“It is a gloomy moment in history. Not in the lifetime of any man who reads this paper has there been so much grave and deep apprehension; never has the future seemed so dark and incalculable.
In France, the political cauldron seethes and bubbles with uncertainty. England and the English empire is being sorely tried and exhausted in the social and economic struggle. The United States is beset with racial, industrial, and commercial chaos, drifting we know not where. Russia hangs like a storm cloud on the horizon of Europe dark and silent.
It is a solemn moment, and no man can feel indifference. Of our own troubles, no man can see the end.”1
Harper’s Magazine
This passage was published on Sunday, October 10th. It embodies the emotions of many as we look at global and geopolitical events and feel that “no man can see the end” of the troubles we find ourselves in. The eagle-eyed reader may notice that October 10th, 2026, fell on a Saturday, not a Sunday. It’s time to come clean: this passage was not written this year. It wasn’t even written this century. It is dated 1847.
Many of us feel something similar today. We are experiencing a technological upheaval driven by AI, which many fear will lead to widespread job losses. Social unrest seems more widespread than ever, and wealth inequality remains a persistent concern. How will we navigate these so-called unprecedented times?
These are legitimate concerns, and we don’t take them lightly. But the feeling that our moment is distinctively precarious is a common one. It even has a name, chronocentrism: the belief that we happen to be living through an unparalleled time that is uniquely chaotic.2 Every generation has had its own reasons for it, from the Black Death to the Industrial Revolution, the Civil War, two World Wars, the internet, and now AI.
Consider what 1847 looked like; James K. Polk was president and the country was at war with Mexico. Famine in Ireland was driving mass immigration. Railroads and steam power were reshaping how people worked and traveled. The nation was divided between fervent nationalism and an increasingly unavoidable fight over slavery. Fourteen years later came the Civil War, so the anxiety was not misplaced, and the country did not pass through it unscathed.
Still, over the following decades the U.S. economy kept innovating and growing. Today the S&P 500 Index sits within 1% of its all-time high. 3
AI Uncertainty
AI will bring real challenges, and some people will be hurt by it. But the fear that a more efficient technology will simply destroy demand for what it replaces has a long and mixed record. Economists call the pattern Jevons Paradox, after the English economist William Stanley Jevons. The general idea is that when technology makes a resource cheaper to use, total consumption of it often rises rather than falls. 4 Here are two examples.
Early steam engines burned so much coal that they made economic sense only at the mouths of coal mines, where fuel was nearly free, and the job was pumping out water. James Watt’s developed a far more efficient design in the late 1700s, sharply cutting the amount of coal an engine needed for the same work. The intuitive expectation was that coal demand would fall, a catastrophe for the coal industry.
Instead, cheaper power put steam to work in mines, mills, ships, and railroads. Railroads made transport cheaper, which opened new markets and fueled further expansion. That expansion required more iron and, later, steel, both of which consume enormous amounts of coal to produce. Watt’s improvements, and the many that followed, set off a compounding loop that drove coal consumption to historic highs. A technology that looked, narrow-mindedly, like a threat to the coal industry ended up boosting it, and the broader economy with it. 5
You may be thinking, “Yes, Brendan, I get it, but that was 300 years ago. Surely we’re better at seeing past the immediate effects of a new technology today.” So here is a more recent example.
When ATMs spread, many experts predicted they would automate cash handling, eliminate the bank teller, and lead to mass layoffs and branch closures. In one sense they were right: the average branch needed far fewer tellers. But cheaper branches meant banks opened more of them. The number of tellers per branch fell, yet total overall teller employment rose. 6
What this means for how we invest
This is not a forecast. The lesson isn’t that AI will turn out fine, or that it won’t. It’s that confident predictions about technological disruption, in either direction, have a poor track record, and a sound plan shouldn’t depend on getting them right. In practice that means a few things we would say in any market:
Near-term spending needs are funded with cash and bonds, while long-term assets remain invested for growth, helping protect the portfolio from having to sell at an inopportune time.
Broad, low-cost, market cap driven diversification means owning more of the companies likely to benefit from AI and less of the ones likely to be disrupted by it, without needing to know in advance which is which.
Volatility and corrections are normal. They are the cost of long-term returns, not automatic signals to change course.
The writer of that old passage was certainly right about one thing: no one can see the end of our troubles. That is exactly why we build plans that don’t require it.
As always, we’re available if you have questions or would like to talk about your own situation.
