…

We cannot predict the full consequences of what we do, and so we should choose carefully and deliberately as we navigate the world. We should be modest in our ambitions, aware of our own fallibility and mindful of the way things can go wrong.

| Jamelle Bouie

…

It’s clear to me that many of our so-called leaders — political and economic —do not operate by Bouie’s admonition.

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So Much Money Building AI Infrastructure

Karen Weise adds up the hyperspending of the hyperscalers in A.I. Spending Sets a Record, With No End in Sight

In the first three months of the year, the four companies [Amazon, Google, Microsoft and Meta] reported in their financial results, they plowed a total of $130.65 billion into capital expenditures, largely spending on data centers that power A.I. That figure — which was another record — was more than three times what the Manhattan Project cost to develop nuclear bombs and 71 percent higher than what the tech giants spent in the same quarter a year earlier.

All of the companies said they would be spending even more, totaling roughly $700 billion this year. Meta, for one, raised its spending forecast for 2026 to between $125 billion and $145 billion, up from its previous prediction of $115 billion to $135 billion. Google also boosted its projection, to at least $180 billion, and said its spending would be “significantly” higher next year.

Om Malik adds his observations about how much more is off the balance sheets:

What is also true is that funding is increasingly off the balance sheet, that supplier relationships are being prepaid, that lease commitments are being deferred for as long as accounting rules allow, and that a meaningful portion of the AI revenue and AI investment gains are flowing in a circle through the same small set of AI labs.

The platform shift is real. AI engineering is real. So is the financial engineering.

Let the good times roll.

Our economy is structured — by policy, not just market forces — so that these companies have this much money to bet on the AI lottery. Meanwhile, the federal debt just surpassed 2025 GDP, in part because these companies (and their billionaire owners) don’t pay very much in taxes.

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Ticketing Driverless Cars

It had to happen, I guess. Orlando Mayorquín reports:

Police officers in California will soon be able to issue traffic tickets to driverless cars like Waymo robotaxis and require their manufacturers to move them out of the way during emergencies.

The state’s Department of Motor Vehicles adopted the new rules for autonomous vehicles this week, in accordance with a 2024 law that imposed more regulation on the technology.

The rules, which go into effect July 1, are designed to address some of the challenges that have vexed local governments and residents in places where driverless carmakers, like Waymo, have expanded their fleets.

What about driverless police cars issuing tickets?

AI Layoffs Starting To Hit India, Too

Steven Lee Myers, Paul Mozur, and Saumya Khandelwal looked at AI impacts in India:

For a quarter century, India has made itself the world’s back office, providing an educated, English-speaking work force to do tasks more cheaply than in the United States or Europe. The industry today employs more than six million people and is worth nearly $300 billion, more than 7 percent of the country’s gross domestic product.

Now, A.I. threatens to do to India what its outsourcing model did to the rest of the world: replace hundreds of thousands of office workers.

India grew a massive workforce as a low-cost alternative to office workers in the West. Now, we are seeing the impact on new grads, with the same calls to ‘upskill’ as we are seeing here:

The tremors are already being felt. Tata Consultancy Services, one of India’s largest employers, has shrunk its work force to 580,000, a decline of more than 20,000 from a peak in 2022, when it hired 100,000 new workers in one year alone.

Its main rival, Infosys, has also slowed hiring, while dozens of smaller start-ups laid off workers across the country in 2025, according to Inc42, a digital economy news outlet in India.

Graduates of the country’s universities and technical colleges are finding fewer openings, forcing them to scramble to “upskill,” an increasingly popular term in the context of learning the A.I. technology that is reshaping the industry.

Maybe these giant service companies will start building their own data centers to lowball the cost of running AI models? At some point, it comes down to the cost of the chips and electricity, and with the growing concerns about data centers in the US, it might be more politically attractive to locate them on the other side of the planet.

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There is nothing inevitable about either doom or progress. We are neither fated nor guaranteed to experience the status quo for all eternity.

| Thomas Zimmer, No Right Is Ever Safe - but Progress Is Possible

…

Zimmer is on to something here. We have no guarantee that the future will be a continuation of the past, that the road we find ourselves walking will be gently graded and smoothly paved.

However, the world conspires to tell us different. We assume thirty-year mortgages — and the financial system is geared to them — under the premise we will be able to pay the note each month. The tax systems our governments impose, and through which they enable the highways to be fixed, school children to be educated, and public health to be ensured. These wheels all turn because people have jobs, are paid for their labor, and pay their taxes.

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I’d really like readers to sign up for a paid annual subscription, so for the rest of April, I have dropped the annual subscription to $30. Note that I’ve also raised the monthly subscription to $10 per month from $6 per month. Give annual a try. The biggest value is years of posts behind the paywall, and of course, seeing new posts in their entirety.

The Wheels Are Coming Off

Late last month, Rishad Tobaccowala posted Jobs are a phase work is going through, an attempt to make the collapse of stable jobs across the economy seem like a reasonable response to technological and sociological trends.

He starts with a chart, The Evolution Of Work, which lacks agency.

I’ll touch on what I think is missing in this chart, including the drivers of the transitions he lays out. Most especially, who benefits from these transitions, and who is harmed? (Also, I think he left out the post-industrial era, starting in the 70s up to the present, which saw the adoption of information technology across the world, and the dramatic decrease in industrial labor in the developed world. He is condensing more than half a century of the post-industrial economy with the only now emerging AI lottery.)

He writes:

Work and Jobs are uncoupling..

As the chart above shows until the Industrial revolution people had work without jobs and even today 70 million people in the US are free-lancing while 60 percent of people globally work and have an income without holding a job.

In many parts of the world, 2025 will have marked the peak of full-time human jobs.

He doesn’t discuss how many of those freelancing or working part-time jobs would like full-time work, or how these folks pay for their benefits. For example, freelancers have to cover 100% of their social security taxes.

And the impersonal tone of ‘work and jobs are uncoupling’, like it is just happening, like the changing of the seasons.

But then, he addresses the elephant in the room: who is uncoupling who from what jobs? He starts by detailing the layoffs at Meta:

Meta CFO Susan Li said since the start of 2025, “output per engineer has risen 30%, driven largely by adopting AI coding agents, and “power users” have increased output 80% year over year.”

And he makes this claim:

The implications of this shift are significant both for society but also for companies which have tended to be organized around filling and managing jobs versus getting work done. As work can get done with fewer full time humans we will see today’s full time jobs being replaced by a mix of a smaller number of full time jobs and an explosion of freelance and fractionalized jobs plus agentic workers.

Just like how people compile playlists to customize music for mood and occasion rather than buying full compact discs or vinyl records, companies are going to compile and access skills and expertise to get work done. Hiring human workers for specific skills to use on term-fixed projects, instead of full-time employment.

This model is widely prevalent in fields such as entertainment where talent gets together around a project whether it be a play, a movie or a tv show and then move on to the next opportunity.

We will all feel the societal impact of the loss of so many full time jobs. In the US, the linkage between full-time employment and health care access will become a key election issue by 2028, if not earlier.

He takes it as a given that this is not limited to tech companies, and implicitly that all companies will — if unchecked — will cut as many full-time workers as AI will support. He imagines a future where the fabric of job-based work is picked apart, and all that will be left are a few leavings [emphasis mine]:

At the start of this decade most companies’ employees were a mix of full-time employees, contract employees and free-lance employees. Well before the end of this decade the majority of most companies’ employees will be agentic employees and fractionalized employees (individuals with the equity and health benefits of full-time employees, but work for, and are compensated for, 50 to 80 percent of a full-time employee as AI requires less of them and aging populations causes people to work fewer hours).

Let’s unpack that. ‘Fractionalized employees’ will be paid 50% to 80% of what full-time employees make, he says. So, let’s imagine someone who is a full-time employee now, making $100,000/year, and they wake up in 2028 making $60,000/year. How do they pay their mortgage, their car loan, save for retirement, or their kid’s education? And this tectonic change will take place before the end of this decade?

And this notion that they would still be getting full benefits? Why would companies do that? That’s the opposite of what happens when a company transitions employees into consultants, for example. Is he anticipating some new government regulation compelling companies to do that? Aside from a minority of progressives, I haven’t heard any groundswell of support for policies like that. And during a Trump administration? A fantasy.

The rest of his proposition can be boiled down to a dystopic fever dream, where everyone becomes a ‘company of one’, having to ‘constantly honing skills and keeping them up to tomorrow so people will call us for our expertise’ and ‘building a reputation and network long before one needs it’, while seeing their pay cut in half.

He tries to make blowing up the world of jobs sound liberating, but I am reminded that the disruptions of the early industrial age, when highly skilled weavers were displaced by mechanical looms, and it took many decades before wages returned to anything like their earlier levels. Tobaccowala’s vision is terrifying, partly because the tech overlords are trying so hard to make it happen, and partly because those that are in a position to slow it don’t seem to be doing much about it.

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First, The Bankers?

A month ago, I wrote in Talking Around The AI Apocalypse, people will tolerate a great deal, even with the ‘inexorable march of AI… and will do what they do, so long as ordinary people can live their lives, save for retirement, and set their kids up for success’.

I was responding to an opinion piece by Michael Steinberger in which he quotes Martin Wolf, of the Financial Times:

‘If lots of “skilled, trained thinking activities” are displaced by machines, it could provoke a furious backlash. “We could have a social and political crisis that makes deindustrialization look trivial,” [Wolf] said. “Deindustrialization, though one of the biggest forces shaping our world, shook the working class, particularly the male working class, from top to bottom. Shaking the prospects of the educated middle class is socially far more dangerous and explosive because it affects them and their parents, who are the people who run our societies in almost every possible way.”’

The remainder of Talking Around The AI Apocalypse is oriented toward the political, financial, industrial, and social institutions — and their leaders — that seem relatively fine with the wheels coming off, and how they need to be stopped. It won’t be easy. Here’s an example.

In less than four months, Brian Moynihan, CEO of Bank of America, has flip-flopped on whether AI will replace human workers, from ‘not a threat to jobs’ at the bank to AI ‘eliminating work and applying technology’, leading to $8.6B in profits in the first quarter and 1,000 jobs gone.

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The pathology of decision-making in wars like this is that leaders often fall into sunk cost fallacies. Wars tend to go on well beyond the point when either side can attain a decisive advantage. At a certain point, leaders often want to believe that something will break their way simply if they persist, even though there’s no evidence of that happening.

| Michael Kofman, ‘Time Is Not on Russia’s Side’: A Q & A With Michael Kofman

…

Watching three wars at once — in Iran, Ukraine, and Lebanon — brings Kofman’s observation into high relief. His ‘wars like this’ was referring to Russia’s war in Ukraine, but Israel has been invading, occupying, and retreating in and out of Lebanon since 1978. And now, the US has gone to war with Iran. And the protagonists have fallen into the trap: desire is not a strategy.

These lessons are directly applicable to business, too. For example, look at the history of media mergers and acquisitions in the past few decades. Remember the $99 billion write off at AOL Time Warner? And the drunk-falling-down-the-stairs history of Time Warner since?

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I’d really like readers to sign up for a paid annual subscription, so for the rest of April, I have dropped the annual subscription to $30. Note that I’ve also raised the monthly subscription to $10 per month from $6 per month. Give annual a try. The biggest value is years of posts behind the paywall, and of course, seeing new posts in their entirety.

Grift, Plain and Simple

Financial shenanigans — especially those that increase debt for nebulous economies — are overwhelmingly bad, strategically, and only line the pockets of financiers.

Perhaps the most remarkable fact about modern finance is that it fails on its own terms. Mergers and acquisitions tend to destroy value even as they sate the appetites of empire-building chief executives. In 2016, the Harvard Business Review highlighted “the rule confirmed by nearly all studies: M&A is a mug’s game, in which typically 70 percent to 90 percent of acquisitions are abysmal failures.”

| Oren Cass, The Finance Industry Is a Grift. Let’s Start Treating It That Way.

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Tech Layoffs Go Viral

Oracle lays off 18% of its workforce, around 30,000 people. Block laid off 40%, Snap 16%, Meta plans another 10% cut in May. Brian Elliott writes in Contagion:

Oracle’s Larry Ellison, Block’s Jack Dorsey and Meta’s Mark Zuckerberg are among the notable tech founders now racing to see who can make the most of the AI opportunity to transform work. They may also be in a contest to see who can be the most badass when it comes to cutting people.

Predictably, the contagion is spreading. The WSJ reports this week that Block’s CFO is fielding calls asking for their playbook. I’ve heard the same from a number of people in San Francisco: the tech bros are out to see who can get aggressive the fastest in transforming their business, damn the side effects.

More cuts in tech are coming. The narrative is taking hold: AI is a human replacement technology.

So guess what? All those economists and senior leadership who countered claims about AI as a job-killing tsunami by saying it would augment, not replace workers, workers who would be freed from drudgery and allowed to develop new skills, new sorts of work… well, it’s turning out to be bullshit, at least in large tech companies.

Brian Elliott argues that these large companies may be using AI as a smokescreen to reduce bureaucratic bloat. But he also cites BCG research that shows serious levels of projected disruption:

71 million jobs impacted by ‘high levels of task automation’: that’s half of the 150 million people working in the US, today.

And the BCG researchers attempt to square the circle saying that most jobs would be reshaped, not replaced, but that with this year’s AI tools. What about AI of 2027, or 2030?

…

As I have argued for over decades, just as soon as it is possible, businesses will cram AI into every nook and cranny, and displace workers without a second thought.

Unless we, the people — presumably through our elected representatives — stop them.

It is time for the Human Spring, when we collectively demand a moratorium on job displacement by AI, or else we may be standing in the ruins of a once-thriving (or at least muddling by) civilization.

Even though our governments seem to flubbing the fight against climate change, we must rise to the challenge of unfettered AI. And soon. Or else.

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The Fall of HR

Ashly Goodall zooms in on how HR is seemingly out of the loop while this is going on [emphasis mine]:

Today, the HR profession finds itself at a crossroads. Its traditional focus has been the supply of talent. It has concerned itself with hiring, compensation, promotion, and learning, all in service of ensuring that an organization has the right people in the right roles for as much of the time as possible. Where it has moved beyond this remit, it has tended to retain the perspective of the C-suite. So its work on culture has typically focused more on describing what a culture should be than on figuring out how one is made, or changed. And its work on performance has typically focused more on how to judge it than on how to generate it.

But much of this work is being automated. For better or worse, AI is now screening applicants, recommending salaries, suggesting e-learning, and writing performance reviews—and even where humans remain in the loop, much of the traditional work of HR is being hollowed out.

Meanwhile, many organizations appear increasingly indifferent to the idea that they owe anything to their employees. Layoffs abound once again. My former employer, Deloitte, is cutting PTO and parental leave for some part of its workforce. Workers are being summoned back to the office in ever sterner terms.

So the technical elements of HR are being done more and more by the machines, even as the human elements are devalued. I heard recently about an HR leader who asked his CEO if he could invest in a leadership development program. The CEO told him his job was to keep HR issues off the CEO’s desk—and that if he succeeded at this, he could invest in whatever programs he wished.

HR was always more like a police force than faculty, but increasingly, they are being marginalized as even the incredibly precarious 21st-century work detente between management and managees is fraying.

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I am not in this world to live up to your expectations, and you are not in this world to live up to mine.

| Fritz Perls

…

I am reading a fascinating book, The Art of Logic in an Illogical World, by Eugenia Cheng, and she reintroduced me to some common logical fallacies, such as one possible misinterpretation of Fritz Perls statement above. Reread it. It may seem implicit that someone — maybe everyone — is in the world to live up to their own expectations, even if not having to live up to others’. But you can’t surmise that from Perls’ line.

To reach that conclusion, you need an additional, independent statement: we are in the world to live up to our own expectations, and not those of others. But that is an emotional response to Perls’ aphorism, not a logical one, Chang shows us. Her example is the statement ‘when you tell students they have to work hard in order to do well, and then they think that if they work hard they should automatically do well. Working hard is a necessary but not sufficient to doing well. It is not sufficient because you also have to work hard in the right sort of way, and if you think otherwise, you are making a converse error.’

But Perls may not have been implying anything about living up to our own expectations: it may just be us inferring that, driven by emotion rather than logic.

I’d really like readers to sign up for a paid annual subscription, so for the rest of April, I have dropped the annual subscription to $30. Note that I’ve also raised the monthly subscription to $10 per month from $6 per month. Give annual a try. The biggest value is years of posts behind the paywall, and of course, seeing new posts in their entirety.

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A Managee’s 1:1 Guide

I am always a bit dubious when senior managers give advice to managees, especially when couched as ‘what top performers do differently’, so I confess I read Polina Russell’s The Employee’s Ultimate Guide to 1:1s - What Top Performers Do Differently with trepidation. But after a quick scan, I returned to the top, very intrigued by her practicality, and I gave it a deep read.

I agree that managees (or ICs — individual contributors — in her dialect) should prepare for 1:1s, considering the purpose and problems inherent to close interaction with managers. (The subtext is the relational and political matrix in which everyone at work is embedded, which Russell mostly leaves unstated. She spent 14 years at Amazon, which is a jungle.)

One of the most helpful parts is the section What not to do in a 1:1. For example:

2 Do not use 1:1 as therapy

4 Don’t write an essay

5 Don’t give your manager action items

Her conspiratorial tone in the final paragraphs is a bit arch, but work relations are not tiddlywinks. Remember, there is a low-scale war underneath it all, between managees and their bosses, and between managees, upwardly striving:

If you apply this guide to your 1:1s consistently, you will stand out. Most people don’t do this. Which is good news for you.

ICs (individual contributors): keep this as your edge. Don’t share with anyone.

Managers: remove that edge and send it to your team.

Everybody keeping everybody off balance, at every turn.

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Think Small

Talya Minsberg reports on a study of athletes that shows focusing on ‘process goals’ — ‘small objectives that were more in my control’ — than ‘outcome goals’, such as her efforts to run her fastest time ever in a marathon.

The rationale is that while big goals can be motivating, the reality is that a great deal of what makes big outcomes big — like winning a race — are out of our control:

Many people naturally gravitate toward ambitious goals that are the traditional markers of success, like landing a dream job or winning an award.

These kinds of targets can be highly motivating, said Ayelet Fishbach, a professor of behavioral science at the University of Chicago Booth School of Business. But, she cautioned, whether you actually achieve them is usually at least partially out of your control.

That’s not all bad. Outcome goals can get you off the blocks, she said. But if you miss your target, she said, falling short can be profoundly disappointing. Had I been singularly focused on running a certain time in Boston, for example, “Well, that may be your last marathon,” she said.

Experts recommend instead focusing on your own performance, building a plan to improve, without ‘normative comparison’, which is measuring yourself against others. She cites the advice of Charles Duhigg, the author of Supercommunicators and The Power of Habit:

“A goal is only useful inasmuch as it helps develop a plan for you,” said Charles Duhigg, the author of “Supercommunicators” and “The Power of Habit.”

If done correctly, he said, once you have a plan in place, you won’t think too much about the goal.

Duhigg used the example of writing a book, which, as he knows well, can be daunting if you try to take in the whole picture at once. But the smaller goals — writing the opening of one chapter, and then the middle of another chapter — are what get you there. “If you just spend enough time sitting there doing these little bits and pieces, you end up with a book,” he said. “The book is the natural byproduct of the plan.”

So, frame your goal as a plan, not as an outcome.

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Never Invented

A 2024 Harris Poll survey of about 1,000 Gen Z adults in the US found that almost half of respondents said they wished social media platforms like TikTok, Snapchat, and X (formerly Twitter) were “never invented.” And 21% said they wished the smartphone had never been invented.

| Sydney Bradley

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Culture has increasingly ceded authority to systems that mistake information for understanding and speed for judgment.

| Yonatan Touval, The Iran War Is a Failure of Imagination

I’d really like readers to sign up for a paid annual subscription, so for the rest of April, I have dropped the annual subscription to $30. Note that I’ve also raised the monthly subscription to $10 per month from $6 per month. Give annual a try. The biggest value is years of posts behind the paywall, and of course, seeing new posts in their entirety.

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Pace Layering

It has become commonplace to characterize the world as moving faster, as if the planet were spinning more energetically around its axis. This is really just a metaphor, but one that is now firmly embedded in our daily psychology.

Stewart Brand (with Brian Eno) created the Pace Layers model to help understand how the various elements of our civilization interact.1

As Brands explains,

I propose six significant levels of pace and size in the working structure of a robust and adaptable civilization. From fast to slow the levels are:

• Fashion/art

• Commerce

• Infrastructure

• Governance

• Culture

• Nature

In a durable society, each level is allowed to operate at its own pace, safely sustained by the slower levels below and kept invigorated by the livelier levels above. “Every form of civilization is a wise equilibrium between firm substructure and soaring liberty,” wrote the historian Eugen Rosenstock-Huessy. Each layer must respect the different pace of the others. If commerce, for example, is allowed by governance and culture to push nature at a commercial pace, then all-supporting natural forests, fisheries, and aquifers will be lost. If governance is changed suddenly instead of gradually, you get the catastrophic French and Russian revolutions.

It is the friction between layers that allows them to influence each other. Fashion trends — like a transition from skinny to baggy jeans — drive changes in commerce, and innovations in commerce — like faster supply chains — drive quicker transitions in fashion.

But Brand points out in the original caption for the diagram above,

The order of a healthy civilization. The fast layers innovate; the slow layers stabilize. The whole combines learning with continuity.

Touval’s opening quote ends with a layer farther down, culture, where we are rejecting time for judgment in favor of speed. In fact, the sense many of us share — that the world is spinning faster — may boil down to our cultural acquiescence to speed over judgment and a confusion between information and understanding.

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Living at Log Level

One reaction to the vertigo imparted by a seemingly sped-up world was recently summarized by Gordon Brander in Dropping to log-level, reminding us that we may be in a situation too complex to effectively model:

A model is a map of the parts of a system that aren’t changing.

But when all parts of our world system are changing, we can’t find any stable parameters. So we have to bail on modeling. What’s left for us to make sense of the world?

Really, when things are moving this quickly, we don’t want a model, we want a log. It’s the simplest narrative structure that could possibly work.2 Just lines of timestamped comments, one after the other: “this happened, then this happened, then this happened”. No higher-level analysis. In software, we log everything that happens during program execution, because logs are invaluable when things spin out of control. We can comb through the log to find patterns, and begin to form higher-level hypotheses about what is happening.

I think this is the right level of sense-making for the moment. It’s time to drop to log-level. You’ll see me shifting away from essays, toward work in progress, technical posts, fragmentary ideas, and raw logging.

I found a great resonance with these insights, which remind me of the motivations behind writing more Short Takes, and fewer longer essays that might be outdated in a few weeks.

Brander constrasts what’s in or out when we drop to log level:

In/out:

Out: essays.
In: logging what I learn, as I learn it.

Out: theory.
In: practice.

Out: good writing.
In: good ideas.

Out: “having an audience”.
In: hoarding things I know how to do.

So, I feel my task — or calling — has shifted. More surfacing of good ideas and insights, fewer authoritative screeds (since I’m deeply uncertain about so much). Less emphasis on ‘audience’ and theory, more focus on sharing what I find, and how I found it.

Flow and Unflow

Flow

One danger lurking in the sense of a sped-up world is a decrease in the happiness that emerges from entering a ‘flow’ state, as researched by Mihaly Csikszentmihalyi. In his work, he interviewed thousands of people to understand when they were most happy. Derek Thompson characterizes his insight this way:

He heard in these diverse testimonies a kind of singular melody—a description of how, in the best parts of life, a feeling of self, time, and anxiety melt away in the face of deep immersion in an activity. He named this phenomenon “flow.”

Thompson then cites a quotation from Csikszentmihalyi’s book, Flow:

The optimal state of inner experience is one in which there is order in consciousness. This happens when psychic energy—or attention—is invested in realistic goals, and when skills match the opportunities for action. The pursuit of a goal brings order in awareness because a person must concentrate attention on the task at hand and momentarily forget everything else. These periods of struggling to overcome challenges are what people find to be the most enjoyable times of their lives

Thompson adds:

Flow suggests a waterway—something liquidly effortless, an unimpeded stream. But the wisdom of Csikszentmihalyi was to recognize that well-being is no lazy river. It is neither ease nor effortlessness that leads to the highest happiness. It is something close to their opposite. It is immersion in an activity that is hard, but just hard enough; it is the discovery of comfort at the outer realm of difficulty. Life feels best, not when it is smoothed with frictionlessness, but when it is filled with achievable challenges.

As with Stewart Brand’s Pace Layers, friction between the layers transmits value in both directions. ‘The fast layers innovate; the slow layers stabilize. The whole combines learning with continuity.’ The friction is essential.

But what if technology could decrease the friction?

Unflow

In our sped-up world, there are opposites to the inherent learning/activity cycle at the heart of flow.

Shishi Wu writes of ‘passive flow’ as a mechanism to understand why people absorb more streaming media than they intend to [emphasis mine]:

Passive flow is a state of low-effort immersion caused by external design features. It includes three main feelings: unclear goals, loss of self-consciousness, and time transformation. It is different from classical flow, which usually involves focus, skill, and challenge. Passive flow appears in low-effort environments where users receive a continuous stream of content. They do not need to make choices. Features such as autoplay, endless scrolling, and personalized feeds help hold their attention. As a result, users stay longer than they intended. Many say they lost track of time. Passive flow does not directly define unplanned use. But it helps explain why unplanned use happens more easily.

Thompson makes the connection between passive flow and the ‘trancelike state’ that gamblers can enter when playing the slots. And the so-called ‘shitty flow’ (from psychologist Paul Bloom) sounds like a synonym of passive flow.

I will collapse those frictionless states into ‘unflow’: a negative psychological state with some superficial similarities to flow — losing track of time, a sense of calm — but lacking the challenges inherent in flow.

But that sense of calm is phony, more like being drugged, or acting like a mindless zombie, as Thompson styles it. Removing the friction of flow leaves a state emptied of challenge, of engagement, and ultimately, of purpose.

We need to make choices if we are to gain from the spinning of the world, and one of those choices has to be to avoid unflow in its many forms, to slow down, and to experience our surroundings.

Remaining grounded in a sped-up world requires throttling passivity, placing judgment over speed, and perhaps living and working at log level, if only to remain functioning and sane.

We should take to heart this thought by Simone Weil:

The authentic and pure values — truth, beauty and goodness — in the activity of a human being are the result of one and the same act, a certain application of the full attention to the object.

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…

In capitalism, the future is an asset and it’s already been sold.

| Total Refusal, Hardly Working (around 14 minutes in.)

…

A great video by the artist collective Total Refusal.

I’d really like readers to sign up for a paid annual subscription, so for the rest of April, I have dropped the annual subscription to $30. Note that I’ve also raised the monthly subscription to $10 per month from $6 per month. Give annual a try. The biggest value is years of posts behind the paywall, and of course, seeing new posts in their entirety.

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The Fall of Pax Americana

The numbers are clear: war is on the rise, globally, and not just this month.

From 1989 to 2014, battle-related deaths from cross-border conflicts averaged fewer than 15,000 a year. Beginning in 2014, the average has risen to over 100,000 a year. As states increasingly disregard limits on the lawful use of force, this may be just the beginning of a deadly new era of conflict.

| Oona A. Hathaway, The Great Unraveling Has Begun

What will it add up to by the end of 2026?

According to the projections, an estimated 28,300 people will be killed in Ukraine in 2026, while 7,700 deaths are forecast in Israel and the Palestinian territories, and 4,300 in Sudan.

| Study warns thousands likely to die in global conflicts in 2026

Ukraine, Sudan, Venezuela, Iran, Lebanon, Gaza, Myanmar, and whatever is coming next. The world contains at least 31 million refugees, and we are creating more all the time.

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The Rise of China

China has managed to stay out of military conflicts, while Russia and the US are up to their armpits in them. What has China been doing, instead?

Already, China’s economy is roughly 30 percent larger than the United States’ by purchasing power, its industrial base twice as large, its power generation twice as high, and its navy is on track to become 50 percent larger by the end of this decade. It leads in new technologies like electric vehicles and next-generation nuclear reactors while the United States increasingly depends on it [China] for everything from antibiotics to rare-earth minerals.

| Rush Joshi

China announced on Wednesday the world’s largest trade surplus ever, even adjusting for inflation, as a tsunami of exports flooded markets around the world last year.

China’s surplus, the value of goods and services it sold abroad versus its imports, reached $1.19 trillion, an increase of 20 percent from 2024, according to data released by the country’s General Administration of Customs. The number had already exceeded $1 trillion through November.

The country’s surplus is still widening: For December alone, China’s surplus reached $114.14 billion, propelled by surging exports to the European Union, Africa, Latin America and Southeast Asia. It was the third-highest monthly surplus on record, trailing only January and June last year.

The enormous trade surplus for the full year came despite efforts by President Trump to use tariffs to contain China’s factories. The tariffs reduced China’s trade surplus with the United States by 22 percent last year. But Chinese factories increased sales to other regions, in many cases bypassing American tariffs by shipping goods to the United States through Southeast Asia and elsewhere.

| Keith Bradsher, China Announces Record Trade Surplus as Its Exports Flood World Markets

Of course, it’s not all sunshine and flowers. China has persistent problems, like the massively overbuilt housing sector which had harmed individual investors, banks, and regional governments that financed the glut. Older workers find themselves pushed out jobs because of institutionalized agism. Young workers are growing disillusioned by a stagnating economy, as reported by Joy Dong, Max Kim:

In past decades, China’s rapid economic growth lifted 800 million people out of poverty and gave rise to a flourishing middle class. But, analysts say, growth and wages have since stagnated, and prospects for social mobility have dimmed. For many young people, the once-idealized life of striving now evokes drudgery, exhaustion and disappointment.

Those sentiments are reflected in the backlash to “996” culture — the expectation of working from 9 a.m. to 9 p.m., six days a week, promoted by prominent figures in the country’s tech sector. They can also be seen in online descriptions of modern life as “garbage time,” an American sports term for the final minutes of a game, when the outcome is already decided but players must still go through the motions.

This perspective explains the rise of an oddball meme, the Sad Toy Horse.

The plushie first appeared last year in a shop in eastern China. It has stumpy legs, a golden bell around its neck and lettering on its side that reads, “wishing you instant wealth.” It also bears a conspicuous manufacturing error: Its mouth is sewn upside down, turning what should have been a content smile into a picture of melancholy.

Known as the “crying horse,” the glum toy has become an online sensation in China ahead of the Lunar New Year, the country’s biggest holiday, which begins on Feb. 17. The “crying horse” hashtag has appeared more than 190 million times on Douyin, the Chinese version of TikTok, and inspired a winking joke: Take the crying version to work, leave the smiling one at home.

The toy’s sudden popularity has resonated beyond novelty. Many young Chinese workers have embraced it as a symbol of their exhaustion and disillusion.

“Its expression perfectly reflects the helplessness of an office worker,” Ms. Hao said. She bought four, two sad and two smiling.

China is no garden of Eden, but even with the sad toy horses — this is the year of the horse, there — and all the social ills of Chinese society, I have to confess that it’s apparent stability relative to what the US is up in geopolitics has its attractions. But I will have to add the caveat that China might use this time of invasions to occupy Taiwan, and that would figure given the strange calculus of 2026.

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More Independents in the US

Gallup reports on a political realignment in the US: more independent voters.

A record-high 45% of U.S. adults identified as political independents in 2025, surpassing the 43% measured in 2014, 2023 and 2024. Meanwhile, equal shares of U.S. adults — 27% each — identified as either Democrats or Republicans. … In most years since Gallup began regularly conducting its polls by telephone in 1988, independents have been the largest political group. However, the independent percentage has increased markedly in the past 15 years, typically registering 40% or higher, a level not reached prior to 2011.

The higher rate of political independence also results from younger adults today being more likely than young adults in the past to identify as independents. The 56% of Gen Z adults identifying as independents today compares with 47% of millennials in 2012 and 40% of Gen X adults in 1992.1

Taking into account Americans’ party identification and political leanings, an average of 47% identified as Democrats or said they were independents who lean toward the Democratic Party, while 42% identified as Republicans or leaned Republican. This breaks a three-year stretch in which Republicans held an edge in party affiliation.

US voters are growing increasingly dissatisfied with both major political parties.

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The fish is in the water, but the water’s also in the fish.

| Arthur Miller

…

There’s a famous line mistakenly attributed to Marshall McLuhan — ‘We shape our tools and thereafter they shape us’ — actually penned by a friend of his, Father John Culkin. It seems that AI is having such an effect on those that use it, and the greatest impact on those that use it the most. And of course, the systems of work are tools (or a tool) as well.

Are we the fish or the water?

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Work Is Not Neutral

I have often said that work in interconnected to everything in our society, and cannot be understood as a thing in isolation, but I have not expressed as clearly as Corinne Murray does, here [emphasis mine]:

The way modern work functions—and what it demands of us—is a byproduct of our cultural and moral frameworks. Underneath all of what we see playing out in our daily lives lies deep-seated religious and secular ideologies that define productivity as a virtue, suffering as a necessity, and worth as something that can only be earned through effort and performance.

If a better future were possible without confronting the depths and power of these systems, we would have arrived in it already. Instead, we’ve only redesigned and rebranded the surface features while leaving the underlying beliefs untouched and unexamined–guaranteeing that the same old outcomes will play out under a different name.

Even if we could isolate modern work in the United States from…everything else…the conditions of modern work are far from ideal. Burnout rates climb to new heights annually. Gen Z still can’t find entry-level work. Parents—primarily women—are leaving the workforce because of childcare shortages and return-to-office mandates. The elderly are working retail to supplement fixed incomes. All while the wealth disparity between billionaires and the rest of us exceeds that of the Gilded Age, and the last vestiges of America’s social safety net get swept away for more tax breaks for them. These are not isolated crises. These are features of our reality, not flaws.

| Corinne Murray, Work Has Never Been Neutral

And they point out we are implicated in the workings of these systems:

Most of us participate in these systems because choosing not to comes at great personal cost, and one we’re rarely allowed to acknowledge. Regardless of our belief and endorsement of what is happening, our participation makes us complicit—willingly or not—in exchange for some semblance of stability and comfort.

As Abraham Joshua Heschel tells us,

In a free society, all are involved in what some are doing. Some are guilty; all are responsible.

One of our tasks is to name those who are guilty, but to accept we are implicated as well. Murray ends with this:

Awareness doesn’t change the fact that we still must participate to survive, but it removes the illusion that unquestioned participation is neutral. Continuing to look away from these systems and their implications is an active choice of disengagement and the grave consequences for all of us.

Breaking through these illusions requires confrontation, and I ask that you stay with me as I do that.

I strongly encourage others to follow Murray. Here’s some background.

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A Burnout Machine

Connie Loizos reports on recent research published in HBR1 that was based on on-the-job observation at a 200-person tech company. Loizos puts the thesis of AI as

The tools work for you, you work less hard, everybody wins.

But a new study published in Harvard Business Review follows that premise to its actual conclusion, and what it finds there isn’t a productivity revolution. It finds companies are at risk of becoming burnout machines.

The UC Berkeley researchers detailed the time sink that AI become:

In our in-progress research, we discovered that AI tools didn’t reduce work, they consistently intensified it. In an eight-month study of how generative AI changed work habits at a U.S.-based technology company with about 200 employees, we found that employees worked at a faster pace, took on a broader scope of tasks, and extended work into more hours of the day, often without being asked to do so. Importantly, the company did not mandate AI use (though it did offer enterprise subscriptions to commercially available AI tools). On their own initiative workers did more because AI made “doing more” feel possible, accessible, and in many cases intrinsically rewarding.

While this may sound like a dream come true for leaders, the changes brought about by enthusiastic AI adoption can be unsustainable, causing problems down the line. Once the excitement of experimenting fades, workers can find that their workload has quietly grown and feel stretched from juggling everything that’s suddenly on their plate. That workload creep can in turn lead to cognitive fatigue, burnout, and weakened decision-making. The productivity surge enjoyed at the beginning can give way to lower quality work, turnover, and other problems.

The researchers detailed the slippery slope of work intensification. Because AI tools can cover for a person’s knowledge gaps, workers began taking on responsibilities that formerly others would have done, like product managers starting to write code. This led to those others having to review that AI-augment output, like engineers reviewing code written by product managers. And because it’s easy to start up an AI project, time that might have been used to rest and reflect was swallowed up by AI-centered or -supported work.

As the researchers — Aruna Ranganathan, Xingqi Maggie Ye — stated the momentum of AI as a ‘partner’ becomes a rollercoaster ride

While this sense of having a “partner” enabled a feeling of momentum, the reality was a continual switching of attention, frequent checking of AI outputs, and a growing number of open tasks. This created cognitive load and a sense of always juggling, even as the work felt productive.

Over time, this rhythm raised expectations for speed—not necessarily through explicit demands, but through what became visible and normalized in everyday work. Many workers noted that they were doing more at once—and feeling more pressure—than before they used AI, even though the time savings from automation had ostensibly been meant to reduce such pressure.

All of this produced a self-reinforcing cycle. AI accelerated certain tasks, which raised expectations for speed; higher speed made workers more reliant on AI. Increased reliance widened the scope of what workers attempted, and a wider scope further expanded the quantity and density of work. Several participants noted that although they felt more productive, they did not feel less busy, and in some cases felt busier than before. As one engineer summarized, “You had thought that maybe, oh, because you could be more productive with AI, then you save some time, you can work less. But then really, you don’t work less. You just work the same amount or even more.”

The researchers suggest band-aids for management to minimize AI burnout, but the most important takeaway is from Loizos. While AI can augment what workers can do on their own

[The research] confirms it, then shows where all that augmentation actually leads, which is “fatigue, burnout, and a growing sense that work is harder to step away from, especially as organizational expectations for speed and responsiveness rise,” according to the researchers.

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The new part is at the bottom, System 3, which is, notably, not occurring in the human mind, but within the workings of an artificial intelligence.

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As described by Matt Gaskell, this is a step beyond cognitive offloading:

Cognitive offloading is strategic. You delegate a discrete task to a tool. You’re still in control. You’re still thinking. The calculator does the maths so you can focus on the problem. The spreadsheet shows you the numbers so you can think about them.

Cognitive surrender is different. You adopt the AI’s output, without verification, without critical evaluation. The tool doesn’t assist your reasoning – it replaces it. And you might not even notice it’s happened.

Worst of all, cognitive surrender comes with real costs. It turns out that those more likely to trust AI are more susceptible to surrender, and they are more confident of the outcome even when the AI has hallucinated.

Again, Gaskell:

Every time someone on your team pastes a question into ChatGPT and copies the answer into a document without reading it critically, that’s cognitive surrender. Every time a manager asks AI to write a performance review and sends it unedited, that’s cognitive surrender. Every time a strategist asks AI for a recommendation and presents it to the board as their own analysis, that’s cognitive surrender.

And the uncomfortable part is: they’ll feel more confident doing it.

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This was the fragment of text where I stumbled upon the concept of cognitive surrender, by Ezra Klein, I Saw Something New in San Francisco:

Researchers have drawn a distinction between “cognitive offloading” and “cognitive surrender.” Cognitive offloading comes when you shift a discrete task over to a tool like a calculator; cognitive surrender comes when, as Steven Shaw and Gideon Mave of the University of Pennsylvania put it, “the user relinquishes cognitive control and adopts the A.I.’s judgment as their own.” In practice, I wonder whether this distinction is so clean: My use of calculators has surely atrophied my math skills, as my use of mapping services has allowed my (already poor) sense of direction to diminish further.

But cognitive surrender is clearly real, and with it will come the atrophy of certain skills and capacities, or the absence of their development in the first place. The work I am doing now, struggling through yet another draft of this essay, is the work that deepens my thinking for later.

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Another fun fact I gleaned from Klein.

According to a new NBC News survey, public opinion on A.I. has turned sharply negative; it now polls beneath ICE or Donald Trump (though above the Democratic Party or Iran). There is an A.I. backlash building, and understandably so: Who wants a technology that may take your job and eventually threaten human sovereignty? At the same time, A.I. is everywhere, and being woven into almost everything, and a staggering number of Americans use it daily. Backlash or no, I expect that trend to accelerate, not reverse.

Who wants a technology that untrains your brain?

But we huddle in the shadow of a near-future world where AI is atop the heap, supported by its myrmidons

Under AI, the robot is the true elite and the next layer down are those who adjust its variables, like worshipers tweaking the corpse of a dead god.

| Kyle Chayka, The Revenge of Elitism

And all the rest become NPCs.

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…

Ganesh Sitaraman, in This Is Why Flying Is So Awful1, summarizes some of the reasons why flying is so bad for us as travelers, and for the industry as a public good.

Wait, you may say: a public good? Yes, the airline industry is a public good, but one that is not treated as one. I will explore that in this post, but first let me quote Sitaraman:

Almost everywhere you look, there’s airline trouble. A tragic crash at LaGuardia Airport. Long lines at airport security. Thousands of cancellations because of bad weather in Dallas and Atlanta. Higher prices. More proposed airline mergers. And a spate of near misses in the sky.

You could blame human error or partisan fights in Washington for some of these issues, but there is a deeper story behind the turbulence: Nearly half a century ago, the U.S. government abandoned its position that regulation and investment were critical elements for America’s transportation infrastructure.

If you remember the days of ample leg room, metal silverware and complimentary drinks, you know flying hasn’t always been like this. That’s largely because of deregulation. After the Wall Street crash of 1929 nearly caused the airline industry to collapse, the government stepped in with a comprehensive regulatory system.

The Civil Aeronautics Board (CAB) was created in 1938 (as the Civil Aeronautics Authority, and renamed in 1940), and treated the airline industry as a natural monopoly, managing nearly all aspects of the industry, including what routes airlines could fly, how much they could charge, and whether airlines could merge or not. The goal was not profits for the companies flying, but for the public good. In particular, the CAB wanted to ensure that air travel to small and medium-sized cities was available at affordable prices. In other words, to treat it like the national highway system.

Sitaraman’s 25 March piece is only the most recent he’s written — or been interviewed — on this topic. I think that Jeff Neal’s interview with him in 2024 — Airline deregulation may be why flying is such a miserable part of holiday travel2 — is perhaps the best.

First off, though, the title should read Airline deregulation is why flying sucks. US air travel sucks because of mistakes made in different eras’ policy regimes.

The first era (starting with the Wright Brothers), airlines were supported by air mail subsidies.

Then, the second era. Sitaraman: ‘in the 1930s, you get what I would call the first modern era of airline policy. It was a period of regulated competition in which Congress created a Civil Aeronautics Board, or CAB. The CAB allocated routes to different airlines to fly between different cities and it regulated the prices of those flights as well.’ ‘And over 40 years, from 1938 to 1978, this system worked pretty well. We had an increasing number of people flying. We had improvements in safety. We had the shift to the jet age. And prices were declining over this period.3 It was a reliable system.’ That lasted until 1978, when neoliberals [Alfred Kahn, head of the CAB, Jimmy Carter, and Ted Kennedy] deregulated airlines.

‘The airlines moved into another phase, which I think about as a kind of Hunger Games. The 1980s were defined by cutthroat competition between the airlines. A lot of new entrants offered no frills service, had no unions4, and took on the high-volume traffic and high traffic routes, for example. This initially meant more competition and lower prices on those routes. But the big airlines fought back and pushed out a lot of these new competitors, raised prices afterwards, and consolidated into large fortress hubs like Atlanta, Dallas, or Charlotte. By the end of the decade, after dozens of bankruptcies and mergers, labor-management strife, declining service quality, congestion, and lost baggage, there was a shakeout in the airlines that led to reconsolidation. The same big airlines that existed under regulation were still dominant, just without the checks of the regulated period. So, we moved from regulated oligopoly to unregulated oligopoly.’ ‘Now what we have is ‘more like monopoly capitalism, a system in which there is very little competition and few choices.’

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The core decision: ‘One of the divides between the regulators of the 1930s and the deregulators of the 1970s was whether airlines are ordinary businesses or more like public utilities. The regulators saw airlines as public utilities. They have high capital costs. There are economies of scale and network effects. Because of these core economic dimensions, plus their role as an essential service for the public, those businesses need to be regulated differently than, say, manufacturers of sofas or coffee mugs.’

Sitaraman rejects two approaches: A single nationalized airline, and a public option, with a private and a public airline, competing.

His final recommendation is to return to regulated competition. ‘three principles for reforming air travel. The first is no flyover country. I think we shouldn’t have an airline system that doesn’t serve large parts of the country, including smaller and midsize cities. The second principle is no bailouts, no bankruptcies. We want a stable airline business, one that isn’t sometimes going through boom years, and other times needing taxpayer bailouts. We want airlines to be reliable all the time. And then third is fair and transparent prices. Over the last few decades, we’ve seen pricing get more and more complicated with increasing numbers of fares and classes of fares, in addition to all the junk fees and additional prices and costs that are added to tickets now. I think passengers need a much simpler system that’s easier for people to understand, easier for people to navigate, and fair for them in the process.’

Yes please.

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The magisterial analysis of the airline mess we have wiggled ourselves into was authored by others. Although it’s from 2012, Terminal Sickness — by Philip Longman and Lina Khan (who became head of the US Federal Trade Commission (FTC) from 2021 to 2025), is a long read, but worth it.

A few excerpts:

‘Until 1978, the United States viewed airline service as a “public convenience and necessity,” and used a government agency—the Civil Aeronautics Board, or CAB—to assign routes and set fares. This regulation was designed to ensure that citizens in cities like Cincinnati^[1] received service roughly equal, in quality and price, to that provided to other comparably sized communities like Charlotte. The government also made sure that smaller cities maintained vital links to the national air network.’

‘In 1978, however, a group of liberals including Ralph Nader, Ted Kennedy, Kennedy’s then Senate aide Stephen Breyer, and an economist named Alfred Kahn, whom President Jimmy Carter chose to run the CAB, conjured up a plan to drive down the cost of airline fares by fostering more price competition among airlines. Though they called it “deregulation,” the practical effect of eliminating the CAB, especially after subsequent administrations abandoned antitrust enforcement as well, was to shift control of the airline industry from experts answerable to the public to corporate boardrooms and Wall Street.’

‘But even strong antitrust enforcement wouldn’t have helped that much, because airlines— just like railroads, waterworks, electrical utilities, and most other networked systems—require concentration both to achieve economies of scale and to enable the cross-subsidization between low- and high-cost service necessary to preserve their value as networks. And when it comes to such natural monopolies that are essential to the public, there is no equitable or efficient alternative to having the government regulate or coordinate entry, prices, and service levels—no matter how messy the process may be.’

‘Why have we become so passive and reluctant to face up to the hard task of governing ourselves and our markets? We don’t need to recite “The Serenity Prayer.” We need to get out from under the thrall of the false prophets of deregulation, conservative and liberal alike, and make the benefits of true capitalism work for us once again.’

‘But now we find ourselves at a moment when nearly all the promises of the airline deregulators have clearly proved false.’ Those promises were greater competition, lower fares, and better service. Instead we’ve seen ongoing concentration of airlines through mergers and bankruptcies, convoluted and hidden fees, and worse service, including many airports that have have little or no service. The authors cite Cincinatti, Pittsburg, and other cities where airlines hav opted not to fly, which is the unintended consequence of deregulation.

‘And it’s about to get worse. Despite a wave of mergers that is fast concentrating control in the hands of three giant carriers, the industry remains essentially insolvent. Absent any coherent outcry, the directors of these private corporations remain free to respond to the crisis in the manner of an electrical utility company that, when it runs short of money, simply cuts off power to the neighborhoods of its own choosing.’

‘Despite massive consolidation, steep cuts in wages and benefits, sharply rising fares, huge direct and indirect subsidies, and a slowly recovering economy, the industry remains unable to service its debt, and its executives—now serving at the whim of Wall Street— see no way out except to continue to merge and to cut capacity.’

The US learned the lessons of unregulated transportation in the robber baron era of railroad expansion, and applied it in the ‘30s to the airline industry. But by the ‘70s the lessons had been forgotten and neoliberal-inspired deregulation led to the unwinding of the Civil Aeronautics Board, and the mess we are confronted with today.

We should fix it like we did in 1938.

And perhaps the biggest takeaway is that so little has changed since Longman and Khan published Terminal Sickness5 in 2012.

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Research is formalised curiosity. It’s poking and prying with purpose.

| Zora Neale Hurston

…

That’s what I’m up to. Poking and prying with purpose.

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Fed Employees Traumatized

Guess what? US federal agency heads characterizing federal employees as ‘the enemy’, trying to inflict ‘trauma’ on them, goading them to quit, and actively criticizing the goals of the federal agencies, results in a highly disengaged workforce. And that, in turn, leads to poor service delivery. So says the Partnership for Public Service in a new report -- the Public Service Viewpoint Survey -- and it reveals a ‘staggering collapse in employee engagement’ at the federal government, as Don Moynihan1 lays it out:

The headline figure is a government-wide Employee Engagement and Satisfaction Index Score of 32 out of 100. To put that in context: prior Best Places to Work scores, even at poorly rated agencies, rarely dropped below 50.

This is not a dip. It is a collapse.

The numbers are awful:

Here are the comparisons between 2024 and 2025 for larger agencies. The Department of the Army registered the highest score among large agencies — at just 48% out of 100 — with only 9% saying Secretary Pete Hegseth’s political team generates high levels of motivation. Every other large agency scored lower, some dramatically so. The average score by agency in 2024 is about 70% and 29.5% in 2025 — just over a 39 percentage point decline.

The picture at mid-size agencies is even worse. In 2024, the average score is 73% and in 2025 it is just over 25% — an astonishing 48 percentage point decline.

Of course, as in the private sector, poor engagement leads to lower productivity, higher churn rates, and worse service to clients. And for those in those jobs, it’s like living in a psychic prison. It may take a decade for the federal government to recover from this crisis.

Elizabeth Linos of the Harvard Kennedy School, comments on the survey results:

By any historical measure, the new data released yesterday by the Partnership for Public Service is documenting the worst employee engagement and workforce sentiment I’ve ever seen for the federal government. To put this into perspective: in a typical year, agencies work hard to get their engagement scores from the high 60s to the mid-70s or even into the 80s (if you’re NASA). This year, the average is 32. No federal agency is a “best place to work” at this point.

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Unsafe For Women

In the United States, women are 73 percent more likely to be severely injured in vehicle crashes than men, and 17 percent more likely to die.

| Eve Van Dyke

Apparently, the National Highway Traffic Safety Administration (NHTSA) has resisted requiring crash test dummies that proxy for actual women. So, terrible stats, and we can’t expect them to get better soon.

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Shift Sulking

When hourly workers begin their day already drained, exhausted, and stressed by increasing pressures to work with understaffed teams and subject to unpredictable schedules, it’s being called ‘shift sulking‘. This goes beyond the disengagement typified by the Gen Z stare, deeper into the way the hourly jobs of today are sapping the reserves of hourly workers, especially when coupled with poly-employment: when workers have to juggle multiple jobs to make ends meet.

Jennifer Mattson reports:

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Instead of a winner-takes-all approach to power, it’s time to consider working toward a system where there is much less power for the winner to take.

| Katherine Mangu-Ward, Libertarians: We Told You So

…

Mangu-Ward’s statement resonates loudly amid the war in Iran and the diminishing congressional oversight of war-making and public policy more generally.

My focus at workfutures.io is on work, not war, but the way corporations are capitulating to the Trump administration's encroachment on how businesses operate must remain high on the agenda here.

I’ve been writing and speaking about the future of work for over 30 years. Along the way, I’ve consulted with and written for Microsoft, IBM, Google, Dell, Cisco, and dozens more. In 2007, I coined the term ‘hashtag’ (yes, hashtag), and many other terms that have shaped the way we think about work, like ‘work management’, ‘social tools’, ‘work media’, and others. If you want to help me continue my work, consider a paid subscription.

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Post-Liberal Conservatism

Vice President JD Vance has embraced the worldview many are calling ‘post-liberal conservatism’, as Thomas Edsall described it1:

Vance described his current way of thinking relatively early on in his transition to MAGA loyalist, at a 2023 event honoring the post-liberal political theorist Patrick Deneen. “We on the right, on the sort of the post-liberal right, the new right,” Vance said, “we are really, really kidding ourselves about the weight of the challenge, and when we talk about changing the regime.”

The corrupting power of liberalism, Vance argued, has infected both the public and private sectors:

The way that lobbyists interact with bureaucrats, interact with corporations — there is no meaningful distinction between the public and the private sector in the American regime; it is all fused together. It is all melded together. It is all, in my view, very much aligned against the people who I represent in the state of Ohio

In fact, Vance continued, “the regime is the public and private sector. It’s the corporate C.E.O.s, it’s the HR professionals at Budweiser, and they are working together, not against one another, in a way that destroys the American common good.”

Vance and his ilk are committed to changing not just public policies or legislating new laws, but changing the culture of the American regime, including what business leaders are working toward, and who they work with to get there.

Edsall quotes Stephanie Slade of the libertarian magazine Reason as saying she

would sum up Vance’s view as follows: The left is willing to use all the power at its disposal — cultural as well as governmental — to impose its way of life on the American people, whether they like it or not, and so if conservatives are to have any hope of saving the country from left-wing tyranny, they must be willing to respond in kind.

This includes putting pressure on business leaders to support the post-liberal right’s agenda. So, out with Diversity, Equity, and Inclusion. Out with secular liberalism. Out with corporate social responsibility.

That may involve dislodging a managerial elite characterized by post-liberal political theorist Patrick Deneen as ‘a new aristocracy that has enjoyed inherited privileges, prescribed economic roles and fixed social positions’.

Deneen expanded on that in Why Liberalism Failed:

A political philosophy that was launched to foster greater equity, defend a pluralist tapestry of different cultures and beliefs, protect human dignity and, of course, expand liberty, in practice generates titanic inequality, enforces uniformity and homogeneity, fosters material and spiritual degradation and undermines freedom.

So, Vance and his cadre would like a new post-liberal managerial elite to take over American business, and to expunge any vestiges of liberal idealism and humanism.

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Following Japan Into Late-Stage Capitalism

Ellie (at Oceandrops) makes that argument that Japan has fallen into late-stage capitalism, and the US is following. She describes Japan’s ‘lost decade’ — the decades following the 1991 financial crisis there:

In 1991, the Bank of Japan tightened monetary policy, cooling speculation which triggered an enormous crash. Asset values plunged by over 80%, and the banks were left with bad loans. This event marked the beginning of the Lost Decade, a period of incredible stagnation that could be better called the Lost Decades — as it plagues the country to this day. Wages peaked in ‘97, the Yen depreciated, and household purchases have flatlined.

We have our own story to tell:

America’s “Lost Decade” started with the housing crisis of 2008, where subprime mortgages and the predatory financialization of consumer debt liquified the entire financial system.

Where the Japanese response to the financial crisis was slow and fragmented, the Americans aggressively flooded the economy with liquidity. TARP bailouts, quantitative easing, zero interest rates, and forced bank recapitalization helped Wall Street recover but not the ordinary American. Asset inflation benefited the rich, and everyone else was handed incredible wealth inequality.

And the social consequences in Japan have been many, and are echoing in America:

Japan has a term for “evil jobs” — ブラック企業 (Black Kigyo or Black Labor). These jobs enforce extreme overtime, unpaid labor disguised as “service overtime”, surveillance, and quotas. Workers internalize this obedience and labor abuse because the alternative is unemployment and social stigma.

(Translation) How to spot a Black Company: Understaffing, low pay, long hours, harassment.

Black kigyo companies comprise a significant portion of entry-level work, drastically altering working norms for young adults.

American analogues are now visible too. This November, the private sector lost 32,000 jobs.2 The pandemic-era white collar work bubble popped, and popped hard. Hiring is flat for the Class of 20263 as layoffs rise and AI takes over menial white collar labor. The American worker is being pushed into gig-worker exploitation, and human dignity violating work conditions. Even industries that were typically seen as relatively stable — healthcare and education — are experiencing major worker burnout.

Overwork is normalized. Karoshi (”death by overwork”) became a recognized cause of death in the 1980s. This includes heart attacks, strokes, and suicides linked to workplace abuses and poor life quality. If you visit Japan you may see signs of this normalized absurdity: salarymen collapsed in random public locations, Konbini selling shower-in-a-can, and people getting most of their calories at vending machines and subway station kissaten.

Asia Minute: Helping and Shaming Japan’s Drunk Salarymen | Hawai’i Public Radio

The average American, unfamiliar with Japanese social norms or cultural history, likely thinks this is some reflection of premodern Japanese values or ritual suicide traditions. It’s not.

Karoshi directly parallels emerging American trends in burnout-related deaths and mental-health collapse. Amazon, for example, had to make a public apology for denying their drivers the right to go to the bathroom.45

Sounds a lot like what is playing out in America circa 2026.

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To see what is in front of one’s nose needs a constant struggle.

| George Orwell

…

I feel that Orwell’s line reads better to my contemporary ears with a ‘just’ at the front and some paraphrasing: ‘Just to see what’s in front of your nose is a constant struggle’.

Struggle on!

Sundays Are The New Mondays

996 culture (9am to 9pm, 6 days a week), a trend transplanted from China, has taken hold in Silicon Valley, according to Amanda Hoover:

As of last year, 5% of white-collar workers in the US logged on during the weekends, a 9% increase from 2023, according to an analysis of the habits of more than 200,000 employees and 777 companies conducted by ActivTrak, a workforce-analytics and productivity-software company. They clocked an average of about 5 hours and 30 minutes on Saturdays and Sundays, and those at mid-size companies of about 1,000 to 5,000 employees were the most likely to work weekends. In 2024, people with bachelor’s degrees worked an average of four hours on a weekend, according to the U.S. Bureau of Labor Statistics, and about 29% of all employed people worked on weekends.

Hoover argues that people are doing this as an act of ‘liberation’, finding time that is ‘distraction-free’:

Others say the weekends are their distraction-free days. A 2025 report from Microsoft found that workers experience some 275 distractions each day, or an interruption about every two minutes during the 9 to 5 workday. The average desk worker sits in meetings for nearly 15 hours a week in 2024, according to a survey from AI-powered calendar app Reclaim.ai.

Heading toward Josef Pieper’s ‘total work’, which I wrote about in the past:

Pieper poses an existential question, which I fear we have sidestepped, these days, as we’ve accepted the tyranny of work:

What is normal is work, and the normal day is the working day. But the question is this: can the world of man be exhausted in being “the working world”? Can the human being be satisfied with being a functionary, a “worker”? Can human existence be fulfilled in being exclusively a work-a-day existence?

To me, the answer is an unequivocal no. But your mileage may vary. But I believe we are confronted by a constant struggle to resist the tyranny of work culture, management, and the clock, and carve out time for leisure, play, and rest.

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Ungovernable Change

In Why Keeping Up with Change Feels Harder Than Ever, Kayla Velnoskey, Ingrid Laman, and Carolina Valencia introduce the concept of ‘ungovernable change’1, an outgrowth of the frantic levels of change initiative corporations are undertaking today:

A March 2025 Gartner survey of more than 980 global leaders found that only 32% of mid- to senior-level leaders were able to implement their last change initiative on time while maintaining employee engagement and performance.

Leaders are managing ungovernable change at a time when their employees are increasingly skeptical of their efforts: 79% of the 2,900 global employees surveyed by Gartner in April 2025 don’t trust their organization’s ability to change effectively. The majority believe that their organization has made poor change decisions in the past and are unlikely to be successful in the future.

The authors summarize the problem:

Leading through change is more difficult than ever because of the convergence of four factors: 1) not only is there a large volume of change, but changes are stacked, one on top of the other; 2) not only are changes happening faster, they’re continuous, without a start or end date; 3) not only are changes larger in scale; they’re interdependent; and 4) not only is change unpredictable, they’re externally driven by technology, geopolitical trends, and more.

The answer in not to slow down, according to the authors, because the world is moving at ungovernable rates.

The companies that are most successful turn the situation inside out. Instead of slowing down, they inculcate routinizing change. They ‘routinizing change—treating it as an everyday business process’.

Note that in their own survey data, ‘79% of employees don’t trust their organization’s ability to change effectively’. This means ‘adopting three strategies’:

1. Communicate that change is a journey, not a destination.

2. Enable change-ready employees, not change enthusiasm.

3. Foresee multiple possible scenarios, not just the current change.

The examples they offer sound rational, but the paper reads more like a PowerPoint than something that can overturn the disruptions of ‘ungovernable change’. And the subtext, again, is Pieper’s ‘total work’ seeping into everything. Not only do you have to complete the work of the day, but have to become a willing confederate in blowing up any sense of stability, and embrace total change — in all dimensions — as the norm.

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I’ve been writing and speaking about the future of work for over 30 years. Along the way, I’ve consulted with and written for Microsoft, IBM, Google, Dell, Cisco, and dozens more. In 2007, I coined the term ‘hashtag’ (yes, hashtag), and many other terms that have shaped the way we think about work, like ‘work management’, ‘social tools’, ‘work media’, and others. If you want to help me continue my work, consider a paid subscription.

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The hour of the barbarian is at hand. The modern barbarian. The American hour. Violence, excess, waste, mercantilism, bluff, conformism, stupidity, vulgarity, disorder.

| Aimé Césaire, Discourse on Colonialism (1950)

…

Events of recent weeks confirm Césaire’s anticipation of the neoliberal world order in the 20th century, and today’s 21st century post-neoliberalism, which looks a great deal like the original form of imperialism: piracy1.

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AI, AI, AI

The title above can be pronounced in two ways: the first is ‘eh-eye, eh-eye, eh-eye’, alluding to artificial intelligence, which is everywhere in the news (but not really in the stats?). The second pronunciation is ‘aye-ee, aye-ee, aye-ee’, like someone screaming because their hair is on fire.

Also, I confess that I am not researching these stories as they flitter by across my radar screen. I am merely repeating anecdotal evidence: mere anecdittos.

…

A whopping 3 percent.

“I can’t really remember a boom with such active hostility to it,” William Quinn, co-author of the 2020 history tome “Boom and Bust: A Global History of Financial Bubbles,” told the NYT. “People usually find new technology exciting. It happened with electricity, bicycles, motorcars. There were fears but also hopes. AI is notable, perhaps unique, for the lack of enthusiasm.”

As consumer sentiment goes from sour to moldy, the CEOs behind the bubble only seem to be doubling down.

“It’s extremely hurtful, frankly,” said Nvidia chief executive Jensen Huang in a January interview about the “battle of [AI] narratives.”

Huang insisted that AI is suffering a “lot of damage” from “very well-respected people who have painted a doomer narrative, end-of-the-world narrative, science fiction narrative.”

OpenAI CEO Sam Altman has concurred, lamenting pushback against the “diffusion, the absorption” of AI in broader society. “Looking at what’s possible, it does feel sort of surprisingly slow,” he said at the recent Cisco AI Summit.

While AI boosters could argue we’re simply living under the tyranny of a vocal, AI-hating minority, evidence suggests the public’s aversion runs deep — and not just against the tech itself. As one Pew Research survey from 2025 found, about 60 percent of respondents said they’d like “more control” over how AI is used in their lives, while only 17 percent are “comfortable” with AI remaining in the hands of a few tech billionaires.

Consumer data paints an even more dramatic story. In mid-2025, when mainstream analyst firms were still parroting uncritical AI hype before investor sentiment turned cold in December, the number of US AI users who regularly paid for the privilege stood at a whopping 3 percent.

| Joe Wilkins, Tech CEOs Confused by Why Everybody Hates AI So Much

I know why people hate AI so much: the billionaires who are steering the economy into a dystopian future, or the ditch.

…

42%

The approximate share of tech-industry workers who said their direct manager expects AI use in day-to-day work as of last October, up from 32% just eight months before, according to a survey from AI consulting firm Section.

| The Future of Everything

…

But what incentives?

Fifty-five percent of companies surveyed say they are offering no premiums, no bonuses, no equity, for employees who have built out their AI skillset. Only 14% offer higher base pay, 10% offer bonuses, and 9% offer long‑term incentives.

| Payscale, Payscale’s 2026 Compensation Best Practices Report Reveals Shifting Pay Strategies Amid AI and Labor Market Volatility

AI adoption in exchange for equity might be a sensible model, come to think of it.

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Blunt-force trauma.

Thirty-six percent of chief marketing officers expect to reduce head count over the next 12 to 24 months “by utilizing AI or eliminating redundancies,” according to a new survey from executive search firm Spencer Stuart based on November interviews with approximately 90 CMOs and other marketing leaders.

At larger companies, the outlook was grimmer. Forty-seven percent of respondents at companies with $20 billion or more in revenue said they expect to cut staff over the next 12 to 24 months, and 32% already did so this year, the survey found.

The key factor is growing pressure to show returns on companies’ significant investments in AI, said Richard Sanderson, who leads Spencer Stuart’s marketing, sales and communications officer practice.

“We’re hearing, particularly from the largest … companies, that they have to deliver, and it may have to be through blunt-force of head-count reduction,” Sanderson said.

| Patrick Coffee

So, AI isn't generating enough work taken on by bots to justify the spend, so, of course, companies will cut jobs instead of cutting back on AI. Totally reasonable. Uh huh. Sure. Got it.

…

Job applicants want to know why they are rejected by AI.

Applicants for AI-screened jobs are suing a recruitment tool company, Eightfold AI, for failing to share the scores and results for their job applications. The novel twist is comparing this to credit ratings and the Fair Credit Reporting Act. Seems reasonable that they should know why the system is rejecting them, in some case for thousands of applications.

| Stacy Cowley2

Seems fair to me. But the companies behind this software, like Eightfold AI, don’t want to share how they do what they do. Or maybe they don’t know how the AI is doing it.

…

And job hunters are using AI, too.

It’s easy to spot when candidates over-rely on AI, some employers said. Oftentimes, executive summaries will look eerily similar to each other, odd phrases that people wouldn’t normally use in conversation creep into descriptions, fancy vocabulary appears, and someone with entry-level experience uses language that indicates they are much more senior, they added.

It’s worse when they use auto-apply AI tools, which will find jobs, fill out applications and submit résumés on the candidate’s behalf, some employers said. Those tend to misinterpret some of the application questions and fill in the wrong information in inappropriate spots. If these applications were evaluated alone, employers say they’d have a harder time identifying AI usage. But when hundreds of applications all have the same issue, they said, AI’s role in it becomes obvious.

Joseph Eitner, chief human resources officer for New York-based investment firm Eaton Capital Management, said he has no issue with candidates turning to AI to add some keywords, clean up their grammar, or even help them think through a question on the application. But ultimately, he said, candidates should do the writing themselves, express their own ideas and personalities, and take the time to manually submit their applications.

“If that’s how you apply and how you work, I don’t want to hire you,” he said. AI auto-apply services are “snake oil. It’s a disservice to yourself and to the people you’re applying to.”

| Danielle Abril, Employers to job seekers: Your AI résumé isn’t fooling anyone3

I’ve been writing and speaking about the future of work for over 30 years. Along the way, I’ve consulted with and written for Microsoft, IBM, Google, Dell, Cisco, and dozens more. In 2007, I coined the term ‘hashtag’ (yes, hashtag), and many other terms that have shaped the way we think about work, like ‘work management’, ‘social tools’, ‘work media’, and others. If you want to help me continue my work, consider a paid subscription.

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Misery generates hate; these sufferers hated the machines which they believed took their bread from them; they hated the buildings which contained those machines; they hated the manufacturers who owned those buildings.

| Charlotte Brontë, Shirley

…

I offer the following close and adversarial reading of Michael Steinberger’s recent Your Job May Already Be in Jeopardy,1 where he embodies the ambivalence and shifting point-of-view that animates so much of the public discussion about AI (meaning Large Language Model-based chat tools). Steinberger is a good writer, but, in my view, fails to take a side in this controversial battleground. And he fails to call out those creating the coming catastrophe as bad actors, unconcerned with the devastation that their golems may cause.

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What Steinberger says.

Michael Steinberger opens his wide-ranging OpEd on the looming AI apocalypse with an anecdote about a young college grad who can’t find work despite his degree in accounting, and instead joins the family tree service company. The classic humanizing a macro trend, a la Gladwell. Then the macroeconomic smokescreen we’re hearing: maybe companies overhired in and after the pandemic? But when the Citrini Research ‘thought experiment’ hit the streets Wall Street freaked:

Many companies went on hiring sprees coming out of the pandemic, and the slowdown is perhaps just the inevitable adjustment. But it is happening against the backdrop of the generative A.I. revolution and fears that vast numbers of knowledge workers will soon be evicted from their cubicles and replaced by machines — fears being amplified by an army of online Cassandras. In a sequence of events that called to mind the 1938 Orson Welles radio adaptation of “The War of the Worlds,” famous for convincing panicked listeners that aliens had really invaded, a recent Substack post imagining the economic hellscape that could result from an A.I.-induced white-collar blood bath helped send the Dow Jones industrial average tumbling 800 points. Anxious times.

The AIpocalypse.

Then, after wondering why the people building all this AI are warning us of its potential for job destruction, the possible political fallout:

While new ones will hopefully emerge, the transition won’t be painless, and if the cracks we are seeing in the labor market become sinkholes, the effect not just on our economy but also our politics could be profound. If millions of college-educated voters have their lives upended by A.I., they will surely make their fury known. That prospect should be causing alarm in Washington and spurring efforts to try to cushion Americans from the blow that may soon befall them — by giving serious consideration, for instance, to something like universal basic income. But it is an election year, Congress is barely functioning, and on this issue, as with so many others, inertia will very likely prevail.

Historians have written a great deal about past circumstances where suddenly disadvantaged elites turn to revolution.

An economy that created only 181,000 jobs in 2025?

According to Lawrence Katz, a professor of economics at Harvard University, what we are experiencing now — a sustained period of “slow job growth and gradually rising unemployment without a real recession” — is virtually unprecedented.

And of course, it’s white-collar jobs being axed, not the usual blue-collar schlubs.

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Steinberg quotes another economist, Gad Levanon, who points out that ‘hiring has come to a virtual standstill in finance, insurance, accounting, consulting and tech, which are pillars of the “knowledge” economy’. He posits that these companies must have found some solution to improve productivity without additional heads, which is LLMs. Supporting that are the claims of CEOs like Jack Dorsey of Block, who Steinberger quotes, ‘“the intelligence tools we’re creating and using, paired with smaller and flatter teams, are enabling a new way of working which fundamentally changes what it means to build and run a company.”’ But former employees (who Steinberger cites without a name or link), ‘contend that poor management left Block with a bloated payroll and that A.I. is just a convenient excuse for the pink slips.’ And another factor is that Wall Street loves this attrition: ‘Whatever the truth, investors responded with delight to the news: Block’s stock soared over 20 percent, which is perhaps indicative of where Wall Street comes down on the job augmentation vs. job elimination question.’

Some white-collar workers might give up, if laid off and searching for jobs for a long time. And while people may be able to learn a trade and join the electricians building data centers for AI, I hear an echo of Judge Smails from Caddy Shack, stating ‘the world needs ditch diggers, too’.

But Steinberger points out that if millions have their white-collar jobs disappear, not all of them are likely to find work for anything like comparable pay: ‘Even now, college-educated workers command an enormous wage premium — more than 70 percent, by most calculations — over those with only high school diplomas’. Of course, these dispossessed workers will still be degreed, but those degrees will simply be less valuable, no matter how much they still owe for college loans.

He returns to the theme of how Americans feel about the seemingly inexorable march of AI: incipient rage at being dispossessed from the neoliberal compact, the Fordist pact: the mass machinery -- institutions, corporations, politicians -- will do what they do, so long as ordinary people can live their lives, save for retirement, and set their kids up for success. He quotes Martin Wolf, the chief economics correspondent of the Financial Times (not a bastion of leftist revolutionary thought):

‘if lots of “skilled, trained thinking activities” are displaced by machines, it could provoke a furious backlash. “We could have a social and political crisis that makes deindustrialization look trivial,” [Wolf] said. “Deindustrialization, though one of the biggest forces shaping our world, shook the working class, particularly the male working class, from top to bottom. Shaking the prospects of the educated middle class is socially far more dangerous and explosive because it affects them and their parents, who are the people who run our societies in almost every possible way.”’

Steinberger mentions a few on Capitol Hill are worrying about the possible rending of the economy by AI, but fairly ineffectively, at least so far.

What Steinberger doesn’t say.

First of all, Steinberger doesn’t say what the enraged former white-collar dispossessed will do. Vote the bums out? Storm the ramparts? Blow up the data centers? Stop spending?

Second, while reading this, I was reminded of an interview with Alan Supiot, the French political historian and theorist, where he quotes FDR:

The freedom of a democracy is not assured if the people tolerate private power growing to such an extent that it becomes stronger than the democratic state itself. From recent history, [FDR] drew the lesson that “economic despotism“ gave rise to fascism and that “government by organized money is as dangerous as government by organized crime.”

The rise of AI and the complacency of our elected officials in the coming dispossession of white-collar workers are outgrowths of private power and moneyed interests coöpting democracy. The elite, those who run institutions theoretically empowered to benefit us all, have abdicated that charge, as Christopher Lasch expounded in The Revolt of the Elites and the Betrayal of Democracy. Rod Dreher decoded Lasch’s arguments in 2021, and they still hold [emphasis mine]:

Lasch wrote the essays in Revolt near the beginning of the modern era of globalization, which can be dated to the end of the Cold War in 1989, the invention of the World Wide Web in 1991, and China’s growing presence as a global economic behemoth. The Clinton Administration, coming to power in 1992, made the Democratic Party’s peace with Reagan-era trade and economic liberalization. At the time, growing economic inequality troubled Lasch, who wrote, “People in the upper 20 percent of the income structure now control half the country’s wealth.”

Those were the days! According to a 2017 analysis by the Washington Post, the top 20 percent of Americans now control 90 percent of the country’s wealth.2 Both the decline of the working class thanks to post-1980s deindustrialization and the hollowing out of the middle class have been well documented.

So has the widening economic and moral chasm between what Charles Murray called the “New Upper Class” and the “New Lower Class.” The divide is not merely economic, but cultural. Lasch saw elites segregating themselves and their progeny into networks and institutions separate from the broader public. Unlike elites in past eras of US history, today’s elites feel less obligation to provide for the commonweal. Rather, they have seceded—socially, intellectually, and often into coastal liberal enclaves—from a country they do not understand and do not wish to understand, regarding it as a land of backward people “at once absurd and vaguely menacing.” Says Lasch, of these elites, “It is a question whether they think of themselves as Americans at all.” This has become so pronounced in the past quarter-century that it’s hard to feel the same sense of urgency Lasch brought to his discussion.

I have to note that Dreher and I differ on who the ‘elite’ are. He thinks of them as coastal liberal professionals, but my definition of elites is limited to the executive class, billionaires, and the elected officials who take their money to serve darks ends.

In the final analysis, Lasch points out what Steinberger is missing: we have become inured to the disassociation of the elite from the world -- and lives -- of ordinary people. The uncomfortable fact that a massive adoption of labor-saving AI tools might -- must? -- lead to millions of workers to not only lose jobs, but to lose their hopes of middle-class life, and, this is being orchestrated so that the elite can collect all -- or at least most -- of the money to be made, thereby. Steinberger simply fails to name names: those who stand to benefit from the catastrophe they are engineering.

As Rebecca Solnit tells us, in Call Them By Their True Names, ‘Naming is the first step in the process of liberation’. Those elites who seek to divorce themselves from our shared fate will fail, but only if we stop them before the point of no return.

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Capitalism is disappearing, but Socialism is not replacing it. What is now arising is a new kind of planned, centralized society which will be neither capitalist nor, in any accepted sense of the word, democratic. The rulers of this new society will be the people who effectively control the means of production: that is,…

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The human condition can almost be summed up in the observation that, whereas all experiences are of the past, all decisions are about the future. It is the great task of human knowledge to bridge this gap and to find those patterns in the past which can be projected into the future as realistic images.

| Kenneth Boulding

Decision-making is critical to business performance, so much more time should spent on systematically and rigorously examining the factors that can impede it. And many of those factors are in our wiring, both as individuals and as organizations.

If Decision-Making Is So Important, Why Do We Take Shortcuts?

Decision-making is critical to business performance. To go even further, it is an existential requirement. Yet so often things go wrong. Why?

Some problems in strategic decision-making seem obvious when you step back far enough. Consider the ‘loss aversion’ problem. Operating unit managers are focused on short-term timeframes and therefore tend to take on only small risks, instead of larger ones that may contribute more to long-term corporate growth. They are more concerned about the possibility of a status decline if things go wrong: the potential loss for the manager may be perceived as worse than any potential upside for the company and the manager.

Senior managers, however, must take a portfolio view and therefore must anticipate a portion of projects to fail in the search for a few that will yield big returns. The incentives — and fear of downside risk — between corporate goals and the individual manager’s goals are mismatched.

The important takeaway is this: the economic downside of loss aversion is only one instance of a much larger case of cognitive biases that distort human behavior whenever decision-making is called for. And like so many other aspects of life, avoiding examination of these influences on our behavior leads to big problems.

The basic assumption is wrong.

The basic assumption in business is that strategic decision-making requires three basic elements:

1. fact-gathering and analysis,

2. the insights and judgment of a defined group of people (stakeholders or advisors), and

3. some process -- ranging between very formal to very informal -- for that group to make a decision, reflecting that analysis and judgment.

However, as Dan Lovallo and Olivier Sibony reveal,

Our research indicates that, contrary to what one might assume, good analysis in the hands of managers who have good judgment won’t naturally yield good decisions.

And why is that? The authors go on to draw our attention to the third part: the process. After extensive analysis of 1,048 major decisions over a five-year period, including investments in new products, M&A decisions, and large capital expenditures they determined that

process mattered more than analysis—by a factor of six.

Process, by extension, is also more valuable than the judgment of those involved in the decision-making, too, since flaws in both analysis and judgment are countered by processes designed to do exactly that.

Process, process, process.

Lovallo and Sibony took a hard look at decision-making effectives, and broke things down:

And a major value of good decision-making processes are to overcome — or at least moderate — cognitive biases that lead to poor decisions. As Lovallo and Sibony put it:

The prevalence of biases in corporate decisions is partly a function of habit, training, executive selection, and corporate culture. But most fundamentally, biases are pervasive because they are a product of human nature—hardwired and highly resistant to feedback, however brutal. For example, drivers laid up in hospitals for traffic accidents they themselves caused overestimate their driving abilities just as much as the rest of us do.

Improving strategic decision making therefore requires not only trying to limit our own (and others’) biases but also orchestrating a decision-making process that will confront different biases and limit their impact. To use a judicial analogy, we cannot trust the judges or the jurors to be infallible; they are, after all, human. But as citizens, we can expect verdicts to be rendered by juries and trials to follow the rules of due process. It is through teamwork, and the process that organizes it, that we seek a high-quality outcome.

Structuring decision-making processes helps, but we need to be vigilant about biases.

Perhaps the core message to be gained from Lovallo and Sibony’s research is this: while awareness of our cognitive blinders is helpful, the best approach to debiasing is to create processes that structure decision-making to intentionally counter biases to the greatest extent possible.

Here are five of the most common groups of biases relevant to business decision-making.

Pattern-recognition biases — We are pattern recognition machines: it is a defining characteristic of our species. But there are some side effects of our pattern-matching prowess that skew our reasoning. For example, confirmation bias leads us to discount new information when it doesn’t support an initial hypothesis. As Sydney Finkelstein, Jo Whitehead, and Andrew Campbell detailed in Think Again: Why Good Leaders Make Bad Decisions and How to Keep It from Happening to You, pattern-matching can lead to huge mistakes [emphasis mine]:

Most of the time, pattern recognition works remarkably well. But there are important exceptions. If we are faced with unfamiliar inputs— especially if the unfamiliar inputs appear familiar-we can think we recognize something when we do not. We refer to this as the problem of misleading experiences. Our brains may contain memories of past experiences that connect with inputs we are receiving. Unfortunately, the past experiences are not a good match with the current situation and hence mislead us.

Another exception is when our thinking has been primed before we receive the inputs, by, for example, previous judgments or decisions we have made that connect with the current situation. If these judgments are inappropriate for the current situation, they disrupt our pattern recognition processes, causing us to misjudge the information we are receiving. We refer to these as misleading prejudgments.

In other words, our pattern recognition process is fallible. We have all experienced the embarrassment of accosting a complete stranger we thought we recognized. We have also experienced some degree of terror because we have misjudged the sharpness of a bend in the road or the speed of an oncoming car. What is less obvious is that our pattern recognition processes can also let us down when we are trying to judge the severity of a financial crisis, the value of an acquisition target, or the threat from an incoming hurricane.

Counter: One technique is to undermine the presuppositions that lead to the pattern match in the first place. Why are the team members so certain about a particular course of action? Explicitly draw our what is the underlying analogy between this circumstance and others from the past. One means is to dramatically enlarge the set of comparison scenarios, and supply known facts for each example.

Action biases — There is a well-known bias toward action among managers which can lead to accepting overly optimistic analysis and yielding to cultural pressures to make decisions quickly without accurately assessing uncertainties.

Counter: Promote the recognition of uncertainty rather than trying to squeeze it out of the conversation. Tools like scenario planning, decision trees, and Gary Klein’s ‘premortems’ explore the many dimensions of uncertainty before pressing ahead with a decision.

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Nothing you do can stop time’s unfolding.

| Willian Stafford, The Way It Is

…

Apologies for my absence from these pages in the past week or so. The recent snowpocalypse — we had 31 inches of snow and an arctic deep freeze lasting more than a week — hit me like a small-scale pandemic lockdown. Trapped inside, grappling with a …

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Under the present brutal and primitive conditions on this planet, every person you meet should be regarded as one of the walking wounded. We have never seen a man or woman not slightly deranged by either anxiety or grief.

| Robert Anton Wilson

…

In his final column at the NY Times, David Brooks offered this:

Only 13 percent of …

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If everyone is thinking alike, then no one is thinking.

| Benjamin Franklin

Out of the blue today I came across a Substack post by Maurizio Cuna (MC) with a somewhat misleading, clickbaity title: The exact 51 questions you can ask in any meeting when you are “not getting it”.

I clicked, and after scrolling past the title, I found something useful.

MC defines his idea — Precision Inquiry — this way:

Successful operators know that asking for clarification is an important tool of high-level execution: when you ask the question everyone else is too afraid to ask, you are both helping yourself and leading the entire conversation.

[…]

To help you navigate these moments with total confidence, I have curated 51 powerful questions categorized into 7 strategic frameworks.

He offers the questions in a tabular cheatsheet:

The cheatsheet drops out information that is quite helpful. Here’s his subtitle for the ‘Wide Lens’ category, parenthetically synonymed as ‘(General Clarification)’: ‘Pick one of these questions when the overall concept feels fuzzy and you need a high-level reset’.

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My Recasting

Rather than ‘the exact 51 questions’, it might be better to use these seven categories of questions as a framework with 51 examples. Also, it might be ‘one less thing’ to drop the ‘archetypes of inquiry’ — like ‘The Architect’ or The Translator’ — and just use the parenthetical categories. Also, I have sequenced a conceptual meeting1 into three phases: At The Outset, Throughout, Toward The End.

I find Cuna’s parenthetical categories more useful than the archetypes, and the subtitles (slightly revised) more useful than the categories. Also, in practice, consider the meeting timeline to discuss the status of a project plan or a company initiative.

At the start, a presenter -- such as the person leading the project or initiative -- will likely lay out the purpose of the project, at which time questions about ‘goals and objectives’ are most salient. As the meeting progresses, other categories become relevant depending on the topic under discussion. When unclear jargon is used, ask questions to clarify the meaning of the jargon, for example, or if the presenter has not provided enough detail, or too much detail, feeding back your understanding could advance shared agreement on what is being said. As the meeting draws toward an end, next steps and accountability are called for before closing the meeting

So, here’s a different organization of the 7 categories, following that thinking:

Feel Free

In practice, the three-phase model is just guidance. It may be appropriate to ask questions about goals at any phase, not just at the outset, but those questions may be more salient then. Likewise, the end of a meeting is not the only time to ask about responsibilities and next steps.

We owe a debt to Maurizio Cuna for his work, even though I stole it and redrafted it for my own purposes2. Artists steal, after all. Feel free to steal my version, too.

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David Leonhardt, an editorial opinion editor at The New York Times, was trying to set up a cage match among three economists: David Autor, Anton Korinek, and Natasha Sarin. But, from my perspective, the important part of the discussion was what wasn’t said.

Leonhardt: Has AI already led to ‘meaningful job loss’?

Autor: ‘The evidence is inconclusive.’

[...]

‘Hiring in these same A.I.-exposed occupations has been sensitive to business cycles and interest rates going back well before the current A.I. era. A.I. may play a role in the recent hiring trends, but it’s quite possible that it does not.’

The last point is repeated by the other two economists.

Autor also hedges, saying slowdown in entry-level jobs in some industries (programming, customer support) but that started in 2022, pre-chatGPT, a sharp rise in Federal Reserve fund rates led to slowdown in hiring, and Trump’s tariffs may also play a role.

Autor: ‘every reason to believe that advancing A.I. will fundamentally change hiring and skill requirements across much of the economy.’

He thinks we will see 1/ fewer people doing the work, 2/ higher skilled workers most likely to remain employed, 3/ we will eventually see that in the data, if we’re not already.

Sarin: ‘there just isn’t evidence in the data [AI hitting jobs] that has happened in a meaningful way so far.’

As Autor said.

Sarin: ‘We don’t see changes in employment in the past few years in the occupations most exposed to AI and those least exposed.’

Sarin is the first to say that tech disruptions create new opportunities: ‘Historically, we’ve had lots of technological disruption, but disruptions also create new opportunities.’

This is a pivot point for people’s concerns: will AI create more jobs than it displaces, and will those jobs pay as well as the ones lost?

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Korinek starts by echoing his fellows:

Korinek: ‘employment data so far is ambiguous.’

But then shifts gears:

Korinek: ‘I want to offer a different lens: the investment data.’

He points out that many tech giants are making 100 billion dollar investments in AI, and points out how strange a/ how few people are involved in the actual work of build out these AI platforms, and b/ ‘I’m struck that this huge spending isn’t creating many jobs even at the A.I. companies themselves.’ It’s a massive investment leading to few jobs.

Is the investment in AI a signal or a cause?

Korinek: ‘The employment effects we are looking for may simply be lagging indicators of a transformation that’s already locked in by the capital being deployed.’

In other words, does the level of investment translate into a pile-on that can’t be piled-off? Are the investors injecting suck large sums in the race for artificial general intelligence so it can’t be stopped?

Korinek is the first to state that AI could lead to revolutions in scientific discovery:

Korinek: ‘A.I. may ultimately be beneficial by revolutionizing scientific discovery, health care and human well-being.’

And Korinek is first to raise the issue of labor market disruption, and (implicitly) our lack of preparation for it:

Korinek: ‘We should be preparing now for the possibility of significant labor market disruption, rather than waiting for it to show up conclusively in the statistics.’

Note that he says we should be preparing, but doesn’t specifically advocate regulating AI adoption to lessen the impact of this potentially enormous market disruption. In fact, none of them discusses regulation at all.

Autor jumps in to advance the ‘disruption leads to new opportunities/past is prologue’ argument:

Autor: ‘Look to history. New technologies don’t merely replace labor in existing industries; new technologies create entirely new industries. Centuries ago, there were no automobiles, airplanes or telecommunications, and those industries all employ people.’

But he doesn’t mention telephone operators, Waymo, or autonomous drones in Ukraine, all of which are evidence of disruption ending occupations.

Korinek returns to the investment argument, saying there are a lot of other investors, too:

Korinek: ‘Their success is not guaranteed. They are betting on relationships such as scaling laws, which predict that more computing power will lead to more powerful A.I. systems. So far, they have had a good track record, but we cannot be sure that these relationships will continue to hold.’

He also counters Autor’s ‘past is prologue’ argument:

Korinek: ‘In the past, new technologies have led to rising employment and wages, but we cannot be sure that this will be true in the future.’

Sarin counters the ‘investor lens’ that Korinek teed up:

Sarin: ‘I am not super swayed by the fact the labs are making big bets. If you work at these firms, haven’t you somewhat drunk the Kool-Aid?’

Leonhardt nudges the conversation to confront the elephant in the room:

Leonhardt: ‘You pointed out that technological disruption has never before caused humanity to run out of jobs, despite centuries of Luddite-like worries to the contrary. Can you sketch out a relatively optimistic scenario, in which A.I. is revolutionary but does not create mass unemployment?’

Sarin counters the ‘past is prologue/opportuinities’ argument:

Sarin: ‘This time could be different, and this revolution could reduce the need for labor as a whole. Then maybe the world would shift to some version of the 15-hour workweek John Maynard Keynes famously predicted.’

‘More likely, new jobs will come in, as they have in the past, and will offset jobs that are less necessary in a world where we all have laptops and don’t need typists. There will be winners and losers.’

‘I don’t want to minimize the possible disruption. How well we manage this transition will be the result of choices we make, and it will be important to retrain the work force.’

Sarin is the first economist to suggest retraining as a solution to the labor market disruption caused by AI. Retraining sounds great in theory, but in practice, the history of turning, for example, assembly-line workers and coal miners into computer programmers or marketers has been mixed at best.

Sarin hedges her bets:

Sarin: ‘It is not a foregone conclusion — and it’s not even likely, in my mind — that productivity growth from A.I. will shrink employment overall. If history is any guide, technological progress, even from really revolutionary, life-changing, universally adopted technology, may change the way that we work, but not the fact that we work.’

Yeah, but workers who made $45/hour at Ford wound up working at Home Depot for minimum wage.

Autor takes the discussion in a more productive direction, shifting to the history of industrialization:

Autor: ‘when we worry about the number of jobs, we are worrying about the wrong thing. We should be worried instead about the commodification of human expertise, since expertise is what gives labor its economic value. Without it, many workers may not be able to earn good wages.’

‘In the artisanal era, most goods were handmade by skilled artisans: wagon wheels by wheelwrights, clothing by tailors, shoes by cobblers, timepieces by clockmakers, firearms by blacksmiths. Artisans spent decades mastering their trades, and their expertise was revered. But the value of many forms of artisanal expertise was decimated during the Industrial Revolution of the 18th and 19th centuries, and many artisans themselves never recovered.

Even as innovations spurred a surge in productivity, it was five decades before working-class living standards began to rise. In its initial incarnation, the Industrial Revolution displaced expert work while leaving humans to perform the simple, grueling, inexpert work of feeding what the poet William Blake termed “dark satanic mills.”

Sarin backs Autor’s stance:

Sarin: ‘One fact from Daron Acemoglu and Simon Johnson, the M.I.T. economists and recent Nobel laureates, that I find compelling: Real wages for weavers more than halved in the first two decades of the 1800s.

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Leonhardt brings Korinek back into the thread:

Leonhardt: ‘My sense is that this is part of what you fear, Anton — that even if A.I. leads to big overall gains for economic output, it will hurt many more workers than it helps, at least in the medium term. Is that right? And what should we do to reduce that risk?’

This is perhaps the most inflammatory section of the panel.

Korinek: ‘Yes and no.’

‘If A.I. continues to advance only modestly, then the Industrial Revolution-scale disruption that Natasha and David are describing seems quite plausible: there will be a painful transition, but ultimately new jobs will emerge, as they always have.’

But ’If the quest for artificial general intelligence succeeds, we are not looking at another Industrial Revolution. For two centuries, labor has been the scarcest factor in our economy, leading to wages that have risen far above preindustrial levels. Human workers were the bottleneck, and being the bottleneck made us valuable. But if labor itself becomes optional for the economy, that would be very different.’

‘When a machine can do a worker’s job, the worker’s wage eventually falls toward the machine’s cost. Yes, new jobs will emerge as they always do. But the machines will learn them faster and do them more cheaply. The reassuring historical patterns depended on humans being needed to run the economy. Remove that bottleneck, and we are facing something qualitatively different: a permanent shift in who, or what, captures the gains from economic growth.’

‘‘Historically, wages have been the primary mechanism for broadly distributing the benefits of economic growth. We may soon need new mechanisms that decouple income from labor: broad-based capital ownership, universal basic income or approaches we haven’t yet imagined. We need to start building those institutions now.

‘New mechanisms’ suggests — or perhaps demands? — government involvement, but Korinek never makes it explicit, nor does Leonhardt.

I wrote in 2014, in a survey for Pew Research, ‘The central question of 2025 will be: What are people for in a world that does not need their labor, and where only a minority are needed to guide the ‘bot-based economy?’ These economists never position the question that way. Maybe, as economists, they are happier talking about markets than people.

Sarin tries to undermine Korinek’s call for concern about the potential for AI to disrupt the economy:

Sarin: ‘It is less obvious to me than it is to Anton that we should be building new institutions now to deal with the possibility that we’re at the end of the Industrial Age. Maybe that will happen one day. But when? And which jobs are most at risk? And who is going to capture the gains?’

She advocates only for tweaking existing checks, like unemployment insurance and job retraining. No push for regulation or new government policies.

Sarin: ‘I don’t think we are going to be great at predicting what new tools we need in the policy tool kit at a moment when there is so much uncertainty about how A.I. will change the labor market. So rather than fight yesterday’s war without anything like complete information, I’d advocate getting better at learning about what types of workers are being impacted by labor market changes in real time. We could do that, among other ways, by collecting better data about firms are using A.I. and then combining that with jobs data to help us spot labor market displacement as it occurs.’

I will leave the close reading of this panel with the final comments of Autor:

Autor: ‘I agree that A.I. could ultimately undermine labor scarcity. If so, this would be a wrenching societal challenge that I’m not at all sure we’d manage successfully. We should begin to insure against this possibility. Two ideas that my M.I.T. colleague Neil Thompson and I sketch in a recent essay are “Universal Basic Capital” and “Wage Insurance”’

I will leave an analysis of that essay for another time1, since this has already been a long post. At least Autor is making new policy proposals.

The Absence of Regulation or Extra-Market Solutions

The biggest takeaway from this conversation is the absence of any discussion of regulating the use of AI, to protect workers’ jobs and slow the potential devolution of a work-based economy. It’s taken as a given that capital can do what it likes to increase productivity, and that labor has little voice in the unraveling of the industrail, pre-AI economy.

Korinek is the most vocal about this danger, and his paragraphs regarding artificial general intelligence should strike fear in all readers of this post. Korinek explicitly calls for new policies, presumably directed by the federal government: ‘We may soon need new mechanisms that decouple income from labor: broad-based capital ownership, universal basic income or approaches we haven’t yet imagined. We need to start building those institutions now.’

Korinek — and to a lesser extent Autor2 — are saying we can’t leave the outcome of a possible fork in the road toward a post-industrial economy up to the markets.

Remember the weavers in the 1880s, or the US manufacturing base after the first China Shock. And consider that truly general artificial intelligence could have ten times — or a hundred times — the impact on the world of today as the steam engine and industrialization had in the first decades of the industrial age.

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