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From Muscle to Mind: Earn More with Fewer Calories and Fewer Deaths

Blog Post | Labor & Employment

From Muscle to Mind: Earn More with Fewer Calories and Fewer Deaths

Office workers use 77.8 percent less energy and experience a 95.3 percent lower fatality rate than construction workers.

Summary: Work has changed dramatically over time, shifting significantly from physical to mental labor. Today, office jobs demand far less physical energy and carry far lower risks of injury or death compared to physically demanding trades. This transition shows how progress has allowed us to create more value with less strain on our bodies—and with far greater safety than workers of the past could have imagined.


Economist George Gilder points out that using blue-collar hourly wage rates to calculate time prices underestimates the gains we’re enjoying in an economy that’s no longer driven by muscle but by mind. Knowledge workers earn more in an hour, consume fewer calories, and risk far less death or injury than other workers. In other words, they do far more with far less. This is the true compounding of progress—and we can see it mapped on a single chart.

Calories Per Hour of Work

I asked several AI models about the number of calories per hour that different kinds of work require and this is what I got:

The energy demands of physical work versus knowledge work reveals a dramatic difference in caloric expenditure. Workers in physically demanding jobs burn significantly more calories than do their office counterparts:

High-energy physical work:

  • Construction tasks such as masonry or hanging sheetrock: 400–500 calories per hour (equivalent to running or high-intensity aerobics)
  • Heavy lifting and transport: 285–300 calories per hour for a 170-pound worker

Moderate physical work:

  • Manufacturing: 228 calories per hour (men), 180 calories per hour (women)

Office work:

  • Standing desk: 186 calories per hour for a 170-pound person
  • Sitting desk work: 100 calories per hour

As we transition from working with atoms to working with knowledge our bodies require a lot less energy to perform that work. Moving from construction work to sitting at a desk in an office requires 77.8 percent fewer calories per hour. Put another way, the calories needed to fuel one construction worker can power 4.5 office workers. The result is an economic system that creates more value with less resource consumption.

Fatal work injury rate

The Bureau of Labor Statistics reports on fatalities on the job:

Farming, fishing, and forestry are the most dangerous professions at 24.4 fatal injuries, with transportation and material moving at 13.6, and construction and extraction at 12.9. Office and administrative support are the least risky professions at 0.6. Farmers, fishermen, and loggers are more than 40 times likely than an office worker to be killed on the job. Moving from construction work to sitting at a desk in an office reduces the risk of a work fatality by 95.3 percent. Adjusted for population size, construction workers experience a work-related fatality rate more than 21 times higher than that of office workers.

And it was much worse in the past—something that we tend to forget when looking at present statistics. In 1900, deaths in the mining and oil extractions fields (lumped under mining) was estimated at 333 per 100,000 workers and remained that high through the 1920s. We can hardly comprehend just how good we’ve got it now.

Calorie-fatality index

If we combine these two factors into a calorie-fatality index and compare the construction and office industries, we note that office work is 99 percent lower than construction work on the index. Moving from blue-collar construction work to an office job indicates an overall improvement factor of 96.75 (or 9,575 percent) on the calorie-fatality index.

Find more of Gale’s work at his Substack, Gale Winds.

Blog Post | Computing

Machines Can Crunch Numbers, but Humans Still Need to Understand Them

In the age of AI, humans remain the ultimate resource.

Summary: Recent AI breakthroughs invite the question of whether human minds are still the “ultimate resource,” as Julian Simon claimed. AI can generate answers far faster than humans can check them, but Joel Mokyr’s distinction between knowing that something works and knowing why shows that both kinds of knowledge must advance together. Candidate proofs are not theorems, and predictions are not findings. That is why the age of AI calls for more educated minds, not fewer.


Last week, OpenAI published more than 370 mathematical results spanning algebra, theoretical computer science, and mathematical logic. Most came from a single prompt and about three hours of computing time. “If a human did this, it would be an instant Fields Medal, no questions asked,” wrote Alex Kontorovich, a professor of mathematics at Rutgers University.

In his 1981 book, economist Julian Simon called people “the ultimate resource” because nothing else can create new ideas. Simon argued that new human ideas lead to innovation, and innovation drives economic growth and underpins modern prosperity. Forty-five years on, and with announcements of AI breakthroughs dominating headlines almost every week, it’s easy to be skeptical of Simon’s claim.

OpenAI announced last month that after 88 hours of work, its AI had cracked the Navier-Stokes existence and smoothness puzzle, one of the seven Millennium Prize Problems that has baffled mathematicians for almost a century. On the same day, Google DeepMind released AlphaGenome Atlas, which maps the predicted molecular effects of all nine billion possible single-letter changes to human DNA. If the nature of innovation is changing, are we still “the ultimate resource”?

Ideas, as the science writer Matt Ridley wrote in 2010, “have sex.” In The Rational Optimist, Ridley argued that innovation is not the work of solitary geniuses but happens when human minds come together. As societies become increasingly open and specialized, more people with different expertise meet, discuss, and recombine ideas. The result is what Ridley called the “collective brain”: the knowledge spread across society that no individual holds.

For recent AI breakthroughs, every system runs on humanity’s collective brain. OpenAI’s agents worked from a cached copy of the internet, and AlphaGenome was trained on thousands of experiments on human and mouse DNA, all of it somebody’s lab work. For years it was implied – simply because no alternative existed – that for ideas to have sex, both partners had to be human. If ideas no longer need two human minds to have sex, as Ridley put it, one might conclude the collective brain has found a replacement for humans altogether.

That assessment is overly pessimistic, however. Since these breakthroughs recombine ideas humans had already written down (albeit at inhuman speeds), the machines look less like second parents and more like a very fast medium through which human ideas meet and get down to business. One-nil, humans.

Beyond depending on existing human knowledge, OpenAI’s breakthrough also relied on human methods. To tackle Navier-Stokes, OpenAI ran about 10,000 agents at once, divided into groups that explored different areas and talked among themselves. As groups made progress, the most useful insights were consolidated and fed back to the other agents. Roughly 2.7 million messages were exchanged before an answer emerged.

Many rival agents working in parallel, immense specialization, most of them failing while the rest built on what was most valuable. Free market galore!

Humanity is surely better off with the additional knowledge AIs produce. Yet whether it is agents creating mathematical proofs that could solve centuries-old problems, or my own AI delivering thousands of words of research reports I haven’t found time to read, the bottleneck to innovation still lies with us.

In DeepMind’s case, AlphaGenome Atlas can predict what genetic variants can do, but it doesn’t comprehend the biology beneath them. A list of predictions settles nothing on its own. A human mind must decide which subset to study, and Atlas’s predictions are not findings until somebody in a laboratory confirms them. Knowing that a DNA variant matters isn’t the same as knowing why.

In OpenAI’s case, mathematicians worldwide are still assessing the proof’s validity. Three days after the announcement, without naming any company, 25 Fields Medal winners published an open letter about AI and the discipline of mathematics, noting that “solving problems is only a tool and proxy for achieving the primary goal of conceptual understanding and insight.” The signatories note that AI can accelerate mathematical work, but human understanding produces the next question worth asking.

The gap between output and understanding predates AI. The economic historian Joel Mokyr, who won the 2025 Nobel, distinguishes two types of knowledge. “Prescriptive knowledge” is knowing that something works. Think Edward Jenner knowing that exposure to cowpox provides immunity against smallpox, long before anyone knew why. “Propositional knowledge” is understanding why something works, such as comprehending the underlying science or natural laws. Mokyr’s answer to why the Industrial Revolution did not stall like earlier golden ages is that Europe got both types of knowledge moving together.

The latest wave of AI discoveries could make one pessimistic about humanity’s role in the future of innovation. But Mokyr’s point is that the two types of knowledge should move together, and understanding why something works matters just as much as knowing that it does. Candidate proofs are not theorems, and predictions are not findings.

AIs can crunch statistical correlations, draw on what humans already know, and generate prescriptive knowledge far faster than any human mind can convert the output into something verified, trusted, and useful. That is why we need more educated minds, not fewer. The scarce resource, indeed the ultimate resource, is still us.

A version of this article was first published in the Washington Examiner on 10/13/2026.

Wall Street Journal | Work-Related Injuries

Remote Bulldozer Improves Landfill Safety

“Inside a cool, dimly lighted trailer where the quiet is broken mostly by the crackle of radio traffic, Abrin Vasquez drives a 40-ton bulldozer hundreds of yards away.

Vasquez is remotely piloting a Caterpillar dozer spreading trash across the Marana Regional Landfill near Tucson, Ariz. He used to be out in the heat running the machine from the cab, a potentially dangerous job in the landfill’s rough, trash-filled terrain. Now, he is safely away from that in the control trailer, doing the same job from a virtual cab…

What Vasquez and others are doing at the Marana landfill is likely a preview of the future of operating machinery in dusty and hazardous conditions. For Houston-based WM, the landfill’s owner, the site has become a proving ground for remote-controlled and autonomous machines aimed at improving safety and reducing operating costs.

Besides the bulldozer, the company plans to deploy an autonomous Caterpillar compactor at the landfill in the coming months. The driverless tractor with spiked steel wheels will run over a programmed course, flattening newly spread trash to create space to bury more garbage.”

From Wall Street Journal.

The Economist | Employment

AI-Related Jobs Are Growing in the US

“"The Economist tracked five industries at the heart of the data-centre build-out, from electrical contracting to equipment manufacturing. Since 2023 employment in them has risen by roughly 320,000 more than broader construction and manufacturing trends would suggest. The Bureau of Labour Statistics (BLS) expects utilities to be the fastest-growing big sector between now and 2035…

AI is also creating a new class of white-collar jobs…

Preliminary research by Gad Levanon, chief economist at the Burning Glass Institute, uses the research outfit’s career history database (which mostly relies on LinkedIn career histories) to identify jobs that would not exist without AI, whether at AI-native firms or because they are AI-specific roles at other companies. He reckons roughly 1% of professional jobs are now 'AI jobs'—on the order of 1m positions in America. In computer occupations and life sciences—including researchers using AI to discover new drugs—the share is 4-5%. LinkedIn’s own analysis points to roughly 640,000 new AI-specific jobs between 2023 and 2025…

A third source of jobs comes from productivity gains. AI allows lawyers to draft contracts faster and analysts to comb through financial filings in minutes. If higher productivity lowers the cost of professional services, it is possible that demand for them can rise enough to create more work overall.

Some of the occupations once believed to be most vulnerable to AI appear to be benefiting from this effect. Between 2023 and 2025 employment among paralegals rose by about 11% and among market-research analysts by 6%, compared with a national average of around 2%.””

From The Economist.

Federal Reserve Bank of New York | Technological Readiness

Regional Businesses Adopt AI Without Widespread Layoffs

“The ongoing advancement and adoption of artificial intelligence continues to raise concerns about widespread job losses. Over the past three years, our regional business surveys have asked firms about their AI adoption and its effects on their workforces. This year, we found that AI use among regional businesses has continued to rise sharply, with more than 60 percent of service firms and about half of manufacturers now using AI—a notable increase from 40 percent and 26 percent, respectively, reported in 2025. Despite this rapid adoption, regional firms’ investments in AI are generally modest, usage tends to be concentrated among a small share of workers within firms, and layoffs have remained uncommon. And, while some firms have scaled back hiring due to AI, others have added workers to help them use it. Retraining employees in response to AI remains the primary way firms are adjusting their workforces.”

From Federal Reserve Bank of New York.