Bob Dewey (@macroleverageTP) header
Bob Dewey (@macroleverageTP) profile picture

Most people think the future is collapsing. I study how progress actually happens. Creator of Exploring Prosperity | Money, markets, AI & Bitcoin.

Connecticut, USA
Joined March 2009
Gold is repricing trust. Below is the updated debt-to-gold ratio. For 100+ years it climbed almost in one direction as fiat replaced discipline. As @LynAldenContact has shown, gold couldn’t keep up with the telegraph — money had to move faster than metal. As @saifedean explains, WWI accelerated the break from sound money and ushered in the debt-laden fiat century. The ratio just turned — but not because debt fell.
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Students are making six-figure college decisions with remarkably little individual guidance. Meanwhile, AI is making the careers they're preparing for less predictable. Could AI itself help students navigate those choices? My latest essay explores that possibility, and an entrepreneur who's trying to make it happen.
🤖 Made with AI
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I've built a website I always wished existed: a collection of all the books written about Bitcoin. I've found 110 of them. If I've missed any, let me know in the replies. btcbooks.org
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“Not all escalators go up. Some actually go down.” Jon Carson argues that college has become a riskier decision, especially for students taking on substantial debt without a clear career path. College is one way to build a fulfilling future. It’s not the only way.
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1,010 studies of AI’s impact on jobs in just nine months. That’s what former BLS Commissioner Erica Groshen told me in our recent interview. The results? “Different results all over the place.” I’ve been collecting research and real-world evidence on how AI is changing productivity and employment. Today I’m launching AI at Work | The Evidence, a series every two weeks examining what the research shows, where findings conflict, and what we still don’t know. The first installment is below. 👇
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What does the accumulated evidence suggest? AI's productivity gains on specific tasks are well documented. Its effects on employment are much less certain. Hiring may slow before layoffs rise. Some firms may expand as AI lowers costs, even as opportunities shrink elsewhere. The emerging picture is not simply jobs gained versus jobs lost. It's a changing mix of opportunities, with potentially very different outcomes across workers and industries.
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The open question: What changes is AI already producing that our official labor statistics aren't measuring? As Erica Groshen explained, our statistical systems aren't keeping pace with changes in how people work. That's why I'll be looking across academic research, company data and real-world evidence for economically meaningful changes that conventional statistics may miss. The goal: understand what's actually happening, not wait for it to show up in the official numbers.
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I love Math because it’s logical but English tutors get paid big bucks for a reason 😉🤣
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Critically important safety tip. Even if you know not to answer the calls, listen to how the tricks work.
Social engineers pretending to be Google Support keep calling me, so I took the opportunity to create this educational video showcasing their tactics.
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We believe the bottom is in, selling pressure has diminished, the debasement case has strengthened, and the crowd has left
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AI may make investment analysis abundant while making good investment judgment more valuable. For decades, exceptional investors gained an advantage by gathering information, analyzing companies, and identifying patterns better than competitors. AI is rapidly reducing the cost of those activities. But deciding what an uncertain future is worth requires judgment, not just information processing. My hypothesis: AI will erode some traditional sources of investment alpha while increasing the relative value of the judgment that remains difficult to automate.
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AI is getting extraordinarily good at solving mathematical problems. But @stevenstrogatz raised a more important question in our conversation: Who decides which problems are worth solving? The greatest advances in mathematics haven't always come from solving harder problems. Sometimes they've come from inventing an entirely new way to think about them. If AI becomes vastly better at finding answers, could our ability to recognize valuable questions become even more important?
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So good!
I was recently in a pub near Galway and noticed this on the wall. Between 1900 and 1993, the price of the same pint of Guinness increased 136x. Measured in Guinness, money lost over 99% of its purchasing power. Sometimes the best lessons about money are hanging on pub walls.
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Median weekly earnings for full-time workers rose 1.5% over the past year. Consumer prices rose 3.5%. Last week, the New York Fed reported that productivity and public-company revenue per worker have accelerated since 2019, while operating margins rose from 16% to 18%. None of this proves AI caused the divergence. But it shows why employment is an incomplete measure of AI’s impact. Workers can keep their jobs while producing more—and receive a smaller claim on what they produce. The first distributional effect of AI may appear in wages and margins long before it appears in unemployment.
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At Cato last night, @Marian_L_Tupy's five arguments against wealth taxes: - They're larger than they sound, compounding over time. - They discourage risk-taking. - Billionaire wealth isn't idle cash. - Much of that wealth reflects expected future inventions. - Tax avoidance is legal and effective. Taxing a billionaire's company's market value means taxing expectations of innovations that haven't happened yet. The insidious effect is that a wealth tax reduces the rewards of successful ventures without reducing the losses from failures. Entrepreneurs should be expected to respond by choosing smaller, safer opportunities over the breakthroughs that mark and drive human progress.
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