by Matty Smith
Communications
March 27, 2025
10 min read
Since the 1930s, gross domestic product, the sum of the value of all goods and services produced in a country, has been used to measure the economy. But with the rise of digital goods, traditional methods have become increasingly outdated and inaccurate. That’s why the Lab proposes GDP-B, a better measure of the health of the AI-powered digital economy.
Lab Digital Fellow Avinash Collis, Research Scientist David Nguyen, and Research Scientist Sophia Kazinnik answered questions about the project.
Why do we need a new GDP?
Avinash Collis: GDP does a great job of measuring the production side of the economy. It does a really good job of measuring the monetary value of all final goods produced. The challenge is that it was never meant to measure well-being or economic well-being.
Historically, that was not really a problem because the world was physical. If you consumed more physical goods, GDP would increase. At the same time, the benefits people got from consuming those goods would also increase. So benefits and what showed up in GDP were mostly correlated.
As the world switched from physical to digital, one big challenge with the digital economy was that most of the things we consume don’t have a price.
Sophia Kazinnik: There are many aspects that current metrics are not capturing, but what stands out to me is the growing importance of “free” digital services. It’s hard for me to imagine my day-to-day activities without apps like Waze, Gmail, and WhatsApp. I get countless benefits from apps without paying for them, but traditional GDP doesn’t capture that extra value. This mismatch shows why we need a more complete way to measure our well-being—one that captures all the benefits people get instead of just tracking how much they spend.
David Nguyen: There is a clear demand from society and policy for a new indicator that can measure what we ultimately care about in life. GDP is a good indicator, but it’s limited to market-based production, which is only one–although important–source of people’s welfare.
AC: We call it GDP-B, where ‘B’ stands for benefits. The idea is this metric directly captures the benefits as opposed to looking at the production side. If you look at the information technology sector as a percentage of the overall economy, that share has remained at around 4 to 5 percent for the last 40 years.
The world has gotten more digital, but the IT sector’s share of the economy, as evident in GDP, hasn’t really changed. Most of the benefits we are getting from the digital revolution are not showing up.
What does it mean to measure “well-being?”
AC: One thing I want to clarify is that we are focusing on economic well-being, measuring what in economics is called consumer surplus. Within the standard economic framework, this is basically the extra value you get from consuming something on top of what you’re paying for it. If you think of well-being more broadly, as happiness and more subjective metrics, we are not measuring that.
There are many research groups and agencies that put out happiness rankings every year. In our 2019 HBR article, that’s why we show the spectrum of metrics. On one end, we have GDP. On the other end, we have happiness metrics trying to truly measure well-being. Our GDP metric is somewhere in the middle.
Can you break down your research methodology?
DN: We run massive online choice experiments asking tens of thousands of people how much they value having access to certain goods and services or other quality of life factors.
AC: How much people are paying for various things is not necessarily a good measure because people are not paying for most digital goods. What we try to measure is, how much do we have to compensate people so that they give up access to a certain good? To estimate valuations even when there is no market data on prices.
SK: We can also use large language models to augment the survey data. LLMs have been trained on massive amounts of human-created text, effectively “absorbing” patterns in people’s thinking, behavior, and decision-making. When you provide them with enough context—like demographics or previous behaviors—they can make realistic guesses about how someone might respond.
Is GDP-B adding new metrics to the existing GDP, or does it rebuild it from the ground up?
AC: I would say it’s a different way of thinking about benefits and economic well-being while staying within the framework used by national statistical agencies. We should be looking at GDP numbers and GDP-B numbers side by side. It’s not like we just look at GDP-B because we still care about production.
DN: GDP-B is meant to complement GDP and other economic indicators. We do not advocate for replacing GDP but want to go “beyond” in the sense of moving closer to measuring what people ultimately care about in life. Due to its level of standardization and comparability over time and across countries, GDP will for now remain a relevant measure of market-based production.
SK: Personally, I don’t see the old GDP going away entirely, because it’s still a useful baseline for things like government spending, cross-country comparisons, and historical data tracking. But once we have a “finished” expanded measure, I’d expect it to sit alongside the traditional GDP, giving policymakers and economists a more rounded view of economic well-being.
What has changed since you started working on GDP-B?
AC: We have noticed GDP growth is quite high. The benefits we get from digital goods are steadily increasing since we started tracking in 2016.
SK: I think that one of the biggest shifts has been the rise of generative AI—not just as a research tool, but as part of the very digital ecosystem we’re trying to measure. Previously, the focus was on platforms like Facebook, Wikipedia, and Google Search—things people use. Now we’re seeing people co-create with tools like ChatGPT, Midjourney, or Copilot, which blurs the lines between producer and consumer, and raises new questions about where value is being generated and who’s actually benefiting from it.
AC: We still don’t see GDP and productivity rise due to AI. But on the GDP-B side, what we found is that in 2024, generative AI tools contributed to around $50 billion of benefits to consumers. Consumers are benefiting already, and it shows up in our GDP-B metrics. On the GDP and productivity side, there is no impact yet. I think this makes the case for why we should be also tracking GDP-B in addition to GDP.
Are there any particular areas of research you’ve found challenging?
AC: We’re exploring areas like healthcare and infrastructure, and more durable goods like the value of having a car. What we’re finding in our survey-based approach is that it’s more challenging to measure goods that are essential to survival.
It’s easier to put values on goods when you can live without them. But if it’s impossible to give up something, the perceived values are, of course, very high. People have difficulty saying if it’s 1 million dollars or 10 million dollars, so when the numbers become too high our method doesn’t work as well.
One approach is to focus on changes over time rather than the absolute numbers. How much one values healthcare is a hard question because it’s essential, but the value of the improvement in healthcare from last year to this year is relatively easier.
DN: Since I first started working on economic measurement under Professor Diane Coyle, I realized quickly how large the challenges lying ahead of us are. Now I know that it is possible to come up and establish new indicators if you are willing to build the networks. Walking the line between improving traditional metrics that remain relevant and establishing something complementary can, at times, be challenging.
What are the areas of concern when looking to apply GDP-B internationally?
DN: We are working with other countries to establish GDP-B as a global indicator from the start. There are no theoretical barriers to using GDP-B as an internationally comparable indicator.
AC: When we measure GDP contributions from digital goods across several countries, we find lower-income countries benefit much more from digital goods than higher-income countries. Similarly, within a country, lower-income individuals seem to benefit much more from digital goods related to their income compared to higher-income individuals.
It’s not like Elon Musk gets a better quality Google search, right? Elon gets the same Google search as you and I, and for free provided you have internet access. If you are richer, you have better homes, better cars. But in a digital world, if you are rich or poor, you still have the same Google search, the same YouTube, and so on.
One caveat I want to mention is this is not to say digital goods are amazing. We are only looking at the economic benefits. If you measure happiness and subjective well-being, there could be negative impacts as well.
Does GDP-B examine any potential harms or negative impacts?
AC: It’s hard to measure these externalities. For example, we find that social media apps generate a lot of welfare and billions of dollars, but–this is a question of debate in academic circles–they may impact increasing political polarization at the national level. That wouldn’t show up in our data because we are measuring individual-level benefits.
DN: We are actively working on accounting for the downsides of (over-)consumption like addiction, and defensive expenditures like cleaning up after environmental disasters or conflicts. GDP has the same issues given that many things we might see as welfare decreasing are counted as positive factors. Examples include cigarettes, surgeries after preventable accidents, or securing private property.
SK: From a purely economic standpoint, standard GDP rarely factors in costs like pollution or other negative externalities; it just measures output or consumption. In the same vein, GDP-B zeroes in on consumer benefits. So, GDP-B only captures the upside.
However, this is an important point that we keep coming back to. We would need to develop a methodology to capture potential harms, perhaps something along the lines of surveying users on how much they’d pay to avoid data privacy issues or unwanted attention “taxes.” But these types of questions are more abstract and are inherently more difficult to answer. Ask yourself, how much would you be willing to pay to not feel the urge to check your phone every five seconds?
How do you see policymakers using these new measures?
SK: I think policymakers could use these new measures (like GDP-B) to inform decisions about everything from regulating platform services to prioritizing infrastructure that boosts overall welfare, not just the economy’s “cash flows.” They’d have numbers that more accurately reflect the actual quality of life and benefits that people experience, especially in the realm of “free” digital goods.
As for the general public, a measure that captures the free services they actually use might resonate better than GDP alone, which can feel disconnected from everyday experiences. People might find it validating to see their reliance on messaging apps or streaming services recognized as part of the economy’s real value.
AC: Looking at changes in GDP-B over time would be a great proxy to see, are people better off or worse off because of various technologies?
Let’s say a policymaker is thinking about regulating generative AI technologies. We can, using GDP-B numbers, put a dollar number on what would happen, an increase or decrease in benefits, from a potential regulation. These numbers would help them come up with better policies because they directly capture the benefit.
If you could ensure one specific change in how we measure economic progress in the next decade, what would it be?
AC: We would have a “dashboard” of metrics–this is something Erik Brynjolfsson said a few years back. If you’re driving a car, you have a dashboard, and you have different numbers. We have speed, we have fuel, all these different numbers.
Similarly, for policymaking, we need a dashboard. We already have GDP and productivity. In addition to that, we can measure happiness. We create a dashboard, and we try to focus on looking at this spectrum rather than focusing on one specific number. This would be on my wish list for the future.
DN: GDP-B becomes the default global measure of progress and societies are benchmarked against improving the things that people ultimately care about.
SK: Personally, I’d push for routinely measuring consumer surplus from digital goods—how much value people actually get from services that don’t have a price tag, like Google Maps or Wikipedia. This is something that’s long overdue.
Have you seen any sort of pushback?
AC: Whenever we present GDP-B, it has been very well received. The big challenge, of course, is that statistical agencies–in the US the Bureau of Economic Analysis–have been facing declining budgets over the past several years. They are already struggling to produce the existing GDP numbers. This is a new method requiring survey data collection, which costs a lot of money.
DN: Policymakers have shown a keen interest in our work, demonstrating that there is a demand out there for welfare indicators beyond GDP. One challenge is the decreasing budget and funding for economic measurement and statistics at the federal level. This is ill-guided given that the rapidly changing economic structure requires better measurement techniques to keep track of what is going on.
What steps are being taken to future-proof GDP-B?
SK: The focus needs to be less on specific tools, and more on the framework. What people value changes fast these days, and our focus needs to be on that.
DN: GDP-B is a flexible measure that can easily account for new products in a rapidly changing digital economy – much more so than traditional GDP that struggles whenever the product mix changes.
AC: We’re creating a template for running the service. If someone is interested in digging deeper into a specific industry or geography, they can take our template, do their own surveys, plug the numbers into our framework and come up with GDP-B estimates for their country or region.
The real impact will come from adoption. If countries adopt GDP-B, that’s where the scale will come.
You can learn more about GDP-B by visiting gdp-b.org.
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Avinash Collis is an Assistant Professor at the Heinz College of Information Systems and Public Policy at Carnegie Mellon University. Avi holds a Ph.D. from the MIT Sloan School of Management. He studies the economic implications of information technologies.
David Nguyen‘s research explores new and better ways to measure the modern and digital economy. He is particularly interested in advancing economic metrics and statistics on economic output and welfare. As a research associate, he remains affiliated to the London-based Economic Statistics Centre of Excellence (ESCoE). David received his PhD from the London School of Economics.
Sophia Kazinnik is a research scientist at the Stanford Digital Economy Lab, where she explores the intersection of artificial intelligence and economics. Prior to joining Stanford, Sophia worked as an economist and quantitative analyst at the Federal Reserve Bank of Richmond, where she was part of the Quantitative Supervision and Research group. While there, she contributed to supervisory projects targeting cyber and operational risks and developed NLP tools for supervisory purposes
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