Edward Conard

Top Ten New York Times Bestselling Author

  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “Unintended Consequences represents the most cogent and persuasive analysis of the Financial Crisis to date.” - Andrei Shleifer, 1999 John Bates Clark Medal Winner
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “Unintended Consequences should be read by anyone who takes for granted the superiority of progressive taxation and has not thought carefully about the trade-offs involved.” - The New Republic
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “Unintended Consequences is full of substance, it is one of the must-read books of the year, and once I finish it I will be giving it a second read through right away.” - Tyler Cowen, Professor, George Mason University
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “…reminds us that inequality sends a signal of what society lacks most, in America’s case, entrepreneurship and risk taking.” - Lawrence Lindsey, CEO, The Lindsey Group, former Director of the National Economic Council
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
Upside of Inequality Oxford Unintended Consequences
Buy the Books
  • Macro Roundup
  • About Roundup
  • About Ed Conard
  • Highlights
  • Topics
  • Subscribe
Edward Conard
  • twitter
  • facebook
  • linkedin
  • youtube
  • Email
  • Text Message (SMS)
  • Twitter/X
  • LinkedIn
  • Facebook
  • WhatsApp Message
Subscribe to Macro Roundup Emails
  • Mentions 884
  • Primary focus 485
Showing 485 database articles primarily about Productivity
Currently filtering by:
  • Remove Productivity
  • Remove "primary topics only" restriction
  • Remove 'Database'
Show all 7,192 articles
For whatever topics you select (currently: "Productivity", "Cronyism", "Incentives/Risk-Taking", "Innovation/Research", "Institutional Capabilities", "Intangibles", "Investment", "Startups", "Workforce Reorganization"):
Choose search scope

Your importance filter 'Database' shows fewer articles.

Remove filters to see full article counts

How People Get Rich Now

Paul Graham PaulGraham.com
Date Posted:
April 27, 2021
Is Database:
Database

Risk-adjusted returns to high-impact entrepreneurship have improved significantly from 1982-2021, driven by reduced barriers to entry.

Risk-adjusted returns to high-impact entrepreneurship have improved significantly from 1982-2021, driven by reduced barriers...
From 1982 to 2021, risk-adjusted returns to high-impact entrepreneurship have improved significantly due to reduced barriers to entry. The shift from large capital-intensive firms to smaller, agile startups has enabled more individuals to create wealth through entrepreneurship. In 2020, 73 new fortunes were made, with 56 from founders' or early employees' equity, compared to 1982 when oil and real estate dominated new wealth creation. The rise of tech companies, which venture capitalists favor for their technological edge, marks a qualitative change in wealth generation. The decreasing cost of starting a startup, driven by technological advancements, has shifted the power balance between founders and investors, increasing valuations and making entrepreneurship more accessible. This trend reflects a return to historical norms where individual entrepreneurship was a primary path to wealth creation.

Paul Graham on why inequality has increased, structural shift in the economy from large capital intensive firms to small ones has enable entrepreneurship, “…It’s easier now to start and grow a company than it has ever been. That means more people start them, that those who do get better terms from investors, and that the resulting companies become more valuable. Once you understand how these mechanisms work, and that startups were suppressed for most of the 20th century, you don’t have to resort to some vague right turn the country took under Reagan to explain why America’s Gini coefficient is increasing. Of course the Gini coefficient is increasing. With more people starting more valuable companies, how could it not be?…”

Paul Graham, “How People Get Rich Now,” PaulGraham.com, April 2021, http://paulgraham.com/richnow.html

His evidence, “…How are people making these new fortunes? Roughly 3/4 by starting companies and 1/4 by investing. Of the 73 new fortunes in 2020, 56 derive from founders’ or early employees’ equity (52 founders, 2 early employees, and 2 wives of founders), and 17 from managing investment funds. There were no fund managers among the 100 richest Americans in 1982. Hedge funds and private equity firms existed in 1982, but none of their founders were rich enough yet to make it into the top 100. Two things changed: fund managers discovered new ways to generate high returns, and more investors were willing to trust them with their money. But the main source of new fortunes now is starting companies, and when you look at the data, you see big changes there too. People get richer from starting companies now than they did in 1982, because the companies do different things. In 1982, there were two dominant sources of new wealth: oil and real estate. Of the 40 new fortunes in 1982, at least 24 were due primarily to oil or real estate. Now only a small number are: of the 73 new fortunes in 2020, 4 were due to real estate and only 2 to oil. By 2020 the biggest source of new wealth was what are sometimes called “tech” companies. Of the 73 new fortunes, about 30 derive from such companies. These are particularly common among the richest of the rich: 8 of the top 10 fortunes in 2020 were new fortunes of this type. Arguably it’s slightly misleading to treat tech as a category. Isn’t Amazon really a retailer, and Tesla a car maker? Yes and no. Maybe in 50 years, when what we call tech is taken for granted, it won’t seem right to put these two businesses in the same category. But at the moment at least, there is definitely something they share in common that distinguishes them. What retailer starts AWS? What car maker is run by someone who also has a rocket company? The tech companies behind the top 100 fortunes also form a well-differentiated group in the sense that they’re all companies that venture capitalists would readily invest in, and the others mostly not. And there’s a reason why: these are mostly companies that win by having better technology, rather than just a CEO who’s really driven and good at making deals. To that extent, the rise of the tech companies represents a qualitative change. The oil and real estate magnates of the 1982 Forbes 400 didn’t win by making better technology. They won by being really driven and good at making deals. And indeed, that way of getting rich is so old that it predates the Industrial Revolution. The courtiers who got rich in the (nominal) service of European royal houses in the 16th and 17th centuries were also, as a rule, really driven and good at making deals….”

He argues the post war period was an outlier in American economic history, note he seemingly falls for Lind’s uncompelling argument that the post war economy wasn’t competitive which was a deterrent to entrepreneurship, “…So it’s not 2020 that’s the anomaly here, but 1982. The real question is why so few people had gotten rich from starting companies in 1982. And the answer is that even as the Herald Tribune’s list was being compiled, a wave of consolidation was sweeping through the American economy. In the late 19th and early 20th centuries, financiers like J. P. Morgan combined thousands of smaller companies into a few hundred giant ones with commanding economies of scale. By the end of World War II, as Michael Lind writes, “the major sectors of the economy were either organized as government-backed cartels or dominated by a few oligopolistic corporations.” In 1960, most of the people who start startups today would have gone to work for one of them. You could get rich from starting your own company in 1890 and in 2020, but in 1960 it was not really a viable option. You couldn’t break through the oligopolies to get at the markets. So the prestigious route in 1960 was not to start your own company, but to work your way up the corporate ladder at an existing one. Making everyone a corporate employee decreased economic inequality (and every other kind of variation), but if your model of normal is the mid 20th century, you have a very misleading model in that respect…. But though the breakup of J. P. Morgan’s economy created a new world in the technological sense, it was a reversion to the norm in the social sense. If you only look back as far as the mid 20th century, it seems like people getting rich by starting their own companies is a recent phenomenon. But if you look back further, you realize it’s actually the default. So what we should expect in the future is more of the same. Indeed, we should expect both the number and wealth of founders to grow, because every decade it gets easier to start a startup. Part of the reason it’s getting easier to start a startup is social. Society is (re)assimilating the concept. If you start one now, your parents won’t freak out the way they would have a generation ago, and knowledge about how to do it is much more widespread. But the main reason it’s easier to start a startup now is that it’s cheaper. Technology has driven down the cost of both building products and acquiring customers. The decreasing cost of starting a startup has in turn changed the balance of power between founders and investors. Back when starting a startup meant building a factory, you needed investors’ permission to do it at all. But now investors need founders more than founders need investors, and that, combined with the increasing amount of venture capital available, has driven up valuations.

  • Productivity
    • Innovation/Research
  • Workforce
    • Inequality
Previous articleApril 27, 2021Summers at FT@LHSummers argues that the current fiscal policy under Biden is excessive and not justified by secular stagnation; notes that pre-Covid, a gap btw private savings and investment led to deflationary tendencies, but the current fiscal stimulus, totaling 14% of GDP, far exceeds the necessary response.Next articleApril 27, 2021Americans do not want the woke racism our schools are peddlingPolling data shows 62% of Americans favor diverse perspectives over progressive views in K-12 schools, with 84% agreeing on equal treatment based on merit. @SamuelAbrams
Showing 484 database articles primarily about either Productivity, Cronyism, Incentives/Risk-Taking, Innovation/Research, Institutional Capabilities, Intangibles, Investment, Startups, or Workforce Reorganization

The College Wage Premium in the Generative AI Era

AI Summary. S. 575 between 2022 and 2026, the first sustained decline in relative demand for college-educated labor in four decades. AI exposure in white-collar occupations accounts for roughly 28% of that drop, as wage growth slowed disproportionately in high-AI-exposure jobs where college graduates are concentrated.

José Azar, Mireia Gine and Javier Sanz-Espín Social Science Research Network
Date Posted:
September 4, 2026
Is Database:
Database

The college wage premium flattened in the mid-2010s and has fallen ~8% since 2022. The authors argue that this compression reflects a broad decline in the returns to formal schooling, rather than a decline in the upper tail.

Is the college degree losing its economic value to artificial intelligence?

Core argument: The U.S. college wage premium fell from 0.626 to 0.575 between 2022 and 2026—the first sustained decline in relative demand for college labor after four decades of uninterrupted expansion.

After expanding for four decades, the U.S. college wage premium [dropped] sharply from 0.626 in 2022 to 0.575 in 2026. Current Population Survey data through 2026 implies an unprecedented drop in relative demand for college labor—the first sustained negative relative demand growth. Post-2022 wage growth slowed disproportionately in high-exposure occupations, which employ a disproportionate share of college graduates. By 2026, going from zero occupational AI exposure to full exposure had a negative effect on wages of−0.086. Combined with the college–non-college exposure gap, this mechanism accounts for roughly 28% of the total drop in the college wage premium from 2022 to 2026. While non-causal, these patterns indicate that task displacement in AI-exposed white-collar occupations plays a quantitatively meaningful role in the recent compression of the aggregate skill premium.

Takeaways by Macro Roundup® AI

  1. The U.S. college wage premium fell from 0.626 to 0.575 between 2022 and 2026—the first sustained decline in relative demand for college labor after four decades of uninterrupted expansion.
  2. Moving from zero to full occupational AI exposure reduced wages by 0.086 log points by 2026.
  3. the college–non-college AI-exposure gap accounts for roughly 28% of the total premium compression over that period.

Related Articles:

  • Looking for the Ladder — The downtick in hiring in AI-exposed occupations started 6 months prior to the release of ChatGPT, and is “perfectly” aligned with the start of Fed rate hikes…
  • How Students and Recent Grads are Responding to the Rise of AI — Far from shying away from AI, American undergraduates “are flocking towards the most-AI-exposed degrees,” with enrollment in these majors up 8% last year…
  • AI and Young-adult Jobs: The Real Mystery — Since the summer of 2023, the employment rate for Americans 22–25 has declined for both college grads and non-college workers, a phenomenon beyond both…
  • Innovation/Research
  • Productivity
  • Workforce
    • Education
      • College
    • Unemployment/Participation

Gross and Net US Investment

AI Summary. Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment merely replaces depreciating assets. The shift toward faster-depreciating information technology assets requires larger gross investment increases to achieve any given gain in productive capital per worker.

Timothy Taylor Conversable Economist
Date Posted:
September 4, 2026
Is Database:
Database

U.S. real net private domestic investment—which adds to the American capital stock—is now only ~25% as large as gross investment, down from ~40% in the 1970s. Taylor suggests the widening gap between gross and net investment reflects the relatively rapid depreciation of IT-related capital.

Does faster asset depreciation explain slowing productivity growth?

Core argument: Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment now merely replaces depreciating capital rather than expanding the productive stock.

The figure divides net investment by gross investment. Back in the 1970s, net investment was often around 40% of gross investment, but the share has been slumping over time. For the last decade or so, net investment has been about 25% of the gross–that is, about three-quarters of gross investment is just making up for depreciation of the pre-existing capital stock. The likely reason for the growing gap between gross and net investment is that modern investment is more likely to be related to information technology [which] depreciates more rapidly and thus needs to be replaced and updated more often. If we want the average US worker to be using a greater amount of capital on the job–which was one of the key drivers of rising labor productivity in the past–it now takes a bigger rise in gross investment to lead to a given rise in net investment.

Takeaways by Macro Roundup® AI

  1. Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment now merely replaces depreciating capital rather than expanding the productive stock.
  2. The shift toward information technology — which depreciates faster than physical machinery — is the primary driver of the widening gap between gross and net investment.
  3. Raising capital per worker, a historic engine of labor productivity growth, now requires a substantially larger increase in gross investment than it did several decades ago.

Related Articles:

  • US Stock Market To Stop Shrinking For First Time In 23 Years — US equity supply is turning positive for the first time in over two decades, as a surge in IPOs and large share sales by major technology companies outweighs the buybacks and privatizations that have shrunk the stock market since 2003.
  • Capital Is Making a Comeback — Btw 1985-2021 the capital intensity of the American economy was relatively flat as a rise in intangible investment was offset by a decline in tangible…
  • The Transition to a Higher Cost of Capital — Bridgewater Associates co-CIO Karen Karniol-Tambour expects 10-year Treasury yields to rise from the current ~4.5% to compensate for structurally higher fiscal…
  • Investment
  • GDP
    • Financial Markets
    • Growth
  • Productivity
    • Innovation/Research

The AI Re-Acceleration That Wasn’t

AI Summary. 615). Claims of re-acceleration result from cherry-picking frontier observations, selecting a breakpoint, ignoring variance collapse, and fitting separate lines on either side.

Paul Kedrosky Applied Complexity
Date Posted:
September 3, 2026
Is Database:
Database

Kedrosky argues AI capabilities continue to improve, but “the full composite data shows flattening relative gains, not acceleration…rolling relative model gains have fallen from their 2024 peak, while model dispersion has narrowed sharply.”

Are AI performance gains accelerating or just appearing to through selective measurement?

Core argument: Epoch’s Capabilities Index shows no statistically significant AI performance acceleration when controlling for developer and model family, with a breakpoint test returning p = 0.615 and a slope-change confidence interval of -8.4 to +23.4 pts per year.

Using all Epoch’s Capabilities Index observations, and controlling for developer and model family, there is no statistically significant breakpoint. A piecewise model—which splits the series into intervals and applies a sub-function to each segment—does not improve on a purely linear trend: p = 0.615, The estimated change in slope has a confidence interval of -8.4 to +23.4 points per year. In short, the maths shows there is no model acceleration, contrary to claims, and as expected. The result comes from selecting frontier observations only, choosing a breakpoint, ignoring variance collapse, and fitting separate lines on either side.

Takeaways by Macro Roundup® AI

  1. Epoch’s Capabilities Index shows no statistically significant AI performance acceleration when controlling for developer and model family, with a breakpoint test returning p = 0.615 and a slope-change confidence interval of -8.4 to +23.4 pts per year.
  2. Claims of AI re-acceleration rest on a methodological artifact: selecting only frontier model observations, pre-choosing a breakpoint, ignoring variance collapse, and fitting separate trend lines on each side of that breakpoint.

Related Articles:

  • Why .400 Hitters Disappeared — and What It Means for AI — As AI model performance converges toward a ceiling, relative gains per improvement cycle shrink, transforming frontier capability from a pricing moat into a commodity where price becomes the primary differentiator and margin pressure intensifies across leading providers.
  • Chart of the Day: Small Models are Closing the Gap to Frontier AI — Small AI models are closing the gap with large ones, achieving the same reasoning benchmarks with 142x fewer parameters than required two years ago. This makes on-device AI viable without data centers, compressing the economic case for cloud-based, per-query AI services.
  • Anthropic’s Best AI Model Struggles To Attract Users As Cheaper Tools Thrive — Spending on the most expensive AI model from a leading provider has plateaued at 11% of total outlay, as cheaper, older models prove capable of handling most business tasks.
  • Innovation/Research
  • Productivity
    • Investment

Understanding AI and Productivity

AI Summary. U.S. productivity growth has accelerated to ~2.2% annually since mid-2022, above the 2010s baseline, though pandemic-era labor market and business formation dynamics likely contributed alongside AI. Historical general-purpose technology booms sustained labor productivity growth above 2.5% for a decade or more, making the current acceleration substantial but not unprecedented.

Chad Syverson Economic Innovation Group
Date Posted:
August 28, 2026
Is Database:
Database

Syverson is skeptical that AI initiated the rise in productivity growth that began in 2023. The acceleration began while AI investment was small, and pandemic-era labor market churn and business dynamism match the acceleration’s start.

Is AI-driven productivity growth sustainable at historical technology boom levels?

Core argument: U.S. labor productivity has grown at roughly 2.2% annually since mid-2022, a pace exceeding the 2010s trend and, if sustained, implying GDP per capita roughly 7% higher within a decade than the prior trajectory.

Productivity from mid-2022 on has maintained a faster-than-2010s trajectory involving annual growth of about 2.2%. Could this acceleration be due to AI? Perhaps. The timing leans against AI being the sole initial cause. Additionally, there were well-documented increases in economic dynamism (labor market churn and business formation) during the pandemic emergence whose timing matches the acceleration’s start. Regardless of AI’s current effect, the longer the aggregate productivity acceleration continues, the more plausible it is that AI is an important driver. As for the magnitude, a sustained increase from 1.5 to 2.2% annual productivity growth would be substantial (after a decade, GDP per capita would be 7% higher than otherwise), but hardly unprecedented. The 1995–2004 productivity boom saw annual productivity growth of nearly 3% per year, and other past general-purpose-technology-related productivity boosts saw labor productivity growth in excess of 2.5% for a decade or longer.

Takeaways by Macro Roundup® AI

  1. U.S. labor productivity has grown at roughly 2.2% annually since mid-2022, a pace exceeding the 2010s trend and, if sustained, implying GDP per capita roughly 7% higher within a decade than the prior trajectory.
  2. The 1995–2004 productivity boom averaged nearly 3.0% annual growth, establishing that a durable AI-driven acceleration to 2.2% would be meaningful but well within historical precedent for general-purpose-technology cycles.
  3. Pandemic-era surges in labor market churn and business formation align more precisely with the productivity acceleration’s start date than AI adoption does, complicating AI-as-sole-cause narratives.

Related Articles:

  • AI and Productivity — Rising US labor productivity is driven by higher capital utilization—factories, servers, and hotel rooms running harder—rather than new investment or efficiency gains at the individual task level.
  • Google’s AI & Economy ATLAS v1.0: Mapping Gemini Usage in the Economy — Google’s new AI & Economy ATLAS maps 15M AI interactions to occupations, tasks, and activities, showing AI use is pervasive but not intensive…
  • Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools — Event studies indicate that adoption of AI coding tools raised “commits” (saved code updates) ~180%, but releases by only ~30%. Large upstream…
  • Investment
  • GDP
    • Growth
  • Productivity
    • Innovation/Research

US Widens AI-Driven Investment Gap With Europe

AI Summary. US corporate investment in equipment and facilities is projected to grow 40% in real terms by the end of next year, versus 12% in the euro area, widening a productivity gap where output per hour worked rose $14 in the US compared with $2 in Europe since 2018.

Sam Fleming, Amy Borrett and Olaf Storbeck Financial Times
Date Posted:
August 24, 2026
Is Database:
Database

Oxford Economics projects US real business investment will rise 40% over 2021–2027, ~3x the euro area’s 12%. US investment growth since 2024 has been largely information processing and software, but high US growth in GDP/hour is not “merely digital.”

Is artificial intelligence investment widening the transatlantic productivity divide?

Core argument: U.S. corporate investment in equipment and facilities is projected to rise 40% in real terms between 2021 and end-2026, versus 12% in the euro area and near-zero growth in Germany, sharply widening the transatlantic capital-spending gap.

Corporate spending on new equipment and facilities in the US is projected to increase 40% in real terms between 2021 and the end of next year, according to forecasts from Oxford Economics. The US surge compared with a real-terms increase of just 12% in the euro area, while German business investment is expected to have all but stagnated over the same period. Europe also faces a large and growing productivity gap with the US. “The United States has recently pulled further ahead of Europe,” Bart van Ark, a professor at the University of Manchester, told policymakers at the ECB Forum in Sintra. GDP per hour worked increased $14 in the US between 2018 and 2025, compared with just $2 in Europe. “The gap is not only a digital sector story,” added van Ark, stressing that the US outperformance extended to other sectors, including wholesale and retail as well as professional services.

Takeaways by Macro Roundup® AI

  1. U.S. corporate investment in equipment and facilities is projected to rise 40% in real terms between 2021 and end-2026, versus 12% in the euro area and near-zero growth in Germany, sharply widening the transatlantic capital-spending gap.
  2. U.S. labor productivity rose $14 per hour worked between 2018 and 2025, versus $2 in Europe, with outperformance spanning wholesale, retail, and professional services—not solely the digital sector.

Related Articles:

  • The Two Europes — The European Union contains two divergent economies: a reforming frontier energized by security threats, and a stagnant interior where structural reform pressure remains absent.
  • Ed Conard Debates Furman On “The Expected Value of Risk Taking” — I debate @JasonFurman—Pres. Obama’s Chair of the Council of Economic Advisors—at Harvard over the effect of tax increases on the expected value of innovative…
  • The Future of European Competitiveness – A Competitiveness Strategy for Europe — An EC study of European competitiveness finds that EU gross value-added per hour worked increased by 0.7%/year from 2000-19, vs. 1.2%/year in the US. “Europe…
  • Investment
  • Comparisons
    • Europe USA Relative Performance
  • GDP
    • Growth
  • Productivity
    • Innovation/Research

Why Big Tech’s AI Spending Is $3 Trillion Higher Than It Seems

AI Summary. Nine major technology companies carry ~$3tn in off-balance-sheet AI commitments — 5x their ~$600bn in reported capital spending — obligations that are growing faster than traditional investment and triple their combined lease and debt liabilities.

Peter Rudegeair and Peter Santilli Wall Street Journal
Date Posted:
August 17, 2026
Is Database:
Database

A WSJ analysis finds 9 firms involved in the data center buildout have ~$3T in off-balance-sheet commitments largely tied to AI infrastructure. The growth in such obligations has outpaced the firms’ capex growth over the last year.

Are technology companies hiding the true cost of artificial intelligence?

Nine top tech companies had some $3 trillion of off-balance-sheet commitments mostly related to AI, according to a Wall Street Journal analysis of footnotes in their most recent securities filings. Those obligations are growing faster than traditional “capex,” which totaled about $600 billion over the past year they reported, and were about triple what the companies owe under their outstanding leases and long-term borrowings.

Related Articles:

  • The Hyperscalers’ Exploding ‘Purchase Commitments’ Reach $1.5tn — Major technology companies have accumulated $1.5tn in lease commitments and $982bn in purchase obligations for chips, computing power, and energy, totaling roughly $2.5tn in future spending. Much of this debt does not appear on standard financial statements, understating true leverage and future cash demands.
  • The Market Is Asking Questions — AI infrastructure debt spreads are widening as markets question whether returns on massive, front-loaded capital spending will outpace financing costs before assets depreciate. If compute demand plateaus from efficiency gains or slow adoption, the industry faces a glut of expensive, rapidly depreciating capacity.
  • Big Tech Credit Risks Rise Sharply As AI Spending Soars — The cost of insuring major technology companies' debt against default has reached record highs, driven by surging AI infrastructure spending that is straining balance sheets and pushing credit ratings toward junk status.
  • Investment
  • GDP
    • Financial Markets
  • Productivity
    • Innovation/Research
© Copyright 2026 Coherent Research Institute · All Rights Reserved · Privacy · Terms