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How Destructive Is Innovation

Daniel Garcia-Macia, Chang-Tai Hsieh and Peter Klenow Econometrica

Date Posted:: May 26, 2021
Is Database:: Database
Is Important:: Important

Innovation drove 70% of TFP growth from 2003-2013, while creative destruction contributed 22% and new varieties added ~5%. @PeteKlenow

From 2003 to 2013, innovation was the primary driver of Total Factor Productivity (TFP) growth, accounting for 70% of the increase, while creative destruction contributed 22% and new varieties added approximately 5%. The data suggests that most productivity growth stems from incumbents improving their own products rather than from new entrants or the creation of entirely new product varieties. This highlights the significant role of incumbents in driving economic growth through quality improvements. Creative destruction, while vital for understanding job dynamics, plays a smaller role in overall productivity gains. The findings imply that policies promoting research and development should consider the balance between fostering innovation within existing firms and encouraging new market entrants.

Takeaways by Macro Roundup^® AI

From 2003 to 2013, innovation accounted for 70% of Total Factor Productivity growth, while creative destruction contributed 22% and new varieties roughly 5%.
The data suggests most productivity growth stems from incumbents improving their own products rather than from new entrants or entirely new product varieties, with creative destruction playing a smaller role in overall productivity gains.
The findings imply that research and development policy should weigh fostering innovation within existing firms against encouraging new market entrants, with creative destruction remaining vital to understanding job dynamics.

Daniel Garcia-Macia, Chang-Tai Hsieh and Peter Klenow, "How Destructive Is Innovation," Econometrica, http://klenow.com/DestructiveInnovation_GHK.pdf

Bottomline, “..How much innovation takes the form of creative destruction versus new varieties versus firms improving their own products? How much occurs through entrants versus incumbents? We try to infer the sources of innovation from the employment dynamics of U.S. firms in the nonfarm private sector from 1983 to 2013. We conclude that creative destruction is vital for understanding job destruction and accounts for around one-fourth of growth. Own-product quality improvements by incumbents appear to be the biggest source of growth. Net variety growth contributes much less than quality improvements doOur findings are relevant for innovation policy. According to Atkeson and Burstein (forthcoming), the consumption-equivalent welfare gain from devoting about 1% more of GDP to research every year is between 0.17 and 0.73 percent in a model calibrated similarly to ours (but with endogenous research investments). They estimated the gains would be larger—0.26 to 2.01 percent—if creative destruction played no role in U.S. growth. Creative destruction is a force raising the private return relative to the social return to research, diminishing the gains from promoting research….”

“…. Table IV presents TFP growth due to creative destruction (row 1), new varieties (row 2), and own innovation (row 3). TFP growth due to each source of innovation is the product of arrival rate of innovation and the quality improvement conditional on innovation. We use equation (2) for this calculation.18 The first column shows that TFP growth due to own innovation was about 1% per year in 1983-1993. Growth due to creative destruction was about half that, at 0.44% per year. And new varieties generate growth of 0.23% per year. The rows in Table V show the contribution of each source of innovation to aggregate TFP growth. About 27% of the 1.66% growth rate in the 1983-1993 period comes from creative destruction. Own-variety improvements by incumbents account for 60%. New varieties à la Romer (1990) are the remainder at around 14%.The columns in Table V also decompose aggregate TFP growth into the percentage contribution of entrants versus incumbents using equation (3). In the ten years between 1983 and 1993, incumbents account for 68% of aggregate TFP growth, with entrants contributing the remaining 32%. Aghion, Akcigit, and Howitt (2014) provided complementary evidence for the importance of incumbents based on their share of R&D spending and patents…”

TFP driver estimates, “Table III presents the eight parameter values inferred from the data using the procedure described above. Based on the data moments from 1983 to 1993, we infer a 70% arrival rate of own-variety quality improvements per 5-year period. Conditional on no own innovation, quality improvements through creative destruction occur 30% of the time by other incumbents. Conditional on no own innovation and creative destruction by another incumbent, quality improvement through creative destruction by entrants occur with probability 1. The unconditional probability that a given product improves due to creative destruction by an incumbent is thus 8.9%, and the unconditional probability of creative destruction by an entrant is 21.0%.16 The unconditional probability that a product is improved upon in a 5-year period is thus 100%, of which 69% is from own innovation and 31% is from creative destruction (the latter from entrants or incumbents). The employment-weighted average step size for quality improvements on existing varieties is given by sq = (θ/(θ − (σ − 1)))1/(σ−1). Given that θ = 154 and σ = 4, the average improvement in quality (conditional on innovation) is 7.5%.17 New varieties are only created by entrants, arrive with 12.3% probability per existing variety, and have an average quality that is 31% of the average quality of existing varieties. Overhead costs imply that the average quality of exiting products ψ is 2% of the average quality of existing varieties, and that the probability a variety exits due to overhead cost δo is essentially zero. The net number of varieties thus grows by 12.3% every five years, which matches the growth of total employment and number of firms from 1983 to 1993.

Core of paper, “…Entrants and incumbents can create new products and displace the products of competitors. Incumbents can also improve their existing products. How much of aggregate productivity growth occurs through each of these channels? Using data from the U.S. Longitudinal Business Database on all nonfarm private businesses from 1983 to 2013, we arrive at three main conclusions:First, most growth appears to come from incumbents. We infer this from the modest employment share of entering firms (defined as those less than 5 years old). Second, most growth seems to occur through improvements of existing varieties rather than creation of brand new varieties. Third, own-product improvements by incumbents appear to be more important than creative destruction. We infer this because the distribution of job creation and destruction has thinner tails than implied by a model with a dominant role for creative destruction…”

Ben Comment:Pete Klenow got back to me re breaking TFP growth down into its components (improved processes, improved products, new products). He has a paper called Missing Growth from Creative Destruction (linked in his email below) that argues that TFP (and therefore TFP growth) is mismeasured because it doesn't properly account for new products replacing old (creatively destroyed) products. The idea is that if a product replaces a product it is a *bigger* gain than the gains achieved by product survival. Their empirical section says this effect is about.5 percentage points per year (pretty big, that takes TFP growth from 2 to 2.5 in a given year) but is mostly in hotels and restaurants and doesn't explain the slow down since 2005. I have to say - I don't really buy it, honestly. Because these results are concentrated in Hotel and Restaurants, it just leaves me a little flat. That's not really the kind of innovation (I think) we want to think about. Sure, it's better if there are more better restaurants around so from a standard of living aspect that's important but it's not really innovation. A second paper he referred me to (and linked below) is HOW DESTRUCTIVE IS INNOVATION? This paper is much closer to what we want. From the abstract: "First, most growth appears to come from incumbents. We infer this from the modest employment share of entering firms (defined as those less than 5 years old). Second, most growth seems to occur through improvements of existing varieties rather than creation of brand new varieties. Third, own-product improvements by incumbents appear to be more important than creative destruction."

Above is the table that is analogous to Ufuk's paper. Looking 2003-2013, it looks like CD accounts for 22% (.29/1.32) of TFP, NV accounts for a bout 5% and own innovation accounts for the rest ~ 70% of TFP growth. Compare that to Ufuk's table (pasted below).

Creative destruction in Klenow is equivalent to New Entry in Ufuk. So the big difference is that Klenow and Ufuk estimate very different growth processes within firms, but they estimate very similar amounts of TFP growth coming from new entrants. I find that reassuring. It also looks like 2003-2013, firms were really focused on improving their current stable of products and less interested in expanding into other areas. I doubt anyone has updated these numbers to see if 2014-today looks any different than what we're seeing here.The final paper that Klenow sent looks specifically at the package goods sector and finds most productivity gains come from product innovation. I don't find this particularly insightful since. My assumption is that packaged goods is pretty competitive so there isn't tons of room for better processes or for new start ups: Heinz is Heinz and Heinz knows how to pack and ship their stuff efficiently; the way for this sector to improve is for Heinz to launch a new product. That's pretty intuitive to me but the smaller scope leaves me cold. Anyway - long story short: the two papers that most closely do the calculation we're interested in both estimate that new entrants only contribute ~25% of growth and that about 75% within firm innovations account for the rest. What's not covered is exactly how changes in sectors' competitive structure would affect incentives to drive that 75% of growth. Clearly it's very important. Let me know what you guys think.
Pete Klenow Comment:And in this paper we argue in passingthat all innovation is pretty much product innovation, though we do not confront process innovations head

How Much Revenue Does The AI Buildout Need In 2030?

William Gildea and Azeem Azhar Exponential View

Date Posted:: October 1, 2026
Is Database:: Database

Beginning with capex projections, Gildea/Azhar derive for their high-growth case a $1.3T annual AI-infrastructure revenue hurdle for 2030. This covers $771B of depreciation and operating costs, plus a 15% return on $3.5T of net capital in service.

We estimate AI CapEx to be $560B in 2026, 64% of the total declared by the hyperscalers. Unfinished facilities can’t generate revenue, so the investment bill and the productive asset base will grow at different speeds. [In the high investment case], by 2030 we see $3.5T of net AI capital in service. The largest component [of the revenue hurdle] is the $555B in compute depreciation. We model lifespans at the current 5-6 years. Shell depreciation and OpEx combined contribute about a further $216B to the required revenue hurdle. [Adding a 15% return on net capital brings required annual infrastructure revenue to $1.3T]. Reaching the $1.3T hurdle would represent 7.4x growth (59% CAGR) from today’s annualized revenues. For its customers to earn 30-50% gross margins, downstream revenues need to be $1.9-2.6T. [Editor’s note: Van Nieuwerburgh’s far larger figure of $3.7T covers mature revenues from projects completed through 2032, using commissioning capital and a 50% infrastructure operating cash-flow margin. This estimate instead charges depreciation and operating costs plus 15% on net capital in service in 2030. Different horizons and cost conventions prevent a direct comparison. Van Nieuwerburgh also has a much higher capex estimate.]

Takeaways by Macro Roundup^® AI

In a high investment case with $3.5tn of net AI capital in service, the authors estimate AI infrastructure must generate $1.3tn of annual revenue by 2030, requiring 7.4x growth (59% CAGR) from today’s annualized revenues.
Compute depreciation of $555bn, modeled on 5-6 year lifespans, is the largest component of the 2030 revenue hurdle, with shell depreciation and operating expenses adding about $216bn and a 15% return on net capital completing the $1.3tn total.

AI and Entrepreneurship: What is Really Happening?

Nathan Goldschlag Economic Innovation Group

Date Posted:: September 30, 2026
Is Database:: Database

Nathan Goldschlag finds that available federal data do not yet establish that AI is boosting entrepreneurship. “Across different cuts of the data, there is no clear evidence of AI affecting entrepreneurship, either for likely employers or nonemployers.”

Employer entrepreneurship is at best rising modestly, and at worst has been falling for several years already. The number of business applications likely to become an employer business was flat in 2025 and began rising in 2026. Fewer of those applications have been the kind that have the highest chance of becoming an employer. Nonemployer entrepreneurship, or “solopreneurship,” is likely rising, but not as the main job of the solopreneurs. These are probably side hustles. Likely nonemployer business applications rose significantly in 2025 and early 2026. But the share of workers claiming to be self-employed nonemployers in their main job — in other words, business owners with no employees — is flat (though noisy). AI-intensive sectors and industries are no exception. Establishment entry in AI-intensive sectors (information and professional services) has been flat or falling in recent years.

Takeaways by Macro Roundup^® AI

Business applications likely to become employer businesses were flat in 2025 and began rising in 2026, with fewer applications of the kind having the highest chance of becoming an employer business.
Likely nonemployer business applications rose significantly in 2025 and early 2026, going vertical in the most AI-intensive sectors of information and professional services, while other sectors kept pace until early 2026 and then flattened.
The share of workers reporting self-employment as nonemployers in their main job is flat, though noisy, including among workers in AI-exposed occupations in 2025 and early 2026.

Tech's Trillion-Dollar Internal Inconsistency

Torsten Sløk Apollo

Date Posted:: September 29, 2026
Is Database:: Database

Tech analysts expect sector operating cash flow to more than double to ~$2.4T by 2028. Analysts covering other S&P 500 sectors (tech’s customers) see far less cash growth in their sectors. Slok “raises the question of who exactly will be writing all those checks to buy AI services.”

Wall Street equity analysts work in sector silos, and when you add up their forecasts, the numbers are internally inconsistent. The analysts covering tech expect the sector's operating cash flow to more than double to roughly $2.4 trillion by 2028, an increase of over $1.2 trillion. Meanwhile, the analysts covering the other sectors in the S&P 500, which are tech's customers, expect those companies to add much less operating cash flow. In other words, the tech silo is betting on a future in which demand for AI and tech services explodes, while the silos covering the companies that would pay for those services see a much more modest outlook. Both cannot be right at the same time. The bottom line is that either tech's customers will generate a lot more cash than their analysts expect, or tech's cash flow forecasts are too optimistic, which raises the question of who exactly will be writing all those checks to buy AI services.

Takeaways by Macro Roundup^® AI

Analysts covering the tech sector expect its operating cash flow to more than double to roughly $2.4tn by 2028, an increase of over $1.2tn.
Analysts covering the other S&P 500 sectors, which are tech's customers, expect those companies to add much less operating cash flow, leaving the combined forecasts internally inconsistent.
Either tech's customers will generate much more cash than their analysts expect, or tech's cash flow forecasts are too optimistic, which raises the question of who will pay for AI services.

Nine Percent of GDP

Hanno Lustig The Two Cents

Date Posted:: September 28, 2026
Is Database:: Database

The US AI-buildout is projected to average ~3.6% of GDP over 2025–32. Lustig notes data-center operators will need ~$3.725T in annual compute revenue by 2032, 9.2% of projected GDP, to earn a 10% unlevered return on the buildout.

What do you have to believe about future revenue in order for all data center owners to to break even on their AI investment? Using a 10% discount rate, Stijn’s baseline number is 3.725 trillion of annual revenue by 2032, which would amount to roughly 9.2% of projected 2032 GDP. This number is the top line earned from selling compute in aggregate by whoever operates the 182.7 GW of US capacity completed by 2032.2. The assumptions behind that number: 183 GW of U.S. data-center capacity completed between 2025 and 2032. $8.54 trillion of construction cost, grossed up to a $9.61 trillion capital base to account for the return on capital tied up during construction. A 10% discount rate. A 6-year life for IT equipment (68% of cost) and a 20-year life for everything else (32%). A 50% operating cash flow margin.

Takeaways by Macro Roundup^® AI

Stijn’s baseline estimate holds that data center owners would need $3.725 trillion of annual revenue by 2032 — roughly 9.2% of projected 2032 GDP — to break even on AI investment at a 10% discount rate.
The break-even calculation assumes 183 GW of U.S. data-center capacity completed between 2025 and 2032 at $8.54 trillion of construction cost, grossed up to a $9.61 trillion capital base for return on capital tied up during construction.
The estimate assumes a 6-year life for IT equipment, which accounts for 68% of cost, a 20-year life for the remaining 32%, and a 50% operating cash flow margin.

Financing the AI Buildout

Stijn Van Nieuwerburgh Brookings Papers on Economic Activity

Date Posted:: September 24, 2026
Is Database:: Database
Is Important:: Important

Stijn Van Nieuwerburgh projects ~$10.3T will be spent on the AI buildout over 2025–2032, averaging 3.63% of GDP. He estimates 2/3 of that spending reflects IT equipment and one-third real estate and associated power infrastructure.

The required investment is enormous. A representative 200 MW AI training campus costs roughly $8.2 billion, about two-thirds of which reflects IT equipment and one-third the real estate and associated power infrastructure. We estimate that 183 GW is completed by 2032 with a further 118 GW completed after 2032. This pipeline will trigger about $10.3 trillion dollars of investment over 2025–2032. At an average of 3.63% of GDP per year, the projected buildout would be larger relative to the economy than the major U.S. canal, railroad, electrification, highway, and telecommunications investment booms. It would double the electricity consumption of the entire U.S. residential sector.

Takeaways by Macro Roundup^® AI

Using Cleanview project-level data from July 2026, the authors estimate roughly 57 GW of operating U.S. data center capacity against a planned development pipeline of roughly 509 GW, which they stress is not a completion forecast.
The authors’ central scenario allocates the announced pipeline so that 182.8 GW of additional U.S. capacity becomes operational during 2025–32, including 19.0 GW tied to projects already operational in 2025 or 2026.
The authors’ scenario has 117.2 GW of planned data center capacity completed after 2032 and 226.9 GW never completed, applying lower realization rates to more distant and less fully documented projects.

Moonshot Capitalism: AI Rewrites The Venture Capital Playbook

Tim Bradshaw Financial Times

Date Posted:: September 10, 2026
Is Database:: Database

Since the start of 2024, more than $150B of venture capital has been invested into non-AI “deep tech” firms whose products are rooted in significant engineering advances, exceeding the $133B invested in such firms btw 2010 and 2019.

The AI boom is fuelling a resurgence in ambitious “moonshot” bets, as early SpaceX backers’ huge returns and falling valuations for traditional software companies force tech investors to embrace riskier and more capital-intensive dealmaking. Excluding the giant sums ploughed into AI start-ups, global investment in “deep tech” — companies whose products are rooted in big scientific or engineering advances — has exceeded $150bn since the start of 2024, more than the $133bn in the entire decade to the end of 2019, according to Dealroom. This year’s deep-tech investments have not yet surpassed 2021’s peak, which was propelled by battery and electric vehicle deals for the likes of Rivian and Northvolt — many of which turned sour, highlighting the risks involved in moonshot dealmaking.

Takeaways by Macro Roundup^® AI

Global "deep tech" investment excluding AI start-ups has exceeded $150bn since the start of 2024, more than the $133bn invested in the entire decade through 2019, according to Dealroom.
Huge returns for early SpaceX backers and falling valuations for traditional software companies are pushing tech investors toward riskier, more capital-intensive "moonshot" dealmaking.
Deep-tech investment has not yet surpassed the 2021 peak, which was propelled by battery and electric vehicle deals for companies such as Rivian and Northvolt, many of which turned sour.

Innovation drove 70% of TFP growth from 2003-2013, while creative destruction contributed 22% and new varieties added ~5%. @PeteKlenow

Does most productivity growth come from incumbent firms improving their own products?

Takeaways by Macro Roundup® AI

Beginning with capex projections, Gildea/Azhar derive for their high-growth case a $1.3T annual AI-infrastructure revenue hurdle for 2030. This covers $771B of depreciation and operating costs, plus a 15% return on $3.5T of net capital in service.

How much annual revenue must AI infrastructure generate to justify investment?

Takeaways by Macro Roundup® AI

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Nathan Goldschlag finds that available federal data do not yet establish that AI is boosting entrepreneurship. “Across different cuts of the data, there is no clear evidence of AI affecting entrepreneurship, either for likely employers or nonemployers.”

Is AI driving a boom in new employer businesses?

Takeaways by Macro Roundup® AI

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Tech analysts expect sector operating cash flow to more than double to ~$2.4T by 2028. Analysts covering other S&P 500 sectors (tech’s customers) see far less cash growth in their sectors. Slok “raises the question of who exactly will be writing all those checks to buy AI services.”

Are tech cash flow forecasts inconsistent with customers' projected growth?

Takeaways by Macro Roundup® AI

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The US AI-buildout is projected to average ~3.6% of GDP over 2025–32. Lustig notes data-center operators will need ~$3.725T in annual compute revenue by 2032, 9.2% of projected GDP, to earn a 10% unlevered return on the buildout.

How much annual revenue must AI data centers generate to break even?

Takeaways by Macro Roundup® AI

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Stijn Van Nieuwerburgh projects ~$10.3T will be spent on the AI buildout over 2025–2032, averaging 3.63% of GDP. He estimates 2/3 of that spending reflects IT equipment and one-third real estate and associated power infrastructure.

How much of the planned data center pipeline gets built?

Takeaways by Macro Roundup® AI

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Since the start of 2024, more than $150B of venture capital has been invested into non-AI “deep tech” firms whose products are rooted in significant engineering advances, exceeding the $133B invested in such firms btw 2010 and 2019.

Is the AI boom pushing venture capital toward riskier moonshot bets?

Takeaways by Macro Roundup® AI

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Takeaways by Macro Roundup^® AI