The early Disney company had several near-death experiences, some due to exogenous shocks and some self-inflicted (bad labor practices, weird technology choices), and all through it, a power struggle between "Roy people" (finance) and "Walt people" (tech/art).

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After WWII, the company was transformed, with Roy ascendant and financial discipline the order of the day. Walt - who struggled with depression - sought desperately for an escape from the company.

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He came up with plans to outfit a train with traveling exhibits that he would drive around the country, putting thousands of miles between him and his brother.

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Another plan called for Walt to build an ambitious model train system and transform his role in the company to showing people around his trains.

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In 1953, Walt came up with the plan that stuck: a park called "Disneyland" that would be his own fiefdom, a commercial project that would justify the kinds of R&D and experimentation that Walt doted on and Roy looked on as high-risk, potentially ruinous follies.

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We hear a lot about Facebook as a platform for manipulation - using machine learning to bypass our critical faculties and trick us into believing things that are bad for us - but the real show is in Facebook's ability to target, not manipulate.

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People who hold disfavored views struggle to find one another and mobilize. To find other people that feel the same way as you and make common cause with them to effect political change, you have to reveal your views and suffer social sanction.

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Search allows people who hold these views to find one another. If you have a deep feeling about your gender being nonbinary but don't know the words for it, you can search for communities of people who have those words, join them, and discover who you've been all along.

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This is why, in this moment, so many ideas are migrating from the fringe to the center: ideas about racial justice, gender identity, alternatives to market systems, etc. People have harbored these views all along, but have held back on expressing them.

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Being able to express yourself in private, among people who share your views, is a prelude to going public and putting your case to the wider world in hopes of effecting change.

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Trump was a very specific kind of disaster: a chaos agent, who lacked the wit, patience and executive function to recruit the powerful institutions of the US military-industrial complex to fight his corner.

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The kind of guy who demands the impossible and then fires and publicly humiliates any allies who fail to deliver - or dare to contradict him - is not the kind of guy who can build new, enduring, evil institutions.

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The best he can hope for is to get some trumpalike goblins into positions of power (where they will replicate his chaos, but without the broad impunity of the presidential office, resulting in a series of resignations and prosecutions), back by executive orders.

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The last months of the Trump admin were an orgy of executive branch rulemakings that dismantled public safety, health, labor, anticorruption and (especially) environmental protections.

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Trump is a debt kingpin. His MO is running across a river on the backs of alligators, hoping to move so fast that none of them takes a leg: borrow, refinance, restructure, borrow, blow town, borrow, restructure, bribe, blow town.

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It took decades after the passage of America's landmark antitrust laws - the Sherman Act and the Clayton Act - for trustbusting to occur in earnest, and what spurred the action wasn't mere corporate bullying, not just price hikes and labor abuses.

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What tipped America over into a state where a leader (FDR) who told activists "I want to do it, now make me do it" found the political will to "do it" was the corruption that attended the extreme concentration of wealth.

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Monopoly was never merely an issue of economics - it's fundamentally an issue about POLITICS. Yes, the monopolist bleeds workers and suppliers, sucks them dry and amasses a tremendous fortune, but that's just accumulating ammunition.

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What the monopolist does with that ammunition is far more consequential: when the powerful are small in number and command vast fortunes, they can come to a consensus about how to deploy their fortunes to corrupt the political process.

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The economic harms are just a warmup, the political harms are the real deal.

Hoover was beholden to plutes, had a cabinet full of them, turned over the nation's treasury to a sociopathic monster called Andrew Mellon whose stated ambition was to own all the world's aluminum.

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When I think about our historical, profound shifts in attitude and discourse, the model I apply is "peak indifference."

Say you have real, existential problem. More often than not, these are systemic problems, and those are the hardest problems.

https://t.co/US1YbwIikM

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Not just because systemic problems involve collective action (you can't recycle your way out of climate change), but because the cause-and-effect relationships of systemic problems can't be easily known, so it's hard to know what you need to do to avert the problem.

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Systemic problems pose a third difficulty: they enrich small minorities, and those minorities can exploit causal ambiguity to deliberately sow doubt.

To make that more concrete: think about cancer-tobacco denial.

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Not everyone who smokes gets cancer. When it does give you cancer, the tumor comes years after the puff that damages your genes. There's lots of social pressure to smoke, and getting your friends to quit is even harder than quitting yourself.

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And on top of all of it, the tobacco industry made tons of money from giving us cancer, and they could use some of that to fund doubt-merchants who deliberately worsened the difficulty in linking smoking and

When we say that "an algorithm is biased" we usually mean, "biased people made an algorithm." This explains why so much machine learning prediction turns into phrenology.

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Researchers with phrenological delusions ask machines to find statistical correlates of personalities or emotions, and machines dutifully provides them. It's high-stakes, machine-human collaborative apophenia, detecting patterns where none

Regrettably, this junk science gets published in respected journals. In 2017, @nature published a study by Stanford's Michal Kosinski claiming that machine learning could detect the facial correlates of homosexuality, creating an alleged AI gaydar.

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Unsurprisingly, Kosinski's study spectacularly failed to replicate. But as is so often the case, the blockbuster finding gets all the press, the careful replication work that calls it into doubt is roundly

Kosinski hasn't given up on AI phrenology. His lab's latest paper (published by Nature...again!) claims that he can detect political affiliation from social media