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When it comes to the philanthropic ecosystem, we live in exciting times. A duo of recent posts \u2014 one from one of SF\u2019s great thinkers, the other from one of its great general managers \u2014 are dedicated to this fact.
Last week, Dwarkesh Patel closed submissions for a blog prize searching for the best answers to the question: How do we deploy (possibly) hundreds of billions of dollars to \u201cmake AI go well?\u201d He specifically wanted to know how readers might approach the problem of converting money into impact from the POV of the OpenAI Foundation.1
In a second blog post, The third wave of American philanthropy, Stripe Climate GM Nan Ransohoff put the scale of the opportunity in context. She points out that if $50 billion from AI donors were to enter the philanthropic ecosystem each year, that would be enough to fund the annual budgets of the following organizations:
6 Gates Foundations (~$9B/yr), or
67 Coefficient Givings, formerly OpenPhil (~$1B/yr), or
100 GiveWells (~$500M/yr), or
333 Arc Institutes (~$150M/yr), or
5000 Institutes for Progress (~$10M/yr)
Nan then breaks down the challenge into a set of seven subproblems that, from an ecosystem design perspective, we must overcome to use these dollars well. Today\u2019s piece zeroes in on a solution to one of these problems: figuring out how these new funders can leverage their unusually large risk appetites to world-changing effect.
Incidentally, this is exactly what my submission to Dwarkesh\u2019s blog competition was about. In it, I profiled the best scientific philanthropist of all time, Warren Weaver, and his (exceptionally risk-tolerant) playbook for willing new fields into existence. Like great VCs, he only took big swings. Unlike VCs, he did next to no diversification. To Weaver, field creation was an endeavor that required focus; responsibility was not about hedging your bets, but giving carefully selected fields everything they needed to flourish.
Over the course of twenty years, Weaver bootstrapped the field of molecular biology into existence. He then funded a course of crop research that would grow into the Green Revolution. Weaver had the courage to live with concentrated bets for years on end, and the world is immeasurably better for it.
Given the blog competition word count, I\u2019m briefer than usual in many spots. If you\u2019re curious to learn more about any topic mentioned, check out my long piece on Weaver, my many Weaver tweets, or ping me. And if you have the urge to act on today\u2019s piece, contact me \u2014 egillia3@alumni.stanford.edu, or on my Twitter. If urgent, I can fly to SF.
Two more of Nan\u2019s subproblems \u2014 (1) the need to create new philanthropic capital allocators and (2) to make the problems that need solving more legible \u2014 will be the subject of a coming piece.
To become a technology of historical consequence, we must proactively make good things happen using AI. In the not-so-distant past, we find kinks in mortality graphs representing hundreds of millions of lives saved \u2014 kinks sparked by the action of a few smart people. Human brains did that. In partnership with a superhuman AI, we should have no less ambition.
Our ambition should extend beyond longer, healthier lives. I take a somewhat Robert Gordonian view of human flourishing: people working less without getting poorer, finding ways to make housing and food more affordable, and so on.
In pursuit of these goals, the philanthropic efforts of the AI labs should focus on problems that take advantage of two Silicon Valley comparative advantages: R&D and risk-tolerant capital allocation.2
Key individuals at the OpenAI Foundation have the opportunity to cement themselves as historically great, as more consequential than even J.C.R. Licklider or Bell Labs\u2019 Mervin Kelly. But to do so, they must internalize the lessons of history\u2019s best research funders. Why? Turning money into impact is closer to a problem of organized complexity than one of simplicity. Working from empirical data trumps \u201cfirst principles\u201d in areas of this sort. Funders need to decide on some heroes, and learn from them.
If I ran the OpenAI Foundation, a photo of Warren Weaver would hang in the lobby. From his perch at the Rockefeller Foundation, which he took over in 1932, Weaver made two contributions that cement him as the greatest scientific grant funder of all-time. He:
Funded molecular biology into existence.
Was key in funding the Green Revolution into existence.
(If the reader wonders whether Weaver simply got lucky twice, he also spotted the computing wave. Purchase the book-form of Claude Shannon\u2019s The Mathematical Theory of Communication, and you\u2019ll find that Weaver is co-author. Search for the grant that funded the 1956 Dartmouth Summer AI Conference, \u201cWeaver.\u201d)3
I\u2019d structure the foundation to empower modern-day visionaries deploying the Weaver playbook. The playbook, in a line: true specialization in an extremely young field.
How young is young?
In the case of molecular biology, ~80% of Weaver\u2019s Natural Sciences Division budget went into the field for ~two decades pre-Watson and Crick. For the first five years, many key experiments didn\u2019t run or barely worked. They fell forward. Even in 1948 \u2014 15 years into this focused bet, 5 years pre-Watson and Crick \u2014 one could find Leo Szilard hemming and hawing over whether it was too risky to enter such a young field.4
How did Weaver choose to specialize in molecular biology?
Weaver, an applied mathematician, worked in physics during the era in which physics\u2019 tools and models fruitfully invaded chemistry.5 He believed a branch could be similarly willed into existence at the intersection of biology and physics.6 Biology, at that point, was a ~single instrument field \u2014 the optical microscope. Biologists didn\u2019t work at the small scales physicists had now learned to study. A biological question on the scale of heredity was the perfect place to start.
Why specialize?
Before Weaver arrived, the Natural Sciences Division operated somewhat typically. They let scientists line up in general areas, and funded the best ideas until money ran out. Weaver saw that as inefficient, and believed focus would be super-additive. \u201cA highly selective procedure is necessary if the available funds are not to lose significance through scattering.\u201d7
It worked. By 1965, 18 Nobel Prizes would be given out for molecular biology. 15 of them were beneficiaries of Rockefeller Foundation funding, on average receiving their funds ~2 decades in advance of the prize \u2014 in an era in which scientists won the prize in middle age. Weaver would then move all his chips into his next branch, one that would grow into the Green Revolution.8
I\u2019ve simplified a bit.9 Still, the simplicity of the approach begs the question: \u201cWhy doesn\u2019t everyone do it?\u201d
The answer is likely also simple: courage and trust are in short supply. The field Weaver tied up almost all his money in did not have a name \u2014 he named it \u201cmolecular biology\u201d in 1938. The bet was overseen by someone named \u201cWeaver,\u201d not \u201cRockefeller.\u201d And he didn\u2019t have all that much to show for it in the early years. The bet is unthinkable at almost every large philanthropy today. Most who tried would get impatient, scared, or fired before the seeds bore fruit.10
But it can be done. AI\u2019s philanthropists have enough money to fund multiple Weavers at once, but not enough to diversify their way to Weaver.11 The work will require courage and a steady hand; it\u2019s a Berkshire Hathaway-style portfolio of focused bets.12
How to choose which could-be Weavers to empower? Focusing on areas where the R&D funding ecosystem systematically under-invests is a natural approach. Several include:
New Field Creation
Instrumentation
Doing-Heavy Discovery Work
AI might give us better next experiment ideas in biology, but that doesn\u2019t change the structure of the NIH or the incentives of VC. NIH panels can err towards consensus work in fields that already exist, and VCs toward things that are VC-profitable within 10 years. Neither specializes in creating new molecular biologies. The OpenAI Foundation can, if it chooses.
On instrumentation, neither academia nor VC fund biological tooling at a societally optimal level. The status quo may deliver super-human intelligence that is wildly under-sensed. There\u2019s surely a Weaver who would dedicate themselves to the problem.
Opportunity also exists beyond AIxBio. Doing-heavy discovery work is under-emphasized in general; for example, crop test fields for construction are not a university specialty. A related historical example worth dredging up comes from the memoir of James Killian, my least favorite dead MIT President. Polaroid cofounder Edwin Land once proposed that, at MIT\u2019s Sloan School, Killian \u201cestablish a model company \u2014 a practice school \u2014 to explore other ways to create other noble prototypes of industry.\u201d That strikes me as a good problem for a Weaver in the age of AI.
Thanks for reading.
Stay tuned for my piece on founding new philanthropic capital allocators that scale, and doing so in not-yet-legible areas of R&D, coming in early June.
FreakTakes is, in many ways, a ~200,000-word exploration of exactly this \u2014 from several dozen different angles.
Naturally, this piece is primarily focused on those eager to focus their philanthropy on R&D. This is, after all, an R&D history Substack. And even among those giving to R&D, there are obviously heroes you can pick other than Weaver! J.C.R. Licklider (of early BBN and ARPA fame) and MIT c. 1920 are both worthy heroes, for example. I\u2019ve written longer FreakTakes pieces on both.
In the next couple of months, I\u2019ll also publish a piece breaking down why Weaver, in the 1940s, thought it was clear that the computer would eventually cement itself as one of (if not the) most important instruments in the study of biology.
This Substack\u2019s original Weaver piece spends many thousands of words detailing what I had to distill in a few sentences here. So if you want to hear stories from the nascent labs, breakdowns of Rockefeller budgets before and after Weaver\u2019s arrival, and how he used the other 20% of his portfolio to explore areas that could become his next branch, see that piece.
Many who read this Substack will be familiar with the fact that the physicists and chemists won each other's Nobels quite frequently, in this period.
To my eye, this heuristic for field creation feels timeless. Heuristics only take you so far, but it\u2019s a great place to start.
Weaver felt that philanthropic divisions (like his) were not above the sort of specialization expected of firms. He focused his funds where they were needed, giving the young (speculative) field he was building the resources it needed to flourish. New Silicon Valley funders will be well-placed to impose this focus on specific activities happening in an organization. But doing so in the service of long-term, (often) not very measurable goals might be a bit of a balancing act. Empowering single individuals, as companies do with executives, is essential. But extending them more rope than is typical of executives is also essential if you want a Weaver. Most philanthropies, frankly, do not have the stomach to fund a Weaver. I write this piece in the hopes that a few will have the stomach and steady hand to do so.
As I wrote in the conclusion of my original Weaver piece: \u201cWeaver was not committed to molecular biology at the expense of all other things. He was committed to creating high-value branches at the expense of all other things.
The Division would not abandon its young branch \u2014 molecular biology \u2014 because it was not publicly producing in a way that would make headlines, but they would abandon their baby if they felt it didn\u2019t need them anymore. There had to be some new, undiscovered branch out there in need of their support. The follow-on funders could take it from there.
For some people, like Weaver and Szilard, life\u2019s too short to spend basking in the glory of what they\u2019ve built. They\u2019d rather do the damn thing again.\u201d
Of course, Weaver didn\u2019t do it alone. He relied on excellent field strategists, most prominently Max Delbr\u00fcck and Salvador Luria, who recruited and pitched great young people into the field one at a time, over many years. And many other key Weaver practices contributed to the success. For example, he deeply studied the field, maintaining a sense of what failures were productive vs. dead ends. Etc. Etc. Read the longer Weaver piece to learn more about all of this.
This is a real problem. The solution to which one might be just as likely to find in religion and folk wisdom as in any business or R&D history book. The mechanism by which courage and patience, for the long haul, are created in individuals is curious. For modern exemplars of steady-handed confidence of this form, I\u2019d recommend watching several dozen hours of Warren Buffett and Charlie Munger reacting to the manias, panics, and fads of their times.
Upon reading Nan\u2019s piece, I might hedge this statement a bit. I was imagining yearly giving numbers more in the ~$5 billion range, give or take. But even if it were ~$1 billion per year, upon further reflection, that might be enough to diversify your way to Weaver in cheaper subject areas. A social science-flavored focus would be one example where this could be possible. Computational law, computational applied history and experimental history, and the \u201cnoble prototypes of industry\u201d idea mentioned in the conclusion are all areas that could potentially warrant their own Weaver.
It should be noted that Weaver, Buffett, and Munger are all aggressively Midwestern \u2014 a factor this Midwestern author finds material. In his life, Munger made an unwieldy number of quips along the lines of, \u201cThe big money is not in the buying or the selling, but in the waiting.\u201d
![\"\"](\"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_biz_cover.png?resize=150%2C150&ssl=1\")
\n
America continues to confuse military spending with true strength.
\n\u2014 David Rothkopf
\nAccording to US warmongers, the American military is the most powerful fighting force that has ever existed \u2014 a war machine so vast and terrible that enemies everywhere tremble in its path. Boasts aside, the US military is surely unrivalled in at least one regard. It is by far the most expensive armed force on the planet.
\nIn 2025, the US government funnelled $842 billion through Pentagon coffers. And if Donald Trump gets his way, that figure will rise to $1.5 trillion in 2027. No matter how you slice it, that\u2019s a staggering pile of cash. But what exactly does this money buy?
\nA recent New York Times piece complains that the Pentagon\u2019s enormous budget seems to buy \u201cinertia and incompetence\u201d. And they have a point. Since external audits began in 2017, the Pentagon has notoriously failed every single one. Then again, charges of \u2018incompetence\u2019 assume that the purpose of the Pentagon is to spend money wisely \u2014 to maximize the war-making return on investment. But what if the Pentagon\u2019s purpose is something different?
\nIn 2015, Senator John McCain made the case for sanctions against Russia by dismissing the state as \u201ca gas station masquerading as a country\u201d. Turning closer to home, I think we can say something similar about the Pentagon; it\u2019s a bureaucratic regime for channelling public funds into private coffers \u2014 a money funnel masquerading as a military. Of course, that\u2019s not to say that the US military has no firepower. (It does.) My point is that it\u2019s foolish to use Pentagon spending to judge US military might.
\nFor an illustration of this foolishness, look to the ongoing debacle in Iran. Although the Pentagon outspends the Iranian military by more than two orders of magnitude, the US military has been unable to accomplish any of Trump\u2019s (quixotic) objectives.1 Is this strategic defeat simply a matter of Iranian good luck combined with US poor planning?
\nI doubt it.
\nWhat seems more likely is that the US humiliation demonstrates that Pentagon spending is a misleading measure of US military power. The reason is simple: based on spending alone, we cannot differentiate between a military that\u2019s expensive because it is powerful, versus a military that\u2019s expensive because it (and its coterie of contractors) is well paid.
\nIn this essay, I examine the problem of measuring military power. Along the way, I review the long-term history of US military spending, I analyze the rise and fall of US military hegemony, and I discuss how the \u2018war on terror\u2019 has foreshadowed US imperial weakness. Finally, I quantify the US military\u2019s transformation from a war-making machine into a money funnel for US business. All told, the evidence suggests that Pentagon spending vastly overstates US military power.
\nIf there is a unifying lesson from military history, it\u2019s the maxim that \u201cGod always favors the big battalions\u201d.2 Of course, the assumption here is that we know what it means for a military to be \u2018big\u2019.
\nThroughout most of history, the definition of a \u2018big\u2019 military was obvious; it was a simple matter of manpower. Thus, when Napoleon invaded Russia with an army of over 400,000 soldiers, there was no question that he had a massive military.3 Yet as war became mechanized during the early 20th century, the question of military scale became more complicated. Suddenly, armies could be strong not just because of their manpower, but also because of their technological power.
\nThis use of technology, in turn, made the measurement of military scale more difficult because it created an aggregation problem. That is, while manpower can be easily summed (just count soldiers), the quantity of technological power cannot be measured so readily. For example, if a military is armed with 1000 rifles and 2 aircraft carriers, what is its total stock of technology? To answer this question, we need a dimension of aggregation \u2014 a common property shared by both rifles and aircraft carriers.
\nEnter economists. For centuries, economists have solved their aggregation problems by turning to money. Looking at prices, economists put on their accounting hats and proceed to aggregate the monetary value of everything. But unlike accountants, who take monetary quantities at face (financial) value, economists pretend that money reveals something deeper about material stocks and flows. Thus, economists presume that GDP \u2014 a measure of aggregate income \u2014 is a meaningful measure of economic \u2018output\u2019. (It\u2019s not.)
\nBack to the military. Using economists\u2019 aggregation trick, it\u2019s easy to \u2018discover\u2019 that the US military is the \u201cgreatest and most powerful [armed force] anywhere in the world\u201d (Trump\u2019s words). To gaze at the superiority of the US military, we simply look at its gargantuan budget, which dwarfs all competitors. Figure\u00a01 shows the spending disparity in 2024.
\n\n![](\"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_pie.png?w=723&ssl=1\"/)
Backing out of this monetary foolishness, my goal in this essay is to demonstrate the problems with equating military spending with military power. In a world not dominated by economics dogma, the key issue would scarcely need stating. Military spending tells us about the income flowing to the armed forces (including its civilian bureaucracy and its private contractors). On its own, this income tells us nothing about military power.
\nDiving into US history, let\u2019s look at the long-term trend in US military spending. From 1789 to 2025, the dollar value of US military expenditures rose by a factor of a million, with conspicuous bumps along the way during periods of war. Figure\u00a02 shows the ascent.
\n\n![](\"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_nominal.png?w=723&ssl=1\"/)
Although this spectacular rise in nominal military spending might excite US warmongers, it\u2019s fairly meaningless on its own. To gain meaning, spending data needs context. So, with context in mind, here are three different views of the history of US military expenditures, each based on a different assumption about what the armed forces should purchase.
\nFirst, let\u2019s compare US military spending to the consumer price index. By doing so, we imply that the purpose of the military is to purchase consumer commodities. (This assumption is silly, of course, but let\u2019s see where it goes.)
\nFigure\u00a03 shows the US military\u2019s power to purchase consumer commodities. Compared to nominal military spending (Figure\u00a02) the notable difference here comes after World War II, where we see a conspicuous flatline. Today, the US military\u2019s consumer-commodity purchasing power is about half the value of its WWII peak.
\n\n![](\"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_rel_cpi.png?w=723&ssl=1\"/)
Since the purpose of a military is to wage war, its ability to purchase consumer commodities is fairly meaningless. Indeed, one could argue that the optimal military is a spartan one \u2014 an organization that spends the bare minimum on troops\u2019 living standards, leaving the maximum budget for warfare.
\nOf course, the problem with this spartan approach is that it becomes difficult to enforce if citizens\u2019 living standards rise. Sure, a totalitarian regime can build a spartan army based on compulsory military service. But in a capitalist society with a professionalized military, this method doesn\u2019t fly. If a professional military pays poorly, no one will join. Hence, when living standards rise, the military is forced to pay the going rate.
\nThis necessity, in turn, gives rise to a form of cost disease; as living standards rise, mobilizing the population becomes more expensive. For example, a selling point of American living is that US income per capita is about six times greater than in China.4 But the flip side of this greater income is that it makes a war effort more expensive. For the same level of spending, China can mobilize six times more of its citizens. So in terms of military power, high American incomes act as a dead weight that Pentagon planners must drag.
\nFigure\u00a04 illustrates the impact of rising incomes on the US military\u2019s ability to mobilize American citizens. Here, I\u2019ve pegged US military spending against American income per capita. From 1790 to 1945, the US military\u2019s mobilization ability grew nearly 5000-fold. But after World War II, it shrank steadily, as military spending failed to keep pace with rising American income. Today, the US military\u2019s power to mobilize citizens is less than 20% of its WWII peak.
\n\n![](\"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_rel_gdp_pc.png?w=723&ssl=1\"/)
While we\u2019re on the topic of military cost diseases, let\u2019s discuss the burden of paying for corporate profits. During World War II, Harry Truman rose to fame campaigning against war profiteers. \u201cTheir greed knows no limit,\u201d he said bluntly.
\nIronically, today\u2019s military contractors are far more greedy than those of Truman\u2019s era. Yet there are no modern Truman Committees working to curb excessive profits. And that\u2019s largely because American culture has since been corrupted by neoliberal ideology, which rebrands fat profits as a sign of \u2018productivity\u2019.
\nThe roots of this cultural sea change date to the Reagan era in the 1980s. But it was in the mid-1990s when the US military officially donned a neoliberal hat. In 1994, the Pentagon created the \u2018Secretary of Defense Executive Fellows\u2019 program, which sent promising military officers to work for top defense contractors and other large corporations. When officers returned from this revolving door, journalist Freddy Brewster notes that they often had a predictable message: \u201coutsource everything not core to DoD\u201d (the Department of Defense).
\nNow in broad terms, there\u2019s nothing new about Pentagon outsourcing. Historically, the US military has relied heavily on corporate America for its procurement, typically sending about a quarter of its expenditures to the top 100 military contractors. (See Figure\u00a05 for the picture since 1958.) However, in recent decades, there\u2019s been a significant change in what this outsourced spending can buy.
\n\n![](\"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/top_contractors.png?w=723&ssl=1\"/)
As corporate profits have fattened, the Pentagon\u2019s ability to pay for them has dwindled. Figure\u00a06 illustrates this corporate cost disease. Here, I\u2019ve pegged US military spending against the earnings per share of the S&P 500. The goal is to get a rough sense for the US military\u2019s ability to subsidize the returns to corporate shareholders.5
\nLooking at the trend, it seems that the military\u2019s ability to subsidize capitalists peaked in World War II, when spending was high and shareholder earnings were low. But since the 1990s, Pentagon spending hasn\u2019t kept pace with rising corporate payouts. As a consequence, the US military\u2019s ability to subsidize corporate owners now sits at just 4% of its WWII peak.
\n\n![](\"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_rel_eps.png?w=723&ssl=1\"/)
When journalists report government spending, they have a tendency to emphasize the big-number factor. (As in, the federal deficit is $1.8 trillion!) But the truth is that big numbers can turn out to be comparatively small, depending on the context.
\nThe Pentagon budget is a case in point. Whether the current budget is \u2018large\u2019 or \u2018small\u2019 depends on the context. Of course, in nominal terms, Pentagon spending is larger than ever. But relative to consumer commodity prices, Pentagon spending now sits at about half its WWII peak. In terms of the ability to mobilize Americans, things are worse; the current budget sits at 19% of its WWII peak. And in terms of the ability to subsidize corporate shareholders, today\u2019s Pentagon budget is shockingly small \u2014 less than 4% of its WWII peak.
\nTable 1 summarizes these different viewpoints. The lesson here is that despite the eye-popping dollar values, the modern Pentagon budget is not the behemoth it once was.
\nTable 1: Spending big or small? Framing the 2025 Pentagon budget.
\n| Observation | \n2025 Pentagon spending compared to WWII peak | \n
|---|---|
| Nominal spending | \n1000% | \n
| Spending relative to consumer price index | \n56% | \n
| Spending relative to average US income | \n19% | \n
| Spending relative to S&P 500 earnings per share | \n3.9% | \n
For data sources, see the appendix.
\n\n
Staying within the realm of military spending, let\u2019s pivot now and look at the road to US empire. Since the end of World War II, the US has maintained hundreds of military bases throughout the world, with US soldiers acting effectively as a global police force. Of course, under Trump, the US military has morphed into more of a pirate force for Washington plutocrats. But before we discuss this devolution, let\u2019s look at how the US empire was formed.
\nOne way to view the US empire is that it emerged suddenly out of the ashes of World War II. The backstory here is that prior to WWII, American politicians favored an isolationist foreign policy (the Monroe Doctrine notwithstanding). And they had inherited from the constitutional founders a deep distrust of standing armies.6
\nGiven this stance, US military spending tended to be quite modest. During periods of peace, it was typically close to 1% of US aggregate income (GDP). Of course, when war erupted, military ranks swelled, as did spending. But when peace returned, the military would shrink to its pre-war stature. Figure\u00a07 shows this cyclical behavior, which lasted from 1790 to 1939.
\n\n![](\"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_us_gdp.png?w=723&ssl=1\"/)
Continuing to look at Figure\u00a07, note how World War II brought a halt to the spending rhythm of war and peace. When the war ended in 1945, the United States retained, for the first time, a massive standing army that was stationed throughout the world. As a consequence, military spending didn\u2019t return to pre-war levels, but instead remained high. Thus was born the imperial epoch of US history.
\nSort of.
\nThe problem with this story of \u2018sudden\u2019 empire is that it ignores the colonial expansion of the United States itself. For example, in 1800, the US was a small nation of 16 states clumped along the Eastern seaboard. Its population was just 5 million \u2014 about 0.5% of the world\u2019s total population. Over the next century, a steady stream of immigration would swell the American population by a factor of ten, and a series of territorial conquests would see the country expand across the continent.
\nWhen we take into account the colonial expansion of the United States itself, we get the more gradual road to US empire shown in Figure\u00a08. Here, I\u2019ve measured US military spending as a share of world income (GDP). From 1789 to 1939, US military expenditures rose steadily, increasing their slice of world income by two orders of magnitude. During World War II, the US war machine bolstered this value another forty-fold. At its peak, the US war effort commanded something like a fifth of the world\u2019s income.
\n\n![](\"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_world_gdp.png?w=723&ssl=1\"/)
Now to the present. Listening to Trump and his cabinet of swaggering morons, we get the impression that the US is at the height of its military power. But then again, when the US was actually at the height of its power (during World War II), its leaders weren\u2019t blathering about their military supremacy. They were sowing the diplomatic seeds for the US-led world order that would follow the war.
\nFor example, at the Moscow conference in 1943, the US drafted and signed (along with the United Kingdom, the Soviet Union, and China) the Four Power Declaration, which laid the groundwork for the United Nations. And in 1944, the US hosted the Bretton Woods Conference, which established the post-war financial order.
\nIn short, it seems that the peak of US military power coincided with the peak of US diplomacy. And if you understand how power works, that\u2019s not surprising. You see, brute force is the most brittle form of power. Yes it works, if one maintains constant armed oppression. But the moment that weapons are sheathed, coercive power is prone to collapse. In contrast, power through diplomatic consensus is far more robust because it involves buy-in from local populations. Hence, through diplomacy, a powerful military can be transformed from a would-be oppressor into a legitimate international police force.
\nIt was this combination of diplomatic and military power that led to the creation and maintenance of the US-led world order. And today, it is the lack of diplomatic and military power that is causing the US-led world order to collapse. In 2026, US statecraft reads like a dark satire. For Trump, the favored tactic is mafia-like extortion. Hence, we get US financial extortion through Trump\u2019s vindictive use of tariffs. And we get US armed extortion through Trump\u2019s mercurial use of the military. Both of these methods are likely to fail, for the simple reason that the US is not the hegemon it once was.
\nThis decline in power is particularly severe for the US military. Yes, the Pentagon remains the world\u2019s most profligate military spender. But the truth is that in relative terms, the Pentagon\u2019s global spending power now sits at just 4% of its WWII peak. And as we will soon see, this monetary view likely overstates the US military\u2019s fighting power. First, though, let\u2019s look at the historical roots of Trump\u2019s imperial death throes.
\nA consistent feature of world history is that when empires are strong, they preside over periods of relative peace. For example, from 27 BC to 180 AD, the Roman Empire ruled over a period of peace known as the Pax Romana. Similarly, the British Empire prevailed over the Pax Britannica, an era of global peace that lasted from 1815 to 1914. And from 1945 onward, the US empire presided over the post-WWII peace, sometimes called the Pax Americana.
\nOf course, the flip side of imperial peace is the chaos that comes as empires die. Not only do rival states fight over the ensuing power vacuum, but the empires themselves often lash out in vain attempts to resurrect past glory. Today, the US empire has entered its (attempted) resurrection stage.
\nThings are not going well.
\nFuture historians will probably point to Trump\u2019s war in Iran as the moment when the US empire entered into terminal decline. Yet the roots of Trump\u2019s imperial debacle date back to 2001 \u2014 the year when George Bush declared his global \u2018war on terror\u2019. In a way, Bush\u2019s language was as important as his actions. As Ian Welsh notes, the word \u2018terrorism\u2019 has become code for \u201cviolence by people who are our enemies\u201d. The effect of this label is to take diplomacy off the table. (You can negotiate with a \u2018rival\u2019 or even an \u2018enemy\u2019. But you can\u2019t negotiate with a \u2018terrorist\u2019.)
\nWith diplomacy negated by the threat of \u2018terrorism\u2019, the US began to ramp up its military interventions around the globe. Figure\u00a09 shows the resulting explosion of conflict. From 1947 to 2001, the US military engaged in an average of 0.75 conflicts per year. (Admittedly, some of these conflicts were brutal wars, as in Korea in the 1950s and Vietnam in the 1960s). However, from 2001 onward, the number of US conflicts rose dramatically. At the same time, US military tactics changed. Airborne assassination became the norm, prompting all the public admiration that one might expect from an empire that conducts extrajudicial executions from the sky.
\n\n![](\"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/us_conflicts.png?w=723&ssl=1\"/)
![](\"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/conflicts_map.png?w=723&ssl=1\"/)
Even more evocative than the growing number of US conflicts has been the changing location of these military engagements. Once a tool for enforcing global peace (and suppressing the occasional communist movement), the \u2018war on terror\u2019 saw the US military become a cudgel for terrorizing Muslim populations in the Middle East and North Africa. Figure\u00a010 shows this evolving geography of violence.
\nIt\u2019s within this geographic (and demographic) context that we should understand Trump\u2019s war with Iran. After two decades of targeting ragtag militant groups throughout the Islamic world, the Iran War saw the US pick a fight with a major military power. Or at least, that\u2019s what the battle damage would suggest. In the Persian Gulf, many US military bases now lie in ruins, as does a significant portion of the oil-and-gas infrastructure (which the US military guaranteed it would protect, but apparently could not). And of course, the Strait of Hormuz is now controlled by Iran.
\nLooking at these battlefield outcomes, what\u2019s odd about the Iranian victory is that on paper, Trump\u2019s war had all the markings of a US blowout. In 2024, the Pentagon outspent the Iranian military more than 100-fold. In light of this spending dominance, there are two ways to interpret the US humiliation. Either Iran got lucky and the US fell victim to remarkably poor planning, or Pentagon spending offers a gross mismeasurement of US military power.
\nLet me build the case for the latter scenario.
\nThe belief that military spending indicates military power derives from the broader belief in neoclassical economics, which asserts that income (the flip side of spending) always stems from productive \u2018output\u2019. This belief system is a lie.
\nA quick look at the real world shows that many types of income stem from doing nothing productive at all. Such is the case with copyleft trolls, who exploit loopholes in early Creative Commons licenses to extract money from people who\u2019ve made minor attribution errors for content that\u2019s otherwise designed to be free. Now, we commonly call this extortion technique a \u2018scam\u2019 or a \u2018fraud\u2019. But if the political economist Thorstein Veblen was alive today, he\u2019d probably just call it business.
\nYou see, Veblen (who lived through the 19th-century heyday of robber-baron capitalism) had a dark view of capitalist enterprise. For Veblen, the goal of \u2018business\u2019 was not to produce useful things, but instead to impose property rights onto society, thereby creating the institutional power to command income. So as Veblen would see it, copyleft trolls appeal to the purest form of \u2018business\u2019, which is to receive money by sabotaging an otherwise free activity. The point here is that when we look at income (and its flip side, expenditure), we\u2019re seeing the effects of \u2018business\u2019 success.
\nNow for Veblen, the antithesis of \u2018business\u2019 was the unmonetized human desire to create and produce useful things \u2014 a tendency that he called industry. Thus, when a farmer grows corn, he engages in \u2018industry\u2019. But when a commodity trader speculates on the price of corn futures, he engages in \u2018business\u2019. What\u2019s important about Veblen\u2019s distinction is that it allows for a divergence between the scale of \u2018business\u2019 income and the scale of social \u2018industry\u2019. Or put another way, it allows for the existence of the modern United States.
\nTo frame the (seemingly) underwhelming returns to Pentagon spending, it helps to first understand the wider pathology of US power. Once the center of global manufacturing, today the United States more closely resembles a patent troll. It is a country where \u2018business\u2019 is booming but homespun \u2018industry\u2019 is anemic.
\nTellingly, Trump\u2019s State Department boasts that about 40% of US income and 80% of US exports stem from the enforcement of intellectual property rights. So what\u2019s wrong with that? Well, in a business sense, nothing. For the person receiving money, all income is the same, no matter how it\u2019s generated. But in a broader social sense, the source of one\u2019s income matters. To put it crudely, income from professional murder is different than income from nursing.
\nIn a slightly less pathological vein, IP-based income is socially detrimental because it inflates the price of goods and services that could otherwise be cheap, or even free. (Absent the copyleft troll, the use of Creative Commons images costs nothing.) In other words, intellectual property is a tool for extracting \u2018business\u2019 profits by choking off human \u2018industry\u2019.
\nTo have a closer look at this business chokehold, I\u2019m going to turn to a metric that I call the business-to-industry index. The goal here is to quantify the relation between Veblenian \u2018business\u2019 (the act of profiting from property rights) and Veblenian \u2018industry\u2019 (the act of providing useful goods and services). For its part, Veblenian \u2018business\u2019 is the easier activity to quantify, because the goal is always to command an income stream. Hence, the success of \u2018business\u2019 can be measured in terms of some form of relative income.
\nIn contrast, Veblenian \u2018industry\u2019 is more difficult to quantify, because it encompasses a wide variety of activities that resist simple aggregation. Here, I\u2019ll sidestep this problem by ignoring industrial \u2018output\u2019. Instead, I\u2019ll measure the input of primary energy. The idea is that energy is essentially a biophysical currency \u2014 it\u2019s a thermodynamic transaction that must be paid (to the universe) to do anything materially useful. So with thermodynamic payments in mind, I\u2019ll measure the scale of \u2018industry\u2019 in terms of energy consumption.
\nThe business-to-industry index consists of the ratio of these two views of society \u2014 the ratio of relative income to relative energy use. In the case of the United States, I define the business-to-industry index as the ratio between the US share of world income and the US share of world energy use:
\nFigure\u00a011 shows these two views of US power. The red curve plots the \u2018business view\u2019 \u2014 the US share of world income. And the blue curve shows the \u2018industry view\u2019 \u2014 the US share of world energy consumption.
\n\n![](\"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/us_gdp_energy.png?w=723&ssl=1\"/)
Eyeballing Figure\u00a011, it\u2019s clear that historically, the rise and fall of US \u2018business\u2019 power stemmed in large part from the rise and fall of industrial hegemony. And fundamentally, that makes sense. If claims on property rights aren\u2019t backed by material power, then they become tenuous to enforce and easily undercut.
\nThat said, when we look more closely at the relation between the two views of US power, a fascinating long-term pattern emerges. Figure\u00a012 illustrates the trend. Here, I\u2019ve calculated the US business-to-industry index \u2014 the US share of world income relative to the US share of world energy use. What\u2019s remarkable (and in my mind unexpected) is that for over two centuries, this index has trended north.
\nIn the early 19th century, the US was an industry-dominated country, meaning its share of world energy use was significantly larger than we\u2019d expect from its share of world income. But by the late 20th century, the US had become a business-dominated country, meaning its share of world income significantly outstripped its share of world energy use. All told, the US business-to-industry index is now (as of 2025) more than three times higher that it was in 1790.
\n\n![](\"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/us_bti-1.png?w=723&ssl=1\"/)
Now, since this essay is ultimately about the US military (and not US society in general), I won\u2019t dwell on the evidence in Figure\u00a012. But I can\u2019t help but connect the trend in the business-to-industry index to a point that Steve Keen recently made about the double-edged sword of empire.
\nNote that it was shortly after World War II that the US business-to-industry index entered business-dominated terrain. And it was around the same time that the US dollar became the world\u2019s reserve currency. I doubt this mutual timing is a coincidence. Keen observes that although control over the world\u2019s reserve currency comes with well-known opportunities for profit, it also comes with a major downside, which is that it kills homegrown industry. That\u2019s because when a currency attains reserve status, it tends to become overvalued, thereby making exports in the currency-issuing country less competitive. The net effect, according to Keen, is that issuing a reserve currency is \u201cnot a spoil of Empire, but a spoiler of Empires.\u201d
\nLooking ahead, there\u2019s definitely more to be said on the theme of booming business and anemic industry. But for now, let\u2019s return to the topic at hand, which is US military power. If the United States as a whole has become \u2018business dominated\u2019, it seems plausible that the US military has undergone a similar transformation.
\nLet\u2019s have a look.
\nHaving defined the business-to-industry index for the United States, it\u2019s easy to apply this metric to the US military. Looking at the Pentagon, its business-to-industry (BTI) index consists of US military expenditures as a share of world income, relative to the US military\u2019s share of world energy use:7
\nNow, before we get to the data, it\u2019s worth noting that while the notion of a war \u2018business\u2019 (the act of profiting from violence) is fittingly Veblenian, the idea of a war \u2018industry\u2019 is \u2026 not. You see, outside of capitalism, Veblen had a fairly optimistic view of human nature. Commenting on Veblen\u2019s thinking, political economists Jonathan Nitzan and Shimshon Bichler argue that the purpose of Veblenian \u2018industry\u2019 is the \u201cefficient production of quality goods and services for the betterment of human life\u201d [my emphasis].
\nObviously, if we speak of a \u2018war industry\u2019, the notion of \u2018bettering human life\u2019 takes on a darker tone. Whereas Veblenian \u2018industry\u2019 is positive-sum for the whole of humanity, the notion of a \u2018war industry\u2019 is at best, zero-sum. The goal of the \u2018war industry\u2019 is to produce a powerful military that triumphs over rivals, thereby bettering the lives of the victors (by ruining the lives of the losers).
\nAcknowledging this dark side of human behavior, let\u2019s see how the \u2018business\u2019 view of the US military lines up with the \u2018industry\u2019 view. The short answer is that it doesn\u2019t. Figure\u00a013 tells the story. Compared to the \u2018business\u2019 view of Pentagon expenditures, the \u2018industry\u2019 view of Pentagon energy consumption is far more anemic. Not only does the Pentagon consume significantly less energy than we would expect from its share of world income, this energy share has declined dramatically.
\nThe net result, as Figure\u00a014 demonstrates, is that the US military\u2019s business-to-industry index has more than doubled over the last fifty years. And if we take the absolute value of this index seriously (which is a speculative exercise), it suggests that the Pentagon\u2019s stupendous budget may overestimate its war-making power by more than a factor of seven.
\n\n![](\"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_gdp_energy.png?w=723&ssl=1\"/)
![](\"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_bti-1.png?w=723&ssl=1\"/)
Since the United States is now a business-dominated country (Figure\u00a012), it makes sense that the US military would exhibit similar behavior (Figure\u00a014). But what\u2019s somewhat surprising is the degree to which Pentagon spending overstates its consumption of energy. (And to be clear, the use of energy is the more realistic indicator of war-making power.)
\nTo characterize this mismatch, it seems fitting to borrow another idea from Thorstein Veblen. Actually, economist Michael Hudson beat me to the analogy. In a recent interview, Hudson compared US weapons to a Rolls-Royce. They\u2019re a technology that exists largely to be seen. Now, the military has a suitably stern phrase for this ostentatious behavior. They call it \u2018power projection\u2019. But given the US military\u2019s apparent deficit of power, perhaps a better term would be conspicuous consumption.
\nThis was Veblen\u2019s term for the behavior of Gilded-Age elites, who had a pathological need to put their wealth on display by parading around objects of great expense. Today, it seems that US military planners have a similar impulse. They feel compelled to procure weapons of ludicrous expense, and to parade them around as a show of force.
\nOf course, this is not to say that US weapons don\u2019t work. They do. But they \u2018work\u2019 in the same way that a Rolls-Royce \u2018works\u2019 as a commuter car. Yes, it gets the job done, but at a cost that doesn\u2019t scale. Or put another way, while the US military boasts about its ability to buy Rolls-Royce weapons, less wealthy armies are busy building unassuming weapons that can be manufactured cheaply at scale \u2014 the war-making equivalent of mass transit.
\nLet me demonstrate this weapons scaling problem with some simple math.
\nWhen Trump launched his unprovoked assault on Iran, it seems that US planners were not prepared for the effectiveness of Iranian drones. And one can understand why. In terms of their ability to \u2018project power\u2019, Iran\u2019s Shahed drones are unimpressive. They\u2019re built from inexpensive fiberglass and styrofoam, piloted by consumer-grade GPS, and deliver a modest explosive payload of up to 100 pounds. But as the US military learned the hard way, this unimpressiveness is the point. The Shahed drone can be mass-produced for as low as $20,000 each, which corresponds to roughly $200 per pound of delivered explosive. Nothing in the US arsenal can compete with this budget-based power.
\nAs an example, take the famed Tomahawk missile, a mainstay of US air assault. Developed in the 1970s, each Tomahawk missile now costs about $2 million to procure. For that price, it delivers about 1000 pounds of explosive payload. Sure, that\u2019s more destructive power than the Shahed drone. But at $2000 per pound of explosive, the Tomahawk is also about ten times more expensive, pound for pound. Hence, for the same price, an arsenal of Shahed drones could deliver far more destruction than an arsenal of Tomahawks.
\nUpping the ante of conspicuous consumption, let\u2019s turn to the F-35 program. With a projected total cost of over $2 trillion, the F-35 project is expected to deliver about 2400 fighter jets. That corresponds to a lifetime cost of over $800 million per jet. Now, if we assume that these jets are used mostly for power projection, a reasonable estimate is that each plane might deliver 80,000 pounds of explosive during its lifetime. (See my calculations in the appendix.) Doing the math, that comes out to about $10,000 per pound of delivered explosive \u2014 a pound-for-pound price tag that\u2019s roughly 50 times more than the Shahed drone.
\nNow, the irony is that in the 21st century, the F-35 is a baroque technology that no one needs, but that US weapons contractors desperately want to build. And in a sense, that\u2019s the point. The F-35 exists not because it\u2019s an efficient war-making investment, but because it\u2019s an extremely profitable weapon to sell. Its bespoke construction allows monopolistic contractors ample opportunity for markup. And so the US military now finds itself in an odd situation. As analyst Alastair Crooke observes, the Pentagon wants not for money, yet is nonetheless plagued by \u201csclerotic supply-lines, long production cycles and minimal weapon inventories.\u201d In short, the Pentagon finds that its booming war \u2018business\u2019 is built on an anemic war \u2018industry\u2019.
\nThe gods of history no doubt had a sense of irony when they gave Donald Trump the keys to the world\u2019s most expensive military. Not every politician is so foolish to mistake stupendous military spending for great military power. But with Trump \u2014 a man who\u2019s never seen a room that couldn\u2019t use more gold-plated decor \u2014 the gods found their mark.
\nAnd so here we are. Convinced of its unmatched power, Trump let his Rolls-Royce military loose on a third-rate army, only to see it humiliated. The gods continue to laugh. While Trump may never understand the joke, we can easily unearth the punchline. You see, unlike the Pentagon, which is a business-dominated institution, the Iranian military is likely the opposite sort of organization \u2014 a place where \u2018business\u2019 is subservient to \u2018industry\u2019.
\nLet me make the case by returning to the business-to-industry index. Figure\u00a015 shows the business-to-industry index for the Pentagon, the United States, and Iran. Unlike the business-leaning United States and the business-dominated Pentagon, Iran is an industry-dominated country. After decades of trade-suppressing US-led sanctions, Iran\u2019s share of global income is now markedly less than its share of global energy use.
\n\n![](\"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/bti_compare.png?w=723&ssl=1\"/)
Of course, the business-to-industry index for the Iranian military itself remains unknown. But let\u2019s suppose that the Iranian military is similar to Iran as a whole. If so, we can immediately see why the Pentagon\u2019s spending power mismeasures its military advantage over Iran.
\nIn 2024, the Pentagon\u2019s business-to-industry index was 7.7, while Iran\u2019s business-to-industry index was 0.22. If the Iranian military exists in similar territory, we can surmise that compared to the Pentagon, every dollar of Iranian military spending mobilized more than 30 times more energy. Or put another way, although the Pentagon outspends the Iranian military by two orders of magnitude, its energy advantage is likely much smaller \u2014 potentially as little as a factor of four. If we add in Iran\u2019s fortress geography and the globe-spanning nature of US forces, we can see how Iran might prevail against a military that, in terms of finance, seems far more powerful.
\nAt any rate, it\u2019s fitting that Donald Trump is the politician to discover this trick of accounting, because he\u2019s the last person who\u2019ll get the joke. Indeed, there seems to be no irony in Trump\u2019s proposal for a \u2018golden dome\u2019 \u2014 a missile-defense boondoggle that (if it ever gets built) will be a gilded prize for military contractors. And then there\u2019s the proposed Arc de Trump. Sure, it\u2019s a grotesque nod to Napoleon. But it\u2019s also an unwitting metaphor for Trump\u2019s unfolding Waterloo moment. Money may buy glittering gold, but it doesn\u2019t always buy military might.
\nHi folks, Blair Fix here. I\u2019m a crowdfunded scientist who shares all of my (painstaking) research for free. If you think my work has value, consider becoming a supporter. You\u2019ll help me continue to share data-driven science with a world that needs less opinion and more facts.
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Share of world military spending in 2024 (Figure\u00a01)
\nData is from the World Bank, series MS.MIL.XPND.CD (Military expenditure in current USD).
\nUS military spending (Figures\u00a02 \u2013 4, 6 \u2013 8, 13 \u2013 15)
\nData is from the following sources:
\nUS consumer price index (Figure\u00a03)
\nData is from the following sources:
\nUS GDP and GDP per capita (Figure\u00a04, 7, 11, 12, & 15)
\nFor GDP per capita calculations, population data is from:
\nPentagon spending paid to top 100 US defense contractors (Figure\u00a05)
\nSpending data is from the following sources:
\nS&P 500 earnings per share (Figure\u00a06)
\nData is from Robert Shiller\u2019s website.
\nWorld GDP (Figures\u00a08, 11 \u2013 15)
\nData is from the following sources:
\nNote that the data prior to 1960 comes with some major caveats. The Maddison database reports global \u2018real\u2019 GDP, measured in terms of purchasing power parity. That is, within each country, GDP is measured relative to some common basket of goods. Hence, the Maddison-database goal is not to measure nominal income, but rather to measure the standard of living, as captured by consumer purchasing power. Given this premise, it\u2019s not ideal to use the Maddison data as a measurement of nominal world income. Nonetheless, when it comes to deep historical GDP data, the Maddison database is the only game in town.
\nHere\u2019s how I convert the Maddison data into a measure of nominal world GDP. First, I assemble a long-term dataset for the US GDP deflator as follows:
\nWith this GDP deflator data, I re-inflate the Maddison \u2018real\u2019 GDP data (reported in PPP USD) to create a proxy for world nominal GDP, measured in USD. Like I said, this calculation makes some conceptual leaps that are not strictly valid, so treat it with a grain of salt.
\nUS military conflicts (Figures\u00a09 & 10)
\nData is from the Uppsala Conflict Data Program, UCDP/PRIO Armed Conflict Dataset version 25.1. (I crawl the UCDP and search for any conflicts in which the United States is a belligerent.) For conflicts in which the US attacked a non-state actor, I\u2019ve placed the conflict inside the country where this non-state actor was active. Note that in Figure\u00a010, the location of individual conflict points is randomly generated by sampling within the geography of the host country.
\nUS energy consumption (Figures\u00a011, 12, & 15)
\nData is from the following sources:
\nWorld energy consumption (Figures\u00a011 \u2013 15)
\nData is from the following sources:
\nPentagon energy use (Figures\u00a013 \u2013 15)
\nEnergy-use data for the Department of Defense is from the Federal Energy Management Program, Comprehensive Annual Energy Data, Table A-4: Primary Energy Use by End-Use Sector and Energy Type, by Federal Agency. (Note that I use data for \u2018primary energy\u2019, not the also-reported \u2018site-delivered energy\u2019.)
\nIranian GDP and energy use (Figure\u00a015)
\nData for Iranian GDP is from the World Bank, series NY.GDP.MKTP.CD (GDP in current USD). Data for Iranian energy use is from the Energy Institute Statistical Review of World Energy, series TES_EJ (total energy supply in exajoules).
\nF-35 calculations
\nHere is my calculations for the mass of explosives dropped by an F-35 during its lifespan. I assume that the vast majority (99%) of sorties are for power projection or training, and not for battle:
\nNote: If war breaks out and F-35s are used intensively for dropping bombs, then the combat rate will increase significantly. But at the same time, flying into a battle zone involves the risk of getting shot down, which would reduce the average service life per plane. At any rate, strapping pilots onto flying bomb-dropping machines is a relic of the 20th century. Today, it\u2019s little more than an expensive stunt (much like manned space flight).
\n![\"\u21a9\"](\"https://s.w.org/images/core/emoji/17.0.2/72x72/21a9.png\")
\ufe0e\n\ufe0e\n\ufe0e\n\ufe0e\n\ufe0e\nMatters came to a head during the English Civil War (1642 to 1651), which saw a decade of conflict between Royalists and Parliamentarians. Although the Parliamentarians won the war, the monarchy remained intact, and English kings continued to test the limits of their military powers. In 1688, King James II went a bit too far and was deposed in the Glorious Revolution. A year later, Parliament passed the Bill of Rights of 1689, which, among other things, prohibited the king from keeping a peacetime standing army without parliamentary consent.
\nFast forward to the American Revolution. When American colonists overthrew British rule, they framed their grievances in terms of the English Bill of Rights. In particular, the Declaration of Independence charged the British king with maintaining a peacetime standing army without the consent of colonial legislatures. When colonists later drafted the American Constitution, they made sure to guard against standing armies by giving Congress control over military spending, and by putting a two-year limit on all military appropriations.\ufe0e\n
\ufe0e\n\n
\nDoctorow, C., & Giblin, R. (2022). Chokepoint capitalism: How big tech and big content captured creative labor markets and how we\u2019ll win them back. Beacon Press.\n
\n\nFix, B. (2019). The aggregation problem: Implications for ecological and biophysical economics. BioPhysical Economics and Resource Quality, 4(1), 1.\n
\n\nNitzan, J., & Bichler, S. (2009). Capital as power: A study of order and creorder. New York: Routledge.\n
\n\nVeblen, T. (1904). The theory of business enterprise. New York: Martino Fine Books.\n
\n\nVeblen, T. (1923). Absentee ownership: Business enterprise in recent times: The case of America. Transaction Pub.\n
\nThe post The Business of War and the Mismeasurement of Military Might appeared first on Economics from the Top Down.
","doi":"https://doi.org/10.59350/mzq7f-nne35","funding_references":null,"guid":"https://economicsfromthetopdown.com/?p=15178","id":"649b55d2-eb9e-41e5-816a-d1ee02351de0","image":null,"images":[{"height":"150","sizes":"(max-width: 150px) 100vw, 150px","src":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_biz_cover.png?resize=150%2C150&ssl=1","srcset":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_biz_cover.png?resize=150%2C150&ssl=1, https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_biz_cover.png?resize=450%2C450&ssl=1, https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_biz_cover.png?resize=60%2C60&ssl=1, https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_biz_cover.png?resize=550%2C550&ssl=1, https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_biz_cover.png?zoom=2&resize=150%2C150&ssl=1","width":"150"},{"src":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_pie.png?w=723&ssl=1"},{"src":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_nominal.png?w=723&ssl=1"},{"src":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_rel_cpi.png?w=723&ssl=1"},{"src":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_rel_gdp_pc.png?w=723&ssl=1"},{"src":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/top_contractors.png?w=723&ssl=1"},{"src":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_rel_eps.png?w=723&ssl=1"},{"src":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_us_gdp.png?w=723&ssl=1"},{"src":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_world_gdp.png?w=723&ssl=1"},{"src":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/us_conflicts.png?w=723&ssl=1"},{"src":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/conflicts_map.png?w=723&ssl=1"},{"src":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/us_gdp_energy.png?w=723&ssl=1"},{"src":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/us_bti-1.png?w=723&ssl=1"},{"src":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_gdp_energy.png?w=723&ssl=1"},{"src":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/military_bti-1.png?w=723&ssl=1"},{"src":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2026/05/bti_compare.png?w=723&ssl=1"},{"alt":"member_button","src":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2023/05/supporter_button-1.png?w=220&ssl=1"},{"src":"https://i0.wp.com/economicsfromthetopdown.com/wp-content/uploads/2021/03/by.png?w=150&ssl=1"},{"alt":"\u21a9","src":"https://s.w.org/images/core/emoji/17.0.2/72x72/21a9.png"},{"alt":"\u21a9","src":"https://s.w.org/images/core/emoji/17.0.2/72x72/21a9.png"},{"alt":"\u21a9","src":"https://s.w.org/images/core/emoji/17.0.2/72x72/21a9.png"},{"alt":"\u21a9","src":"https://s.w.org/images/core/emoji/17.0.2/72x72/21a9.png"},{"alt":"\u21a9","src":"https://s.w.org/images/core/emoji/17.0.2/72x72/21a9.png"},{"alt":"\u21a9","src":"https://s.w.org/images/core/emoji/17.0.2/72x72/21a9.png"},{"alt":"\u21a9","src":"https://s.w.org/images/core/emoji/17.0.2/72x72/21a9.png"}],"indexed":true,"indexed_at":1779469119,"language":"en","parent_doi":null,"published_at":1779468058,"reference":[],"registered_at":0,"relationships":[],"rid":"reebk-d5t80","status":"active","summary":"Your browser does not support the audio tag. Download: PDF | EPUB | MP3 | WATCH VIDEO\n\n America continues to confuse military spending with true strength.\n\n\u2014 David Rothkopf According to US warmongers, the American military is the most powerful fighting force that has ever existed \u2014 a war machine so vast and terrible that enemies everywhere tremble in its path. Boasts aside, the US military is surely unrivalled in at least one regard.","tags":["Business-to-industry Index","Energy","Iran","Iran War","Military Contractors"],"title":"The Business of War and the Mismeasurement of Military Might","updated_at":1779468394,"url":"https://economicsfromthetopdown.com/2026/05/22/the-business-of-war-and-the-mismeasurement-of-military-might/","version":"v1"}},{"document":{"abstract":null,"archive_url":null,"authors":[{"contributor_roles":[],"family":"Turner","given":"Stephen D."}],"blog":{"archive_collection":null,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":[{"name":"Stephen Turner"}],"canonical_url":null,"category":"biologicalSciences","community_id":"382941a7-2ffa-41df-8bbb-5f772188517f","created_at":1734172613,"current_feed_url":null,"description":"A practicing data scientist's take on AI, genomics, biosecurity, and the ways AI is reshaping how science gets done. Weekly updates from the field. Occasional notes on programming.","doi":null,"doi_as_guid":false,"favicon":null,"feed_format":"application/rss+xml","feed_url":"https://blog.stephenturner.us/feed","filter":null,"funding":null,"generator":"Substack","generator_raw":"Substack","home_page_url":"https://blog.stephenturner.us/","id":"bffe125c-3dfa-4f25-998f-e62878677c7c","indexed":true,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":"https://bsky.app/profile/stephenturner.us","prefix":"10.59350","registered_at":0,"relative_url":null,"ror":null,"secure":true,"slug":"stephenturner","status":"active","subfield":"1311","subfield_validated":true,"title":"Paired Ends","updated_at":1779440786.366907,"use_api":null,"use_mastodon":false,"user_id":"ae63ef98-7475-4cc1-b3eb-244d5e096f0f"},"blog_name":"Paired Ends","blog_slug":"stephenturner","content_html":"Big week in AI in life sciences (AIxBio). The Nature drop this week included three papers on AI scientists alongside an editorial and a comment piece pushing back on the whole project. Add \u2019s three-part series on AI for biology, a new RAND/Helena workshop report on AIxBio mitigations, \u2019s take on AI in genomics, and a new study on AI in peer review and I\u2019m landing on a theme this week: how fast should we let AI into the production of biological knowledge, and what gets lost if we don\u2019t slow down to ask?
\u2019s case for shaping AI-for-biology before it shapes us
RAND and Helena on AIxBio mitigations
on the state of AI in genomics
Nature\u2019s AI scientists week, and the editorial pushback
45 expert scientists review the reviewers
(Johns Hopkins, Center for Health Security) published a three-part series last week on shaping AI progress for biology and biosecurity.
Part 1 sets up the series. AI will compress decades of biological research into years, but cures won\u2019t arrive by default, and the same systems that enable them can lower the barrier to weaponizing pathogens. We need proactive policy on both sides.
Part 2 is where it gets interesting. Pannu lays out what she calls autonomous biological discovery: AI systems that automate every step of the research cycle, including managing the cycle itself, with orgs like Isomorphic Labs, FutureHouse, Ginkgo Bioworks and others entering the fray.
AI-enabled feedback loops will be able to extend beyond this, exploring parts of biological space that nature has not.
Evolution selects for reproductive success and gets stuck in fitness valleys. AI-driven design doesn\u2019t have that constraint. Whether that\u2019s a feature or a terrifying bug depends on what you\u2019re designing.
Part 3 centers on smallpox eradication: 171 years between Jenner\u2019s cowpox demonstration in 1796 and Henderson\u2019s 1967 campaign, and only 10 of those years were spent actually eradicating. Her conclusion is that the bottleneck wasn\u2019t tech, it was coordination and political will, so even if AI drives the marginal cost of biology research to zero, we shouldn\u2019t expect cures to deploy themselves.
RAND and Helena released the conference proceedings (full PDF here) from a January 2026 workshop on AI-enabled biological threats. 22 participants from frontier labs, biotech, biosecurity, and academia, working over two days in DC under Chatham House rules, with three threat scenarios: a millenarian nonstate group releasing a novel influenza A, an agroterrorism scenario targeting US wheat with an engineered fungal pathogen, and a state-sponsored insider attack on a semiconductor plant using a biofilm-forming bacterium. Scenarios were deliberately compressed and the document withholds specifics for infohazard reasons.
![\"\"](\"https://substackcdn.com/image/fetch/$s_!9tUv!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb0f9b385-73bc-47f3-96db-79701af448b6_1012x1192.png\")
The pandemic group prioritized pathogen-agnostic physical defenses (high-quality PPE, indoor air quality with filtration and UV), a voluntary credentialing system called \u201cBioTrust\u201d modeled on ORCID, and AI \u201cguardian models\u201d for intent monitoring. The agroterrorism group went after holistic biosurveillance, information-sharing modeled on the Kansas Intelligence Fusion Center, and synthetic DNA screening for agricultural pathogens (which gets less attention than human-pathogen screening, and the participants thought that was the most well-scoped problem of the bunch). The critical infrastructure group went hardest on LLM-side interventions: investing in safeguards to better infer intent from prompt patterns, information-sharing between LLM companies via something like the Frontier Model Forum, federated cross-platform behavior analysis, and (this one is interesting) training LLMs to de-escalate malicious intent by adapting techniques from suicide prevention hotlines.
The participants (which included Twist Bioscience, Anthropic, Microsoft, SecureBio, Los Alamos, and others) kept running into the same wall, which the report names explicitly:
A central theme was that technical feasibility and political backing together determine a mitigation\u2019s success.
I.e., most of these will fail without sustained funding and political will, and almost none of them have either. The other recurring caveat was attribution. AI-enabled biological incidents may remain unattributed indefinitely, which weakens deterrence and complicates response authority.
ran the University of Chicago\u2019s annual genetics, genomics, and systems biology symposium last week, and turned the speaker lineup into seven points on the current state of AI in genomics.
The whole post is worth reading (<10 minutes). I\u2019m just going to highlight a few.
First, the scaling laws may not hold for DNA. Evo 2 is 40 billion parameters trained on 9 trillion nucleotides spanning every domain of life. But Vishniakov et al. (2025) compared seven genomic foundation models against randomly initialized baselines of matched architecture across 52 tasks. The random baselines often matched or beat the pretrained models. Tang et al. (2025) found that raw one-hot encoded sequences are competitive with learned DNA-LM representations on regulatory genomics tasks. As Alex Lu put it at the symposium, DNA isn\u2019t natural language: low signal-to-noise, vast repetitive tracts, no obvious word or sentence analogs, and sparse functional elements that interact combinatorially across long distances.
Second, Arjun Krishnan\u2019s rule of thumb on benchmarks:
The best model is usually the one that is consistently number 2 in benchmarks across the literature.
Whoever publishes a model also designs the benchmark, and the benchmark almost always flatters the model. A model that\u2019s consistently competitive but rarely first-place is more likely to be genuinely strong than one that wins on the benchmark its own authors built. I\u2019m stealing this.
Third, toward the end, is a succinct rule for trainees using AI tools:
If you can validate what the AI produces, namely, if you can do the task yourself comfortably and check whether the AI did it correctly, then you can probably use AI to do the task. Otherwise, you should probably do it yourself, even if it feels hard.
This is the cleanest articulation of the trainee-and-AI problem I\u2019ve seen. I\u2019ve written about this before, highlighting work from a new colleague and co-author, Arjun Krishnan:
The friction of doing it the slow way is often the friction of actually learning, the \u201cproductive struggle\u201d I\u2019ve written about here before. An AI tool that produces output you can\u2019t evaluate is just a black box you\u2019re forced to trust. I\u2019d extend that beyond trainees, frankly.
On Tuesday, Nature published three full-length papers on AI scientists, an editorial that hedges, and a comment piece that pushes back. All on the same day.
![\"\"](\"https://substackcdn.com/image/fetch/$s_!OMAo!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F83677cd0-18cf-4891-87d4-066d4be0443b_3594x1887.jpeg\")
Ghareeb, A. E. et al. A multi-agent system for automating scientific discovery. Nature 1\u20133 (2026) doi:10.1038/s41586-026-10652-y.
Ayg\u00fcn, E. et al. An AI system to help scientists write expert-level empirical software. Nature 1\u20133 (2026) doi:10.1038/s41586-026-10658-6.
Gottweis, J. et al. Accelerating scientific discovery with Co-Scientist. Nature 1\u20133 (2026) doi:10.1038/s41586-026-10644-y.
Messeri, L. & Crockett, M. J. The uncritical adoption of AI in science is alarming \u2014 we urgently need guard rails. Nature 653, 675\u2013676 (2026).
Why AI cannot do good science without humans. Nature 653, 650\u2013650 (2026).
The three papers: Google DeepMind\u2019s ERA, an LLM-plus-tree-search system that discovered 40 novel single-cell analysis methods that outperformed the top human methods on a public leaderboard, and 14 COVID hospitalization forecasting models that beat the CDC ensemble. Google\u2019s Co-Scientist, a multi-agent system built on Gemini that helped identify in vitro\u2013validated drug repurposing candidates for acute myeloid leukemia and (in a now-famous demo) recovered an antibiotic-resistance hypothesis that a Imperial College team had spent a decade developing but hadn\u2019t yet published, in days. And FutureHouse\u2019s Robin, which autonomously proposed enhancing RPE phagocytosis as a strategy for dry AMD, identified ripasudil (a clinically used ROCK inhibitor never previously proposed for AMD) as a candidate, validated it in vitro, then proposed an RNA-seq follow-up that fingered ABCA1 as a possible novel target. All hypotheses, experimental directions, data analyses, and main-text figures in the Robin paper were produced by Robin.
These are real results. With that throat-clearing out of the way\u2014
Then there\u2019s the editorial, \u201cWhy AI cannot do good science without humans\u201d, which is mostly anodyne until the closing paragraph:
Scientists should not allow a negative view of AI to drive them away from exploring the possibilities that AI co-scientists might hold for research. Equally, however, they must rise above the din of AI hype and advocate for their own importance, to remind the wider public, funders and fellow researchers that science still needs humanity, and that not every grant proposal need include an AI project.
Again: not every grant proposal need include an AI project.
Once more: not every grant proposal need include an AI project.
I read this as the editorial board, deliberately, on the day they published three papers about AI scientists, telling reviewers and program officers not to use \u201cno AI angle\u201d as a reason to triage a proposal.
The comment piece is more pointed. Lisa Messeri (Yale anthropology) and M. J. Crockett (Princeton psychology) wrote \u201cUncritical use of AI in science needs reality check\u201d. Some empirical claims: Hao et al. (2026) analyzed 41.3 million papers across biology, medicine, chemistry, physics, materials science, and geology and concluded that AI adoption seems to \u201cinduce authors to converge on the same solutions to known problems rather than create new ones.\u201d I wrote about this earlier this year:
Kusumegi et al. (2025) looked at 264,125 papers and found that in LLM-assisted papers, good writing stopped being a useful heuristic for scientific quality. Organization Science audited 6,957 submissions from 2021 to 2026 and found LLM-assisted papers had poorer scientific quality by acceptance rate. The closing argument is about deskilling: cleaning raw data, reading and summarizing the literature, the entry-level grunt work that AI is now offered as a solution for, is also how scientists develop the tacit knowledge needed to supervise AI-assisted workflows. If trainees don\u2019t develop those skills, who oversees the AI?
Three papers showcasing autonomous discovery, an editorial gently telling reviewers not to fetishize AI angles in proposals, and a comment piece arguing that the productivity gains may be hollowing out the next generation. Read them together. Or, if you don\u2019t have time, listen to Nature\u2019s podcast.
One more, and this connects to a paper I co-authored. A preprint went up at arXiv:2605.20668 titled \u201cOn the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists.\u201d Big study. 45 domain scientists annotated reviews of Nature-family papers, comparing official human reviewers against three frontier LLM agents. Headline results:
On aggregate review-item quality, all three AI reviewers exceed the lowest-rated human, and GPT-5.2 exceeds the top-rated human.
AI reviewers raise more significant items but with lower correctness.
Replacing one human reviewer with one AI reviewer minimally erodes panel diversity, because human reviewers themselves surface largely disjoint sets of criticisms.
AI reviewers can augment but not replace a human panel.
Current frontier AI reviewers in an agentic framework provide genuine value on the rigor- and code-heavy aspects of peer review, while systematically failing on the field-context aspects.
On that last point: it\u2019s the same argument that Agnieszka Swiatecka-Urban, Arjun Krishnan, and I argued for in our preprint earlier this year.
Our claim was that AI is best deployed as a rubric enforcer for the systematic, criterion-checkable parts of review (consistency between scores and comments, statistical reporting, completeness of evaluation, internal consistency of reviewer reasoning) while humans retain authority on the parts that depend on argumentative-world knowledge (novelty, feasibility, recognizing creative leaps, judging whether an ambitious proposal might fail spectacularly or succeed brilliantly). The arXiv paper, working with a completely different methodology and 45 domain scientists doing item-level annotation of real Nature-family reviews, lands in the same place. AI is strong on rigor and code, weak on field context.
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Pannu on AI and biosecurity, RAND/Helena AIxBio biosecurity mitigations, Blekhman on genomics AI, Nature\u2019s AI scientists week, AI in peer review.","tags":["Papers","Biosecurity","AI"],"title":"Five Things (May 23, 2026): AI in life sciences","updated_at":1779455466,"url":"https://blog.stephenturner.us/p/five-things-may-23-2026-aixbio","version":"v1"}},{"document":{"abstract":"La sombra de la revoluci\u00f3n mexicana. Usos pol\u00edticos del pasado y la tradici\u00f3n cardenista a inicios de la era de Luis Echeverr\u00eda (1969-1970), por Andr\u00e9s Funes Revisi\u00f3n de c\u00f3mo Luis Echeverr\u00eda \u00c1lvarez utiliz\u00f3 la memoria de L\u00e1zaro C\u00e1rdenas como recurso simb\u00f3lico para reconstruir la legitimidad del r\u00e9gimen priista tras la masacre de Tlatelolco de 1968.","archive_url":null,"authors":[{"contributor_roles":[],"name":"Atarraya"}],"blog":{"archive_collection":null,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":null,"canonical_url":null,"category":"humanities","community_id":"c45eb77a-1580-4fbb-a9a2-11d7b258ec05","created_at":1723914704,"current_feed_url":null,"description":"Nuestras historias","doi":null,"doi_as_guid":false,"favicon":"https://rogue-scholar.org/api/communities/f17066f5-0dbf-48d0-a413-b22a79861a94/logo","feed_format":"application/atom+xml","feed_url":"https://blogatarraya.com/feed/atom/","filter":null,"funding":null,"generator":"WordPress.com","generator_raw":"WordPress.com","home_page_url":"https://blogatarraya.com","id":"7c191eac-fe88-4488-b12c-54a91a009dfb","indexed":true,"issn":null,"language":"es","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.59350","registered_at":1729715978,"relative_url":null,"ror":null,"secure":true,"slug":"atarraya","status":"active","subfield":"1202","subfield_validated":null,"title":"BLOG ATARRAYA","updated_at":1779439011.948398,"use_api":true,"use_mastodon":false,"user_id":"3a4c9f2c-4d20-406c-a15a-25e435f6313b"},"blog_name":"BLOG ATARRAYA","blog_slug":"atarraya","content_html":"\n![\"\"](\"https://i0.wp.com/blogatarraya.com/wp-content/uploads/2026/05/18-Funes-La-sombra.png?resize=770%2C770&ssl=1\")
La sombra de la revoluci\u00f3n mexicana. Usos pol\u00edticos del pasado y la tradici\u00f3n cardenista a inicios de la era de Luis Echeverr\u00eda (1969-1970), por Andr\u00e9s Funes
\n\n\n\nRevisi\u00f3n de c\u00f3mo Luis Echeverr\u00eda \u00c1lvarez utiliz\u00f3 la memoria de L\u00e1zaro C\u00e1rdenas como recurso simb\u00f3lico para reconstruir la legitimidad del r\u00e9gimen priista tras la masacre de Tlatelolco de 1968. Muestra c\u00f3mo el pasado revolucionario se convirti\u00f3 en un terreno de disputa pol\u00edtica en los inicios de los a\u00f1os setenta en M\u00e9xico.
\n\n\n\nAndr\u00e9s Nicol\u00e1s Funes, \u00abLa sombra de la Revoluci\u00f3n mexicana: Usos pol\u00edticos del pasado y la tradici\u00f3n cardenista a inicios de la era de Luis Echeverr\u00eda (1969-1970)\u00bb, Revista Outros Tempos, vol. 22, n\u00fam. 40, 2025, pp. 357-394.
\n\n\n\nTambi\u00e9n disponible en otras plataformas
\n\n\n\n![\"\"](\"https://i0.wp.com/blogatarraya.com/wp-content/uploads/2026/05/Captura-de-pantalla-2026-05-20-a-las-2.53.51-p.m.png?resize=770%2C347&ssl=1\")
Implementing FAIR Workflows: A Proof of Concept Study in the Field of Consciousness is a project funded by the Templeton World Charity Foundation. In this project, DataCite works with a number of partners on providing an exemplar workflow that researchers can use to implement FAIR practices throughout their research lifecycle. This post shares an outcome of the project: a new chapter on persistent identifiers (PIDs) contributed to The Turing Way during the November 2025 Book Dash and was merged into the book in February 2026.
\n\n\n\n\n\n\n\nThe Turing Way is a community-driven, openly developed handbook for reproducible, ethical, and collaborative research, supported by The Alan Turing Institute. Organized across five guides and a community handbook, it brings together contributors from research, data science, and infrastructure communities to co-create guidance on open and reproducible practices. Its modular structure and openly licensed content have made it a widely referenced resource across the open scholarship ecosystem.
\n\n\n\nThe Turing Way\u2019s core mechanism for developing new content is the\u00a0Book Dash: a week-long collaborative event during which contributors propose, draft, review, and merge chapters. In November 2025, the Implementing FAIR Workflows project team participated in a Book Dash and integrated key outputs from the project into the Turing Book.
\n\n\n\nThe Implementing FAIR Workflows project has been a venue through which DataCite and partners develop practical guidance for PID and metadata adoption across the research lifecycle. The alignment with The Turing Way was clear from the outset: both initiatives aim to lower the barrier to FAIR practices for working researchers.
\n\n\n\nA review of the existing handbook showed that “assign a DOI” appeared more than fifty times across multiple chapters, but no single chapter explained what PIDs are, how they work, or how major PID systems \u2014 such as DataCite, Crossref, ORCID, and ROR \u2014 connect to support open scholarly infrastructure. The project team\u2019s contribution had two aims: to provide a single reference point for PIDs, and to cross-link PID and metadata content throughout existing chapters on data repositories, documentation and metadata, the FAIR principles, and citable research outputs, so that recommendations to “use a PID” sit alongside clear, practical guidance on how to do so.
\n\n\n\nMaterial developed through the FAIR Workflows project was adapted to The Turing Way’s audience and editorial style. The bulk of the content was drafted during the Book Dash, with the following months dedicated to peer review and revision through GitHub pull requests. The result is a new\u00a0Persistent Identifiers chapter in the Research Data Management section, two new chapters on connection metadata and version management in the Communication guide, and cross-references woven throughout the book.
\n\n\n\nThis contribution would not have reached completion without the thorough review and sustained support of Jim Madge (The Alan Turing Institute) and Esther Plomp (University of Aruba), whose guidance on structure, tone, and technical accuracy shaped the final chapter.
\n\n\n\nThe Turing Way is always open to contributions. Researchers, educators, and practitioners are welcome to consult the new chapter, suggest edits, or propose related additions through the GitHub repository.\u00a0
\n\n\n\nWe look forward to the wider dissemination and adoption of open and FAIR research best practices through the Turing Way community. From the perspective of the FAIR Workflows project, we also hope to see a more embedded approach to PID implementation throughout the research lifecycle, from which both researchers and research organizations can benefit.\u00a0
\n\n\n\n\n\n\n\n![\"\"](\"https://datacite.org/wp-content/uploads/2026/05/image-1024x218.png\")
This project was made possible through the support of a grant from Templeton World Charity Foundation, Inc. The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of Templeton World Charity Foundation, Inc.
\n\n\n\n\n","doi":"https://doi.org/10.5438/aah4-gj16","funding_references":null,"guid":"https://datacite.org/?p=15168","id":"be728b36-4324-43e6-a085-edee459cae4f","image":"https://datacite.org/wp-content/uploads/2026/05/Datacite_Social_Media_Blog_post_banner_Turing_Way_2026_1.png","images":[{"src":"data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7"},{"height":"218","sizes":"(max-width: 1024px) 100vw, 1024px","src":"https://datacite.org/wp-content/uploads/2026/05/image-1024x218.png","srcset":"https://datacite.org/wp-content/uploads/2026/05/image-1024x218.png, https://datacite.org/wp-content/uploads/2026/05/image-300x64.png, https://datacite.org/wp-content/uploads/2026/05/image-768x164.png, https://datacite.org/wp-content/uploads/2026/05/image.png","width":"1024"}],"indexed":false,"indexed_at":0,"language":"en","parent_doi":null,"published_at":1779361980,"reference":[],"registered_at":0,"relationships":[],"rid":null,"status":"active","summary":"\n Implementing FAIR Workflows\n\n\n : A Proof of Concept Study in the Field of Consciousness is a project funded by the Templeton World Charity Foundation. In this project, DataCite works with a number of partners on providing an exemplar workflow that researchers can use to implement FAIR practices throughout their research lifecycle.\n","tags":["FAIR","FAIR Workflows","Projects"],"title":"FAIR Workflows Meets The Turing Way: A Community Contribution on Persistent Identifiers for Reproducible Research Practices","updated_at":1779377334,"url":"https://datacite.org/blog/fair-workflows-meets-the-turing-way-a-community-contribution-on-persistent-identifiers-for-reproducible-research-practices/","version":"v1"}},{"document":{"abstract":"Submitting a blog to the Rogue Scholar science blog archive requires filling out a simple form, and sometimes answering a few additional questions via email. In recent months I haven't caught up with the submissions and decided to implement a few changes. Participating in Rogue Scholar doesn't cost anything to authors or readers.","archive_url":null,"authors":[{"affiliation":[{"name":"Front Matter"}],"contributor_roles":[],"family":"Fenner","given":"Martin","url":"https://orcid.org/0000-0003-1419-2405"}],"blog":{"archive_collection":22096,"archive_host":null,"archive_prefix":"https://wayback.archive-it.org/22096/20231101172748/","archive_timestamps":[20231101172748,20240501180447,20241101172601],"authors":[{"name":"Martin Fenner","url":"https://orcid.org/0000-0003-1419-2405"}],"canonical_url":null,"category":"computerAndInformationSciences","community_id":"91dd2c24-5248-4510-9c2b-30b772bf8b60","created_at":1672561153,"current_feed_url":null,"description":"The Front Matter Blog covers the intersection of science and technology since 2007.","doi":null,"doi_as_guid":false,"favicon":"https://rogue-scholar.org/api/communities/15a362ea-8138-42b8-917f-1840a92addf8/logo","feed_format":"application/atom+xml","feed_url":"https://blog.front-matter.de/atom","filter":null,"funding":null,"generator":"Ghost","generator_raw":"Ghost 5.52","home_page_url":"https://blog.front-matter.de","id":"74659bc5-e36e-4a27-901f-f0c8d5769cb8","indexed":true,"issn":"2749-9952","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":"https://hachyderm.io/@mfenner","prefix":"10.53731","registered_at":1729685319,"relative_url":null,"ror":null,"secure":true,"slug":"front_matter","status":"active","subfield":"1710","subfield_validated":null,"title":"Front Matter","updated_at":1779439649.343466,"use_api":true,"use_mastodon":true,"user_id":"8498eaf6-8c58-4b58-bc15-27eda292b1aa"},"blog_name":"Front Matter","blog_slug":"front_matter","content_html":"Submitting a blog to the Rogue Scholar science blog archive requires filling out a simple form, and sometimes answering a few additional questions via email. In recent months I haven't caught up with the submissions and decided to implement a few changes.
Participating in Rogue Scholar doesn't cost anything to authors or readers. In turn, participating science blogs must make their content available as full-text via an RSS (or Atom or JSON) Feed with a Creative Commons Attribution (CC-BY) license.
Starting today, blog authors have to agree to these two additional statements in the blog submission form:
Also starting today, all blog submissions (author name and blog URL) are posted in the Rogue Scholar Slack Community (in the new public #blog-submissions channel) for two weeks. This allows the Rogue Scholar community to ask questions and blog authors to give more information about their blog.
Rogue Scholar is a free resource for authors and readers, but it costs money to maintain the infrastructure and support the community. For this reason, not all blog submissions can be accepted, or it takes longer until new blogs are included. Please reach out if your submission is delayed or you have questions.
With currently 191 participating science blogs and 49,965 blog posts, Rogue Scholar is on track to archive 50,000 blog posts in May, and I look forward to many more participating science blogs. Please reach out with questions or comments via Slack, email, Mastodon, or Bluesky.
Artificial Intelligence (AI) is rapidly gaining ground in science. Whereas the use of algorithms was until recently limited to a few specific fields, the availability of generative AI tools such as ChatGPT offers attractive possibilities for virtually all disciplines. Generative AI (GAI) can automatically generate content, such as text, images, or programming code, at a user\u2019s request. With the help of GAI, researchers can write articles faster, analyse larger datasets, and gather relevant literature more easily. Generative AI can also facilitate communication with parties outside the scientific community, for example, by producing accessible texts or animations.
However, GAI also frequently produces biased and inaccurate analyses. In an open letter, over 1,800 scientists based in the Netherlands warn of the dangers GAI poses to scientific integrity, privacy, sustainability, and other public values. Scientific journals are currently being flooded with fake articles, sometimes even listing authors who are unaware of their inclusion. Furthermore, the use of the most common GAI systems increases the dependence of research institutions on a handful of extremely powerful technology companies based in the United States. Finally, there are serious concerns about the amount of water and energy consumed by data centres, as well as the exploitation of workers in the Global South who are assigned the task of selecting disturbing texts and images into categories such as sexual abuse and violence.
Currently, a large responsibility rests with individual scientists to weigh these opportunities and risks and to assess if and how GAI can be used responsibly in science. Dutch universities advise scientists against using tools like ChatGPT but allow their staff the freedom to make their own decisions.
I wonder whether it is fair to ask individual researchers to handle this responsibility. The workload at universities is high, and competition is fierce. For young scientists, that one extra publication in a prestigious journal or that additional grant from NWO can make the difference between a permanent position and a temporary contract. Do scientists have sufficient knowledge of the technical limitations and sustainability impact of the use of large language models to make an informed decision? And can you trust that they will always prioritise public and scientific values, even when GAI can (seemingly) take a lot of work off their hands? Surveys among scientists worldwide show that many scientists are well aware of the errors and inaccuracies caused by the use of GAI, yet still use the available tools to generate summaries or have parts of their publications written.
Universities must therefore quickly establish standards or rules for the responsible use of GAI. This first and foremost requires a political assessment of conflicting values such as efficiency, autonomy, sustainability, and justice, on which the Dutch Parliament must decide: how important is it to accelerate and broaden scientific research, even in the current geopolitical landscape where data and AI are used as instruments of power? What is the moral bottom line in terms of sustainability and (global) justice, and at what cost are we willing to increase the efficiency of scientific research? In addition, a translation is needed from traditional scientific values to a new reality. What do integrity, reliability, and independence mean when using GAI, and how can we safeguard these values? For which applications is the use of GAI permissible, and what conditions must GAI tools meet? It is not certain that the Dutch science system can provide a single universal answer to this. It may be necessary for different disciplines to set out to formulate their own rules, because it is important to do justice to the great diversity of scientific practices and the varying ways in which scientists use GAI.
Drafting and enforcing new rules will, of course, not be easy. But scientific communities must do it, nonetheless. GAI challenges established mechanisms of scientific quality assurance, and we must find a response to this. My colleagues and I at the Rathenau Instituut are currently investigating how GAI is changing the nature of scientific knowledge, the demands its use places on scientific quality assurance, and what competencies scientists need to use GAI effectively.
A promising development is that the Dutch technology institute TNO is developing an alternative large language model, that will be more reliable, independent and transparent, called GPT-NL. Moreover, several Dutch universities are developing their own AI tools for science, which are delivering increasingly better performance. The EU also intends to invest heavily in this area. This offers the opportunity to develop alternative GAI technology that better aligns with European values and the rigor that forms the foundation of responsible science. But we cannot wait for that. As long as these alternatives remain insufficiently developed, scientists will continue to rely on GAI tools, which are fraught with problems. Setting limits on permissible use, therefore, requires our immediate attention.
This blogpost is a translation of a Dutch opinion piece published on ScienceGuide.nl and the Rathenau Institute\u00a0website. The author has made use of DeepL (free version) to translate the text to English.
Header image by\u00a0Anna Riepe & FARI on\u00a0Better Images of AI.
","doi":"https://doi.org/10.59350/mxm8v-ws533","funding_references":null,"guid":"https://www.leidenmadtrics.nl/articles/universities-must-act-now-to-regulate-ai-use-in-science","id":"de4cb48b-244a-4e32-9d3d-e6f9347e3cfd","image":null,"images":[],"indexed":true,"indexed_at":1779355341,"language":"en","parent_doi":null,"published_at":1779351060,"reference":[],"registered_at":0,"relationships":[],"rid":"xwxkb-xw732","status":"active","summary":"Artificial Intelligence (AI) is rapidly gaining ground in science. Whereas the use of algorithms was until recently limited to a few specific fields, the availability of generative AI tools such as ChatGPT offers attractive possibilities for virtually all disciplines. Generative AI (GAI) can automatically generate content, such as text, images, or programming code, at a user\u2019s request.","tags":[],"title":"Universities must act now to regulate AI-use in science","updated_at":1779355335,"url":"https://www.leidenmadtrics.nl/articles/universities-must-act-now-to-regulate-ai-use-in-science","version":"v1"}},{"document":{"abstract":null,"archive_url":null,"authors":[{"contributor_roles":[],"family":"Hartley","given":"Kim","url":"https://orcid.org/0000-0002-4345-9044"},{"contributor_roles":[],"family":"Barker","given":"Michelle","url":"https://orcid.org/0000-0002-3623-172X"},{"contributor_roles":[],"family":"Aragon","given":"Selina","url":"https://orcid.org/0000-0001-9938-0522"},{"contributor_roles":[],"family":"Hong","given":"Neil Chue","url":"https://orcid.org/0000-0002-8876-7606"}],"blog":{"archive_collection":22149,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":[{"name":"Research Software Alliance"}],"canonical_url":null,"category":"computerAndInformationSciences","community_id":"79c5ab82-d540-413c-a8cf-3e55d0135a40","created_at":1682035200,"current_feed_url":null,"description":"Recent content on Research Software Alliance","doi":null,"doi_as_guid":false,"favicon":null,"feed_format":"application/atom+xml","feed_url":"https://www.researchsoft.org/feed.xml","filter":null,"funding":null,"generator":"Hugo","generator_raw":"Hugo 0.76.5","home_page_url":"https://researchsoft.org/","id":"9f582ac6-f8b2-46b6-98ab-a7def5e3faba","indexed":false,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.59350","registered_at":1729930725,"relative_url":null,"ror":null,"secure":true,"slug":"researchsoft","status":"active","subfield":"1802","subfield_validated":null,"title":"Research Software Alliance","updated_at":1779440544.986415,"use_api":null,"use_mastodon":false,"user_id":"9bbc9e05-69d3-481e-838f-33f1acf7aef9"},"blog_name":"Research Software Alliance","blog_slug":"researchsoft","content_html":"\n\n\n![\"My](\"https://researchsoft.org/blog/2026-05-21/IRSC26-Banner_hu_eace69442c8e4fb7.webp\")
By Kim Hartley, Michelle Barker, Selina Aragon, Neil Chue Hong
\nBuilding on a longstanding collaboration, ReSA is delighted to partner with the Software Sustainability Institute (SSI) for the first International Research Software Conference (IRSC).
\nInternationally recognised for shaping research software policy and practice, SSI has led training, community building, and advocacy activities since 2010. As the first organisation dedicated to improving software in research, it has played a vital role in the UK and worldwide in advancing research culture, expanding access to training, and working with partners to develop policies that better recognise and support software as a fundamental component of research. Through its Fellowship Programme, community events, training, policy work, and collaborations with institutions, funders and international partners, SSI has helped establish research software as a fundamental component of modern research.
\nAs a ReSA Founding Member and long-time partner, SSI has also been central to ReSA\u2019s development from the beginning, helping to shape its role as an international alliance for the research software community. ReSA Founding Members express their deep commitment to delivering the ReSA vision that research software and those who develop and maintain it are recognised and valued as fundamental and vital to research worldwide. To do this, Founding Members provide resources needed to support ReSA in its aim to bring research software communities together to collaborate on the advancement of the research software ecosystem.
\nThe partnership builds on shared work to improve how research software is recognised, funded, and supported, such as the Amsterdam Declaration on Funding Research Software Sustainability (ADORE.software) and the ADORE.software Toolkit. SSI also plays an active role across ReSA\u2019s activities, contributing to forums, task forces, resources, and events, including the recent workshop on Research Software Engineering in the Age of Generative AI: Building a Community Vision.
\nThrough its Collaborations Workshop and Research Software Camps series, SSI has built a strong track record of convening the research software community around emerging challenges and shared priorities. The SSI Collaborations Workshop is particularly recognised as a participatory, community-led event that brings together researchers, research software engineers, developers, funders, policy professionals, and infrastructure leaders to exchange practice, build collaborations, and generate practical outputs for the wider community. Alongside the more focused Research Software Camps, these activities demonstrate SSI\u2019s ability to create inclusive spaces where ideas are tested and communities are strengthened. Together, ReSA and SSI bring complementary strengths to deliver the first International Research Software Conference: ReSA\u2019s leadership in global research software community coordination and international collaboration, and SSI\u2019s longstanding expertise in research software policy, practice, training, and community building.
\n\n\n\n \u201cIRSC is an important and timely opportunity to bring the international research software community together around a shared agenda: recognising research software as essential research infrastructure, supporting the people who develop and maintain it, and strengthening the policies and practices that enable it to thrive. SSI is delighted to partner with ReSA in helping to shape and deliver this inaugural conference.\u201d\n
\n
\n \u2014 Neil Chue Hong, Director, Software Sustainability Institute\n
This partnership reflects that shared commitment. Neil Chue Hong also serves on the ReSA Steering Committee and chairs its financial subcommittee, further strengthening ties between the organisations. In addition, Kyro Hartzenberg, SSI Event Manager, is providing in-kind support for IRSC26, contributing valuable expertise to the delivery of the conference.
\nReSA is proud to partner with SSI in delivering IRSC and building a global platform that brings together the research software community.
\nLearn more about SSI at www.software.ac.uk.
\nSponsorship opportunities for IRSC are still available. Organisations interested in supporting the conference and engaging with the global research software community can learn more at: https://www.researchsoft.org/irsc/sponsorship/ or contact ReSA at info[at]researchsoft.org.
\nToday is Global Accessibility Awareness Day, and accessibility has been on our minds lately. We\u2019ve recently completed an internal audit of all our user interfaces, and have added a new accessibility page to our website, where you can find the accessibility documentation that we put together as part of the audit.
\nOf course we want to keep the barriers to participation in Crossref as low as possible for users with various disabilities. But also, more accessible tools work better for everyone. A person\u2019s access needs can change really quickly: even if you consider yourself to be relatively able-bodied, you are only one minor inconvenience away from at least a temporary disability. All it takes is some dazzling sunlight hitting your eye or your phone screen, or perhaps your dog going after a rabbit in an awkward direction while you are holding the lead (ask me how I know!) - and before you know it, you will be relying on accessibility features to navigate the digital and/or physical world for a while.
\nAn accessible user interface is one that you can navigate and interact with by various methods, including a mouse or touchpad, keyboard, screen reader, voice control, and other assistive technologies. It can be used on various screen sizes and supports zooming in or out without losing any content or functionality. It has sufficient colour contrast, doesn\u2019t flash fast-moving images at you, and has a clear structure that can be understood by both humans and machines.
\nIt is worth mentioning that we didn\u2019t only start thinking about accessibility when we started tackling the full audit of our user interfaces in March 2026. For example, Patrick Vale has previously written in this blog about a browser extension he has created to improve the accessibility of DOI links anywhere on the Internet. And we have known for a long time that there were accessibility gaps in many of our tools, but we didn\u2019t have this centrally documented anywhere.
\nWhen we did begin testing all our interfaces for compliance with level AA of the Web Content Accessibility Guidelines (WCAG 2.2) as part of the audit, we knew that some of what we would find was not going to be pretty. In the 26+ years of working with and for the scholarly community, Crossref has built countless tools and reports to offer to members and users, many of which we still maintain today. These are often decades old and have been built in a way that makes it virtually impossible to make them more accessible without rebuilding them entirely. So we know that we will continue to have accessibility gaps for the foreseeable future, but at least now we have a better idea of the scale of the challenge.
\nIt\u2019s also not all doom and gloom: more recently created user interfaces, such our new Metadata Manager, performed much better in the audit than legacy alternatives such as the web deposit form. We found a similar trend when looking at our report interfaces. To illustrate this, compare what happens when running the axe DevTools extension for Google Chrome on a member\u2019s participation report - this is a user interface that was completely re-implemented in 2025. Doing this brings up 26 issues:
\n![\"Screenshot](\"https://www.crossref.org/images/blog/2026/accessibility-issues-participation-reports.png\")
\nMeanwhile, the browsable title list, which has completed a few more trips around the sun, has 254 issues listed:
\n![\"Screenshot](\"https://www.crossref.org/images/blog/2026/accessibility-issues-title-list.png\")
\nIf you\u2019ve read this far, I hope you are convinced that accessibility is more than just ticking boxes on a conformance report. But especially for a global community like ours, there are other, less technical barriers to participation that we have to consider. For example, language is a major accessibility factor: much of what we as Crossref staff write and say is in English. When we host community events, we enable captions, and we try to leave space for these captions at the bottom of our slides.
\nWe have also started experimenting with simultaneous interpretation during our online events, such as our recent project showcase event for the 2026 metadata sprint in S\u00e3o Paulo. You can find recordings of this event in Spanish, Portuguese, and English on our YouTube channel to see the promising results of these efforts.
\nWe are currently addressing the accessibility issues identified in our audit of the Crossmark service. Many Crossref members have implemented the Crossmark button and pop-up on their own platforms and websites, so we thought this was a great place to start the remediation efforts following our audit.
\nWe are also in the process of redesigning our main website, www.crossref.org, following an information architecture review completed in 2025. Making changes to the design and navigation of our website will be the perfect opportunity to make our content not just more discoverable and more understandable, but also more accessible.
\nClearly there is even more to be done, so watch this space for more updates on our accessibility roadmap and improvements. And if you have first-hand experience of using Crossref services and interfaces with assistive technologies, or you have other input or feedback you\u2019d like to share, leave a comment below or start a discussion in our community forum.
\n![](\"https://medical-education.pages.ub.uni-bielefeld.de/research/mes-blog/posts/2026-05-21-workload-publications/FriederichsH_Workload.png\"/)
Wer eine wissenschaftliche Karriere plant, sollte ein realistisches Gef\u00fchl daf\u00fcr haben, wie viel Zeit eine einzelne Originalarbeit verschlingt. Die Ideen sprudeln meist schnell, der Rest dauert.
\nWenn von \u201eZeit f\u00fcr ein Paper\u201d die Rede ist, sind meist zwei sehr unterschiedliche Dinge gemeint. Die eine ist die Kalenderzeit, also die Spanne, die zwischen den Meilensteinen verstreicht, Wochen im Peer Review, Monate bis zur Online-Publikation. Die andere ist die aktive Arbeitszeit, die tats\u00e4chlich am Schreibtisch verbracht wird. Beide korrelieren nur schwach. Ein Manuskript kann monatelang in der Redaktion liegen, ohne dass jemand daran arbeitet. Drei intensive Schreibwochen wiederum bringen oft mehr als hundert Arbeitsstunden zustande, ohne nennenswert Kalender zu verbrauchen.
\nDie \u00fcberw\u00e4ltigende Mehrheit publizierter Studien zur Publikationsdauer misst die Kalenderzeit, weil sie sich aus Journal-Metadaten rekonstruieren l\u00e4sst. Die aktive Arbeitszeit dagegen erfordert Selbstausk\u00fcnfte und ist methodisch deutlich anspruchsvoller. Dieser Blogbeitrag folgt dem Weg eines Manuskripts und sammelt unterwegs ein, was die Forschung jeweils belegen kann.
\nBeginnen wir mit der Arbeitszeit, weil sie am leichtesten untersch\u00e4tzt wird. Song und Kolleg*innen erfassten in einer retrospektiven Befragung den Personenstundenaufwand von 171 publizierten retrospektiven Studien, indem sie die beteiligten Chirurg*innen baten, die Stunden \u00fcber acht Phasen des Forschungszyklus zu sch\u00e4tzen, von der Studienplanung \u00fcber Datenerhebung und Analyse bis zur Revision nach Einreichung. Der Median lag bei 177 Stunden pro Publikation, also rund 22 Achtstundentagen konzentrierter Arbeit einer einzelnen Person, bei einer Spanne von 29 (!) bis 1.287 Stunden (Song et al., 2013). Bemerkenswert ist, dass weder die Zahl der Autor*innen noch die Zahl der untersuchten F\u00e4lle mit dem Gesamtaufwand korrelierte. Mehr Mitautor*innen bedeuten also nicht automatisch weniger Arbeit f\u00fcr die einzelnen K\u00f6pfe.
\nEin erheblicher Teil dieser Stunden flie\u00dft dabei nicht in Erkenntnis, sondern in die Form. LeBlanc und Kolleg*innen befragten 372 Forschende aus 41 L\u00e4ndern und fanden einen Median von 14 Stunden allein f\u00fcr die formelle Formatierung eines einzigen Manuskripts, also f\u00fcr das Anpassen von Layout, Referenzstil und Einreichungsvorgaben. \u00dcber das Jahr summiert sich das auf rund 52 Stunden pro Person, was Lohnkosten von etwa 1.900 US-Dollar entspricht, nur f\u00fcrs Formatieren (!) (LeBlanc et al., 2019). Und diese Stunden verteilen sich selten auf geregelte B\u00fcrozeiten. Barnett und Kolleg*innen werteten \u00fcber 49.000 Manuskripteinreichungen und 76.000 Gutachten bei BMJ-Zeitschriften aus und fanden, dass Einreichungen mit einer Wahrscheinlichkeit von 14 bis 18 Prozent am Wochenende und von 8 bis 13 Prozent an Feiertagen erfolgten, am h\u00e4ufigsten arbeiteten chinesische Forschende am Wochenende und um Mitternacht (Barnett et al., 2019). Wissenschaftliches Schreiben frisst also nicht nur Stunden, es frisst oft Freizeit.
\nIst das Manuskript eingereicht, beginnt die erste lange Wartezeit. Huisman und Smits werteten 3.500 Begutachtungserfahrungen der Plattform SciRev aus und fanden eine erste Antwortzeit, die stark nach Fach variiert, mit 8 bis 9 Wochen in der Medizin am unteren Ende und 16 bis 18 Wochen in den Geistes- und Wirtschaftswissenschaften am oberen Ende (Huisman & Smits, 2017). Ein nennenswerter Teil der Verz\u00f6gerung entsteht dabei nicht im Gutachten selbst, sondern in der Redaktion, immerhin ein Drittel der Desk-Rejections dauerte l\u00e4nger als zwei Wochen, obwohl daf\u00fcr kein externes Gutachten n\u00f6tig ist.
\nWie lang die Gesamtspanne von der Einreichung bis zur Publikation ausf\u00e4llt, l\u00e4sst sich kaum auf eine einzige Zahl bringen. Andersen, Fonnes und Rosenberg fassten in einem systematischen Review 69 Studien zu biomedizinischen Zeitschriften zusammen und fanden eine mittlere Submission-to-Publication-Zeit, die zwischen 91 und 639 Tagen schwankte, je nach Fachgebiet und Journal (Andersen et al., 2021). Die Daten waren zu heterogen f\u00fcr eine sinnvolle Metaanalyse, und es zeigte sich kein systematischer Unterschied zwischen der Zeit bis zur Annahme und der Zeit von der Annahme bis zur Publikation. Mit anderen Worten, beide Teilstrecken k\u00f6nnen sich ziehen.
\nDie Wartezeit hat eine Ursache, die in keiner individuellen Zeitrechnung auftaucht, n\u00e4mlich den kollektiven Aufwand der Begutachtung. Kovanis und Kolleg*innen sch\u00e4tzten f\u00fcr das Jahr 2015 rund 63,4 Millionen Stunden, die weltweit in den Peer Review der biomedizinischen Literatur flossen, wobei ein kleiner Teil der Forschenden die Hauptlast trug, 20 Prozent erstellten zwischen 69 und 94 Prozent aller Gutachten (Kovanis et al., 2016). Auf die einzelne Begutachtung heruntergebrochen sind das im Schnitt rund 9,6 Stunden pro Review, allein f\u00fcr eine einzige Zeitschrift summierten Golden und Schultz so \u00fcber 360.000 ehrenamtliche Stunden im Jahr (Golden & Schultz, 2012). Aczel und Kolleg*innen kamen f\u00fcr 2020 auf \u00fcber 100 Millionen Stunden j\u00e4hrlich, mit einem gesch\u00e4tzten Gegenwert von mehr als 1,5 Milliarden US-Dollar allein f\u00fcr US-amerikanische Gutachter*innen (Aczel et al., 2021). Eine aktuellere Erhebung von LeBlanc und Kolleg*innen beziffert die Kosten auf rund 1.272 US-Dollar pro Gutachter*in und Jahr und unter Einrechnung abgelehnter Manuskripte global auf rund 6 Milliarden US-Dollar, wobei die gro\u00dfe Mehrheit der Begutachtungen unverg\u00fctet bleibt (LeBlanc et al., 2023). Dieser Aufwand ist die Kehrseite der Wartezeit, jede Woche, die ein Manuskript auf sein Gutachten wartet, ist auch eine Woche, in der jemand anderes unbezahlt f\u00fcr ein anderes Manuskript liest.
\nNach dem ersten Feedback folgt nat\u00fcrlich noch fast immer mindestens eine Revisionsrunde, bevor ein Manuskript endg\u00fcltig angenommen wird.
\nBei klinischen Studien dehnt sich die Zeitskala noch einmal erheblich, und zwar schon vor der Einreichung. Hopewell und Kolleg*innen zeigten in einem Cochrane-Methoden-Review, dass Studien mit positiven Ergebnissen eine fast vierfach h\u00f6here Chance auf Publikation hatten als solche mit negativen (Odds Ratio 3,90) und zudem schneller erschienen, im Median nach vier bis f\u00fcnf Jahren gegen\u00fcber sechs bis acht Jahren bei negativen Befunden (Hopewell et al., 2009). Dieser Time-Lag-Bias zugunsten positiver Ergebnisse ist seit Jahrzehnten dokumentiert und bedeutet, dass gerade die ern\u00fcchternden Befunde besonders lange auf sich warten lassen.
\nWer in der medizinischen Ausbildungsforschung publiziert, kennt das Ph\u00e4nomen aus eigener Erfahrung (und Geduld). Maggio und Kolleg*innen analysierten die Publikationszeiten in 24 Medical-Education-Journalen zwischen 2018 und 2022 und fanden eine durchschnittliche Submission-to-Publication-Zeit von gut 300 Tagen (Maggio et al., 2024). Das liegt im oberen Bereich der breiten biomedizinischen Spanne, die Andersen berichtet hatte.
\nDie Gr\u00fcnde f\u00fcr die generell langen Zeiten sind nicht abschlie\u00dfend gekl\u00e4rt. Vermutlich spielen kleinere Reviewer-Pools, die methodische Heterogenit\u00e4t qualitativer und gemischter Designs sowie l\u00e4ngere Revisionsrunden eine Rolle. F\u00fcr junge Medical Educators mit Tenure-Druck ist die lange Bearbeitungszeit eine Erkenntnis von praktischer Bedeutung, die man bei der Karriereplanung lieber kennen sollte, als sie erst nach drei eingereichten Manuskripten selbst zu entdecken.
\nZwei Entwicklungen versprechen, den Weg zu verk\u00fcrzen. Preprints machen Ergebnisse verf\u00fcgbar, lange bevor die formale Publikation abgeschlossen ist. Allerdings ersetzen sie diese nicht, Drzymalla und Kolleg*innen verfolgten 39.243 COVID-bezogene Preprints und fanden, dass nur 20 Prozent je in einer Fachzeitschrift erschienen, im Median 178 Tage nach dem Preprint (Drzymalla et al., 2022). Die Erkenntnis-Verf\u00fcgbarkeit ist also beschleunigt, die formale Publikation dauert weiter Monate. Die Pandemie hat zudem gezeigt, dass es schneller geht, wenn man will, Horbach fand f\u00fcr COVID-Artikel eine um 49 Prozent verk\u00fcrzte Bearbeitungszeit, w\u00e4hrend sich f\u00fcr Nicht-COVID-Artikel nichts beschleunigte (Horbach, 2020).
\nGenerative KI ist die zweite, noch offene Abk\u00fcrzung. Fr\u00fche journalistische Analysen in Nature beschreiben Zeitersparnis beim Schreiben, Korrekturlesen und Referenzieren, mit besonderem Nutzen f\u00fcr nicht-englische Muttersprachler*innen, weisen aber zugleich auf Risiken wie Paper-Mills und eine m\u00f6gliche Flut minderwertiger Manuskripte hin (Conroy, 2023a). In einem vielzitierten Experiment lie\u00df sich mit ChatGPT in rund einer Stunde ein vollst\u00e4ndiges, sprachlich fl\u00fcssiges Manuskript erzeugen, das aber an Neuheit und Genauigkeit krankte (Conroy, 2023b). Belastbare Vorher-Nachher-Vergleiche zum tats\u00e4chlichen Stundenaufwand fehlen bislang.
\nReine Arbeitszeit: Song et al.\u00a02013 (retrospektive Survey, 171 chirurgische Studien, 13 Chirurg*innen, 81 % R\u00fccklauf): Median 177 h pro Publikation (Range 29\u20131.287), entspricht rund 22 Achtstundentagen; keine Korrelation mit Autoren- oder Probandenzahl; Hauptanteile Datenerhebung 23 %, Manuskripterstellung 22 %, Datenanalyse 13 % (Song et al., 2013).
\nFormatierungsaufwand: LeBlanc et al.\u00a02019 (Survey, n = 372 aus 41 L\u00e4ndern): Median 14 h pro Manuskript, 52 h pro Person und Jahr, rund 1.908 US-Dollar Lohnkosten pro Person und Jahr (LeBlanc et al., 2019).
\nArbeitszeitmuster: Barnett et al.\u00a02019 (> 49.000 Einreichungen, > 76.000 Reviews, BMJ-Journale 2012\u20132019): Wochenend-Wahrscheinlichkeit 0,14\u20130,18, Feiertags-Wahrscheinlichkeit 0,08\u20130,13; chinesische Forschende am h\u00e4ufigsten am Wochenende und um Mitternacht (Barnett et al., 2019).
\nErste Antwortzeit: Huisman & Smits 2017 (n = 3.500 SciRev-Berichte): Median der Erstantwort 8\u20139 Wochen (Medizin) bis 16\u201318 Wochen (Geistes-/Wirtschaftswissenschaften); ein Drittel der Desk-Rejections dauerte \u00fcber zwei Wochen (Huisman & Smits, 2017).
\nGesamtspanne biomedizinisch: Andersen, Fonnes & Rosenberg 2021 (systematisches Review, 69 Studien): Submission-to-Publication 91\u2013639 Tage, zu heterogen f\u00fcr Metaanalyse, kein systematischer Unterschied zwischen Submission-to-Acceptance und Acceptance-to-Publication (Andersen et al., 2021).
\nPeer-Review-Aufwand: Kovanis et al.\u00a02016: rund 63,4 Mio. Stunden (2015), 20 % der Forschenden erstellen 69\u201394 % der Gutachten (Kovanis et al., 2016). Golden & Schultz 2012 (310 Gutachter*innen): im Schnitt 9,6 h pro Review, \u00fcber 360.000 ehrenamtliche Stunden j\u00e4hrlich allein f\u00fcr eine Zeitschrift (Golden & Schultz, 2012). Aczel et al.\u00a02021: \u00fcber 100 Mio. Stunden (2020), Gegenwert > 1,5 Mrd. US-Dollar (nur USA) (Aczel et al., 2021). LeBlanc et al.\u00a02023 (n = 308, 33 L\u00e4nder): rund 1.272 US-Dollar pro Person und Jahr, global 1,1\u20131,7 Mrd., mit abgelehnten Manuskripten rund 6 Mrd. US-Dollar; 87,5 % unverg\u00fctet (LeBlanc et al., 2023).
\nKlinische Studien (Time-Lag): Hopewell et al.\u00a02009 (Cochrane, 5 Kohorten): positive Studien OR 3,90 f\u00fcr Publikation, Median 4\u20135 Jahre vs.\u00a06\u20138 Jahre bei negativen Befunden (Hopewell et al., 2009).
\nMedical Education: Maggio et al.\u00a02024 (24 Journale, 2018\u20132022): Submission-to-Publication im Schnitt 300,8 Tage (SD 200,8); COVID-Overlap 539 Tage, COVID-endemisch 226 Tage (F(2, 7473) = 2150,7; p < 0,001) (Maggio et al., 2024).
\nPreprints und Pandemie: Drzymalla et al.\u00a02022 (39.243 COVID-Preprints): 20 % publiziert, Median 178 Tage bis zur Journalpublikation (Drzymalla et al., 2022). Horbach 2020 (14 Journale, 669 Artikel): COVID-Artikel 49 % schneller, Nicht-COVID unver\u00e4ndert (Horbach, 2020).
\nTransparenzhinweis:
\n![](\"https://substackcdn.com/image/fetch/$s_!ytmH!,w_56,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ffabebe05-d8d0-4141-821f-4fb29b38a346_3871x3871.jpeg\")
![](\"https://substackcdn.com/image/fetch/$s_!ciq4!,w_56,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe4252d92-80bd-4a83-81aa-9ec866a51fe7_608x608.png\")
@misc{friederichs2026,\n author = {Friederichs, Hendrik},\n title = {*Science Friction* -\\/- Wie viel Arbeit macht eine\n Publikation?},\n date = {2026-05-21},\n url = {https://medical-education.pages.ub.uni-bielefeld.de/research/mes-blog/posts/2026-05-21-workload-publications/},\n langid = {de}\n}\nWhen it comes to the philanthropic ecosystem, we live in exciting times. A duo of recent posts \u2014 one from one of SF\u2019s great thinkers, the other from one of its great general managers \u2014 are dedicated to this fact.
Last week, Dwarkesh Patel closed submissions for a blog prize searching for the best answers to the question: How do we deploy (possibly) hundreds of billions of dollars to \u201cmake AI go well?\u201d He specifically wanted to know how readers might approach the problem of converting money into impact from the POV of the OpenAI Foundation.1
In a second blog post, The third wave of American philanthropy, Stripe Climate GM Nan Ransohoff put the scale of the opportunity in context. She points out that if $50 billion from AI donors were to enter the philanthropic ecosystem each year, that would be enough to fund the annual budgets of the following organizations:
6 Gates Foundations (~$9B/yr), or
67 Coefficient Givings, formerly OpenPhil (~$1B/yr), or
100 GiveWells (~$500M/yr), or
333 Arc Institutes (~$150M/yr), or
5000 Institutes for Progress (~$10M/yr)
Nan then breaks down the challenge into a set of seven subproblems that, from an ecosystem design perspective, we must overcome to use these dollars well. Today\u2019s piece zeroes in on a solution to one of these problems: figuring out how these new funders can leverage their unusually large risk appetites to world-changing effect.
Incidentally, this is exactly what my submission to Dwarkesh\u2019s blog competition was about. In it, I profiled the best scientific philanthropist of all time, Warren Weaver, and his (exceptionally risk-tolerant) playbook for willing new fields into existence. Like great VCs, he only took big swings. Unlike VCs, he did next to no diversification. To Weaver, field creation was an endeavor that required focus; responsibility was not about hedging your bets, but giving carefully selected fields everything they needed to flourish.
Over the course of twenty years, Weaver bootstrapped the field of molecular biology into existence. He then funded a course of crop research that would grow into the Green Revolution. Weaver had the courage to live with concentrated bets for years on end, and the world is immeasurably better for it.
Given the blog competition word count, I\u2019m briefer than usual in many spots. If you\u2019re curious to learn more about any topic mentioned, check out my long piece on Weaver, my many Weaver tweets, or ping me. And if you have the urge to act on today\u2019s piece, contact me \u2014 egillia3@alumni.stanford.edu, or on my Twitter. If urgent, I can fly to SF.
Two more of Nan\u2019s subproblems \u2014 (1) the need to create new philanthropic capital allocators and (2) to make the problems that need solving more legible \u2014 will be the subject of a coming piece.
To become a technology of historical consequence, we must proactively make good things happen using AI. In the not-so-distant past, we find kinks in mortality graphs representing hundreds of millions of lives saved \u2014 kinks sparked by the action of a few smart people. Human brains did that. In partnership with a superhuman AI, we should have no less ambition.
Our ambition should extend beyond longer, healthier lives. I take a somewhat Robert Gordonian view of human flourishing: people working less without getting poorer, finding ways to make housing and food more affordable, and so on.
In pursuit of these goals, the philanthropic efforts of the AI labs should focus on problems that take advantage of two Silicon Valley comparative advantages: R&D and risk-tolerant capital allocation.2
Key individuals at the OpenAI Foundation have the opportunity to cement themselves as historically great, as more consequential than even J.C.R. Licklider or Bell Labs\u2019 Mervin Kelly. But to do so, they must internalize the lessons of history\u2019s best research funders. Why? Turning money into impact is closer to a problem of organized complexity than one of simplicity. Working from empirical data trumps \u201cfirst principles\u201d in areas of this sort. Funders need to decide on some heroes, and learn from them.
If I ran the OpenAI Foundation, a photo of Warren Weaver would hang in the lobby. From his perch at the Rockefeller Foundation, which he took over in 1932, Weaver made two contributions that cement him as the greatest scientific grant funder of all-time. He:
Funded molecular biology into existence.
Was key in funding the Green Revolution into existence.
(If the reader wonders whether Weaver simply got lucky twice, he also spotted the computing wave. Purchase the book-form of Claude Shannon\u2019s The Mathematical Theory of Communication, and you\u2019ll find that Weaver is co-author. Search for the grant that funded the 1956 Dartmouth Summer AI Conference, \u201cWeaver.\u201d)3
I\u2019d structure the foundation to empower modern-day visionaries deploying the Weaver playbook. The playbook, in a line: true specialization in an extremely young field.
How young is young?
In the case of molecular biology, ~80% of Weaver\u2019s Natural Sciences Division budget went into the field for ~two decades pre-Watson and Crick. For the first five years, many key experiments didn\u2019t run or barely worked. They fell forward. Even in 1948 \u2014 15 years into this focused bet, 5 years pre-Watson and Crick \u2014 one could find Leo Szilard hemming and hawing over whether it was too risky to enter such a young field.4
How did Weaver choose to specialize in molecular biology?
Weaver, an applied mathematician, worked in physics during the era in which physics\u2019 tools and models fruitfully invaded chemistry.5 He believed a branch could be similarly willed into existence at the intersection of biology and physics.6 Biology, at that point, was a ~single instrument field \u2014 the optical microscope. Biologists didn\u2019t work at the small scales physicists had now learned to study. A biological question on the scale of heredity was the perfect place to start.
Why specialize?
Before Weaver arrived, the Natural Sciences Division operated somewhat typically. They let scientists line up in general areas, and funded the best ideas until money ran out. Weaver saw that as inefficient, and believed focus would be super-additive. \u201cA highly selective procedure is necessary if the available funds are not to lose significance through scattering.\u201d7
It worked. By 1965, 18 Nobel Prizes would be given out for molecular biology. 15 of them were beneficiaries of Rockefeller Foundation funding, on average receiving their funds ~2 decades in advance of the prize \u2014 in an era in which scientists won the prize in middle age. Weaver would then move all his chips into his next branch, one that would grow into the Green Revolution.8
I\u2019ve simplified a bit.9 Still, the simplicity of the approach begs the question: \u201cWhy doesn\u2019t everyone do it?\u201d
The answer is likely also simple: courage and trust are in short supply. The field Weaver tied up almost all his money in did not have a name \u2014 he named it \u201cmolecular biology\u201d in 1938. The bet was overseen by someone named \u201cWeaver,\u201d not \u201cRockefeller.\u201d And he didn\u2019t have all that much to show for it in the early years. The bet is unthinkable at almost every large philanthropy today. Most who tried would get impatient, scared, or fired before the seeds bore fruit.10
But it can be done. AI\u2019s philanthropists have enough money to fund multiple Weavers at once, but not enough to diversify their way to Weaver.11 The work will require courage and a steady hand; it\u2019s a Berkshire Hathaway-style portfolio of focused bets.12
How to choose which could-be Weavers to empower? Focusing on areas where the R&D funding ecosystem systematically under-invests is a natural approach. Several include:
New Field Creation
Instrumentation
Doing-Heavy Discovery Work
AI might give us better next experiment ideas in biology, but that doesn\u2019t change the structure of the NIH or the incentives of VC. NIH panels can err towards consensus work in fields that already exist, and VCs toward things that are VC-profitable within 10 years. Neither specializes in creating new molecular biologies. The OpenAI Foundation can, if it chooses.
On instrumentation, neither academia nor VC fund biological tooling at a societally optimal level. The status quo may deliver super-human intelligence that is wildly under-sensed. There\u2019s surely a Weaver who would dedicate themselves to the problem.
Opportunity also exists beyond AIxBio. Doing-heavy discovery work is under-emphasized in general; for example, crop test fields for construction are not a university specialty. A related historical example worth dredging up comes from the memoir of James Killian, my least favorite dead MIT President. Polaroid cofounder Edwin Land once proposed that, at MIT\u2019s Sloan School, Killian \u201cestablish a model company \u2014 a practice school \u2014 to explore other ways to create other noble prototypes of industry.\u201d That strikes me as a good problem for a Weaver in the age of AI.
Thanks for reading.
Stay tuned for my piece on founding new philanthropic capital allocators that scale, and doing so in not-yet-legible areas of R&D, coming in early June.
FreakTakes is, in many ways, a ~200,000-word exploration of exactly this \u2014 from several dozen different angles.
Naturally, this piece is primarily focused on those eager to focus their philanthropy on R&D. This is, after all, an R&D history Substack. And even among those giving to R&D, there are obviously heroes you can pick other than Weaver! J.C.R. Licklider (of early BBN and ARPA fame) and MIT c. 1920 are both worthy heroes, for example. I\u2019ve written longer FreakTakes pieces on both.
In the next couple of months, I\u2019ll also publish a piece breaking down why Weaver, in the 1940s, thought it was clear that the computer would eventually cement itself as one of (if not the) most important instruments in the study of biology.
This Substack\u2019s original Weaver piece spends many thousands of words detailing what I had to distill in a few sentences here. So if you want to hear stories from the nascent labs, breakdowns of Rockefeller budgets before and after Weaver\u2019s arrival, and how he used the other 20% of his portfolio to explore areas that could become his next branch, see that piece.
Many who read this Substack will be familiar with the fact that the physicists and chemists won each other's Nobels quite frequently, in this period.
To my eye, this heuristic for field creation feels timeless. Heuristics only take you so far, but it\u2019s a great place to start.
Weaver felt that philanthropic divisions (like his) were not above the sort of specialization expected of firms. He focused his funds where they were needed, giving the young (speculative) field he was building the resources it needed to flourish. New Silicon Valley funders will be well-placed to impose this focus on specific activities happening in an organization. But doing so in the service of long-term, (often) not very measurable goals might be a bit of a balancing act. Empowering single individuals, as companies do with executives, is essential. But extending them more rope than is typical of executives is also essential if you want a Weaver. Most philanthropies, frankly, do not have the stomach to fund a Weaver. I write this piece in the hopes that a few will have the stomach and steady hand to do so.
As I wrote in the conclusion of my original Weaver piece: \u201cWeaver was not committed to molecular biology at the expense of all other things. He was committed to creating high-value branches at the expense of all other things.
The Division would not abandon its young branch \u2014 molecular biology \u2014 because it was not publicly producing in a way that would make headlines, but they would abandon their baby if they felt it didn\u2019t need them anymore. There had to be some new, undiscovered branch out there in need of their support. The follow-on funders could take it from there.
For some people, like Weaver and Szilard, life\u2019s too short to spend basking in the glory of what they\u2019ve built. They\u2019d rather do the damn thing again.\u201d
Of course, Weaver didn\u2019t do it alone. He relied on excellent field strategists, most prominently Max Delbr\u00fcck and Salvador Luria, who recruited and pitched great young people into the field one at a time, over many years. And many other key Weaver practices contributed to the success. For example, he deeply studied the field, maintaining a sense of what failures were productive vs. dead ends. Etc. Etc. Read the longer Weaver piece to learn more about all of this.
This is a real problem. The solution to which one might be just as likely to find in religion and folk wisdom as in any business or R&D history book. The mechanism by which courage and patience, for the long haul, are created in individuals is curious. For modern exemplars of steady-handed confidence of this form, I\u2019d recommend watching several dozen hours of Warren Buffett and Charlie Munger reacting to the manias, panics, and fads of their times.
Upon reading Nan\u2019s piece, I might hedge this statement a bit. I was imagining yearly giving numbers more in the ~$5 billion range, give or take. But even if it were ~$1 billion per year, upon further reflection, that might be enough to diversify your way to Weaver in cheaper subject areas. A social science-flavored focus would be one example where this could be possible. Computational law, computational applied history and experimental history, and the \u201cnoble prototypes of industry\u201d idea mentioned in the conclusion are all areas that could potentially warrant their own Weaver.
It should be noted that Weaver, Buffett, and Munger are all aggressively Midwestern \u2014 a factor this Midwestern author finds material. In his life, Munger made an unwieldy number of quips along the lines of, \u201cThe big money is not in the buying or the selling, but in the waiting.\u201d
\n
America continues to confuse military spending with true strength.
\n\u2014 David Rothkopf
\nAccording to US warmongers, the American military is the most powerful fighting force that has ever existed \u2014 a war machine so vast and terrible that enemies everywhere tremble in its path. Boasts aside, the US military is surely unrivalled in at least one regard. It is by far the most expensive armed force on the planet.
\nIn 2025, the US government funnelled $842 billion through Pentagon coffers. And if Donald Trump gets his way, that figure will rise to $1.5 trillion in 2027. No matter how you slice it, that\u2019s a staggering pile of cash. But what exactly does this money buy?
\nA recent New York Times piece complains that the Pentagon\u2019s enormous budget seems to buy \u201cinertia and incompetence\u201d. And they have a point. Since external audits began in 2017, the Pentagon has notoriously failed every single one. Then again, charges of \u2018incompetence\u2019 assume that the purpose of the Pentagon is to spend money wisely \u2014 to maximize the war-making return on investment. But what if the Pentagon\u2019s purpose is something different?
\nIn 2015, Senator John McCain made the case for sanctions against Russia by dismissing the state as \u201ca gas station masquerading as a country\u201d. Turning closer to home, I think we can say something similar about the Pentagon; it\u2019s a bureaucratic regime for channelling public funds into private coffers \u2014 a money funnel masquerading as a military. Of course, that\u2019s not to say that the US military has no firepower. (It does.) My point is that it\u2019s foolish to use Pentagon spending to judge US military might.
\nFor an illustration of this foolishness, look to the ongoing debacle in Iran. Although the Pentagon outspends the Iranian military by more than two orders of magnitude, the US military has been unable to accomplish any of Trump\u2019s (quixotic) objectives.1 Is this strategic defeat simply a matter of Iranian good luck combined with US poor planning?
\nI doubt it.
\nWhat seems more likely is that the US humiliation demonstrates that Pentagon spending is a misleading measure of US military power. The reason is simple: based on spending alone, we cannot differentiate between a military that\u2019s expensive because it is powerful, versus a military that\u2019s expensive because it (and its coterie of contractors) is well paid.
\nIn this essay, I examine the problem of measuring military power. Along the way, I review the long-term history of US military spending, I analyze the rise and fall of US military hegemony, and I discuss how the \u2018war on terror\u2019 has foreshadowed US imperial weakness. Finally, I quantify the US military\u2019s transformation from a war-making machine into a money funnel for US business. All told, the evidence suggests that Pentagon spending vastly overstates US military power.
\nIf there is a unifying lesson from military history, it\u2019s the maxim that \u201cGod always favors the big battalions\u201d.2 Of course, the assumption here is that we know what it means for a military to be \u2018big\u2019.
\nThroughout most of history, the definition of a \u2018big\u2019 military was obvious; it was a simple matter of manpower. Thus, when Napoleon invaded Russia with an army of over 400,000 soldiers, there was no question that he had a massive military.3 Yet as war became mechanized during the early 20th century, the question of military scale became more complicated. Suddenly, armies could be strong not just because of their manpower, but also because of their technological power.
\nThis use of technology, in turn, made the measurement of military scale more difficult because it created an aggregation problem. That is, while manpower can be easily summed (just count soldiers), the quantity of technological power cannot be measured so readily. For example, if a military is armed with 1000 rifles and 2 aircraft carriers, what is its total stock of technology? To answer this question, we need a dimension of aggregation \u2014 a common property shared by both rifles and aircraft carriers.
\nEnter economists. For centuries, economists have solved their aggregation problems by turning to money. Looking at prices, economists put on their accounting hats and proceed to aggregate the monetary value of everything. But unlike accountants, who take monetary quantities at face (financial) value, economists pretend that money reveals something deeper about material stocks and flows. Thus, economists presume that GDP \u2014 a measure of aggregate income \u2014 is a meaningful measure of economic \u2018output\u2019. (It\u2019s not.)
\nBack to the military. Using economists\u2019 aggregation trick, it\u2019s easy to \u2018discover\u2019 that the US military is the \u201cgreatest and most powerful [armed force] anywhere in the world\u201d (Trump\u2019s words). To gaze at the superiority of the US military, we simply look at its gargantuan budget, which dwarfs all competitors. Figure\u00a01 shows the spending disparity in 2024.
\n\nBacking out of this monetary foolishness, my goal in this essay is to demonstrate the problems with equating military spending with military power. In a world not dominated by economics dogma, the key issue would scarcely need stating. Military spending tells us about the income flowing to the armed forces (including its civilian bureaucracy and its private contractors). On its own, this income tells us nothing about military power.
\nDiving into US history, let\u2019s look at the long-term trend in US military spending. From 1789 to 2025, the dollar value of US military expenditures rose by a factor of a million, with conspicuous bumps along the way during periods of war. Figure\u00a02 shows the ascent.
\n\nAlthough this spectacular rise in nominal military spending might excite US warmongers, it\u2019s fairly meaningless on its own. To gain meaning, spending data needs context. So, with context in mind, here are three different views of the history of US military expenditures, each based on a different assumption about what the armed forces should purchase.
\nFirst, let\u2019s compare US military spending to the consumer price index. By doing so, we imply that the purpose of the military is to purchase consumer commodities. (This assumption is silly, of course, but let\u2019s see where it goes.)
\nFigure\u00a03 shows the US military\u2019s power to purchase consumer commodities. Compared to nominal military spending (Figure\u00a02) the notable difference here comes after World War II, where we see a conspicuous flatline. Today, the US military\u2019s consumer-commodity purchasing power is about half the value of its WWII peak.
\n\nSince the purpose of a military is to wage war, its ability to purchase consumer commodities is fairly meaningless. Indeed, one could argue that the optimal military is a spartan one \u2014 an organization that spends the bare minimum on troops\u2019 living standards, leaving the maximum budget for warfare.
\nOf course, the problem with this spartan approach is that it becomes difficult to enforce if citizens\u2019 living standards rise. Sure, a totalitarian regime can build a spartan army based on compulsory military service. But in a capitalist society with a professionalized military, this method doesn\u2019t fly. If a professional military pays poorly, no one will join. Hence, when living standards rise, the military is forced to pay the going rate.
\nThis necessity, in turn, gives rise to a form of cost disease; as living standards rise, mobilizing the population becomes more expensive. For example, a selling point of American living is that US income per capita is about six times greater than in China.4 But the flip side of this greater income is that it makes a war effort more expensive. For the same level of spending, China can mobilize six times more of its citizens. So in terms of military power, high American incomes act as a dead weight that Pentagon planners must drag.
\nFigure\u00a04 illustrates the impact of rising incomes on the US military\u2019s ability to mobilize American citizens. Here, I\u2019ve pegged US military spending against American income per capita. From 1790 to 1945, the US military\u2019s mobilization ability grew nearly 5000-fold. But after World War II, it shrank steadily, as military spending failed to keep pace with rising American income. Today, the US military\u2019s power to mobilize citizens is less than 20% of its WWII peak.
\n\nWhile we\u2019re on the topic of military cost diseases, let\u2019s discuss the burden of paying for corporate profits. During World War II, Harry Truman rose to fame campaigning against war profiteers. \u201cTheir greed knows no limit,\u201d he said bluntly.
\nIronically, today\u2019s military contractors are far more greedy than those of Truman\u2019s era. Yet there are no modern Truman Committees working to curb excessive profits. And that\u2019s largely because American culture has since been corrupted by neoliberal ideology, which rebrands fat profits as a sign of \u2018productivity\u2019.
\nThe roots of this cultural sea change date to the Reagan era in the 1980s. But it was in the mid-1990s when the US military officially donned a neoliberal hat. In 1994, the Pentagon created the \u2018Secretary of Defense Executive Fellows\u2019 program, which sent promising military officers to work for top defense contractors and other large corporations. When officers returned from this revolving door, journalist Freddy Brewster notes that they often had a predictable message: \u201coutsource everything not core to DoD\u201d (the Department of Defense).
\nNow in broad terms, there\u2019s nothing new about Pentagon outsourcing. Historically, the US military has relied heavily on corporate America for its procurement, typically sending about a quarter of its expenditures to the top 100 military contractors. (See Figure\u00a05 for the picture since 1958.) However, in recent decades, there\u2019s been a significant change in what this outsourced spending can buy.
\n\nAs corporate profits have fattened, the Pentagon\u2019s ability to pay for them has dwindled. Figure\u00a06 illustrates this corporate cost disease. Here, I\u2019ve pegged US military spending against the earnings per share of the S&P 500. The goal is to get a rough sense for the US military\u2019s ability to subsidize the returns to corporate shareholders.5
\nLooking at the trend, it seems that the military\u2019s ability to subsidize capitalists peaked in World War II, when spending was high and shareholder earnings were low. But since the 1990s, Pentagon spending hasn\u2019t kept pace with rising corporate payouts. As a consequence, the US military\u2019s ability to subsidize corporate owners now sits at just 4% of its WWII peak.
\n\nWhen journalists report government spending, they have a tendency to emphasize the big-number factor. (As in, the federal deficit is $1.8 trillion!) But the truth is that big numbers can turn out to be comparatively small, depending on the context.
\nThe Pentagon budget is a case in point. Whether the current budget is \u2018large\u2019 or \u2018small\u2019 depends on the context. Of course, in nominal terms, Pentagon spending is larger than ever. But relative to consumer commodity prices, Pentagon spending now sits at about half its WWII peak. In terms of the ability to mobilize Americans, things are worse; the current budget sits at 19% of its WWII peak. And in terms of the ability to subsidize corporate shareholders, today\u2019s Pentagon budget is shockingly small \u2014 less than 4% of its WWII peak.
\nTable 1 summarizes these different viewpoints. The lesson here is that despite the eye-popping dollar values, the modern Pentagon budget is not the behemoth it once was.
\nTable 1: Spending big or small? Framing the 2025 Pentagon budget.
\n| Observation | \n2025 Pentagon spending compared to WWII peak | \n
|---|---|
| Nominal spending | \n1000% | \n
| Spending relative to consumer price index | \n56% | \n
| Spending relative to average US income | \n19% | \n
| Spending relative to S&P 500 earnings per share | \n3.9% | \n
For data sources, see the appendix.
\n\n
Staying within the realm of military spending, let\u2019s pivot now and look at the road to US empire. Since the end of World War II, the US has maintained hundreds of military bases throughout the world, with US soldiers acting effectively as a global police force. Of course, under Trump, the US military has morphed into more of a pirate force for Washington plutocrats. But before we discuss this devolution, let\u2019s look at how the US empire was formed.
\nOne way to view the US empire is that it emerged suddenly out of the ashes of World War II. The backstory here is that prior to WWII, American politicians favored an isolationist foreign policy (the Monroe Doctrine notwithstanding). And they had inherited from the constitutional founders a deep distrust of standing armies.6
\nGiven this stance, US military spending tended to be quite modest. During periods of peace, it was typically close to 1% of US aggregate income (GDP). Of course, when war erupted, military ranks swelled, as did spending. But when peace returned, the military would shrink to its pre-war stature. Figure\u00a07 shows this cyclical behavior, which lasted from 1790 to 1939.
\n\nContinuing to look at Figure\u00a07, note how World War II brought a halt to the spending rhythm of war and peace. When the war ended in 1945, the United States retained, for the first time, a massive standing army that was stationed throughout the world. As a consequence, military spending didn\u2019t return to pre-war levels, but instead remained high. Thus was born the imperial epoch of US history.
\nSort of.
\nThe problem with this story of \u2018sudden\u2019 empire is that it ignores the colonial expansion of the United States itself. For example, in 1800, the US was a small nation of 16 states clumped along the Eastern seaboard. Its population was just 5 million \u2014 about 0.5% of the world\u2019s total population. Over the next century, a steady stream of immigration would swell the American population by a factor of ten, and a series of territorial conquests would see the country expand across the continent.
\nWhen we take into account the colonial expansion of the United States itself, we get the more gradual road to US empire shown in Figure\u00a08. Here, I\u2019ve measured US military spending as a share of world income (GDP). From 1789 to 1939, US military expenditures rose steadily, increasing their slice of world income by two orders of magnitude. During World War II, the US war machine bolstered this value another forty-fold. At its peak, the US war effort commanded something like a fifth of the world\u2019s income.
\n\nNow to the present. Listening to Trump and his cabinet of swaggering morons, we get the impression that the US is at the height of its military power. But then again, when the US was actually at the height of its power (during World War II), its leaders weren\u2019t blathering about their military supremacy. They were sowing the diplomatic seeds for the US-led world order that would follow the war.
\nFor example, at the Moscow conference in 1943, the US drafted and signed (along with the United Kingdom, the Soviet Union, and China) the Four Power Declaration, which laid the groundwork for the United Nations. And in 1944, the US hosted the Bretton Woods Conference, which established the post-war financial order.
\nIn short, it seems that the peak of US military power coincided with the peak of US diplomacy. And if you understand how power works, that\u2019s not surprising. You see, brute force is the most brittle form of power. Yes it works, if one maintains constant armed oppression. But the moment that weapons are sheathed, coercive power is prone to collapse. In contrast, power through diplomatic consensus is far more robust because it involves buy-in from local populations. Hence, through diplomacy, a powerful military can be transformed from a would-be oppressor into a legitimate international police force.
\nIt was this combination of diplomatic and military power that led to the creation and maintenance of the US-led world order. And today, it is the lack of diplomatic and military power that is causing the US-led world order to collapse. In 2026, US statecraft reads like a dark satire. For Trump, the favored tactic is mafia-like extortion. Hence, we get US financial extortion through Trump\u2019s vindictive use of tariffs. And we get US armed extortion through Trump\u2019s mercurial use of the military. Both of these methods are likely to fail, for the simple reason that the US is not the hegemon it once was.
\nThis decline in power is particularly severe for the US military. Yes, the Pentagon remains the world\u2019s most profligate military spender. But the truth is that in relative terms, the Pentagon\u2019s global spending power now sits at just 4% of its WWII peak. And as we will soon see, this monetary view likely overstates the US military\u2019s fighting power. First, though, let\u2019s look at the historical roots of Trump\u2019s imperial death throes.
\nA consistent feature of world history is that when empires are strong, they preside over periods of relative peace. For example, from 27 BC to 180 AD, the Roman Empire ruled over a period of peace known as the Pax Romana. Similarly, the British Empire prevailed over the Pax Britannica, an era of global peace that lasted from 1815 to 1914. And from 1945 onward, the US empire presided over the post-WWII peace, sometimes called the Pax Americana.
\nOf course, the flip side of imperial peace is the chaos that comes as empires die. Not only do rival states fight over the ensuing power vacuum, but the empires themselves often lash out in vain attempts to resurrect past glory. Today, the US empire has entered its (attempted) resurrection stage.
\nThings are not going well.
\nFuture historians will probably point to Trump\u2019s war in Iran as the moment when the US empire entered into terminal decline. Yet the roots of Trump\u2019s imperial debacle date back to 2001 \u2014 the year when George Bush declared his global \u2018war on terror\u2019. In a way, Bush\u2019s language was as important as his actions. As Ian Welsh notes, the word \u2018terrorism\u2019 has become code for \u201cviolence by people who are our enemies\u201d. The effect of this label is to take diplomacy off the table. (You can negotiate with a \u2018rival\u2019 or even an \u2018enemy\u2019. But you can\u2019t negotiate with a \u2018terrorist\u2019.)
\nWith diplomacy negated by the threat of \u2018terrorism\u2019, the US began to ramp up its military interventions around the globe. Figure\u00a09 shows the resulting explosion of conflict. From 1947 to 2001, the US military engaged in an average of 0.75 conflicts per year. (Admittedly, some of these conflicts were brutal wars, as in Korea in the 1950s and Vietnam in the 1960s). However, from 2001 onward, the number of US conflicts rose dramatically. At the same time, US military tactics changed. Airborne assassination became the norm, prompting all the public admiration that one might expect from an empire that conducts extrajudicial executions from the sky.
\n\nEven more evocative than the growing number of US conflicts has been the changing location of these military engagements. Once a tool for enforcing global peace (and suppressing the occasional communist movement), the \u2018war on terror\u2019 saw the US military become a cudgel for terrorizing Muslim populations in the Middle East and North Africa. Figure\u00a010 shows this evolving geography of violence.
\nIt\u2019s within this geographic (and demographic) context that we should understand Trump\u2019s war with Iran. After two decades of targeting ragtag militant groups throughout the Islamic world, the Iran War saw the US pick a fight with a major military power. Or at least, that\u2019s what the battle damage would suggest. In the Persian Gulf, many US military bases now lie in ruins, as does a significant portion of the oil-and-gas infrastructure (which the US military guaranteed it would protect, but apparently could not). And of course, the Strait of Hormuz is now controlled by Iran.
\nLooking at these battlefield outcomes, what\u2019s odd about the Iranian victory is that on paper, Trump\u2019s war had all the markings of a US blowout. In 2024, the Pentagon outspent the Iranian military more than 100-fold. In light of this spending dominance, there are two ways to interpret the US humiliation. Either Iran got lucky and the US fell victim to remarkably poor planning, or Pentagon spending offers a gross mismeasurement of US military power.
\nLet me build the case for the latter scenario.
\nThe belief that military spending indicates military power derives from the broader belief in neoclassical economics, which asserts that income (the flip side of spending) always stems from productive \u2018output\u2019. This belief system is a lie.
\nA quick look at the real world shows that many types of income stem from doing nothing productive at all. Such is the case with copyleft trolls, who exploit loopholes in early Creative Commons licenses to extract money from people who\u2019ve made minor attribution errors for content that\u2019s otherwise designed to be free. Now, we commonly call this extortion technique a \u2018scam\u2019 or a \u2018fraud\u2019. But if the political economist Thorstein Veblen was alive today, he\u2019d probably just call it business.
\nYou see, Veblen (who lived through the 19th-century heyday of robber-baron capitalism) had a dark view of capitalist enterprise. For Veblen, the goal of \u2018business\u2019 was not to produce useful things, but instead to impose property rights onto society, thereby creating the institutional power to command income. So as Veblen would see it, copyleft trolls appeal to the purest form of \u2018business\u2019, which is to receive money by sabotaging an otherwise free activity. The point here is that when we look at income (and its flip side, expenditure), we\u2019re seeing the effects of \u2018business\u2019 success.
\nNow for Veblen, the antithesis of \u2018business\u2019 was the unmonetized human desire to create and produce useful things \u2014 a tendency that he called industry. Thus, when a farmer grows corn, he engages in \u2018industry\u2019. But when a commodity trader speculates on the price of corn futures, he engages in \u2018business\u2019. What\u2019s important about Veblen\u2019s distinction is that it allows for a divergence between the scale of \u2018business\u2019 income and the scale of social \u2018industry\u2019. Or put another way, it allows for the existence of the modern United States.
\nTo frame the (seemingly) underwhelming returns to Pentagon spending, it helps to first understand the wider pathology of US power. Once the center of global manufacturing, today the United States more closely resembles a patent troll. It is a country where \u2018business\u2019 is booming but homespun \u2018industry\u2019 is anemic.
\nTellingly, Trump\u2019s State Department boasts that about 40% of US income and 80% of US exports stem from the enforcement of intellectual property rights. So what\u2019s wrong with that? Well, in a business sense, nothing. For the person receiving money, all income is the same, no matter how it\u2019s generated. But in a broader social sense, the source of one\u2019s income matters. To put it crudely, income from professional murder is different than income from nursing.
\nIn a slightly less pathological vein, IP-based income is socially detrimental because it inflates the price of goods and services that could otherwise be cheap, or even free. (Absent the copyleft troll, the use of Creative Commons images costs nothing.) In other words, intellectual property is a tool for extracting \u2018business\u2019 profits by choking off human \u2018industry\u2019.
\nTo have a closer look at this business chokehold, I\u2019m going to turn to a metric that I call the business-to-industry index. The goal here is to quantify the relation between Veblenian \u2018business\u2019 (the act of profiting from property rights) and Veblenian \u2018industry\u2019 (the act of providing useful goods and services). For its part, Veblenian \u2018business\u2019 is the easier activity to quantify, because the goal is always to command an income stream. Hence, the success of \u2018business\u2019 can be measured in terms of some form of relative income.
\nIn contrast, Veblenian \u2018industry\u2019 is more difficult to quantify, because it encompasses a wide variety of activities that resist simple aggregation. Here, I\u2019ll sidestep this problem by ignoring industrial \u2018output\u2019. Instead, I\u2019ll measure the input of primary energy. The idea is that energy is essentially a biophysical currency \u2014 it\u2019s a thermodynamic transaction that must be paid (to the universe) to do anything materially useful. So with thermodynamic payments in mind, I\u2019ll measure the scale of \u2018industry\u2019 in terms of energy consumption.
\nThe business-to-industry index consists of the ratio of these two views of society \u2014 the ratio of relative income to relative energy use. In the case of the United States, I define the business-to-industry index as the ratio between the US share of world income and the US share of world energy use:
\nFigure\u00a011 shows these two views of US power. The red curve plots the \u2018business view\u2019 \u2014 the US share of world income. And the blue curve shows the \u2018industry view\u2019 \u2014 the US share of world energy consumption.
\n\nEyeballing Figure\u00a011, it\u2019s clear that historically, the rise and fall of US \u2018business\u2019 power stemmed in large part from the rise and fall of industrial hegemony. And fundamentally, that makes sense. If claims on property rights aren\u2019t backed by material power, then they become tenuous to enforce and easily undercut.
\nThat said, when we look more closely at the relation between the two views of US power, a fascinating long-term pattern emerges. Figure\u00a012 illustrates the trend. Here, I\u2019ve calculated the US business-to-industry index \u2014 the US share of world income relative to the US share of world energy use. What\u2019s remarkable (and in my mind unexpected) is that for over two centuries, this index has trended north.
\nIn the early 19th century, the US was an industry-dominated country, meaning its share of world energy use was significantly larger than we\u2019d expect from its share of world income. But by the late 20th century, the US had become a business-dominated country, meaning its share of world income significantly outstripped its share of world energy use. All told, the US business-to-industry index is now (as of 2025) more than three times higher that it was in 1790.
\n\nNow, since this essay is ultimately about the US military (and not US society in general), I won\u2019t dwell on the evidence in Figure\u00a012. But I can\u2019t help but connect the trend in the business-to-industry index to a point that Steve Keen recently made about the double-edged sword of empire.
\nNote that it was shortly after World War II that the US business-to-industry index entered business-dominated terrain. And it was around the same time that the US dollar became the world\u2019s reserve currency. I doubt this mutual timing is a coincidence. Keen observes that although control over the world\u2019s reserve currency comes with well-known opportunities for profit, it also comes with a major downside, which is that it kills homegrown industry. That\u2019s because when a currency attains reserve status, it tends to become overvalued, thereby making exports in the currency-issuing country less competitive. The net effect, according to Keen, is that issuing a reserve currency is \u201cnot a spoil of Empire, but a spoiler of Empires.\u201d
\nLooking ahead, there\u2019s definitely more to be said on the theme of booming business and anemic industry. But for now, let\u2019s return to the topic at hand, which is US military power. If the United States as a whole has become \u2018business dominated\u2019, it seems plausible that the US military has undergone a similar transformation.
\nLet\u2019s have a look.
\nHaving defined the business-to-industry index for the United States, it\u2019s easy to apply this metric to the US military. Looking at the Pentagon, its business-to-industry (BTI) index consists of US military expenditures as a share of world income, relative to the US military\u2019s share of world energy use:7
\nNow, before we get to the data, it\u2019s worth noting that while the notion of a war \u2018business\u2019 (the act of profiting from violence) is fittingly Veblenian, the idea of a war \u2018industry\u2019 is \u2026 not. You see, outside of capitalism, Veblen had a fairly optimistic view of human nature. Commenting on Veblen\u2019s thinking, political economists Jonathan Nitzan and Shimshon Bichler argue that the purpose of Veblenian \u2018industry\u2019 is the \u201cefficient production of quality goods and services for the betterment of human life\u201d [my emphasis].
\nObviously, if we speak of a \u2018war industry\u2019, the notion of \u2018bettering human life\u2019 takes on a darker tone. Whereas Veblenian \u2018industry\u2019 is positive-sum for the whole of humanity, the notion of a \u2018war industry\u2019 is at best, zero-sum. The goal of the \u2018war industry\u2019 is to produce a powerful military that triumphs over rivals, thereby bettering the lives of the victors (by ruining the lives of the losers).
\nAcknowledging this dark side of human behavior, let\u2019s see how the \u2018business\u2019 view of the US military lines up with the \u2018industry\u2019 view. The short answer is that it doesn\u2019t. Figure\u00a013 tells the story. Compared to the \u2018business\u2019 view of Pentagon expenditures, the \u2018industry\u2019 view of Pentagon energy consumption is far more anemic. Not only does the Pentagon consume significantly less energy than we would expect from its share of world income, this energy share has declined dramatically.
\nThe net result, as Figure\u00a014 demonstrates, is that the US military\u2019s business-to-industry index has more than doubled over the last fifty years. And if we take the absolute value of this index seriously (which is a speculative exercise), it suggests that the Pentagon\u2019s stupendous budget may overestimate its war-making power by more than a factor of seven.
\n\nSince the United States is now a business-dominated country (Figure\u00a012), it makes sense that the US military would exhibit similar behavior (Figure\u00a014). But what\u2019s somewhat surprising is the degree to which Pentagon spending overstates its consumption of energy. (And to be clear, the use of energy is the more realistic indicator of war-making power.)
\nTo characterize this mismatch, it seems fitting to borrow another idea from Thorstein Veblen. Actually, economist Michael Hudson beat me to the analogy. In a recent interview, Hudson compared US weapons to a Rolls-Royce. They\u2019re a technology that exists largely to be seen. Now, the military has a suitably stern phrase for this ostentatious behavior. They call it \u2018power projection\u2019. But given the US military\u2019s apparent deficit of power, perhaps a better term would be conspicuous consumption.
\nThis was Veblen\u2019s term for the behavior of Gilded-Age elites, who had a pathological need to put their wealth on display by parading around objects of great expense. Today, it seems that US military planners have a similar impulse. They feel compelled to procure weapons of ludicrous expense, and to parade them around as a show of force.
\nOf course, this is not to say that US weapons don\u2019t work. They do. But they \u2018work\u2019 in the same way that a Rolls-Royce \u2018works\u2019 as a commuter car. Yes, it gets the job done, but at a cost that doesn\u2019t scale. Or put another way, while the US military boasts about its ability to buy Rolls-Royce weapons, less wealthy armies are busy building unassuming weapons that can be manufactured cheaply at scale \u2014 the war-making equivalent of mass transit.
\nLet me demonstrate this weapons scaling problem with some simple math.
\nWhen Trump launched his unprovoked assault on Iran, it seems that US planners were not prepared for the effectiveness of Iranian drones. And one can understand why. In terms of their ability to \u2018project power\u2019, Iran\u2019s Shahed drones are unimpressive. They\u2019re built from inexpensive fiberglass and styrofoam, piloted by consumer-grade GPS, and deliver a modest explosive payload of up to 100 pounds. But as the US military learned the hard way, this unimpressiveness is the point. The Shahed drone can be mass-produced for as low as $20,000 each, which corresponds to roughly $200 per pound of delivered explosive. Nothing in the US arsenal can compete with this budget-based power.
\nAs an example, take the famed Tomahawk missile, a mainstay of US air assault. Developed in the 1970s, each Tomahawk missile now costs about $2 million to procure. For that price, it delivers about 1000 pounds of explosive payload. Sure, that\u2019s more destructive power than the Shahed drone. But at $2000 per pound of explosive, the Tomahawk is also about ten times more expensive, pound for pound. Hence, for the same price, an arsenal of Shahed drones could deliver far more destruction than an arsenal of Tomahawks.
\nUpping the ante of conspicuous consumption, let\u2019s turn to the F-35 program. With a projected total cost of over $2 trillion, the F-35 project is expected to deliver about 2400 fighter jets. That corresponds to a lifetime cost of over $800 million per jet. Now, if we assume that these jets are used mostly for power projection, a reasonable estimate is that each plane might deliver 80,000 pounds of explosive during its lifetime. (See my calculations in the appendix.) Doing the math, that comes out to about $10,000 per pound of delivered explosive \u2014 a pound-for-pound price tag that\u2019s roughly 50 times more than the Shahed drone.
\nNow, the irony is that in the 21st century, the F-35 is a baroque technology that no one needs, but that US weapons contractors desperately want to build. And in a sense, that\u2019s the point. The F-35 exists not because it\u2019s an efficient war-making investment, but because it\u2019s an extremely profitable weapon to sell. Its bespoke construction allows monopolistic contractors ample opportunity for markup. And so the US military now finds itself in an odd situation. As analyst Alastair Crooke observes, the Pentagon wants not for money, yet is nonetheless plagued by \u201csclerotic supply-lines, long production cycles and minimal weapon inventories.\u201d In short, the Pentagon finds that its booming war \u2018business\u2019 is built on an anemic war \u2018industry\u2019.
\nThe gods of history no doubt had a sense of irony when they gave Donald Trump the keys to the world\u2019s most expensive military. Not every politician is so foolish to mistake stupendous military spending for great military power. But with Trump \u2014 a man who\u2019s never seen a room that couldn\u2019t use more gold-plated decor \u2014 the gods found their mark.
\nAnd so here we are. Convinced of its unmatched power, Trump let his Rolls-Royce military loose on a third-rate army, only to see it humiliated. The gods continue to laugh. While Trump may never understand the joke, we can easily unearth the punchline. You see, unlike the Pentagon, which is a business-dominated institution, the Iranian military is likely the opposite sort of organization \u2014 a place where \u2018business\u2019 is subservient to \u2018industry\u2019.
\nLet me make the case by returning to the business-to-industry index. Figure\u00a015 shows the business-to-industry index for the Pentagon, the United States, and Iran. Unlike the business-leaning United States and the business-dominated Pentagon, Iran is an industry-dominated country. After decades of trade-suppressing US-led sanctions, Iran\u2019s share of global income is now markedly less than its share of global energy use.
\n\nOf course, the business-to-industry index for the Iranian military itself remains unknown. But let\u2019s suppose that the Iranian military is similar to Iran as a whole. If so, we can immediately see why the Pentagon\u2019s spending power mismeasures its military advantage over Iran.
\nIn 2024, the Pentagon\u2019s business-to-industry index was 7.7, while Iran\u2019s business-to-industry index was 0.22. If the Iranian military exists in similar territory, we can surmise that compared to the Pentagon, every dollar of Iranian military spending mobilized more than 30 times more energy. Or put another way, although the Pentagon outspends the Iranian military by two orders of magnitude, its energy advantage is likely much smaller \u2014 potentially as little as a factor of four. If we add in Iran\u2019s fortress geography and the globe-spanning nature of US forces, we can see how Iran might prevail against a military that, in terms of finance, seems far more powerful.
\nAt any rate, it\u2019s fitting that Donald Trump is the politician to discover this trick of accounting, because he\u2019s the last person who\u2019ll get the joke. Indeed, there seems to be no irony in Trump\u2019s proposal for a \u2018golden dome\u2019 \u2014 a missile-defense boondoggle that (if it ever gets built) will be a gilded prize for military contractors. And then there\u2019s the proposed Arc de Trump. Sure, it\u2019s a grotesque nod to Napoleon. But it\u2019s also an unwitting metaphor for Trump\u2019s unfolding Waterloo moment. Money may buy glittering gold, but it doesn\u2019t always buy military might.
\nHi folks, Blair Fix here. I\u2019m a crowdfunded scientist who shares all of my (painstaking) research for free. If you think my work has value, consider becoming a supporter. You\u2019ll help me continue to share data-driven science with a world that needs less opinion and more facts.
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Share of world military spending in 2024 (Figure\u00a01)
\nData is from the World Bank, series MS.MIL.XPND.CD (Military expenditure in current USD).
\nUS military spending (Figures\u00a02 \u2013 4, 6 \u2013 8, 13 \u2013 15)
\nData is from the following sources:
\nUS consumer price index (Figure\u00a03)
\nData is from the following sources:
\nUS GDP and GDP per capita (Figure\u00a04, 7, 11, 12, & 15)
\nFor GDP per capita calculations, population data is from:
\nPentagon spending paid to top 100 US defense contractors (Figure\u00a05)
\nSpending data is from the following sources:
\nS&P 500 earnings per share (Figure\u00a06)
\nData is from Robert Shiller\u2019s website.
\nWorld GDP (Figures\u00a08, 11 \u2013 15)
\nData is from the following sources:
\nNote that the data prior to 1960 comes with some major caveats. The Maddison database reports global \u2018real\u2019 GDP, measured in terms of purchasing power parity. That is, within each country, GDP is measured relative to some common basket of goods. Hence, the Maddison-database goal is not to measure nominal income, but rather to measure the standard of living, as captured by consumer purchasing power. Given this premise, it\u2019s not ideal to use the Maddison data as a measurement of nominal world income. Nonetheless, when it comes to deep historical GDP data, the Maddison database is the only game in town.
\nHere\u2019s how I convert the Maddison data into a measure of nominal world GDP. First, I assemble a long-term dataset for the US GDP deflator as follows:
\nWith this GDP deflator data, I re-inflate the Maddison \u2018real\u2019 GDP data (reported in PPP USD) to create a proxy for world nominal GDP, measured in USD. Like I said, this calculation makes some conceptual leaps that are not strictly valid, so treat it with a grain of salt.
\nUS military conflicts (Figures\u00a09 & 10)
\nData is from the Uppsala Conflict Data Program, UCDP/PRIO Armed Conflict Dataset version 25.1. (I crawl the UCDP and search for any conflicts in which the United States is a belligerent.) For conflicts in which the US attacked a non-state actor, I\u2019ve placed the conflict inside the country where this non-state actor was active. Note that in Figure\u00a010, the location of individual conflict points is randomly generated by sampling within the geography of the host country.
\nUS energy consumption (Figures\u00a011, 12, & 15)
\nData is from the following sources:
\nWorld energy consumption (Figures\u00a011 \u2013 15)
\nData is from the following sources:
\nPentagon energy use (Figures\u00a013 \u2013 15)
\nEnergy-use data for the Department of Defense is from the Federal Energy Management Program, Comprehensive Annual Energy Data, Table A-4: Primary Energy Use by End-Use Sector and Energy Type, by Federal Agency. (Note that I use data for \u2018primary energy\u2019, not the also-reported \u2018site-delivered energy\u2019.)
\nIranian GDP and energy use (Figure\u00a015)
\nData for Iranian GDP is from the World Bank, series NY.GDP.MKTP.CD (GDP in current USD). Data for Iranian energy use is from the Energy Institute Statistical Review of World Energy, series TES_EJ (total energy supply in exajoules).
\nF-35 calculations
\nHere is my calculations for the mass of explosives dropped by an F-35 during its lifespan. I assume that the vast majority (99%) of sorties are for power projection or training, and not for battle:
\nNote: If war breaks out and F-35s are used intensively for dropping bombs, then the combat rate will increase significantly. But at the same time, flying into a battle zone involves the risk of getting shot down, which would reduce the average service life per plane. At any rate, strapping pilots onto flying bomb-dropping machines is a relic of the 20th century. Today, it\u2019s little more than an expensive stunt (much like manned space flight).
\n\ufe0e\n\ufe0e\n\ufe0e\n\ufe0e\n\ufe0e\nMatters came to a head during the English Civil War (1642 to 1651), which saw a decade of conflict between Royalists and Parliamentarians. Although the Parliamentarians won the war, the monarchy remained intact, and English kings continued to test the limits of their military powers. In 1688, King James II went a bit too far and was deposed in the Glorious Revolution. A year later, Parliament passed the Bill of Rights of 1689, which, among other things, prohibited the king from keeping a peacetime standing army without parliamentary consent.
\nFast forward to the American Revolution. When American colonists overthrew British rule, they framed their grievances in terms of the English Bill of Rights. In particular, the Declaration of Independence charged the British king with maintaining a peacetime standing army without the consent of colonial legislatures. When colonists later drafted the American Constitution, they made sure to guard against standing armies by giving Congress control over military spending, and by putting a two-year limit on all military appropriations.\ufe0e\n
\ufe0e\n\n
\nDoctorow, C., & Giblin, R. (2022). Chokepoint capitalism: How big tech and big content captured creative labor markets and how we\u2019ll win them back. Beacon Press.\n
\n\nFix, B. (2019). The aggregation problem: Implications for ecological and biophysical economics. BioPhysical Economics and Resource Quality, 4(1), 1.\n
\n\nNitzan, J., & Bichler, S. (2009). Capital as power: A study of order and creorder. New York: Routledge.\n
\n\nVeblen, T. (1904). The theory of business enterprise. New York: Martino Fine Books.\n
\n\nVeblen, T. (1923). Absentee ownership: Business enterprise in recent times: The case of America. Transaction Pub.\n
\nThe post The Business of War and the Mismeasurement of Military Might appeared first on Economics from the Top Down.
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Download: PDF | EPUB | MP3 | WATCH VIDEO\n\n America continues to confuse military spending with true strength.\n\n\u2014 David Rothkopf According to US warmongers, the American military is the most powerful fighting force that has ever existed \u2014 a war machine so vast and terrible that enemies everywhere tremble in its path. Boasts aside, the US military is surely unrivalled in at least one regard.","tags":["Business-to-industry Index","Energy","Iran","Iran War","Military Contractors"],"title":"The Business of War and the Mismeasurement of Military Might","updated_at":1779468394,"url":"https://economicsfromthetopdown.com/2026/05/22/the-business-of-war-and-the-mismeasurement-of-military-might/","version":"v1"},{"abstract":null,"archive_url":null,"authors":[{"contributor_roles":[],"family":"Turner","given":"Stephen D."}],"blog":{"archive_collection":null,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":[{"name":"Stephen Turner"}],"canonical_url":null,"category":"biologicalSciences","community_id":"382941a7-2ffa-41df-8bbb-5f772188517f","created_at":1734172613,"current_feed_url":null,"description":"A practicing data scientist's take on AI, genomics, biosecurity, and the ways AI is reshaping how science gets done. Weekly updates from the field. Occasional notes on programming.","doi":null,"doi_as_guid":false,"favicon":null,"feed_format":"application/rss+xml","feed_url":"https://blog.stephenturner.us/feed","filter":null,"funding":null,"generator":"Substack","generator_raw":"Substack","home_page_url":"https://blog.stephenturner.us/","id":"bffe125c-3dfa-4f25-998f-e62878677c7c","indexed":true,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":"https://bsky.app/profile/stephenturner.us","prefix":"10.59350","registered_at":0,"relative_url":null,"ror":null,"secure":true,"slug":"stephenturner","status":"active","subfield":"1311","subfield_validated":true,"title":"Paired Ends","updated_at":1779440786.366907,"use_api":null,"use_mastodon":false,"user_id":"ae63ef98-7475-4cc1-b3eb-244d5e096f0f"},"blog_name":"Paired Ends","blog_slug":"stephenturner","content_html":"Big week in AI in life sciences (AIxBio). The Nature drop this week included three papers on AI scientists alongside an editorial and a comment piece pushing back on the whole project. Add \u2019s three-part series on AI for biology, a new RAND/Helena workshop report on AIxBio mitigations, \u2019s take on AI in genomics, and a new study on AI in peer review and I\u2019m landing on a theme this week: how fast should we let AI into the production of biological knowledge, and what gets lost if we don\u2019t slow down to ask?
\u2019s case for shaping AI-for-biology before it shapes us
RAND and Helena on AIxBio mitigations
on the state of AI in genomics
Nature\u2019s AI scientists week, and the editorial pushback
45 expert scientists review the reviewers
(Johns Hopkins, Center for Health Security) published a three-part series last week on shaping AI progress for biology and biosecurity.
Part 1 sets up the series. AI will compress decades of biological research into years, but cures won\u2019t arrive by default, and the same systems that enable them can lower the barrier to weaponizing pathogens. We need proactive policy on both sides.
Part 2 is where it gets interesting. Pannu lays out what she calls autonomous biological discovery: AI systems that automate every step of the research cycle, including managing the cycle itself, with orgs like Isomorphic Labs, FutureHouse, Ginkgo Bioworks and others entering the fray.
AI-enabled feedback loops will be able to extend beyond this, exploring parts of biological space that nature has not.
Evolution selects for reproductive success and gets stuck in fitness valleys. AI-driven design doesn\u2019t have that constraint. Whether that\u2019s a feature or a terrifying bug depends on what you\u2019re designing.
Part 3 centers on smallpox eradication: 171 years between Jenner\u2019s cowpox demonstration in 1796 and Henderson\u2019s 1967 campaign, and only 10 of those years were spent actually eradicating. Her conclusion is that the bottleneck wasn\u2019t tech, it was coordination and political will, so even if AI drives the marginal cost of biology research to zero, we shouldn\u2019t expect cures to deploy themselves.
RAND and Helena released the conference proceedings (full PDF here) from a January 2026 workshop on AI-enabled biological threats. 22 participants from frontier labs, biotech, biosecurity, and academia, working over two days in DC under Chatham House rules, with three threat scenarios: a millenarian nonstate group releasing a novel influenza A, an agroterrorism scenario targeting US wheat with an engineered fungal pathogen, and a state-sponsored insider attack on a semiconductor plant using a biofilm-forming bacterium. Scenarios were deliberately compressed and the document withholds specifics for infohazard reasons.
The pandemic group prioritized pathogen-agnostic physical defenses (high-quality PPE, indoor air quality with filtration and UV), a voluntary credentialing system called \u201cBioTrust\u201d modeled on ORCID, and AI \u201cguardian models\u201d for intent monitoring. The agroterrorism group went after holistic biosurveillance, information-sharing modeled on the Kansas Intelligence Fusion Center, and synthetic DNA screening for agricultural pathogens (which gets less attention than human-pathogen screening, and the participants thought that was the most well-scoped problem of the bunch). The critical infrastructure group went hardest on LLM-side interventions: investing in safeguards to better infer intent from prompt patterns, information-sharing between LLM companies via something like the Frontier Model Forum, federated cross-platform behavior analysis, and (this one is interesting) training LLMs to de-escalate malicious intent by adapting techniques from suicide prevention hotlines.
The participants (which included Twist Bioscience, Anthropic, Microsoft, SecureBio, Los Alamos, and others) kept running into the same wall, which the report names explicitly:
A central theme was that technical feasibility and political backing together determine a mitigation\u2019s success.
I.e., most of these will fail without sustained funding and political will, and almost none of them have either. The other recurring caveat was attribution. AI-enabled biological incidents may remain unattributed indefinitely, which weakens deterrence and complicates response authority.
ran the University of Chicago\u2019s annual genetics, genomics, and systems biology symposium last week, and turned the speaker lineup into seven points on the current state of AI in genomics.
The whole post is worth reading (<10 minutes). I\u2019m just going to highlight a few.
First, the scaling laws may not hold for DNA. Evo 2 is 40 billion parameters trained on 9 trillion nucleotides spanning every domain of life. But Vishniakov et al. (2025) compared seven genomic foundation models against randomly initialized baselines of matched architecture across 52 tasks. The random baselines often matched or beat the pretrained models. Tang et al. (2025) found that raw one-hot encoded sequences are competitive with learned DNA-LM representations on regulatory genomics tasks. As Alex Lu put it at the symposium, DNA isn\u2019t natural language: low signal-to-noise, vast repetitive tracts, no obvious word or sentence analogs, and sparse functional elements that interact combinatorially across long distances.
Second, Arjun Krishnan\u2019s rule of thumb on benchmarks:
The best model is usually the one that is consistently number 2 in benchmarks across the literature.
Whoever publishes a model also designs the benchmark, and the benchmark almost always flatters the model. A model that\u2019s consistently competitive but rarely first-place is more likely to be genuinely strong than one that wins on the benchmark its own authors built. I\u2019m stealing this.
Third, toward the end, is a succinct rule for trainees using AI tools:
If you can validate what the AI produces, namely, if you can do the task yourself comfortably and check whether the AI did it correctly, then you can probably use AI to do the task. Otherwise, you should probably do it yourself, even if it feels hard.
This is the cleanest articulation of the trainee-and-AI problem I\u2019ve seen. I\u2019ve written about this before, highlighting work from a new colleague and co-author, Arjun Krishnan:
The friction of doing it the slow way is often the friction of actually learning, the \u201cproductive struggle\u201d I\u2019ve written about here before. An AI tool that produces output you can\u2019t evaluate is just a black box you\u2019re forced to trust. I\u2019d extend that beyond trainees, frankly.
On Tuesday, Nature published three full-length papers on AI scientists, an editorial that hedges, and a comment piece that pushes back. All on the same day.
Ghareeb, A. E. et al. A multi-agent system for automating scientific discovery. Nature 1\u20133 (2026) doi:10.1038/s41586-026-10652-y.
Ayg\u00fcn, E. et al. An AI system to help scientists write expert-level empirical software. Nature 1\u20133 (2026) doi:10.1038/s41586-026-10658-6.
Gottweis, J. et al. Accelerating scientific discovery with Co-Scientist. Nature 1\u20133 (2026) doi:10.1038/s41586-026-10644-y.
Messeri, L. & Crockett, M. J. The uncritical adoption of AI in science is alarming \u2014 we urgently need guard rails. Nature 653, 675\u2013676 (2026).
Why AI cannot do good science without humans. Nature 653, 650\u2013650 (2026).
The three papers: Google DeepMind\u2019s ERA, an LLM-plus-tree-search system that discovered 40 novel single-cell analysis methods that outperformed the top human methods on a public leaderboard, and 14 COVID hospitalization forecasting models that beat the CDC ensemble. Google\u2019s Co-Scientist, a multi-agent system built on Gemini that helped identify in vitro\u2013validated drug repurposing candidates for acute myeloid leukemia and (in a now-famous demo) recovered an antibiotic-resistance hypothesis that a Imperial College team had spent a decade developing but hadn\u2019t yet published, in days. And FutureHouse\u2019s Robin, which autonomously proposed enhancing RPE phagocytosis as a strategy for dry AMD, identified ripasudil (a clinically used ROCK inhibitor never previously proposed for AMD) as a candidate, validated it in vitro, then proposed an RNA-seq follow-up that fingered ABCA1 as a possible novel target. All hypotheses, experimental directions, data analyses, and main-text figures in the Robin paper were produced by Robin.
These are real results. With that throat-clearing out of the way\u2014
Then there\u2019s the editorial, \u201cWhy AI cannot do good science without humans\u201d, which is mostly anodyne until the closing paragraph:
Scientists should not allow a negative view of AI to drive them away from exploring the possibilities that AI co-scientists might hold for research. Equally, however, they must rise above the din of AI hype and advocate for their own importance, to remind the wider public, funders and fellow researchers that science still needs humanity, and that not every grant proposal need include an AI project.
Again: not every grant proposal need include an AI project.
Once more: not every grant proposal need include an AI project.
I read this as the editorial board, deliberately, on the day they published three papers about AI scientists, telling reviewers and program officers not to use \u201cno AI angle\u201d as a reason to triage a proposal.
The comment piece is more pointed. Lisa Messeri (Yale anthropology) and M. J. Crockett (Princeton psychology) wrote \u201cUncritical use of AI in science needs reality check\u201d. Some empirical claims: Hao et al. (2026) analyzed 41.3 million papers across biology, medicine, chemistry, physics, materials science, and geology and concluded that AI adoption seems to \u201cinduce authors to converge on the same solutions to known problems rather than create new ones.\u201d I wrote about this earlier this year:
Kusumegi et al. (2025) looked at 264,125 papers and found that in LLM-assisted papers, good writing stopped being a useful heuristic for scientific quality. Organization Science audited 6,957 submissions from 2021 to 2026 and found LLM-assisted papers had poorer scientific quality by acceptance rate. The closing argument is about deskilling: cleaning raw data, reading and summarizing the literature, the entry-level grunt work that AI is now offered as a solution for, is also how scientists develop the tacit knowledge needed to supervise AI-assisted workflows. If trainees don\u2019t develop those skills, who oversees the AI?
Three papers showcasing autonomous discovery, an editorial gently telling reviewers not to fetishize AI angles in proposals, and a comment piece arguing that the productivity gains may be hollowing out the next generation. Read them together. Or, if you don\u2019t have time, listen to Nature\u2019s podcast.
One more, and this connects to a paper I co-authored. A preprint went up at arXiv:2605.20668 titled \u201cOn the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists.\u201d Big study. 45 domain scientists annotated reviews of Nature-family papers, comparing official human reviewers against three frontier LLM agents. Headline results:
On aggregate review-item quality, all three AI reviewers exceed the lowest-rated human, and GPT-5.2 exceeds the top-rated human.
AI reviewers raise more significant items but with lower correctness.
Replacing one human reviewer with one AI reviewer minimally erodes panel diversity, because human reviewers themselves surface largely disjoint sets of criticisms.
AI reviewers can augment but not replace a human panel.
Current frontier AI reviewers in an agentic framework provide genuine value on the rigor- and code-heavy aspects of peer review, while systematically failing on the field-context aspects.
On that last point: it\u2019s the same argument that Agnieszka Swiatecka-Urban, Arjun Krishnan, and I argued for in our preprint earlier this year.
Our claim was that AI is best deployed as a rubric enforcer for the systematic, criterion-checkable parts of review (consistency between scores and comments, statistical reporting, completeness of evaluation, internal consistency of reviewer reasoning) while humans retain authority on the parts that depend on argumentative-world knowledge (novelty, feasibility, recognizing creative leaps, judging whether an ambitious proposal might fail spectacularly or succeed brilliantly). The arXiv paper, working with a completely different methodology and 45 domain scientists doing item-level annotation of real Nature-family reviews, lands in the same place. AI is strong on rigor and code, weak on field context.
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Pannu on AI and biosecurity, RAND/Helena AIxBio biosecurity mitigations, Blekhman on genomics AI, Nature\u2019s AI scientists week, AI in peer review.","tags":["Papers","Biosecurity","AI"],"title":"Five Things (May 23, 2026): AI in life sciences","updated_at":1779455466,"url":"https://blog.stephenturner.us/p/five-things-may-23-2026-aixbio","version":"v1"},{"abstract":"La sombra de la revoluci\u00f3n mexicana. Usos pol\u00edticos del pasado y la tradici\u00f3n cardenista a inicios de la era de Luis Echeverr\u00eda (1969-1970), por Andr\u00e9s Funes Revisi\u00f3n de c\u00f3mo Luis Echeverr\u00eda \u00c1lvarez utiliz\u00f3 la memoria de L\u00e1zaro C\u00e1rdenas como recurso simb\u00f3lico para reconstruir la legitimidad del r\u00e9gimen priista tras la masacre de Tlatelolco de 1968.","archive_url":null,"authors":[{"contributor_roles":[],"name":"Atarraya"}],"blog":{"archive_collection":null,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":null,"canonical_url":null,"category":"humanities","community_id":"c45eb77a-1580-4fbb-a9a2-11d7b258ec05","created_at":1723914704,"current_feed_url":null,"description":"Nuestras historias","doi":null,"doi_as_guid":false,"favicon":"https://rogue-scholar.org/api/communities/f17066f5-0dbf-48d0-a413-b22a79861a94/logo","feed_format":"application/atom+xml","feed_url":"https://blogatarraya.com/feed/atom/","filter":null,"funding":null,"generator":"WordPress.com","generator_raw":"WordPress.com","home_page_url":"https://blogatarraya.com","id":"7c191eac-fe88-4488-b12c-54a91a009dfb","indexed":true,"issn":null,"language":"es","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.59350","registered_at":1729715978,"relative_url":null,"ror":null,"secure":true,"slug":"atarraya","status":"active","subfield":"1202","subfield_validated":null,"title":"BLOG ATARRAYA","updated_at":1779439011.948398,"use_api":true,"use_mastodon":false,"user_id":"3a4c9f2c-4d20-406c-a15a-25e435f6313b"},"blog_name":"BLOG ATARRAYA","blog_slug":"atarraya","content_html":"\nLa sombra de la revoluci\u00f3n mexicana. Usos pol\u00edticos del pasado y la tradici\u00f3n cardenista a inicios de la era de Luis Echeverr\u00eda (1969-1970), por Andr\u00e9s Funes
\n\n\n\nRevisi\u00f3n de c\u00f3mo Luis Echeverr\u00eda \u00c1lvarez utiliz\u00f3 la memoria de L\u00e1zaro C\u00e1rdenas como recurso simb\u00f3lico para reconstruir la legitimidad del r\u00e9gimen priista tras la masacre de Tlatelolco de 1968. Muestra c\u00f3mo el pasado revolucionario se convirti\u00f3 en un terreno de disputa pol\u00edtica en los inicios de los a\u00f1os setenta en M\u00e9xico.
\n\n\n\nAndr\u00e9s Nicol\u00e1s Funes, \u00abLa sombra de la Revoluci\u00f3n mexicana: Usos pol\u00edticos del pasado y la tradici\u00f3n cardenista a inicios de la era de Luis Echeverr\u00eda (1969-1970)\u00bb, Revista Outros Tempos, vol. 22, n\u00fam. 40, 2025, pp. 357-394.
\n\n\n\nTambi\u00e9n disponible en otras plataformas
\n\n\n\nImplementing FAIR Workflows: A Proof of Concept Study in the Field of Consciousness is a project funded by the Templeton World Charity Foundation. In this project, DataCite works with a number of partners on providing an exemplar workflow that researchers can use to implement FAIR practices throughout their research lifecycle. This post shares an outcome of the project: a new chapter on persistent identifiers (PIDs) contributed to The Turing Way during the November 2025 Book Dash and was merged into the book in February 2026.
\n\n\n\n\n\n\n\nThe Turing Way is a community-driven, openly developed handbook for reproducible, ethical, and collaborative research, supported by The Alan Turing Institute. Organized across five guides and a community handbook, it brings together contributors from research, data science, and infrastructure communities to co-create guidance on open and reproducible practices. Its modular structure and openly licensed content have made it a widely referenced resource across the open scholarship ecosystem.
\n\n\n\nThe Turing Way\u2019s core mechanism for developing new content is the\u00a0Book Dash: a week-long collaborative event during which contributors propose, draft, review, and merge chapters. In November 2025, the Implementing FAIR Workflows project team participated in a Book Dash and integrated key outputs from the project into the Turing Book.
\n\n\n\nThe Implementing FAIR Workflows project has been a venue through which DataCite and partners develop practical guidance for PID and metadata adoption across the research lifecycle. The alignment with The Turing Way was clear from the outset: both initiatives aim to lower the barrier to FAIR practices for working researchers.
\n\n\n\nA review of the existing handbook showed that “assign a DOI” appeared more than fifty times across multiple chapters, but no single chapter explained what PIDs are, how they work, or how major PID systems \u2014 such as DataCite, Crossref, ORCID, and ROR \u2014 connect to support open scholarly infrastructure. The project team\u2019s contribution had two aims: to provide a single reference point for PIDs, and to cross-link PID and metadata content throughout existing chapters on data repositories, documentation and metadata, the FAIR principles, and citable research outputs, so that recommendations to “use a PID” sit alongside clear, practical guidance on how to do so.
\n\n\n\nMaterial developed through the FAIR Workflows project was adapted to The Turing Way’s audience and editorial style. The bulk of the content was drafted during the Book Dash, with the following months dedicated to peer review and revision through GitHub pull requests. The result is a new\u00a0Persistent Identifiers chapter in the Research Data Management section, two new chapters on connection metadata and version management in the Communication guide, and cross-references woven throughout the book.
\n\n\n\nThis contribution would not have reached completion without the thorough review and sustained support of Jim Madge (The Alan Turing Institute) and Esther Plomp (University of Aruba), whose guidance on structure, tone, and technical accuracy shaped the final chapter.
\n\n\n\nThe Turing Way is always open to contributions. Researchers, educators, and practitioners are welcome to consult the new chapter, suggest edits, or propose related additions through the GitHub repository.\u00a0
\n\n\n\nWe look forward to the wider dissemination and adoption of open and FAIR research best practices through the Turing Way community. From the perspective of the FAIR Workflows project, we also hope to see a more embedded approach to PID implementation throughout the research lifecycle, from which both researchers and research organizations can benefit.\u00a0
\n\n\n\n\n\n\n\nThis project was made possible through the support of a grant from Templeton World Charity Foundation, Inc. The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of Templeton World Charity Foundation, Inc.
\n\n\n\n\n","doi":"https://doi.org/10.5438/aah4-gj16","funding_references":null,"guid":"https://datacite.org/?p=15168","id":"be728b36-4324-43e6-a085-edee459cae4f","image":"https://datacite.org/wp-content/uploads/2026/05/Datacite_Social_Media_Blog_post_banner_Turing_Way_2026_1.png","images":[{"src":"data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7"},{"height":"218","sizes":"(max-width: 1024px) 100vw, 1024px","src":"https://datacite.org/wp-content/uploads/2026/05/image-1024x218.png","srcset":"https://datacite.org/wp-content/uploads/2026/05/image-1024x218.png, https://datacite.org/wp-content/uploads/2026/05/image-300x64.png, https://datacite.org/wp-content/uploads/2026/05/image-768x164.png, https://datacite.org/wp-content/uploads/2026/05/image.png","width":"1024"}],"indexed":false,"indexed_at":0,"language":"en","parent_doi":null,"published_at":1779361980,"reference":[],"registered_at":0,"relationships":[],"rid":null,"status":"active","summary":"\n Implementing FAIR Workflows\n\n\n : A Proof of Concept Study in the Field of Consciousness is a project funded by the Templeton World Charity Foundation. In this project, DataCite works with a number of partners on providing an exemplar workflow that researchers can use to implement FAIR practices throughout their research lifecycle.\n","tags":["FAIR","FAIR Workflows","Projects"],"title":"FAIR Workflows Meets The Turing Way: A Community Contribution on Persistent Identifiers for Reproducible Research Practices","updated_at":1779377334,"url":"https://datacite.org/blog/fair-workflows-meets-the-turing-way-a-community-contribution-on-persistent-identifiers-for-reproducible-research-practices/","version":"v1"},{"abstract":"Submitting a blog to the Rogue Scholar science blog archive requires filling out a simple form, and sometimes answering a few additional questions via email. In recent months I haven't caught up with the submissions and decided to implement a few changes. Participating in Rogue Scholar doesn't cost anything to authors or readers.","archive_url":null,"authors":[{"affiliation":[{"name":"Front Matter"}],"contributor_roles":[],"family":"Fenner","given":"Martin","url":"https://orcid.org/0000-0003-1419-2405"}],"blog":{"archive_collection":22096,"archive_host":null,"archive_prefix":"https://wayback.archive-it.org/22096/20231101172748/","archive_timestamps":[20231101172748,20240501180447,20241101172601],"authors":[{"name":"Martin Fenner","url":"https://orcid.org/0000-0003-1419-2405"}],"canonical_url":null,"category":"computerAndInformationSciences","community_id":"91dd2c24-5248-4510-9c2b-30b772bf8b60","created_at":1672561153,"current_feed_url":null,"description":"The Front Matter Blog covers the intersection of science and technology since 2007.","doi":null,"doi_as_guid":false,"favicon":"https://rogue-scholar.org/api/communities/15a362ea-8138-42b8-917f-1840a92addf8/logo","feed_format":"application/atom+xml","feed_url":"https://blog.front-matter.de/atom","filter":null,"funding":null,"generator":"Ghost","generator_raw":"Ghost 5.52","home_page_url":"https://blog.front-matter.de","id":"74659bc5-e36e-4a27-901f-f0c8d5769cb8","indexed":true,"issn":"2749-9952","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":"https://hachyderm.io/@mfenner","prefix":"10.53731","registered_at":1729685319,"relative_url":null,"ror":null,"secure":true,"slug":"front_matter","status":"active","subfield":"1710","subfield_validated":null,"title":"Front Matter","updated_at":1779439649.343466,"use_api":true,"use_mastodon":true,"user_id":"8498eaf6-8c58-4b58-bc15-27eda292b1aa"},"blog_name":"Front Matter","blog_slug":"front_matter","content_html":"Submitting a blog to the Rogue Scholar science blog archive requires filling out a simple form, and sometimes answering a few additional questions via email. In recent months I haven't caught up with the submissions and decided to implement a few changes.
Participating in Rogue Scholar doesn't cost anything to authors or readers. In turn, participating science blogs must make their content available as full-text via an RSS (or Atom or JSON) Feed with a Creative Commons Attribution (CC-BY) license.
Starting today, blog authors have to agree to these two additional statements in the blog submission form:
Also starting today, all blog submissions (author name and blog URL) are posted in the Rogue Scholar Slack Community (in the new public #blog-submissions channel) for two weeks. This allows the Rogue Scholar community to ask questions and blog authors to give more information about their blog.
Rogue Scholar is a free resource for authors and readers, but it costs money to maintain the infrastructure and support the community. For this reason, not all blog submissions can be accepted, or it takes longer until new blogs are included. Please reach out if your submission is delayed or you have questions.
With currently 191 participating science blogs and 49,965 blog posts, Rogue Scholar is on track to archive 50,000 blog posts in May, and I look forward to many more participating science blogs. Please reach out with questions or comments via Slack, email, Mastodon, or Bluesky.
Artificial Intelligence (AI) is rapidly gaining ground in science. Whereas the use of algorithms was until recently limited to a few specific fields, the availability of generative AI tools such as ChatGPT offers attractive possibilities for virtually all disciplines. Generative AI (GAI) can automatically generate content, such as text, images, or programming code, at a user\u2019s request. With the help of GAI, researchers can write articles faster, analyse larger datasets, and gather relevant literature more easily. Generative AI can also facilitate communication with parties outside the scientific community, for example, by producing accessible texts or animations.
However, GAI also frequently produces biased and inaccurate analyses. In an open letter, over 1,800 scientists based in the Netherlands warn of the dangers GAI poses to scientific integrity, privacy, sustainability, and other public values. Scientific journals are currently being flooded with fake articles, sometimes even listing authors who are unaware of their inclusion. Furthermore, the use of the most common GAI systems increases the dependence of research institutions on a handful of extremely powerful technology companies based in the United States. Finally, there are serious concerns about the amount of water and energy consumed by data centres, as well as the exploitation of workers in the Global South who are assigned the task of selecting disturbing texts and images into categories such as sexual abuse and violence.
Currently, a large responsibility rests with individual scientists to weigh these opportunities and risks and to assess if and how GAI can be used responsibly in science. Dutch universities advise scientists against using tools like ChatGPT but allow their staff the freedom to make their own decisions.
I wonder whether it is fair to ask individual researchers to handle this responsibility. The workload at universities is high, and competition is fierce. For young scientists, that one extra publication in a prestigious journal or that additional grant from NWO can make the difference between a permanent position and a temporary contract. Do scientists have sufficient knowledge of the technical limitations and sustainability impact of the use of large language models to make an informed decision? And can you trust that they will always prioritise public and scientific values, even when GAI can (seemingly) take a lot of work off their hands? Surveys among scientists worldwide show that many scientists are well aware of the errors and inaccuracies caused by the use of GAI, yet still use the available tools to generate summaries or have parts of their publications written.
Universities must therefore quickly establish standards or rules for the responsible use of GAI. This first and foremost requires a political assessment of conflicting values such as efficiency, autonomy, sustainability, and justice, on which the Dutch Parliament must decide: how important is it to accelerate and broaden scientific research, even in the current geopolitical landscape where data and AI are used as instruments of power? What is the moral bottom line in terms of sustainability and (global) justice, and at what cost are we willing to increase the efficiency of scientific research? In addition, a translation is needed from traditional scientific values to a new reality. What do integrity, reliability, and independence mean when using GAI, and how can we safeguard these values? For which applications is the use of GAI permissible, and what conditions must GAI tools meet? It is not certain that the Dutch science system can provide a single universal answer to this. It may be necessary for different disciplines to set out to formulate their own rules, because it is important to do justice to the great diversity of scientific practices and the varying ways in which scientists use GAI.
Drafting and enforcing new rules will, of course, not be easy. But scientific communities must do it, nonetheless. GAI challenges established mechanisms of scientific quality assurance, and we must find a response to this. My colleagues and I at the Rathenau Instituut are currently investigating how GAI is changing the nature of scientific knowledge, the demands its use places on scientific quality assurance, and what competencies scientists need to use GAI effectively.
A promising development is that the Dutch technology institute TNO is developing an alternative large language model, that will be more reliable, independent and transparent, called GPT-NL. Moreover, several Dutch universities are developing their own AI tools for science, which are delivering increasingly better performance. The EU also intends to invest heavily in this area. This offers the opportunity to develop alternative GAI technology that better aligns with European values and the rigor that forms the foundation of responsible science. But we cannot wait for that. As long as these alternatives remain insufficiently developed, scientists will continue to rely on GAI tools, which are fraught with problems. Setting limits on permissible use, therefore, requires our immediate attention.
This blogpost is a translation of a Dutch opinion piece published on ScienceGuide.nl and the Rathenau Institute\u00a0website. The author has made use of DeepL (free version) to translate the text to English.
Header image by\u00a0Anna Riepe & FARI on\u00a0Better Images of AI.
","doi":"https://doi.org/10.59350/mxm8v-ws533","funding_references":null,"guid":"https://www.leidenmadtrics.nl/articles/universities-must-act-now-to-regulate-ai-use-in-science","id":"de4cb48b-244a-4e32-9d3d-e6f9347e3cfd","image":null,"images":[],"indexed":true,"indexed_at":1779355341,"language":"en","parent_doi":null,"published_at":1779351060,"reference":[],"registered_at":0,"relationships":[],"rid":"xwxkb-xw732","status":"active","summary":"Artificial Intelligence (AI) is rapidly gaining ground in science. Whereas the use of algorithms was until recently limited to a few specific fields, the availability of generative AI tools such as ChatGPT offers attractive possibilities for virtually all disciplines. Generative AI (GAI) can automatically generate content, such as text, images, or programming code, at a user\u2019s request.","tags":[],"title":"Universities must act now to regulate AI-use in science","updated_at":1779355335,"url":"https://www.leidenmadtrics.nl/articles/universities-must-act-now-to-regulate-ai-use-in-science","version":"v1"},{"abstract":null,"archive_url":null,"authors":[{"contributor_roles":[],"family":"Hartley","given":"Kim","url":"https://orcid.org/0000-0002-4345-9044"},{"contributor_roles":[],"family":"Barker","given":"Michelle","url":"https://orcid.org/0000-0002-3623-172X"},{"contributor_roles":[],"family":"Aragon","given":"Selina","url":"https://orcid.org/0000-0001-9938-0522"},{"contributor_roles":[],"family":"Hong","given":"Neil Chue","url":"https://orcid.org/0000-0002-8876-7606"}],"blog":{"archive_collection":22149,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":[{"name":"Research Software Alliance"}],"canonical_url":null,"category":"computerAndInformationSciences","community_id":"79c5ab82-d540-413c-a8cf-3e55d0135a40","created_at":1682035200,"current_feed_url":null,"description":"Recent content on Research Software Alliance","doi":null,"doi_as_guid":false,"favicon":null,"feed_format":"application/atom+xml","feed_url":"https://www.researchsoft.org/feed.xml","filter":null,"funding":null,"generator":"Hugo","generator_raw":"Hugo 0.76.5","home_page_url":"https://researchsoft.org/","id":"9f582ac6-f8b2-46b6-98ab-a7def5e3faba","indexed":false,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.59350","registered_at":1729930725,"relative_url":null,"ror":null,"secure":true,"slug":"researchsoft","status":"active","subfield":"1802","subfield_validated":null,"title":"Research Software Alliance","updated_at":1779440544.986415,"use_api":null,"use_mastodon":false,"user_id":"9bbc9e05-69d3-481e-838f-33f1acf7aef9"},"blog_name":"Research Software Alliance","blog_slug":"researchsoft","content_html":"\n\n\n
By Kim Hartley, Michelle Barker, Selina Aragon, Neil Chue Hong
\nBuilding on a longstanding collaboration, ReSA is delighted to partner with the Software Sustainability Institute (SSI) for the first International Research Software Conference (IRSC).
\nInternationally recognised for shaping research software policy and practice, SSI has led training, community building, and advocacy activities since 2010. As the first organisation dedicated to improving software in research, it has played a vital role in the UK and worldwide in advancing research culture, expanding access to training, and working with partners to develop policies that better recognise and support software as a fundamental component of research. Through its Fellowship Programme, community events, training, policy work, and collaborations with institutions, funders and international partners, SSI has helped establish research software as a fundamental component of modern research.
\nAs a ReSA Founding Member and long-time partner, SSI has also been central to ReSA\u2019s development from the beginning, helping to shape its role as an international alliance for the research software community. ReSA Founding Members express their deep commitment to delivering the ReSA vision that research software and those who develop and maintain it are recognised and valued as fundamental and vital to research worldwide. To do this, Founding Members provide resources needed to support ReSA in its aim to bring research software communities together to collaborate on the advancement of the research software ecosystem.
\nThe partnership builds on shared work to improve how research software is recognised, funded, and supported, such as the Amsterdam Declaration on Funding Research Software Sustainability (ADORE.software) and the ADORE.software Toolkit. SSI also plays an active role across ReSA\u2019s activities, contributing to forums, task forces, resources, and events, including the recent workshop on Research Software Engineering in the Age of Generative AI: Building a Community Vision.
\nThrough its Collaborations Workshop and Research Software Camps series, SSI has built a strong track record of convening the research software community around emerging challenges and shared priorities. The SSI Collaborations Workshop is particularly recognised as a participatory, community-led event that brings together researchers, research software engineers, developers, funders, policy professionals, and infrastructure leaders to exchange practice, build collaborations, and generate practical outputs for the wider community. Alongside the more focused Research Software Camps, these activities demonstrate SSI\u2019s ability to create inclusive spaces where ideas are tested and communities are strengthened. Together, ReSA and SSI bring complementary strengths to deliver the first International Research Software Conference: ReSA\u2019s leadership in global research software community coordination and international collaboration, and SSI\u2019s longstanding expertise in research software policy, practice, training, and community building.
\n\n\n\n \u201cIRSC is an important and timely opportunity to bring the international research software community together around a shared agenda: recognising research software as essential research infrastructure, supporting the people who develop and maintain it, and strengthening the policies and practices that enable it to thrive. SSI is delighted to partner with ReSA in helping to shape and deliver this inaugural conference.\u201d\n
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\n \u2014 Neil Chue Hong, Director, Software Sustainability Institute\n
This partnership reflects that shared commitment. Neil Chue Hong also serves on the ReSA Steering Committee and chairs its financial subcommittee, further strengthening ties between the organisations. In addition, Kyro Hartzenberg, SSI Event Manager, is providing in-kind support for IRSC26, contributing valuable expertise to the delivery of the conference.
\nReSA is proud to partner with SSI in delivering IRSC and building a global platform that brings together the research software community.
\nLearn more about SSI at www.software.ac.uk.
\nSponsorship opportunities for IRSC are still available. Organisations interested in supporting the conference and engaging with the global research software community can learn more at: https://www.researchsoft.org/irsc/sponsorship/ or contact ReSA at info[at]researchsoft.org.
\nToday is Global Accessibility Awareness Day, and accessibility has been on our minds lately. We\u2019ve recently completed an internal audit of all our user interfaces, and have added a new accessibility page to our website, where you can find the accessibility documentation that we put together as part of the audit.
\nOf course we want to keep the barriers to participation in Crossref as low as possible for users with various disabilities. But also, more accessible tools work better for everyone. A person\u2019s access needs can change really quickly: even if you consider yourself to be relatively able-bodied, you are only one minor inconvenience away from at least a temporary disability. All it takes is some dazzling sunlight hitting your eye or your phone screen, or perhaps your dog going after a rabbit in an awkward direction while you are holding the lead (ask me how I know!) - and before you know it, you will be relying on accessibility features to navigate the digital and/or physical world for a while.
\nAn accessible user interface is one that you can navigate and interact with by various methods, including a mouse or touchpad, keyboard, screen reader, voice control, and other assistive technologies. It can be used on various screen sizes and supports zooming in or out without losing any content or functionality. It has sufficient colour contrast, doesn\u2019t flash fast-moving images at you, and has a clear structure that can be understood by both humans and machines.
\nIt is worth mentioning that we didn\u2019t only start thinking about accessibility when we started tackling the full audit of our user interfaces in March 2026. For example, Patrick Vale has previously written in this blog about a browser extension he has created to improve the accessibility of DOI links anywhere on the Internet. And we have known for a long time that there were accessibility gaps in many of our tools, but we didn\u2019t have this centrally documented anywhere.
\nWhen we did begin testing all our interfaces for compliance with level AA of the Web Content Accessibility Guidelines (WCAG 2.2) as part of the audit, we knew that some of what we would find was not going to be pretty. In the 26+ years of working with and for the scholarly community, Crossref has built countless tools and reports to offer to members and users, many of which we still maintain today. These are often decades old and have been built in a way that makes it virtually impossible to make them more accessible without rebuilding them entirely. So we know that we will continue to have accessibility gaps for the foreseeable future, but at least now we have a better idea of the scale of the challenge.
\nIt\u2019s also not all doom and gloom: more recently created user interfaces, such our new Metadata Manager, performed much better in the audit than legacy alternatives such as the web deposit form. We found a similar trend when looking at our report interfaces. To illustrate this, compare what happens when running the axe DevTools extension for Google Chrome on a member\u2019s participation report - this is a user interface that was completely re-implemented in 2025. Doing this brings up 26 issues:
\n\nMeanwhile, the browsable title list, which has completed a few more trips around the sun, has 254 issues listed:
\n\nIf you\u2019ve read this far, I hope you are convinced that accessibility is more than just ticking boxes on a conformance report. But especially for a global community like ours, there are other, less technical barriers to participation that we have to consider. For example, language is a major accessibility factor: much of what we as Crossref staff write and say is in English. When we host community events, we enable captions, and we try to leave space for these captions at the bottom of our slides.
\nWe have also started experimenting with simultaneous interpretation during our online events, such as our recent project showcase event for the 2026 metadata sprint in S\u00e3o Paulo. You can find recordings of this event in Spanish, Portuguese, and English on our YouTube channel to see the promising results of these efforts.
\nWe are currently addressing the accessibility issues identified in our audit of the Crossmark service. Many Crossref members have implemented the Crossmark button and pop-up on their own platforms and websites, so we thought this was a great place to start the remediation efforts following our audit.
\nWe are also in the process of redesigning our main website, www.crossref.org, following an information architecture review completed in 2025. Making changes to the design and navigation of our website will be the perfect opportunity to make our content not just more discoverable and more understandable, but also more accessible.
\nClearly there is even more to be done, so watch this space for more updates on our accessibility roadmap and improvements. And if you have first-hand experience of using Crossref services and interfaces with assistive technologies, or you have other input or feedback you\u2019d like to share, leave a comment below or start a discussion in our community forum.
\n
Wer eine wissenschaftliche Karriere plant, sollte ein realistisches Gef\u00fchl daf\u00fcr haben, wie viel Zeit eine einzelne Originalarbeit verschlingt. Die Ideen sprudeln meist schnell, der Rest dauert.
\nWenn von \u201eZeit f\u00fcr ein Paper\u201d die Rede ist, sind meist zwei sehr unterschiedliche Dinge gemeint. Die eine ist die Kalenderzeit, also die Spanne, die zwischen den Meilensteinen verstreicht, Wochen im Peer Review, Monate bis zur Online-Publikation. Die andere ist die aktive Arbeitszeit, die tats\u00e4chlich am Schreibtisch verbracht wird. Beide korrelieren nur schwach. Ein Manuskript kann monatelang in der Redaktion liegen, ohne dass jemand daran arbeitet. Drei intensive Schreibwochen wiederum bringen oft mehr als hundert Arbeitsstunden zustande, ohne nennenswert Kalender zu verbrauchen.
\nDie \u00fcberw\u00e4ltigende Mehrheit publizierter Studien zur Publikationsdauer misst die Kalenderzeit, weil sie sich aus Journal-Metadaten rekonstruieren l\u00e4sst. Die aktive Arbeitszeit dagegen erfordert Selbstausk\u00fcnfte und ist methodisch deutlich anspruchsvoller. Dieser Blogbeitrag folgt dem Weg eines Manuskripts und sammelt unterwegs ein, was die Forschung jeweils belegen kann.
\nBeginnen wir mit der Arbeitszeit, weil sie am leichtesten untersch\u00e4tzt wird. Song und Kolleg*innen erfassten in einer retrospektiven Befragung den Personenstundenaufwand von 171 publizierten retrospektiven Studien, indem sie die beteiligten Chirurg*innen baten, die Stunden \u00fcber acht Phasen des Forschungszyklus zu sch\u00e4tzen, von der Studienplanung \u00fcber Datenerhebung und Analyse bis zur Revision nach Einreichung. Der Median lag bei 177 Stunden pro Publikation, also rund 22 Achtstundentagen konzentrierter Arbeit einer einzelnen Person, bei einer Spanne von 29 (!) bis 1.287 Stunden (Song et al., 2013). Bemerkenswert ist, dass weder die Zahl der Autor*innen noch die Zahl der untersuchten F\u00e4lle mit dem Gesamtaufwand korrelierte. Mehr Mitautor*innen bedeuten also nicht automatisch weniger Arbeit f\u00fcr die einzelnen K\u00f6pfe.
\nEin erheblicher Teil dieser Stunden flie\u00dft dabei nicht in Erkenntnis, sondern in die Form. LeBlanc und Kolleg*innen befragten 372 Forschende aus 41 L\u00e4ndern und fanden einen Median von 14 Stunden allein f\u00fcr die formelle Formatierung eines einzigen Manuskripts, also f\u00fcr das Anpassen von Layout, Referenzstil und Einreichungsvorgaben. \u00dcber das Jahr summiert sich das auf rund 52 Stunden pro Person, was Lohnkosten von etwa 1.900 US-Dollar entspricht, nur f\u00fcrs Formatieren (!) (LeBlanc et al., 2019). Und diese Stunden verteilen sich selten auf geregelte B\u00fcrozeiten. Barnett und Kolleg*innen werteten \u00fcber 49.000 Manuskripteinreichungen und 76.000 Gutachten bei BMJ-Zeitschriften aus und fanden, dass Einreichungen mit einer Wahrscheinlichkeit von 14 bis 18 Prozent am Wochenende und von 8 bis 13 Prozent an Feiertagen erfolgten, am h\u00e4ufigsten arbeiteten chinesische Forschende am Wochenende und um Mitternacht (Barnett et al., 2019). Wissenschaftliches Schreiben frisst also nicht nur Stunden, es frisst oft Freizeit.
\nIst das Manuskript eingereicht, beginnt die erste lange Wartezeit. Huisman und Smits werteten 3.500 Begutachtungserfahrungen der Plattform SciRev aus und fanden eine erste Antwortzeit, die stark nach Fach variiert, mit 8 bis 9 Wochen in der Medizin am unteren Ende und 16 bis 18 Wochen in den Geistes- und Wirtschaftswissenschaften am oberen Ende (Huisman & Smits, 2017). Ein nennenswerter Teil der Verz\u00f6gerung entsteht dabei nicht im Gutachten selbst, sondern in der Redaktion, immerhin ein Drittel der Desk-Rejections dauerte l\u00e4nger als zwei Wochen, obwohl daf\u00fcr kein externes Gutachten n\u00f6tig ist.
\nWie lang die Gesamtspanne von der Einreichung bis zur Publikation ausf\u00e4llt, l\u00e4sst sich kaum auf eine einzige Zahl bringen. Andersen, Fonnes und Rosenberg fassten in einem systematischen Review 69 Studien zu biomedizinischen Zeitschriften zusammen und fanden eine mittlere Submission-to-Publication-Zeit, die zwischen 91 und 639 Tagen schwankte, je nach Fachgebiet und Journal (Andersen et al., 2021). Die Daten waren zu heterogen f\u00fcr eine sinnvolle Metaanalyse, und es zeigte sich kein systematischer Unterschied zwischen der Zeit bis zur Annahme und der Zeit von der Annahme bis zur Publikation. Mit anderen Worten, beide Teilstrecken k\u00f6nnen sich ziehen.
\nDie Wartezeit hat eine Ursache, die in keiner individuellen Zeitrechnung auftaucht, n\u00e4mlich den kollektiven Aufwand der Begutachtung. Kovanis und Kolleg*innen sch\u00e4tzten f\u00fcr das Jahr 2015 rund 63,4 Millionen Stunden, die weltweit in den Peer Review der biomedizinischen Literatur flossen, wobei ein kleiner Teil der Forschenden die Hauptlast trug, 20 Prozent erstellten zwischen 69 und 94 Prozent aller Gutachten (Kovanis et al., 2016). Auf die einzelne Begutachtung heruntergebrochen sind das im Schnitt rund 9,6 Stunden pro Review, allein f\u00fcr eine einzige Zeitschrift summierten Golden und Schultz so \u00fcber 360.000 ehrenamtliche Stunden im Jahr (Golden & Schultz, 2012). Aczel und Kolleg*innen kamen f\u00fcr 2020 auf \u00fcber 100 Millionen Stunden j\u00e4hrlich, mit einem gesch\u00e4tzten Gegenwert von mehr als 1,5 Milliarden US-Dollar allein f\u00fcr US-amerikanische Gutachter*innen (Aczel et al., 2021). Eine aktuellere Erhebung von LeBlanc und Kolleg*innen beziffert die Kosten auf rund 1.272 US-Dollar pro Gutachter*in und Jahr und unter Einrechnung abgelehnter Manuskripte global auf rund 6 Milliarden US-Dollar, wobei die gro\u00dfe Mehrheit der Begutachtungen unverg\u00fctet bleibt (LeBlanc et al., 2023). Dieser Aufwand ist die Kehrseite der Wartezeit, jede Woche, die ein Manuskript auf sein Gutachten wartet, ist auch eine Woche, in der jemand anderes unbezahlt f\u00fcr ein anderes Manuskript liest.
\nNach dem ersten Feedback folgt nat\u00fcrlich noch fast immer mindestens eine Revisionsrunde, bevor ein Manuskript endg\u00fcltig angenommen wird.
\nBei klinischen Studien dehnt sich die Zeitskala noch einmal erheblich, und zwar schon vor der Einreichung. Hopewell und Kolleg*innen zeigten in einem Cochrane-Methoden-Review, dass Studien mit positiven Ergebnissen eine fast vierfach h\u00f6here Chance auf Publikation hatten als solche mit negativen (Odds Ratio 3,90) und zudem schneller erschienen, im Median nach vier bis f\u00fcnf Jahren gegen\u00fcber sechs bis acht Jahren bei negativen Befunden (Hopewell et al., 2009). Dieser Time-Lag-Bias zugunsten positiver Ergebnisse ist seit Jahrzehnten dokumentiert und bedeutet, dass gerade die ern\u00fcchternden Befunde besonders lange auf sich warten lassen.
\nWer in der medizinischen Ausbildungsforschung publiziert, kennt das Ph\u00e4nomen aus eigener Erfahrung (und Geduld). Maggio und Kolleg*innen analysierten die Publikationszeiten in 24 Medical-Education-Journalen zwischen 2018 und 2022 und fanden eine durchschnittliche Submission-to-Publication-Zeit von gut 300 Tagen (Maggio et al., 2024). Das liegt im oberen Bereich der breiten biomedizinischen Spanne, die Andersen berichtet hatte.
\nDie Gr\u00fcnde f\u00fcr die generell langen Zeiten sind nicht abschlie\u00dfend gekl\u00e4rt. Vermutlich spielen kleinere Reviewer-Pools, die methodische Heterogenit\u00e4t qualitativer und gemischter Designs sowie l\u00e4ngere Revisionsrunden eine Rolle. F\u00fcr junge Medical Educators mit Tenure-Druck ist die lange Bearbeitungszeit eine Erkenntnis von praktischer Bedeutung, die man bei der Karriereplanung lieber kennen sollte, als sie erst nach drei eingereichten Manuskripten selbst zu entdecken.
\nZwei Entwicklungen versprechen, den Weg zu verk\u00fcrzen. Preprints machen Ergebnisse verf\u00fcgbar, lange bevor die formale Publikation abgeschlossen ist. Allerdings ersetzen sie diese nicht, Drzymalla und Kolleg*innen verfolgten 39.243 COVID-bezogene Preprints und fanden, dass nur 20 Prozent je in einer Fachzeitschrift erschienen, im Median 178 Tage nach dem Preprint (Drzymalla et al., 2022). Die Erkenntnis-Verf\u00fcgbarkeit ist also beschleunigt, die formale Publikation dauert weiter Monate. Die Pandemie hat zudem gezeigt, dass es schneller geht, wenn man will, Horbach fand f\u00fcr COVID-Artikel eine um 49 Prozent verk\u00fcrzte Bearbeitungszeit, w\u00e4hrend sich f\u00fcr Nicht-COVID-Artikel nichts beschleunigte (Horbach, 2020).
\nGenerative KI ist die zweite, noch offene Abk\u00fcrzung. Fr\u00fche journalistische Analysen in Nature beschreiben Zeitersparnis beim Schreiben, Korrekturlesen und Referenzieren, mit besonderem Nutzen f\u00fcr nicht-englische Muttersprachler*innen, weisen aber zugleich auf Risiken wie Paper-Mills und eine m\u00f6gliche Flut minderwertiger Manuskripte hin (Conroy, 2023a). In einem vielzitierten Experiment lie\u00df sich mit ChatGPT in rund einer Stunde ein vollst\u00e4ndiges, sprachlich fl\u00fcssiges Manuskript erzeugen, das aber an Neuheit und Genauigkeit krankte (Conroy, 2023b). Belastbare Vorher-Nachher-Vergleiche zum tats\u00e4chlichen Stundenaufwand fehlen bislang.
\nReine Arbeitszeit: Song et al.\u00a02013 (retrospektive Survey, 171 chirurgische Studien, 13 Chirurg*innen, 81 % R\u00fccklauf): Median 177 h pro Publikation (Range 29\u20131.287), entspricht rund 22 Achtstundentagen; keine Korrelation mit Autoren- oder Probandenzahl; Hauptanteile Datenerhebung 23 %, Manuskripterstellung 22 %, Datenanalyse 13 % (Song et al., 2013).
\nFormatierungsaufwand: LeBlanc et al.\u00a02019 (Survey, n = 372 aus 41 L\u00e4ndern): Median 14 h pro Manuskript, 52 h pro Person und Jahr, rund 1.908 US-Dollar Lohnkosten pro Person und Jahr (LeBlanc et al., 2019).
\nArbeitszeitmuster: Barnett et al.\u00a02019 (> 49.000 Einreichungen, > 76.000 Reviews, BMJ-Journale 2012\u20132019): Wochenend-Wahrscheinlichkeit 0,14\u20130,18, Feiertags-Wahrscheinlichkeit 0,08\u20130,13; chinesische Forschende am h\u00e4ufigsten am Wochenende und um Mitternacht (Barnett et al., 2019).
\nErste Antwortzeit: Huisman & Smits 2017 (n = 3.500 SciRev-Berichte): Median der Erstantwort 8\u20139 Wochen (Medizin) bis 16\u201318 Wochen (Geistes-/Wirtschaftswissenschaften); ein Drittel der Desk-Rejections dauerte \u00fcber zwei Wochen (Huisman & Smits, 2017).
\nGesamtspanne biomedizinisch: Andersen, Fonnes & Rosenberg 2021 (systematisches Review, 69 Studien): Submission-to-Publication 91\u2013639 Tage, zu heterogen f\u00fcr Metaanalyse, kein systematischer Unterschied zwischen Submission-to-Acceptance und Acceptance-to-Publication (Andersen et al., 2021).
\nPeer-Review-Aufwand: Kovanis et al.\u00a02016: rund 63,4 Mio. Stunden (2015), 20 % der Forschenden erstellen 69\u201394 % der Gutachten (Kovanis et al., 2016). Golden & Schultz 2012 (310 Gutachter*innen): im Schnitt 9,6 h pro Review, \u00fcber 360.000 ehrenamtliche Stunden j\u00e4hrlich allein f\u00fcr eine Zeitschrift (Golden & Schultz, 2012). Aczel et al.\u00a02021: \u00fcber 100 Mio. Stunden (2020), Gegenwert > 1,5 Mrd. US-Dollar (nur USA) (Aczel et al., 2021). LeBlanc et al.\u00a02023 (n = 308, 33 L\u00e4nder): rund 1.272 US-Dollar pro Person und Jahr, global 1,1\u20131,7 Mrd., mit abgelehnten Manuskripten rund 6 Mrd. US-Dollar; 87,5 % unverg\u00fctet (LeBlanc et al., 2023).
\nKlinische Studien (Time-Lag): Hopewell et al.\u00a02009 (Cochrane, 5 Kohorten): positive Studien OR 3,90 f\u00fcr Publikation, Median 4\u20135 Jahre vs.\u00a06\u20138 Jahre bei negativen Befunden (Hopewell et al., 2009).
\nMedical Education: Maggio et al.\u00a02024 (24 Journale, 2018\u20132022): Submission-to-Publication im Schnitt 300,8 Tage (SD 200,8); COVID-Overlap 539 Tage, COVID-endemisch 226 Tage (F(2, 7473) = 2150,7; p < 0,001) (Maggio et al., 2024).
\nPreprints und Pandemie: Drzymalla et al.\u00a02022 (39.243 COVID-Preprints): 20 % publiziert, Median 178 Tage bis zur Journalpublikation (Drzymalla et al., 2022). Horbach 2020 (14 Journale, 669 Artikel): COVID-Artikel 49 % schneller, Nicht-COVID unver\u00e4ndert (Horbach, 2020).
\nTransparenzhinweis:
\n@misc{friederichs2026,\n author = {Friederichs, Hendrik},\n title = {*Science Friction* -\\/- Wie viel Arbeit macht eine\n Publikation?},\n date = {2026-05-21},\n url = {https://medical-education.pages.ub.uni-bielefeld.de/research/mes-blog/posts/2026-05-21-workload-publications/},\n langid = {de}\n}\n