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The changing structure of American innovation: Some cautionary remarks for economic growth

AI Summary. The authors argue that the shift of research from large corporate labs to startups and universities has not fully replaced those labs' capabilities, weakening innovation and growth. Research fell from 30% of total business R&D in 1985 to under 20% in 2015, and scientific publications per firm dropped about 20% per decade.

Ashish Arora, Sharon Belenzon, Andrea Patacconi, and Jungkyu Suh National Bureau of Economic Growth
Date Posted:
June 10, 2019
Is Database:
Database

Research shifted from corporate labs to startups & universities hasn’t fully compensated for decline of corporate labs, impacting innovation & economic growth.

Has the decline of corporate research labs weakened innovation and growth?

Core argument: NSF data show research fell from 30% of total U.S. business R&D in 1985 to below 20% in 2015, as innovation shifted from corporate labs to startups and universities.

The shift of research from corporate labs to startups and universities has not fully compensated for the decline of corporate labs, impacting innovation and economic growth. NSF data shows the share of research in total business R&D fell from 30% in 1985 to below 20% in 2015. The number of scientific publications per firm dropped by 20% per decade from 1980 to 2006, with a more than 30% decline in high-quality journals. Fortune 500 firms' share of R&D 100 awards fell from 41% in 1971 to 6% in 2006. While university research has grown, it is often less mission-driven, requiring further development to become commercially viable. This division of labor has not yet realized its full potential, as large corporate labs had unique capabilities that are difficult to replace.

Takeaways by Macro Roundup® AI

  1. NSF data show research fell from 30% of total U.S. business R&D in 1985 to below 20% in 2015, as innovation shifted from corporate labs to startups and universities.
  2. Scientific publications per firm declined about 20% per decade from 1980 to 2006, with a more than 30% drop in high-quality journals, and Fortune 500 firms' share of R&D 100 awards fell from 41% in 1971 to 6% in 2006.
  3. University research has grown but is often less mission-driven and requires further development to become commercially viable, leaving the shift away from large corporate labs short of replacing their unique capabilities.

new NBER theorizes that moving research off corporate balance sheets to"...Spinoffs, startups, and university licensing offices...." quote"....has not fully filled the gap left by the decline of the corporate lab...".lots of good factoids

"...NSF data indicate that share of research (both basic and applied) in total business R&D in the U.S. fell from about 30 percent in 1985 to below 20 percent in 2015 (figure 2).The figure also shows that the absolute amount of research in industry, after increasing over the 1980s, barely grew over the 20 year period between 1990 to 2010. Other data show the same decline...Utilizing data on scientific c publications, Arora et al. (2018) show that the number of publications per rm fell at a rate of 20 percent per decade from 1980 to 2006 for R&D performing American listed firms. The authors also find that the drop is even more dramatic for established firms in high quality journals. For articles within the top quartile of Journal Impact Factor scores, the magnitude of the drop increases to over 30 percent. Large firms' withdrawal from science can also be gleaned from the list of R&D 100 awards winners. Fortune 500 firms won 41 percent of the awards in 1971, but only 6 percent in 2006 (Block and Keller, 2009). Over the same period, total industry R&D and patenting grew steadily, as did university performed research (see figure 6 below). This evidence points to the emergence of a new division of innovative labor, with universities focusing on research, large firms focusing on development and commercialization, and spinoffs, startups, and university technology licensing offices responsible for connecting the two.....Anecdotal evidence suggests that neural network research published by Google brain has been implemented by follow-own research at firms. It is now standard practice among researchers to test their algorithm's performance against Alexnet or LSTM | both of which were re fined at Google. We find that ML papers published by large firms are cited more often in patents than other ML papers: large firms published 12 percent of the papers in KDD, AAAI, ICML, IJCAI, and NIPS between 2011 and 2018, but accounted for 32 percent of the papers that are cited by patents.Bikard (2015) finds corporate publications to be 23 percent more likely to be cited than university publications one the same scientific discovery. We add wider correlational evidence in support of this prediction by comparing the likelihood of a U.S. utility patent issued between 1980 and 2006 citing a corporate scientific publication versus a university counterpart in its non-patent literature section.Using a linear probability model, we estimate that corporate publicationsare on average 11 percent more likely to be cited as a university publications. We control for the possibility that these results are driven by lower-quality universities, "applied" journals, or industry level differences in scientific quality, and find that the results hold. Panel (A) of Figure 12 visualizes the citation likelihood differences between these two groups, while panel (B) shows that corporate publications first order stochastically dominate university publications in terms of the number of citations they receive from patents...we suggest that this division of innovative labor has not, perhaps, lived up to its promise. The translation of scientific knowledge generated in universities to productivity enhancing technical progress has proved to be more difficult to accomplish in practice than expected.Spinoffs, startups, and university licensing offices have not fully filled the gap left by the decline of the corporate lab.Corporate research has a number of characteristics that make it very valuable for science-based innovation and growth. Large corporations have access to significant resources, can more easily integrate multiple knowledge streams, and direct their research toward solving specific practical problems, which makes it more likely for them to produce commercial applications. University research has tended to be curiosity-driven rather than mission-focused. It has favored insight rather than solutions to specific problems, and partly as a consequence, university research has required additional integration and transformation to become economically useful. This is not to deny the important contributions that universities and small firms make to American innovation.Rather, our point is that large corporate labs may have distinct capabilities which have proved to be difficult to replace....During the so-called Golden Age of American Capitalism, large corporate labs were important loci of research, and important sources of scientific and technical advances. At the start of the period, the university research sector was small (certainly compared to the current period) and uneven in quality. Over time, university research grew, bolstered by significant support from the federal government. This period also coincided with (and perhaps this was more than a coincidence) incumbent firms enjoying significant market power but restrained by aggressive anti-trust actions. Despite the apparent successes, corporate research, and the large corporate labs in particular, fell out of favor with investors, and eventually, also with managers. The focus shifted to university research, and startups, often venture funded, that aimed to capitalize on the scientific and technical advances in university labs. Corporations turned to sourcing ideas and inventions from the outside, hoping to combine it with their downstream development and commercialization abilities. These hopes have not been fully realized, at least not yet. Even as this division of innovative labor has progressed, so have the challenges it faces become more evident. University research is different from corporate research: it is less likely to be mission-driven. Its smaller scale and greater disciplinary focus mean that university research typically produces insights which then need further development and integration to produce commercializable inventions. This requirement of converting insight to product has proved more onerous and challenging than commonly appreciated. It seems unlikely that corporate research will rediscover its glory days. The boost in employment of data scientists, machine learning experts, and even economists, in large firms would appear to prognosticate a different future. We disagree. For some time, quick wins from low-hanging fruit (such as optimizing auction or advertising formats) may cover up the problem, but the fundamental challenge of managing long-run research inside a for-profit corporation remains a formidable one. Put differently, although there are significant efficiency gains that companies have realized from hiring data scientists and economists, there are only a handful of cases of significantly new markets created from such efforts, and incumbent firms continue to rely on outside inventions to fuel their growth. In the longer run, therefore, university research will remain the principal source of new ideas for such inventions. And therefore the ongoing economic experiments of discovering efficient ways to translate scientific insights in universities into technical advances that eventually manifest in productivity growth will remain crucial to our future prosperity...."

Ashish Arora, Sharon Belenzon, Andrea Patacconi, and Jungkyu Suh, "The changing structure of American innovation: Some cautionary remarks for economic growth," National Bureau of Economic Growth, May 22, 2019, https://www.nber.org/chapters/c14259.pdf

  • Productivity
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Previous articleJune 10, 2019Aggregate Implications of Changing Sectoral TrendsConstruction sector accounts for 30% of decline in TFP growth. Sector-specific disturbances have reduced trend GDP growth by 2-3pp over last 6 decades.Next articleJune 11, 2019Does the UK Benefit From Chinese Investment?@michaelxpettis: Countries with net foreign investment inflows necessarily see domestic investment rise or domestic savings drop. If capital inflows don’t cause investment to rise, they must force down the savings rate.
Showing 489 database articles primarily about either Productivity, Cronyism, Incentives/Risk-Taking, Innovation/Research, Institutional Capabilities, Intangibles, Investment, Startups, or Workforce Reorganization

How Much Revenue Does The AI Buildout Need In 2030?

AI Summary. The authors estimate AI capital spending of $560bn in 2026 and, in a high-investment case, $3.5tn of net AI capital in service by 2030, requiring roughly $1.3tn in annual infrastructure revenue once depreciation, operating costs and a 15% return are covered. That implies 7.4x growth from current annualized revenues, with downstream revenues of $1.9–2.6tn.

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.

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

Core argument: 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.

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

  1. 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.
  2. 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.

Related Articles:

  • Financing the AI Buildout — The authors estimate roughly 57 gigawatts of operating US data center capacity against a planned development pipeline of about 509 gigawatts, which is not a completion forecast since projects may be delayed, scaled back, or abandoned. Their central scenario has 182.8 gigawatts of added capacity becoming operational through 2032, 117.2 gigawatts completed later, and 226.9 gigawatts never completed.
  • What Investment Data Implies about the AI Transition — The five largest U.S. technology firms are scaling AI capital spending about 50% a year, from $155bn to a forecast $755bn. Embedding this in a two-sector model with rare productivity booms, the authors estimate AI adds roughly 5, 20, or 58pp to cumulative growth over a seven-year transition, depending on scenario.
  • Rear Window — Major cloud providers posted annual revenue growth of 37% at Amazon Web Services, 43% at Microsoft Azure and 82% at Google Cloud, with rising revenue backlogs of $514bn and $496bn respectively at Google Cloud and Amazon Web Services. Earnings growth appears highly contingent on continued hyperscaler capital spending and research, which rose from $460bn to $870bn.
  • Investment
  • GDP
    • Financial Markets
  • Productivity
    • Innovation/Research

AI and Entrepreneurship: What is Really Happening?

AI Summary. Entrepreneurship by businesses with employees is flat or declining, including in AI-intensive information and professional services, where establishment entry has been flat or falling and young-firm employment in AI-exposed industries is falling. Applications for likely solo businesses with no employees rose steeply in those AI-intensive sectors, but self-employment as a main job is flat, suggesting side hustles.

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.”

Is AI driving a boom in new employer businesses?

Core argument: 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.

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

  1. 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.
  2. 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.
  3. 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.

Related Articles:

  • The Age Of The Solopreneur — A surge in US business formation is being driven by solo entrepreneurs, with growth in AI tool usage and in solopreneur activity in high-AI-adoption sectors suggesting artificial intelligence accounts for a meaningful portion of the increase. The authors argue AI is lowering barriers to starting and growing a business by expanding what one-person firms can do.
  • America Has Become An Entrepreneur’s Paradise — US growth in new-business applications is concentrated in professional services (up 25% over four years), retail (18%), and health care and social assistance (18%) rather than artificial-intelligence startups. The share of applications the Census Bureau deems likely to hire at least one employee has fallen to 30% from 38%.
  • Which Entrepreneurs Boost Productivity? — The authors find that a minority of entrepreneurs are "transformative" — generating disproportionate productivity gains, scoring high on IQ tests, being well-educated, and hiring research and development workers. They argue that economies with more such entrepreneurs and R&D workers sustain higher long-run productivity growth.
  • Startups
  • Productivity
    • Innovation/Research
    • Investment

Tech's Trillion-Dollar Internal Inconsistency

AI Summary. Wall Street analysts covering technology project the sector's operating cash flow will more than double to roughly $2.4tn by 2028, a gain of over $1.2tn, while analysts covering the rest of the S&P 500 — technology's customers — project those firms will add far less cash flow. These sector-by-sector forecasts are internally inconsistent.

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.”

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

Core argument: 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.

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

  1. 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.
  2. 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.
  3. 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.

Related Articles:

  • A Catalyst For The AI Bubble Break — Historically, controlling for valuations, a 1% rise in the share of the market represented by initial public offerings has been associated with roughly a 4% decline in prices. With large artificial-intelligence company listings expected and share issuance exceeding buybacks, equity supply could shift from shrinking about 1% a year to growing near 5%, implying returns roughly 20% below average.
  • Financing the AI Buildout — The authors estimate roughly 57 gigawatts of operating US data center capacity against a planned development pipeline of about 509 gigawatts, which is not a completion forecast since projects may be delayed, scaled back, or abandoned. Their central scenario has 182.8 gigawatts of added capacity becoming operational through 2032, 117.2 gigawatts completed later, and 226.9 gigawatts never completed.
  • Rear Window — Major cloud providers posted annual revenue growth of 37% at Amazon Web Services, 43% at Microsoft Azure and 82% at Google Cloud, with rising revenue backlogs of $514bn and $496bn respectively at Google Cloud and Amazon Web Services. Earnings growth appears highly contingent on continued hyperscaler capital spending and research, which rose from $460bn to $870bn.
  • Investment
  • GDP
    • Financial Markets
  • Productivity
    • Innovation/Research

Nine Percent of GDP

AI Summary. An analysis estimates that for US data center owners to break even on AI investments, the sector would need about $3.725tn in annual compute revenue by 2032 — roughly 9.2% of projected 2032 GDP. The estimate assumes 183 GW of US capacity completed, a $9.61tn capital base, a 10% discount rate, and a 50% operating cash flow margin.

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.

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

Core argument: 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.

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

  1. 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.
  2. 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.
  3. 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.

Related Articles:

  • Financing the AI Buildout — The authors estimate roughly 57 gigawatts of operating US data center capacity against a planned development pipeline of about 509 gigawatts, which is not a completion forecast since projects may be delayed, scaled back, or abandoned. Their central scenario has 182.8 gigawatts of added capacity becoming operational through 2032, 117.2 gigawatts completed later, and 226.9 gigawatts never completed.
  • A Catalyst For The AI Bubble Break — Historically, controlling for valuations, a 1% rise in the share of the market represented by initial public offerings has been associated with roughly a 4% decline in prices. With large artificial-intelligence company listings expected and share issuance exceeding buybacks, equity supply could shift from shrinking about 1% a year to growing near 5%, implying returns roughly 20% below average.
  • Rear Window — Major cloud providers posted annual revenue growth of 37% at Amazon Web Services, 43% at Microsoft Azure and 82% at Google Cloud, with rising revenue backlogs of $514bn and $496bn respectively at Google Cloud and Amazon Web Services. Earnings growth appears highly contingent on continued hyperscaler capital spending and research, which rose from $460bn to $870bn.
  • Investment
  • GDP
    • Financial Markets
  • Productivity
    • Innovation/Research

Financing the AI Buildout

AI Summary. The authors estimate roughly 57 gigawatts of operating US data center capacity against a planned development pipeline of about 509 gigawatts, which is not a completion forecast since projects may be delayed, scaled back, or abandoned. Their central scenario has 182.8 gigawatts of added capacity becoming operational through 2032, 117.2 gigawatts completed later, and 226.9 gigawatts never completed.

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.

How much of the planned data center pipeline gets built?

Core argument: 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 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

  1. 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.
  2. 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.
  3. 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.

Related Articles:

  • Rear Window — Major cloud providers posted annual revenue growth of 37% at Amazon Web Services, 43% at Microsoft Azure and 82% at Google Cloud, with rising revenue backlogs of $514bn and $496bn respectively at Google Cloud and Amazon Web Services. Earnings growth appears highly contingent on continued hyperscaler capital spending and research, which rose from $460bn to $870bn.
  • AI Set to be Largest CapEx Cycle Ever … and Soon Majority Externally Financed — Hyperscaler and data-center investment is tracking toward $1.5tn annually by 2030, which in real terms would exceed the 2005-06 housing boom, the 1990s fiber peak, and the 1882 railroad peak combined. External debt and equity are projected to fund close to 90% of the buildout, making it dependent on credit markets.
  • The Summer I Turned Pretty — A warning sign in a boom cycle is when companies closest to final demand weaken while capital spending beneficiaries keep thriving, as when telecom operators' shares rolled over while communications equipment stocks kept rising. Semiconductor stock prices and free cash flow are soaring while large cloud providers' shares stagnate and their free cash flow plummets.
  • Investment
  • GDP
    • Financial Markets
    • Growth
  • Productivity
    • Innovation/Research

Moonshot Capitalism: AI Rewrites The Venture Capital Playbook

AI Summary. Global investment in "deep tech" — companies whose products rest on major scientific or engineering advances — has exceeded $150bn since the start of 2024, more than the $133bn invested in the decade through 2019, excluding sums poured into AI start-ups. Failed battery and electric-vehicle bets from an earlier peak illustrate the risks of such moonshot dealmaking.

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.

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

Core argument: 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.

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

  1. 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.
  2. Huge returns for early SpaceX backers and falling valuations for traditional software companies are pushing tech investors toward riskier, more capital-intensive "moonshot" dealmaking.
  3. 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.

Related Articles:

  • Capital Is Making a Comeback — Tangible investment in property, factories and equipment fell from 12.5% to 8.5% of private gross domestic product, while intangible investment in intellectual property, software and process knowledge rose from about 11.5% to 16.75%. The author argues demand for tangible capital investment will now have to rise.
  • Public to Private Equity in the United States: A Long-Term Look — An analysis of more than 31,000 global venture capital deals finds 62% lost money and over half lost 50–100% of invested capital, though the distribution of returns has much fatter tails than buyouts or public equities. Venture funds averaged a public market equivalent ratio of about 1.4, indicating returns above public markets.
  • Gross and Net US Investment — Net US investment has fallen from roughly 40% of gross investment in the 1970s to about 25% in the past decade, meaning about three-quarters of gross investment now just replaces depreciating capital. The author attributes this gap to modern investment being more information-technology-related, which depreciates faster and requires more frequent replacement.
  • Investment
  • GDP
    • Financial Markets
  • Productivity
    • Innovation/Research
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