2026-05-31XThread

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UnclassifiedUnclassifiedUnclassifiedUnclassifiedEnergyEquityMacroAI and productivity

1/2 Yesterday, I posted the chart below, which shows that the entire "war rally" in the stock market has been driven by AI. Take out AI, and the rest of the stock market has done little since gasoline started soaring (blue). Is this a bubble? Probably. But the more important question, when in the "bubble cycle" are we, rather than just a proclamation that there is one? Remember that bubbles can be both the best time to make a lot of money and the hardest time to hold on to that money (when they pop). Using the internet bubble as a benchmark: Is today more like Dec 5, 1996, when Greenspan warned of "irrational exuberance"? If so, and you were worried about it popping on this date, you missed a nearly 300% rally in the NASDAQ over the next few years. Or is it like March 2000, and not worrying about it, exposes you to a NASDAQ correction of almost 80% over the next few years? As I explain in the repost below, it might be closer to 1997. I argue it is NOT overdone and explain why the massive capex spending makes sense. 2/2 In the repost below, I argued that the stock market is the most concentrated in a single theme in 150 years. The last time we saw anything resembling this level of concentration was the railroads in the late 19th century. It should be that way! The railroads literally transformed this country, and no other technology has had this potential until AI. The chart below is the generic Gartner Hype Cycle. Yes, the "Peak of Inflated Expectations" is in the future. But I would argue we a lot of the expectaion curve to rise before we get their. https://t.co/tEmL6v4QUg repost https://t.co/fqglYkBweD

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The fear over AI is palpable. So, it's time for my optimistic take .... Why the AI doom-and-gloom story is missing the bigger picture A lot of people hear “AI” and immediately think one of two things: it’s just Google search on steroids, or it’s a magic machine coming for everyone’s job. Both miss the bigger picture. A job is not one single task; it’s a bundle of tasks supported by a massive, fragmented software stack. Email, spreadsheets, presentations, Slack, CRM platforms, and, in finance, a Bloomberg Terminal, FactSet, and market data feeds. For millions of jobs, the cost of software to provide basic tools for these tasks can run to $1,000 a month, and more for complicated roles. Much of the modern workday is consumed by the friction of this stack: moving data between systems, cleaning spreadsheets, searching for files, and summarizing meetings. AI is emerging as the new interface for enterprise software. Think about the iPhone. It collapsed cameras, GPS devices, and music players into one simple, powerful device. AI is doing something similar for workplace software, turning 10 clunky programs that don't talk to each other into a single conversational prompt. Just as we stopped buying standalone cameras and tape recorders once the smartphone came around, companies will happily pay for an AI layer. It will be far cheaper and eliminate the bloated costs of that fragmented software stack that requires you to perform endless, mundane tasks because these programs do not talk to each other. The immediate fear is that if AI lets three people do the work of five, companies will fire two people. But that ignores economic history. When the electronic spreadsheet was invented, the cost of calculations plummeted. But accounting jobs didn't vanish; demand for complex financial modeling exploded. Accounting clerks became financial analysts, a more in-demand role. Jevons Paradox suggests that making a resource more efficient actually increases total demand for it. By absorbing the drudgery, AI allows the employee to focus on judgment and strategy—making the human element more valuable, not less. In this framework, demand for high-output workers doesn't shrink; it explodes. Does this justify the mind-numbing capital expenditure currently pouring into AI infrastructure? If AI fulfills this promise of enterprise-wide productivity, the investment isn't just justified—it’s a bargain. That said, we are clearly near the peak of a hype cycle, just like the internet was in 1999. But remember: the dot-com crash did not mean the internet was a bust. It simply meant the hype outpaced the infrastructure. After the wreckage cleared, the optimistic predictions about connectivity and productivity were not only fulfilled—they were exceeded. The same path can lie ahead for AI. And instead of the fear that AI will replace workers, it's the joy of replacing soulless busywork, making jobs more fulfilling... and more profitable for employers.
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Last week, Michael Harnett of BofA included this chart in his May 22 "Flow Show." As this reposted thread below details, we have not seen the market this concentrated around a single theme in 150 years. https://t.co/mv42HFM0Sj
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