A "Them" Problem

In academic performance, boys lag well behind girls in elementary school, high school, and college, where female undergraduates now outnumber their male peers roughly 2 to 1. That ratio was about even in the early 1980s.

"The problem with men" The Week. Saturday Wrap.

When I was a freshman in college, in 1986, I went to a HBC/U, and the ratio there was roughly 5 to 1. No one considered this a male problem. Rather, it was a Black problem. Like a lot of things, the idea that boys lag behind girls in school is now considered symptomatic of a larger societal concern because White, middle-class, city-dwellers/suburbanites have come to see it as a problem for themselves.

And what this means is that things can spend a lot of time in minority communities without anyone paying attention to them. Then, when the same factors show up in the "mainstream," it's suddenly a crisis. In 2024, the Wall Street Journal had an article on a generation that they claimed was languishing in a state of arrested development, due to attaining milestones such a marriage and parenthood later, or not at all. But again, Black Americans hit the point where the majority was unmarried in the 1970s or 80s. The perception and reality of poverty has been a bigger problem in that community for a long time. But the alarm bells are ringing because the White people in the article looked like they should have the standard markers of "middle class-ness." When the less well-off were encountering these problems, there wasn't the same generalized concern.

This is a pattern that repeats itself. The problems of non-White communities are ignored, especially when they aren't "middle-class" concerns, and then, when it becomes visible to the "mainstream," there is a sense that it somehow snuck up on everyone.

I know that I tend to harp on the idea that the United States is not a unified polity, but there are times when that lack of unity leads to problems. In this case, there is an idea that Black America and White America are fundamentally different, and have different concerns. The chalking up of problems in the Black community to "pathologies" means that Black people aren't treated as canaries in the coal mine; which would allow problems to be addressed early, to everyone's benefit.

Action Required

Has there ever been an industry rollout, from a PR perspective, sort of worse than AI? Which has sort of been: “It’s coming, there's absolutely nothing you can do about it, it might end the world, it could very well destroy your job. But you know, as a consolation, this chatbot will help you describe how to commit suicide.”
David French, The Opinions Podcast: “Trump's Political Instincts ‘Are Clearly Fading’.” 19 September, 2026

Some of this is that the companies have done a terrible job at messaging. They have effectively said, “We’re going to destroy all your jobs and potentially destroy humanity, but we’re going to IPO for a massive valuation and we’re going to set up camp in your local communities with data centers, which by the way, don’t employ people and drive up the cost of power. But, it’s okay. China.”
Jonathan Kanter, Decoder Podcast: “Does AI need an antitrust exemption so it doesn’t kill everyone????” 19 September, 2026

While Dario Amodei may think that the messaging around generative automation is less important than prior failures of corporate America to live up to its promises to make people's lives better, if this is the impression that people have of the public messaging from Anthropic, OpenAI, XAI et cetera, the companies sure aren't doing themselves any favors.

Back when Mr. Amodei was backing up one of his engineers in a debate with an investor on X, he noted that earning the public's trust would require more than promises to cure cancer; the cure actually needed to be delivered. But when I read Messrs. French and Kanter characterizing the messaging from the industry, neither of them mention curing cancer. And perhaps that's the problem that industry players have; their lofty promises aren't actually making it into the public's consciousness. To be sure, I'm not convinced that many people take the idea that generative automation will be able to come up with a cure for cancer(s) in the short term, but the fact that the pledge isn't even part of the discussion is telling.

And maybe that's because cancer cures have been promised for decades now, yet have never materialized, but maybe it's because it's not anything that anyone expects immediately, and so it becomes a matter of giving companies a lot now, in the hope of some benefit later. And the breakthroughs that the frontier labs are actually announcing aren't immediately useful to the general public. In other words, while it's impressive that an OpenAI model disproved a conjecture concerning the planar unit distance problem, what does that disproof do for the many thousands of people who have lost their jobs because the C-suite of their company decided that agents could do the work more cheaply or simply to free up capital for data center construction? Perhaps the lofty promises are being ignored because there's no concrete indication that the generative automation labs are actual working to bring any of them to fruition. Rather they're the typical technology smokescreens that corporate executives seek to hind behind when they ask for public subsidies for their shareholders and their own salaries.

But as Mr. Amodei pointed out, this is not, fundamentally, an advertising problem; rather, it's an action problem. And while I think that yes, finding a way to do away with the scourge of cancer will show people that the industry is serious about doing something good for people, there are a myriad of much smaller problems that could use solutions in the near term. It occurs to me that the rapid pace of frontier model creation, to the point where the industry claims that they cannot be sure their products are safe, becomes a tacit admission that the current models aren't really useful for much of anything. Chatbots can, allegedly, completely wreck a person's mental health, but they can't reliably shave even one-half of one percent from people's cost of living. They produce little, if anything, that makes life better for the average person. Even people who are more productive as a result of using generative automation in their day-to-day work aren't necessarily seeing their wages rise from it.

While it's not true that the technology moguls are the only people benefiting from this new technology (the trades involved in data center construction are seeing dollar signs), for the average member of the public, this whole enterprise comes across as high-risk, low-reward, buttressed by justifications that seem flimsy on their face. This gives the impression that the technology companies believe that they don't actually need the public's support. Sooner or later, I suspect that the broader public will contest that with them.

Fire In The Hole

There's a decent amount of Internet schadenfreude being aimed at Shopify CEO Tobias Lütke after a podcast interview in which he noted that while generative automation allows employees to create more, faster, when they don't then check that work, it becomes "slop grenades" (perhaps more commonly known as "workslop") lobbed at other employees.

According to a Business Insider article about the interview, "Lütke said AI is most valuable when it makes people's thinking clearer and more concise."

Okay, fair enough, but in the all-hands memo that was reported, he said that "Reflexive AI usage is now a baseline expectation at Shopify."

That's significantly different from "consider your tasks, and where generative automation can make you clearer or more concise, use it." The memo seemed to presuppose that humans simply couldn't match the clarity and conciseness of an LLM. And if people are expected to reflexively turn to generative automation to do work, and are being graded on being seen to do so the "slop grenades" are a feature, not a bug.

In the all-hands memo, it's noted that: "What we have learned so far is that using AI well is a skill that needs to be carefully learned by... using it a lot." Well, this is what everyone "using it a lot" looks like.

The problem, he said, is that the tools make it easy to pass along work that has not been properly evaluated.

From the memo that I saw reported, the message seemed to be "use the tools all the time and let the feedback mechanisms sort it out." There's no call for employees to evaluate their outputs themselves before sending them. Now, I'm going to admit that I don't know if I've seen the memo as it was sent to Shopify employees... screenshots purporting to be the message in question were shared to LinkedIn, and that's what I read.

In any event, it seems to me that Mr. Lütke is running into something that I remember encountering in my professional career... people don't do what someone tells them... they do what they hear. And a hallmark of good communication is narrowing the gap between those two things as much as can be managed. While Mr. Lütke may have intended for Shopify employees to be careful to ensure that their use of generative automation didn't simply create more work for people, when employees read that "In a company growing 20-40% year over year, you must improve by at least that much just to re-qualify. [...] That sounds daunting, but given the nature if the tools, this doesn't even sound terribly ambitious to me anymore," their focus is going to be on meeting that benchmark, especially given that the memo also noted that "We will add AI usage questions to our performance and peer review questionnaire."

Of course Shopify didn't intend for people to use generative automation willy-nilly and then simply lob the raw outputs over the wall to their colleagues. But Mr. Lütke's memo didn't make an expectation that employees would check those outputs to see if they actually provided value clear to anyone. And that's where the miss occurred. Employees internalized the implicit assumption that all automation was good automation, and left it to feedback from others to inform them if they were on the right track. But the sheer volume of material that an LLM can put out in short timeframes can make evaluating all of it difficult, especially for someone who is receiving work items from a number of people.

Perhaps the real lesson from this is that CEOs are people, too, and they're just as susceptible to believing that their words are accurate carriers of their intent as anyone else. And of finding out the hard way that they're mistaken.

Justification

There is a saying, and I have no idea where it first originated, that says: "There is no wrong way to do the right thing." There are a few different ways, it seems, in which people mean this, but it still tends to sound to me as just a different way of saying: "When the end is lawful, the means are also lawful,"* or, more prosaically, "The ends justifies the means."  My own expression of this is commonly "Right makes right," as in "The correctness of my intentions justifies the actions I take in bringing them about."

While this strikes me as a rather crude statement of consequentialist ethics, the problem, as I encounter it in the real world, is not the consequentialism of it, for all that there may be many valid criticisms of consequentialism. It is, instead, the fact that it's commonly a post hoc rationale, deployed as a shield against criticism of an action.

And this may be the fundamental problem. The understanding that there is no wrong way to do the right thing justifies pretty much anything that can be explained as being in the service of a desirable end. Which, on the one hand, makes a certain amount of sense; it's merely the understanding that it makes sense to do what needs to be done to reach appropriate ends. But on the other hand, it has the same problem that many questions of morality and ethics do... whose standards are things intended to be working toward?

Take, for example, President Trump's push to remove millions of immigrants from the United States. The alleged goal of all of this is to create more jobs for American citizens. Leaving aside, for the moment, the idea that it seems unlikely that this goal will actually be attained (and that there are also political goals in play), if that's all that one is interested in, then how the program actually works on the ground doesn't matter. Any amount of suffering and misery that may be caused will be more than counterbalanced if it leads to a net gain in employment for citizens.

And for people who are unemployed or underemployed, they may feel themselves straited enough that other concerns are luxuries that they simply cannot afford at the moment. And maybe that's the bigger problem; "There is no wrong way to do the right thing" tends to show up when someone i really needs "the right thing" to happen, but isn't going to bear the costs of a "wrong way" of bringing it about.

* "Cum finis set licit, etiam media sunt licita." Hermann Busenbaum, Jesuit Theologian.

Payoff

As I see it, the thing about this most recent promise of a "Trump Dividend" is that it wouldn't be necessary if the economy of the United States was working for people in a way that they want it to. If unemployment was low, if median wages were rising and inflation was being kept in check, people would be happy enough with the Trump Administration that (likely) empty promises of cash payouts (which are best a one-time fix, and come with consequences for both inflation and the federal budget) wouldn't be needed. It's a quick fix for an electoral problem that Republicans are facing, intended to paper over the fact that the longer-term economic problems that people want solved aren't being solved.

And there are a number of reasons for this. One of them being that large scale economic dysfunction isn't simple to correct. Things work they way they do because of the incentives that they're responding to, and those incentives have often been built up over years. Another reason is that people's expectations don't have to be realistic. And just because an expectation is unrealistic doesn't mean that someone won't run for office on a promise of fulfilling it.

But perhaps the most important reason is that having things actually working well, and having people feel that things are working well, while they are different goals, aren't easily distinguishable by the general public, more or less by definition. And this incentivizes politicians to focus on public sentiment, because, in the end, that's actually what they're being judged on.

President Trump's primary political skill lies in convincing people that their perceptions are accurate; at least in the sense that if they feel good about the economy, it's because the economy is actually doing well. Likewise, when people have a sense that levels of immigration or the trade deficit are creating problems for them, the President affirms them in that, and assures them that their perceptions are, in fact, accurate.

This skill is not, however, infallible. Because when people come to the conclusion that the problem resides in the White House, the conviction that they're correct in their perceptions becomes a liability. Like President Biden before him, President Trump has attempted, unsuccessfully, to convince people that when their lived experience doesn't line up with positive economic indicators, it's the indicators (at least when he takes credit for them) that are correct.

Which brings us to the idea of a "Trump Dividend." The President has promised dividends before; like the $2,000 tariff dividend or the DOGE Dividend, which, like this current promise, was set at $5,000. Neither of these have materialized, but perhaps more importantly, the billions of dollars of government savings/revenue that would have paid for them never materialized, either.

I'd like to say that the President underestimated the complexity and time that would be needed to bring about what was promised, but that presupposes that the President really engaged on the topic, and I'm dubious about that. And this latest pledge of a dividend seems to be more of the same.