According to this graph, real GDP has grown by roughly a factor of 6 since 1960. That seems… way too low, intuitively. Consider:
I’m typing this post on my laptop (which conveniently has a backspace button and everything I type is backed up halfway around the world and I can even insert images trivially)...
while listening to spotify…
through my noise-canceling earbuds…
and there’s a smartphone on my desk which can give me detailed road maps and directions anywhere in the US and even most of the world, plus make phone calls…
and oh-by-the-way I have an internet connection.
Forgive my language, but this paragraph looks to me like an example of tech people being a bit too full of themselves sometimes. The IT-sector is clearly a cherry-picked example and cannot be extrapolated to the rest of the economy. It’s also not a good proxy for utilons; a million-fold increase in transistor abundance does not correspond to a million-fold more value for society, marginal returns yada yada. One could have picked even more extreme examples, like the triple product in nuclear fusion that has improved even faster than Moore’s law yet has generated approximately zero value for society thus far. On the other hand, the average life expectancy in the US has only improved by 13% since 1960 (and has begun to drop recently), arguably a measure much closer to the wellbeing of people.
1960 real GDP (and 1970 real GDP, and 1980 real GDP, etc) calculated at recent prices is dominated by the things which are expensive today—like real estate, for instance. Things which are cheap today are ignored in hindsight, even if they were a very big deal at the time.
In other words: real GDP growth mostly tracks production of goods which aren’t revolutionized. Goods whose prices drop dramatically are downweighted to near-zero, in hindsight.
And I argue that that’s how it should be—a transistor is on average performing much more important tasks in 1960, like planning trajectories for moon missions or running banking systems, than in 2021, like allowing people to watch TikTok videos or play games in HD. On the other hand, people still need houses to live in no matter how fancy their smartphones become. For average people, real estate is genuinely a bigger deal now than even a massive increase in their phone’s camera resolution.
In fact, the real GDP graph at the beginning of this post uses a different method—the BEA (which calculates the “official” US GDP and produced the numbers in that graph) switched from fixed prices to “chaining” in 1996.
I think this is actually an ingenious way of putting productivity figures into a historical context and thereby allowing us to track progress at all. There are ways it can break, as I will discuss later, but it’s still far superior than pointing to Moore’s Law and saying “Did you know you’re actually trillions of times richer than the average person in 1960?”.
Now, I think the smoothness of real GDP growth tells us basically-nothing about the smoothness of AI takeoff. Even after a hypothetical massive jump in AI, real GDP would still look smooth, because it would be calculated based on post-jump prices, and it seems pretty likely that there will be something which isn’t revolutionized by AI.
I agree it’s silly to use GDP growth as a measure in AI takeoff scenarios, kind of like asking how big of an impact a civilization-ending meteor would have on the stock market (big, approx. 150 km in diameter). I don’t expect our current concepts of private property, ownership or indeed money as a coordination mechanism to survive AI takeoff.
But that’s just AI being AI.
Let’s take a less extreme example: Suppose in the near future, a pill was invented that prolonged your healthspan perfectly by 30 years (if you buy into SENS Foundation’s rejuvenation paradigm, it’s actually somewhat plausible). But like all new technologies, it is very difficult to produce initially and only gradually becomes more affordable over time.
I would expect people to be willing to pay large sums of money to access such a technology even if they could barely afford it—it’s a matter of life and death after all. This would give the longevity pill an enormous initial price tag. As the price comes down and the pill becomes more widely distributed, GDP receives a big boost since it is calculated using the old price tag, until the reference point resets.
But what if the longevity pill technology does not follow previous trends and just got dumped onto the market at dirt-cheap prices? Not only would it not contribute much to GDP itself, it would also completely collapse the existing healthcare sector and render millions of people unemployed. It might actually register as negative growth.
Finally, consider the possibility that the pill made you immortal straight away. In this case, whatever the initial effects the technology had on the economy, once everybody has undergone the treatment its sales number will go to zero and its manufacturer bankrupt, all while immortality becomes a mere background fact of human existence.
So in conclusion, GDP is more of a measure of economic activity than value, and growth is only a meaningful proxy for progress under the limited context of gradual adoption and improvement of new technologies. In a way, GDP growth has slow takeoff built in as an assumption.
Forgive my language, but this paragraph looks to me like an example of tech people being a bit too full of themselves sometimes. The IT-sector is clearly a cherry-picked example and cannot be extrapolated to the rest of the economy. It’s also not a good proxy for utilons...
I think you’ve missed a key point here. The argument did not actually rely on extrapolating to the rest of the economy. The intuitive claim was that the IT sector by itself seemed to have grown enough that GDP should have grown by a factor of hundreds. So cherry-picking is irrelevant; if there is any such sector, then that’s enough for the argument.
I do agree that it’s not a good proxy for utilons.
Now, this part is where I think you’ve correctly identified the key issue (although I disagree with the “how it should be” bit):
And I argue that that’s how it should be—a transistor is on average performing much more important tasks in 1960, like planning trajectories for moon missions or running banking systems, than in 2021, like allowing people to watch TikTok videos or play games in HD. On the other hand, people still need houses to live in no matter how fancy their smartphones become. For average people, real estate is genuinely a bigger deal now than even a massive increase in their phone’s camera resolution.
This is a decreasing marginal returns argument; the billionth iPhone is worth a lot less than the tenth. But it’s not like there’s an easily-identifiable “correct” price point to use on that curve; some iPhones do in fact provide a lot more value than others. After all, the first ten or a thousand iPhones could probably have sold for a price orders of magnitude higher (even without signalling value). If we just use current prices, then we’re underestimating the iPhone’s value contribution.
Your story about the longevity pill is great, and I generally agree with the conclusion at the end.
True. Still, using 1960′s prices with current production assumes a 1960 flat demand curve, right? It’s like using off-season avocado prices when no one buys them to compute real GDP during avocado season.
But it’s not like there’s an easily-identifiable “correct” price point to use on that curve
Shouldn’t we measure something like social surplus produced each year? (In efficient markets without externalities, this would be producer surplus given perfect price discrimination.)
(I think my comment here is slightly wrong but probably right in spirit; I don’t have time to think it out right now.)
So, there’s this general problem in economics where economists want to talk about what we “should” do in policy debates, and that justifies quantifying things in terms of e.g. social surplus (or whatever), on the basis that we want policies to increase social surplus (or whatever).
The problem with this is that such metrics are not chosen for robust generalization to many different use-cases, so unsurprisingly they don’t generalize very well to other use-cases. For instance, if we want to make predictions about the probable trajectory of AI based on the smoothness of some metric of economic impact of technologies, social surplus does not seem like a particularly great metric for that purpose.
I don’t think that’s what I mean. If we use 1950s real prices, then we’re overestimating the value of the transistor production because we’re multiplying quantity by the price very early on the marginal utility curve, when they’re still marginally fulfilling extremely high value use cases. Conversely, if we use current prices, we’re underestimating the GDP contribution. So it seemed to me that we should integrate along the willingness to pay curve, which I think gets us something like total surplus.
(There are a few wrinkles in that the rest of the economy has also changed since the 1950s, and so i imagine that will throw some more subtle problems.)
That would indeed be the right way to estimate total surplus. The problem is that total surplus is not obviously the right metric to worry about. For a use case like forecasting AI, for instance, it’s not particularly central.
No opinion because I haven’t thought about that use case. My comment was intended to answer “how do you actually measure an idealized version of a GDP growth curve”—minimizing strangeness which depends on the reference year—without considering its usefulness for forecasting AI.
Overall, I think the right way to think about GDP growth in relation to utilons is that it’s a combination of removing trivial inconveniences for large numbers of people, while solving mission-critical problems for a few people, and occasionally creating positive or negative externalities through network effects that have to be analyzed on an individual basis.
There’s an argument out there that as the economy tackles low-hanging fruit, increasing innovation becomes harder to achieve. Stagnation sets in. I think this is an incomplete framing that is misleading over longer time scales.
When we think in terms of years, there are a set of tractable technological achievements, and the low-hanging fruit metaphor is appropriate here. There’s a set of problems we basically know how to solve. We put in the work to solve them roughly in order of priority, and see diminishing returns on our investment.
However, one of the knock-on effects of solving these problems is that they open up formerly intractable problems and inaccessible resources.
For example, sequencing the human genome was one an expensive and time-consuming project. It came half a century after the structure of DNA was determined, and represented the culmination of our understanding of the genome to that point.
Once we’d achieved that high-hanging fruit, however, the endeavor itself created a network of highly-skilled scientists with the knowhow to make the process cheaper and more reliable. Now it’s relatively cheap to sequence the genome. Cheap sequencing gives us access to massive amounts of genetic data. Cheap compute lets us gather and process big health data, and interpret it in the light of genetic data. All together, this lets us refocus our scientific efforts in more productive directions.
Doing all this would simply not have been possible at an earlier technological era. But the network effects that make the new wave of growth possible take time to accumulate. It takes time to build out the highway system or the internet, to figure out how to automate production of a useful product.
So we’ll see some time delay between inventing the tech that enables a network, the growth of that network to its full potential, and the harnessing of that network to drive a new wave of technological innovation.
We can’t assume that these “waves of innovation” have diminishing value over time, the way that we can assume that the automation of specific products produces diminishing value as more consumers gain access to them. They deliver value by two different mechanisms. Individual products solve particular problems. “Waves of innovation” give rise to entirely different classes of products, which may turn out to deliver widely varying average levels of utility. Even if a particular wave of innovation delivers a very high level of average utility, even an entirely efficient market can’t shortcut the technological and network growth barriers to implementing that wave. It just takes time, and the work has to be done in a certain order. The exact outcomes are not predictable in advance.
So from a local perspective on the order of years, we should focus on the diminishing returns story. On the order of decades, though, we should focus on the “waves of innovation” and network effects story, where diminishing returns is not operating.
“GDP is more of a measure of economic activity than value”. I think this a very good intuition.
As I understand it, a main source of confusion in discussions like this stems from mixing up the notion of “value” as (A) price*quantity=revenue and (B) the difference between the consumer’s willingness to pay and the price.
“A” is what counts towards GDP—mostly for practical reasons, since prices and quantities are, at least theoretically, are straightforward to measure on a national level. However, this definition goes against the intuition of lots of people, who usually think of value as something like the idea in definition “B”.
My rephrasing of it would go something like this: (1) nowadays very powerful transistors can be had for a few cents, (2) we know that people did pay a lot of money for inferior devices in the past, (3) therefore we are very rich today (at least measured in transistors-speed-per-cent), which leads to the not totally unfounded conclusion (4): GDP is a poor measure of “value” or “national wealth” in the sense of definition “B”.
This is by and large true as long as you accept definition B—which, in turn, turns out to be the (textbook) definition of consumer surplus. Which is something that was never intended to be captured by GDP—mostly, because consumer surplus is just very complex to measure: it is very hard to estimate how much people would be willing to pay for certain items (and how much would be sold at those prices; basically, you are trying to estimate a demand curve).
Quantifying the gain from new products is a sub-sub-field of empirical industrial organization in itself: Petrin’s 2002 seminal paper that tries to do it for the market of minivans (titled, funnily enough “Quantifying the Benefits of New Products: The Case of the Minivan”) is still compulsory reading in graduate IO courses. But, as you can tell from the granularity and specificity of the topic, it looks hopeless to perform these exercises for the entirety of the economy, every year. So we work what we have, which is GDP.
I’ve heard that minivans replaced large station wagons largely because station wagons counted as cars for purposes of fuel economy laws (which mandated that cars sold by a manufacturer achieve a “fleet average” of a certain number of miles per gallon) and minivans didn’t.
I do agree that the distinction should be made and should be known, and that the confusion around the interpretation be reduced. At the same time calling it an “insight” appears to be due to either that very confusion or ignorance of the actual subject matter.
Since its creation, economists who are familiar with GDP have emphasized that GDP is a measure of economic activity, not economic or social well-being. In 1934, Simon Kuznets, the chief architect of the United States national accounting system and GDP, cautioned against equating GDP growth with economic or social well-being.
(Note—I take the meaning of “value” above to refer to the more subjective utility-type meaning and not simply the price value for accounting at some aggregate level.)
Perhaps a more interesting question here might be why so many people, and specifically non-lay people who really should know better (professional economists, professional financial journalist, governmental staff and representatives), keep slipping into the error in framing/rhetoric if not flat out error in thought.
One could have picked even more extreme examples, like the triple product in nuclear fusion that has improved even faster than Moore’s law yet has generated approximately zero value for society thus far.
Side note: this claim about the triple product only seems to have been true until about the early 90s. Since the early 2000s there have been no demonstrated increases at all (though future increases are projected).
Lots of technologies advance rapidly at first, but Moore’s Law was exceptional in terms of how long it continued even after massive research efforts had picked the low hanging fruit.
Forgive my language, but this paragraph looks to me like an example of tech people being a bit too full of themselves sometimes. The IT-sector is clearly a cherry-picked example and cannot be extrapolated to the rest of the economy. It’s also not a good proxy for utilons; a million-fold increase in transistor abundance does not correspond to a million-fold more value for society, marginal returns yada yada. One could have picked even more extreme examples, like the triple product in nuclear fusion that has improved even faster than Moore’s law yet has generated approximately zero value for society thus far. On the other hand, the average life expectancy in the US has only improved by 13% since 1960 (and has begun to drop recently), arguably a measure much closer to the wellbeing of people.
And I argue that that’s how it should be—a transistor is on average performing much more important tasks in 1960, like planning trajectories for moon missions or running banking systems, than in 2021, like allowing people to watch TikTok videos or play games in HD. On the other hand, people still need houses to live in no matter how fancy their smartphones become. For average people, real estate is genuinely a bigger deal now than even a massive increase in their phone’s camera resolution.
I think this is actually an ingenious way of putting productivity figures into a historical context and thereby allowing us to track progress at all. There are ways it can break, as I will discuss later, but it’s still far superior than pointing to Moore’s Law and saying “Did you know you’re actually trillions of times richer than the average person in 1960?”.
I agree it’s silly to use GDP growth as a measure in AI takeoff scenarios, kind of like asking how big of an impact a civilization-ending meteor would have on the stock market (big, approx. 150 km in diameter). I don’t expect our current concepts of private property, ownership or indeed money as a coordination mechanism to survive AI takeoff.
But that’s just AI being AI.
Let’s take a less extreme example: Suppose in the near future, a pill was invented that prolonged your healthspan perfectly by 30 years (if you buy into SENS Foundation’s rejuvenation paradigm, it’s actually somewhat plausible). But like all new technologies, it is very difficult to produce initially and only gradually becomes more affordable over time.
I would expect people to be willing to pay large sums of money to access such a technology even if they could barely afford it—it’s a matter of life and death after all. This would give the longevity pill an enormous initial price tag. As the price comes down and the pill becomes more widely distributed, GDP receives a big boost since it is calculated using the old price tag, until the reference point resets.
But what if the longevity pill technology does not follow previous trends and just got dumped onto the market at dirt-cheap prices? Not only would it not contribute much to GDP itself, it would also completely collapse the existing healthcare sector and render millions of people unemployed. It might actually register as negative growth.
Finally, consider the possibility that the pill made you immortal straight away. In this case, whatever the initial effects the technology had on the economy, once everybody has undergone the treatment its sales number will go to zero and its manufacturer bankrupt, all while immortality becomes a mere background fact of human existence.
So in conclusion, GDP is more of a measure of economic activity than value, and growth is only a meaningful proxy for progress under the limited context of gradual adoption and improvement of new technologies. In a way, GDP growth has slow takeoff built in as an assumption.
I think you’ve missed a key point here. The argument did not actually rely on extrapolating to the rest of the economy. The intuitive claim was that the IT sector by itself seemed to have grown enough that GDP should have grown by a factor of hundreds. So cherry-picking is irrelevant; if there is any such sector, then that’s enough for the argument.
I do agree that it’s not a good proxy for utilons.
Now, this part is where I think you’ve correctly identified the key issue (although I disagree with the “how it should be” bit):
This is a decreasing marginal returns argument; the billionth iPhone is worth a lot less than the tenth. But it’s not like there’s an easily-identifiable “correct” price point to use on that curve; some iPhones do in fact provide a lot more value than others. After all, the first ten or a thousand iPhones could probably have sold for a price orders of magnitude higher (even without signalling value). If we just use current prices, then we’re underestimating the iPhone’s value contribution.
Your story about the longevity pill is great, and I generally agree with the conclusion at the end.
True. Still, using 1960′s prices with current production assumes a 1960 flat demand curve, right? It’s like using off-season avocado prices when no one buys them to compute real GDP during avocado season.
Shouldn’t we measure something like social surplus produced each year? (In efficient markets without externalities, this would be producer surplus given perfect price discrimination.)
(I think my comment here is slightly wrong but probably right in spirit; I don’t have time to think it out right now.)
So, there’s this general problem in economics where economists want to talk about what we “should” do in policy debates, and that justifies quantifying things in terms of e.g. social surplus (or whatever), on the basis that we want policies to increase social surplus (or whatever).
The problem with this is that such metrics are not chosen for robust generalization to many different use-cases, so unsurprisingly they don’t generalize very well to other use-cases. For instance, if we want to make predictions about the probable trajectory of AI based on the smoothness of some metric of economic impact of technologies, social surplus does not seem like a particularly great metric for that purpose.
I don’t think that’s what I mean. If we use 1950s real prices, then we’re overestimating the value of the transistor production because we’re multiplying quantity by the price very early on the marginal utility curve, when they’re still marginally fulfilling extremely high value use cases. Conversely, if we use current prices, we’re underestimating the GDP contribution. So it seemed to me that we should integrate along the willingness to pay curve, which I think gets us something like total surplus.
(There are a few wrinkles in that the rest of the economy has also changed since the 1950s, and so i imagine that will throw some more subtle problems.)
That would indeed be the right way to estimate total surplus. The problem is that total surplus is not obviously the right metric to worry about. For a use case like forecasting AI, for instance, it’s not particularly central.
No opinion because I haven’t thought about that use case. My comment was intended to answer “how do you actually measure an idealized version of a GDP growth curve”—minimizing strangeness which depends on the reference year—without considering its usefulness for forecasting AI.
Overall, I think the right way to think about GDP growth in relation to utilons is that it’s a combination of removing trivial inconveniences for large numbers of people, while solving mission-critical problems for a few people, and occasionally creating positive or negative externalities through network effects that have to be analyzed on an individual basis.
There’s an argument out there that as the economy tackles low-hanging fruit, increasing innovation becomes harder to achieve. Stagnation sets in. I think this is an incomplete framing that is misleading over longer time scales.
When we think in terms of years, there are a set of tractable technological achievements, and the low-hanging fruit metaphor is appropriate here. There’s a set of problems we basically know how to solve. We put in the work to solve them roughly in order of priority, and see diminishing returns on our investment.
However, one of the knock-on effects of solving these problems is that they open up formerly intractable problems and inaccessible resources.
For example, sequencing the human genome was one an expensive and time-consuming project. It came half a century after the structure of DNA was determined, and represented the culmination of our understanding of the genome to that point.
Once we’d achieved that high-hanging fruit, however, the endeavor itself created a network of highly-skilled scientists with the knowhow to make the process cheaper and more reliable. Now it’s relatively cheap to sequence the genome. Cheap sequencing gives us access to massive amounts of genetic data. Cheap compute lets us gather and process big health data, and interpret it in the light of genetic data. All together, this lets us refocus our scientific efforts in more productive directions.
Doing all this would simply not have been possible at an earlier technological era. But the network effects that make the new wave of growth possible take time to accumulate. It takes time to build out the highway system or the internet, to figure out how to automate production of a useful product.
So we’ll see some time delay between inventing the tech that enables a network, the growth of that network to its full potential, and the harnessing of that network to drive a new wave of technological innovation.
We can’t assume that these “waves of innovation” have diminishing value over time, the way that we can assume that the automation of specific products produces diminishing value as more consumers gain access to them. They deliver value by two different mechanisms. Individual products solve particular problems. “Waves of innovation” give rise to entirely different classes of products, which may turn out to deliver widely varying average levels of utility. Even if a particular wave of innovation delivers a very high level of average utility, even an entirely efficient market can’t shortcut the technological and network growth barriers to implementing that wave. It just takes time, and the work has to be done in a certain order. The exact outcomes are not predictable in advance.
So from a local perspective on the order of years, we should focus on the diminishing returns story. On the order of decades, though, we should focus on the “waves of innovation” and network effects story, where diminishing returns is not operating.
“GDP is more of a measure of economic activity than value”. I think this a very good intuition.
As I understand it, a main source of confusion in discussions like this stems from mixing up the notion of “value” as (A) price*quantity=revenue and (B) the difference between the consumer’s willingness to pay and the price.
“A” is what counts towards GDP—mostly for practical reasons, since prices and quantities are, at least theoretically, are straightforward to measure on a national level. However, this definition goes against the intuition of lots of people, who usually think of value as something like the idea in definition “B”.
My rephrasing of it would go something like this: (1) nowadays very powerful transistors can be had for a few cents, (2) we know that people did pay a lot of money for inferior devices in the past, (3) therefore we are very rich today (at least measured in transistors-speed-per-cent), which leads to the not totally unfounded conclusion (4): GDP is a poor measure of “value” or “national wealth” in the sense of definition “B”.
This is by and large true as long as you accept definition B—which, in turn, turns out to be the (textbook) definition of consumer surplus. Which is something that was never intended to be captured by GDP—mostly, because consumer surplus is just very complex to measure: it is very hard to estimate how much people would be willing to pay for certain items (and how much would be sold at those prices; basically, you are trying to estimate a demand curve).
Quantifying the gain from new products is a sub-sub-field of empirical industrial organization in itself: Petrin’s 2002 seminal paper that tries to do it for the market of minivans (titled, funnily enough “Quantifying the Benefits of New Products: The Case of the Minivan”) is still compulsory reading in graduate IO courses. But, as you can tell from the granularity and specificity of the topic, it looks hopeless to perform these exercises for the entirety of the economy, every year. So we work what we have, which is GDP.
I’ve heard that minivans replaced large station wagons largely because station wagons counted as cars for purposes of fuel economy laws (which mandated that cars sold by a manufacturer achieve a “fleet average” of a certain number of miles per gallon) and minivans didn’t.
Upvoting for this insight.
I do agree that the distinction should be made and should be known, and that the confusion around the interpretation be reduced. At the same time calling it an “insight” appears to be due to either that very confusion or ignorance of the actual subject matter.
https://thesolutionsjournal.com/2016/02/22/a-short-history-of-gdp-moving-towards-better-measures-of-human-well-being/
(Note—I take the meaning of “value” above to refer to the more subjective utility-type meaning and not simply the price value for accounting at some aggregate level.)
Perhaps a more interesting question here might be why so many people, and specifically non-lay people who really should know better (professional economists, professional financial journalist, governmental staff and representatives), keep slipping into the error in framing/rhetoric if not flat out error in thought.
Side note: this claim about the triple product only seems to have been true until about the early 90s. Since the early 2000s there have been no demonstrated increases at all (though future increases are projected).
See here: https://www.fusionenergybase.com/article/measuring-progress-in-fusion-energy-the-triple-products
Lots of technologies advance rapidly at first, but Moore’s Law was exceptional in terms of how long it continued even after massive research efforts had picked the low hanging fruit.