the results would be things like worsening Baumol’s cost disease and possibly increasing unemployment in sectors that can be automated.
Fourth, we can think about the role of intelligence in the economy, and argue that we should think about AI-as-intelligence, and try to model how an influx of intelligence into an economy changes it in different ways. A version of this is to say that AI, when advanced enough, will be able to pay attention, and in so doing will affect all institutions and mechanisms in the economy that in some way depend on attention. This perspective on AI is not new, and in fact was one of the reasons economists like Herbert Simon thought we needed AI in the first place (Simon 1969).
Fifth, we can try to look at learning as an economic process, and argue that the changes in the economy that are likely to come are due to increased speed of social learning, and perhaps of scientific discovery – looking at different kinds of scientific productivity measures as keys to understanding how society evolves under AI.
Sixth, we can model AI as a vast increase in the number of agents in the system – and think about if economic systems go through phase shifts as the number of agents in the system increase by orders of magnitude. Does it matter for an economy if it has a billion or a trillion trillion actors?
These are but some of the possible aspects we can explore.
It is also worthwhile teasing out what our assumptions about AI in the economy seem to be – and if there are any root assumptions that are worth exploring and questioning more in detail.
One such root assumption may be about control and complexity. Do we think that the addition of AI to an economy adds more complexity, or do we think it reduces complexity? Or does it re-allocate complexity in different ways?
Why does this matter?
In order to answer this question we need to think about the relationship between complexity and the economy. Complexity can create more robust systems, but it can also reduce the overall predictability of these systems — and so essentially mean that what we end up with is a system that does not collapse, but behaves in ways that are inherently opaque to us.
We might want to use a simple toy example and ask if markets are more complex than weather, or if they are currently in the same complexity class. We should also think about what it would mean if they end up in a different complexity class and how that would affect society.
We live in a world that is premised on the idea that political choice can impact economic development. It is questionable if that is true even now, but we seem to think that the economy is possible to influence to some degree. The weather, not so much. So if we look at this we can imagine two different complexity classes: the first one with systems that we can still influence and impact in different ways, the second with systems that are essentially inaccessible to our will.
Now, we also need to be careful with concepts here — the market is not the same thing as the economy, and weather is not the same thing as climate. We may be able to argue, even, that the market is to the economy what the weather is to climate – an interesting analogy in complexity classes – and then examine what would happen if this were to change in different ways; if the economy shifts into the same complexity class as markets and weather, for example.
Another assumption that is worth looking at is that any sufficiently advanced AGI would indeed be an economic actor at all — and why that would be. We assume that participation in the economy is a natural choice, but why would it be? And what mode of participation should we expect? This is another kind of problem, and it is based on the anthropomorphism that plagues the field of AI and X, where X is any arbitrary subject.
We also do not know exactly what this participatory pattern could look like. The way we engage in the economy is not continuous, but discrete. We buy and sell things, but not all the time. We invest, sometimes, and sell sometimes – but in patterns. These patterns are human; what could completely different patterns look like? What classes of economic participation patterns exist and where should we predict that AI ends up?
AI’s impact on the economy could also be discussed as a consequence of how AI changes the time it takes for us to do different things. We know, with innovations like AlphaFold, that AI can compress the time it takes to perform scientific work – and this has impacts on that work, but also on the nature of science as a whole.
If we only assume that compressed time means that we get more labor, we seem to assume a completely synchronised economy – but those effects are likely to be gated on parts of the economy that have another kind of rhythm. So how do we account for the changes in rhythm in the economy that AI could lead to?
Much more here — worth coming back to after the seminar.