A morsel of reassurance from me about the recent market moves in @TheEconomist's leader pages: "Either AI or public debt could crash markets. But probably not both" And of the risks to fear, a sovereign-debt bust is far more worrying than an AI crash.

A few reflections from travelling (w/ @ed_shackle, @sebwride++) through data-centre country in Michigan/Ohio in my latest for @TheEconomist. 1/ The issue really is moving votes to the Democrats. That's now visible in our polling, too (h/t @OwenWntr), but also in the scale of on-the-ground discontent. I'm quite a bit more bearish on the prospects for big new data-centre projects in blue states after the trip. 2/ I really didn't hear that much direct anti-AI sentiment. The mood much more was a cocktail of worries about local harms (water, electricity bills, house prices), plus a total distrust of businesses and politicians. Against that backdrop, assurances about the water usage of closed-loop cooling systems really don't cut through. And, though developers will doubtless get cannier at this, cash payoffs to communities can easily look like subterfuge and do little to help. 3/ Still, moratoriums are at this stage much more bark than bite (h/t @mayabarkin for the data there). Only about 2GW seems actually delayed. The bigger risk is deterring newer projects, but I'd still guess that affects the exact locations more than whether the build-out happens at all. 4/ The question I'm left with is what comes after data centres as the big AI political issue into next year. A few obvious candidates are: a big cyberattack, the sheer scale of AI-related PAC spending in the 2028 cycle, the early innings of job displacement. 5/ What the data-centre issue should tell us, I think, is that voters' hostility to new technologies that change their lives in unpleasant ways is vast—especially when those changes are less positive-sum than data centres. So, however mad you expect AI as a technology to make the next few years, scale that up accordingly to account for a much more scrambled politics. Do take a look at my piece on this here: And at our Checks and Balances podcast, where I contributed a short diary featuring lots of conversations with voters

IS THE ECONOMIST ALWAYS WRONG? Scandalously, in some circles @TheEconomist has a reputation as a contrarian indicator. This week we fessed up to getting a big call on oil prices from April wrong. Obviously our goal is not perfectly-hedged (and perfectly boring) predictive accuracy: often it is to stimulate, provoke, and challenge. But I did want to test that wider allegation, so I ran a series of LLM scorers across our full leader database since 2000 (7,000 leaders in all.) You can see the results in the chart below: each dot is one of the 1,400 leaders where we identified concrete and falsifiable predictions that were central to the argument. Higher = more accurate, further to the right = more contrarian. We do well, unsurprisingly, when aligned with conventional wisdom. We often do worse when truly out on a limb. But actually, on average, we are a bit likelier to be right than wrong on our somewhat-out-of-consensus calls. All round, a respectable performance. And as @ecurrnomics points out an accompanying leader, there is no shame at all in being beaten by the market: as good free-marketers we believe deeply in the aggregated wisdom of prices. Take a look at my piece here, which includes a canter through our best and worst calls of the last quarter-century:

I took a swing at the grand transatlantic spat over GDP figures, and whether Europe really is falling behind America, in this week's @TheEconomist. Read on to see if I succeeded in sneaking in enough World Cup sugar to make the PPP medicine go down.