Perhaps a more accurate title would be “AI isn’t going to be what you want it to be or are afraid it will be.” And by “you” I mean specifically you. Whatever you have in your mind’s eye, that’s what you should correct your expectations against. Those rare times where we have the slow unveiling of a revolutionary technology, over the span of years or even decades, there is a window of time where we all form an expectation of that it will look like in it’s final form and we’re all wrong. Everyone of us. Except Neal Stephenson, but that is another story.

I think we come by this bias honestly. There’s this tendency to see a new technology and either try to will it into being exactly that thing that would be a optimal for you, or succumb to pessimistic paranoia that this is why you were always fated to lose. In the early 00’s, the start-up tech boom and, later, stock market bubble were driven, I think, but the irresistable optimism that “The Internet” was a way that someone could enter a new market via their garage and bootstrap their way to millions while skipping those less than fun decades of grinding your way to a customer base. If you had a clever concept, then millions of customers were a click away. It was “idea person” catnip. And by idea person I mean someone who has lots of ideas but rarely can be bothered to follow through with anything more a few days. Eventually enough vaporware was a bought and sold that people started to question what was real, Microstrategy got caught cooking the books, everyone had the “maybe this thing isn’t real” thought all at once, and the market tanked. Flash foward 15 years and the internet had radically changed everyone’s life, but how it did so was in hard to foresee ways, through firms that were painstakingly built by experts and/or exploded into market leadership through network effects they’ve been teaching in Econ 101 for at least 30 years.

I observed a similar effect in my own research career. In my early years I was obsessed with agent-based computatonal modeling (something I’ve written about before). For all the optimism I carried for the methodology, it always paled in comparison to expectations and claims made by other. There was an observable pattern, too. What I saw was a way to model things that weren’t tractable in other economic methods, be it classic analytics, game theory, or dynamic stochastic general equilibrium models. What they saw was a way to write and publish economic models without having to learn high level math. Its both a way in and a way around. A way to skip a stage that they wanted to believe was unnecessary to make a scholarly contribution and/or make a career in academic social science. For some it was also a way to retake scientific territory annexed by economists. In either case, their expectations were deeply biased.

What I hear within a lot of a commentators, particularly those most obsessed and optimistic for AI, is wishing into existence the tool that would best serve them. To reimagine the cliche of a hammer in a world full of nails, they are toolbox that is missing a screwdriver, but have no fear, AI will be the universal screwdriver. No need for screwdrivers anymore, everyone will have a near infinite supply of (near) zero marginal cost universal screwdrivers, ready at a moments notice. If you are a professional screwdriver, well, you are out of luck, but that’s how the fates work and bully for me because I can accomplish so much now that I have an infinite supply of the skill I lacked. I am neither constrained by my own personal deficiencies, nor am I constrained by resources insufficient to hire a team of screwdrivers. I am what I always I dreamed I would be: a specialist in what the world still needs that is no longer dependent or deferential to people with the skills I lack. If a prognosticator is predicting a specific future for AI that will greatly increase their relative status among a narrow strata of professionals or scholars, you should index their prediction accordingly.

The inverse of this, of course, is the people who imagine themselves to be the screwdrivers in the previous story. They have specialized in labor product that is soon to be available at zero marginal cost. They’re value will be decimated and thus there is no hope. The irony, of course, is that it is the exact same story but perhaps seems more likely now that it is put in a pessimistic light. Obsolescence happens, after all. They’re both almost guaranteed to be wrong, though. Both sets of expectations are being radically biased by the narcicissm of the imaginer.

My impulses are, of course, similar to everyone elses. I try to keep this in check through my experience with the tech bubble (N=1, I know). AI will change our lives, but it will probably take at least 5-7 years longer than expected, and at least that long before that change is successfully “monetized”. The changes will be significant, it will show up in almost all of our work lives. It will disappoint in many ways. I remember telling someone that our expectations for the internet were too high for it to ever meet them. Then the iPhone came out and suddenly its penetration into our lives was fully actualized.

I don’t know what you think AI will be, but you’re wrong. And that’s ok. We all are.

An overlooked part of being a good Bayesian is revisiting your past failures of imagination, so that past stuggles with Knightian Uncertainty can be transformed into simple failures to accurately forecast probailities.

I posted earlier today about things getting weirder, but it’s worth considering the exercise proposed by Jonathan V. Last over at The Bulwark, where he goes through his own worst case scenario from November of 2024 and then compares it to the current observed reality.

JVL provides the following list of current events that he never even considered as possible. Going through the list below, which of these would you have considered as genuine possibilities? Not whether you predicted they would happen, but whether you would have even considered in your forecast that they could happen.

I think there are only two items, maybe three on that list I would have thought of as >1% chance of happening. That’s a failure of my imagination and I don’t think I am alone. If we’re good Bayesians, I think that means not just updating our priors, but updating how we update, and opening the door to the darker parts of our imagination when forecasting going forward. No, I’m not enjoying it either.

Tomorrow the first of the “deferred resignation program” windows close, adding to the growing sources of a chaos as the signs of a recession continue to mount. The supply chain is filled with tariff uncertainty. The tech sector is scrambling to deal with a potentially crippled H1-B channel (conditional on court rulings). Layoffs are showing up in retail and tech. Employers everywhere are coping with worker absences due to chaotic National Guard call-ups.

Between 150k and 200k employees took the government up on option to defer resignation i.e. collect 8 months of pay before resigning. This is about 2% of the federal workforce, though it’s notable that 20% of the IRS workforce took the offer in anticipation that they were going to be laid off with fewer benefits if they didn’t. For at least half of those who accepted DRP, it appears to simply be letting them coast into an already planned retirement, but it could also feasibly be used to bridge you to your next job, on the only condition that it be outside of the federal government.

A 100k workers showing up in the job market in the next few weeks isn’t catastrophic by any means, but combined with a government shutdown that would turn off benefits weeks or months before individuals planned is just another injection of chaos into an already uncertain labor market.

I’m not telling you if and when a recession will officially hit. I’m not in that kind of forecasting business. But I am comfortable saying their is more uncertainty about the state of the economy and institutions now than at any point since 2008, more concern over a speculative bubble surrounding a new bundle of technologies than any time since 2000, and more uncertainty around the robustness of the rule of law since I was born. Make of that what you will.

The announced $100,000 price tag on H-1B visas is an astonishingly stupid policy that serves no purpose other than create yet another channel for rent-seeking through an anti-immigration mechanism.

There’s nothing to untangle here. No confusion over the underlying economics. No panic or fear mongering through false claims of violent crime. It’s blocking high skilled workers our economy is desperate for in hopes that the prospects of enormous damage will create yet another source of power that will lead to wealth being transferred from industry into the pockets of the administration.

It’s bad. It’s getting worse. It’s the first time I’ve experienced a steady stream of economic policy that there is no one to argue with because there is no earnest belief that this will improve social welfare. It’s just a grift.

The only thing that remains certain is that there will be a new version of this every week and month until they are stopped. I’m pretty sure I can just replace the details of the story, and then copy and paste the rest of this post going forward.

I’ve been making a point to fill in the “gaps” in my film history lately. Yesterday I finally watched the John Cassavettes classic “A Woman Under the Influence” starring Gena Rowlands and Peter Falk. It is a fantastic film, with two incredible performances by the leads, but it is also emotionally exhausting as you watch an already strained woman entirely unravel. It’s the kind of movie that a modicum of chain smoking would probably make for easier viewing. I broke it into two separate sittings.

Nobody needs a new review of a 50 year old film- Roger Ebert already covered it ably, but there is reason to see it with fresh eyes. The principal word used to describle Mabel (played by Rowlands in a jaw dropping performance) is “crazy”. A least one person refers to her as anxious, but insanity is the general catch-all concept.

When you watch it now, though, you see a woman who would likely be be diagnosed with some variation of bipolar disorder, triggered by social anxiety. If she were to grow up today the observation of repeated physical “ticks” might have been associated with Tourettes or identified as the physical coping mechanisms of a child on the autism spectrum dealing with an avalanche of indecipherable social cues. I don’t actually know – the character is fictional and I am not a psychiatric professional. The point is that there are social, medical, and educational mechanisms in place to help a greater variety of people thrive. Maybe it’s just that we recognize a richer set of personal attributes and diversity of personalities than prior decades. There are handles for a person to grab on to before their life spins out of control.

There exists a sentiment that maybe we’ve gone too far, that we’re overdiagnosing, over- compartmenalizing, and over-accomodating a variety of behaviors as mental illness or disorder. And I can see the logic sometimes. But I think we’ve come so far that we can sometimes lose sight of the incredible value of the progress made. There are easily thousands, likely millions, of people who would have in prior generations been expected to endure a life of quiet misery or, barring that, be pushed sufficiently to the periphery that their suffering was just out of earshot. Instead they are provided language to understand themselves and communicate their needs to others, and sometimes the tools to optimize within their diverse set of needs and constraints. That’s much better.

Nirvana fallacies abound, especially when nostalgia paints over the obviously inferior parts of our personal histories. The present is taken for granted, it’s flaws drawn in sharp relief against an imagined perfect future rather than vastly inferior past. There is little to be looked back upon fondly in the formal and informal institutions of mental health. Better to have progressed an overly diagnosed and indulgent inch passed the unknowable social optimum than regress to a past where ignorance obstructed our empathy.

Disappearing people to El Salvador is bad. Unilaterally raising tariffs is unconstitutional and bad. Threatening an American city with violently imposed martial law is really bad. Unilaterally defunding USAID of their legislated resources was bad. The consistent spectacle of cruelty is a spewing sewer geyser of bad. There’s so much bad that I can’t really do it justice here. I’d call it the death of democracy by a thousand papercuts, but these feel more like slashes from raptor claws looking for each and every weakness in an ever-diminishing cage.

Enumerating what is bad and what it means if things get worse is not what I want to write about today. What I want to discuss is how we collectively comment and respond to it. Obviously there is a wide spectrum of responses that we can sift through and evaluate, but broadly there seems to be three categories.

1. This is fantastic
2. This is catastrophically bad
3. Sure, it’s bad, but it’s not that bad.

I don’t care about the first category. If you are cheering this on, well, I can’t help you. You’re either entirely detached or a person whose lens on the world allows them to enjoy personal cruelty and institutional arson. Persuading you otherwise through a blog post is way, way above my pay grade. What I’m interested in understanding, and possibly mediating, is the conversation between types 2 and 3.

Whether you identify as a “highly alarmed” 2 or a “calmly observant” 3, I want you to step back and consider the possibility that you are in 80% agreement with the alternative type, you just don’t know it.

Consider your typical policy expert. They are engaged with the same information ecoystem as everyone else, but there is a policy channel or mechanism they participate in via their expertise. They observe the general sentiment that things are bad, hearing each day about things that are specifically bad. But sometimes there is a news item that either they created (“Here’s a new bad thing I found”) or are impeccably credentialed to comment on (“You can trust me when I say this bad thing is especially bad”). They aren’t going to abstain from contributing or commenting just because other bad things are happening. And neither is anyone else who shares their vein of expertise. Further, those who aggregate or broadly comment on such things will contribute as well. The system quickly becomes oversaturated, and that oversaturation incentivizes and selects for a darker, sometimes panicked tone. There’s a collective action problem here because individuals cannot coordinate to produce an coherent message, ordinal queue, or collective tone.

That’s the primary collective action problem. The secondary problem occurs at the level of commentators who, either because of political or personal temperament, are skeptical of anything that achieves the status of conventional wisdom in the commentariat. Each time a newly weakened democratic guardrail or act of indiscriminate cruelty raises the collective tone beyond what would be a “normal” response in an unsaturated information environment, the skeptic will feel compelled to lower the temperature. This response, however, backfires because it is not engaged with by the uncoordinated collective, but rather an individual. An indvidual, often, who is the relevant expert in question, who knows exactly why it is very bad, and has no interest in the collective temperature, but rather the validity of the narrow and specific bad thing. As experts in narrow fields don’t like being told they’re wrong by non-experts, they likely see the temperature of their own language rise, making the marginal discussion of the bad thing in question more, not less, angry and concerned. The skeptic has not only made things, from their own point of view, worse, they have procured further evidence that the conventional wisdom is overly panicked, compelling them to try to tamp down that much harder on the next wave of concern.

What we are left with is an inner and outer set of collective action problems that are recursively feeding into cohorts of panicking experts fueling doomer fatalism while smug denialists reassure every frog who will listen that their cozy pots of water are not in fact getting warmer.

I’m sure it’s obvious that I’m in the camp that thinks the United States is in greater institutional danger at the moment than at any time since the Civil War. What might not be clear is that I think that the probability of an actual collapse to early 20th century authoritarianism within the next 20 years is about 2 to 4%.** Mathematically, that is a slim chance, but in terms of expected cost its terrifying. Many of you may have read my tone as an implied near inevitability (>90%), a hurricane at sea that is rapidly approaching the shore. Some of you may actually hold that belief, that the US is exactly on track to becoming a failed state, and upon seeing my estimated probability of collapse think me a denialist myself (NB: To be clear, even if the worst doesn’t come to bear there will still be terrible costs along the way). In the context of our discussion, it doesn’t actually matter whether I’m right or wrong. What matters is the failure of collective tone to actually reveal the beliefs held by the individuals that comprise it.

What are the outcomes you are concerned about? What do you think are the odds they will each come to be realized? If we want to take small steps towards improving communication and increasing the quality of collective beliefs, I think we need evolve social norms around communicating our beliefs more directly, even, yes, quantifiably. That way, when our beliefs are internalized in the information zeitgeist, they retain more of their intended meaning, regardless of the tone that emerges after a couple cycles through the collective wash.

** Yes, a 4% chance of democratic collapse within a decade is very large. Think about it this way – if 4% was anywhere near normal, the US would have probabilistically collapse to authoritarianism long ago.

In no particular order:

$50k in cash compensation is always worth more to employees than $50k in water slides and sagely advice. College football programs that don’t have as many resources tied up in highly paid assistant coaches and non-pecuniary amenities have a short term advantage in the new NIL landscape. Programs will adjust over time, but a lot of that money is locked in for the next 3-5 years.

Referee review has been a mixed bag at best, and a net negative in soccer, but baseball pitching has advanced to the point where it is no longer about beating the batter so much as fooling the umpire. It’s not the raw velocity of pitches that is overwhelming the naked eye, it’s the amount that pitches are now breaking when they cross the plate combined with catchers’ acumen at “framing” pitches with small movements of the mitt. Batters are routinely striking out without ever facing a pitch in the strikezone. #RoboUmps

The English Premiere League has long been the perfect of example of bureaucratic and “focus group” failure. I could go on at length. Watching a handful of games this weekend, it is increasingly clear that they are comfortable letting their league turn into mid 1990’s NHL hockey, with clutching and grabbing replacing skill or, counter-intuitively, even effort. There will be much hand-wringing mid season as to why so many great players are injured, why the order of the league table mostly reflects injury luck, and why teams are overly dependent scoring on boring corner kicks and randomly alotted penalties rather than teamwork and skill. Sigh.

Speaking of the Premiere League, it’s also been interesting watching a sort of resource curse play out with Manchester City two years in a row. There are certain players that are truly one of a kind that every team should want, but few can afford. There is a catch though. When you have one of a kind players there is incentive to train for strategies and tactics that only work optimally with those specific players. If those players are unavailable, a team finds itself having to choose between tactics they can no longer execute optimally or a tactics they have not trained in extensively. Last year Manchester City lost the best midfielder in the world to season long injury, a player who by himself can execute the defensive and offensive duties of what would normally be two specialist players. Playing him by himself in a “single pivot” without defensive support lets you have a numerical advantage elsewhere. Forcing a more mortal human to take on that responsibility, however, proved quite risky. This year they are trying to play without their long time goalie who was, without hyperbole, the greatest passer of the ball to ever play in goal. Watching someone else try to do a job that literally only one human being has ever been able to do has been illustrative of the perils of becoming dependent on irreplicable assets.

I’ve been thinking a lot about the loneliness of moderates/centrists/whatever you want to call them, in no small part because that’s the camp in which I place myself. While it’s (perhaps undeservably) flattering to think of yourself as “practical” and “reasonable”, it’s not a fun identity. There’s no good art to fall back on when you need to fill in the missing parts of your personality. You are constantly disappointing the more vocal members of the chattering classes while simultaneously sharing their frustration with the fire-dog-meme “This is fine” folks who don’t seem constitutionally capable of noticing when the room is in fact actively on fire. It’s a tough political identity to pin down because it is, at least ostensibly, an identity defined by it’s relation to two polar extremes. Anarchists, socialists, liberals, conservatives, they have an easier time because they can start from first principles and work upwards. As society progresses, so does the middle. To define yourself as wherever the middle stands is to be plastic, externally shaped, even inauthentic. Such a positional identity may be safe, but it’s not especially useful.

I would like to suggestion a more useful lodestone for moderates: responsibility

You have social responsibility. As a moderate I am uncomfortable with the libertarian fetishism of individualism without an obligation to others. With all due deference to “Naked and Afraid”, we are primates, and as such we are just shambling hunks of nutrition for other species if left on our own. Individuals, wholly independent of others, are completely useless. You are useless on your own. All human achievement is predicated on coordination with others. Through families, communities, and states. Through exchange, markets, and firms. You need other people, whether you like them or not. Admitting you need others is not weakness.

You have personal responsibility. As a moderate I am often uncomfortable with the type of socialism that promises relief from the obligations of toil. That your comfort and care can be assured regardless of the efforts and investments you make for yourself. There is no life without toil. There is no life without risk. The only institutions that can wholly shelter you from toil and risk demand the enslavement of others. Sure, you can be a party elite, but you’re only going to be fed and sheltered because of those toiling in the gulag. Admitting that others have an obligation to action and self-care is not cruelty.

Which is all to say that moderates should be up in arms, protesting and raging alongside progressives, liberals, democrats, and (yes) classic conservatives. Not because the current administration has strayed too far down an abstract one-dimensional range of political positions. But because their destruction, grifting, and hate are in direct opposition to everything we hold dear. They accept no responsibility for their actions while acknowledging no responsibility for the welfare of others. They are the antithesis of responsible adults.

I’m not much of a political philosopher, but maybe if I get stuck in an airport long enough I’ll hammer out my own “Theory of Responsibility”. I mean, that’s how Rawls got his magnum opus done, right?

I’m here to discuss women in the criminal justice system as part of the ongoing BRIDGE series organized by Arnold Ventures. DC remains one of my very favorite cities, one I lived in and around for decades. I arrived with some trepidation, of course, now that the federal government is attempting to “occupy” it while deploying National guard troops (“some armed”) while ICE agents execute their own specific combination of random assault sprinkled in with some light kidnapping. I wasn’t quite sure whether I should expect military vehicles on every other street or just the odd rented van with masked men claiming to be ICE agents pouring out.

What I’ve seen so far is mostly…nothing. I don’t me DC seems normal, not in the slightest. I mean the streets feel emptier. There’s far too few tourists for mid-August. There were families on the steps of the museums, but normally they’d be swarmed. I’m sure to some degree I’m layering my own sensitivies on the scene, but I really do think it is far quieter than it normally is. Than it should be.

Tonight I’m going to head to U street to visit an old friend, have a drink, catch up. I’ve done this a million times, in this exact neighborhood, for going on 20 years. That this time, with a cheap tinny authoritarian claiming to clean up crime while DC is experiencing the lowest rate of violent crime of my lifetime, that this is the only time I’ve really had any sense of insecurity, that something bad could happen around me, is some of a grossest irony I’ve ever experienced first hand.

Anyway, it’s always nice to come home, no matter how hard some are trying to take feeling away.

Economists have become almost comically skeptical of estimated effects. A researcher estimating the effect of X on Y has always had to consider the bias and efficiency of their estimator, where bias is the result of unconsidered or unobserved forces pulling your estimated effect in one particular direction away from the truth (too positive or too negative), and efficiency is the overall noisiness of the estimate, where a less efficient estimater provides too large a range of possible effect sizes.

Under the umbrella of efficiency were concerns about random measurement error – the basic and unavoidable difficulties in accurately recording the the underlying “true” value. Filed under “everywhere and always”, measurement error is often simply the cost of doing business, while nonetheless limiting the precision which the world can be known and, in turn, the precision with which decision making or policy can be calibrated.

Coping with bias has been in many ways the story of empirical economics and the “credibiilty revolution” of the last 25 years. It’s why “identitication strategy” is the fourth slide of almost any microeconomics presentation, why the econometrics of every great applied economics working paper is seemingly obsolete before it finds itself in print, and why there is a genuine possibility I will retire with a half dozen ulcers before I finish this blog post. Economists make themselves crazy thinking, strategizing, and internalizing criticism about the potential bias in their estimates. Selection bias, omitted variable bias, reverse causality, and even observer bias lurk in the shadows of our minds. To be an expert in causal inference is to anticipate and guard against myriad sources of bias in your empirical analysis. For many living economists, however, there is a new bogeyman.

Systemic measurement error.

Sounds banal enough. And if you’re a chemist, it is. The gauge is consistently measuring every temperature too high, mass too low, electromagnetic spectra too red. Something to test for every day. Vigilence and repetition, the solution. For economists, however, the answer is less simple.

What happens when the data is rigged to make the results too good? Unemployment too low. Wages too high. Expenditures too productive. <Redacted> too <redacted>. Economists have looked for cheaters as a research subject and rooted out fraud within scientific endeavor itself. But it is precious few who have made it their job to sift through manipulated public data and carefully distill the true underlying numbers. And for good reason — as soon as you declare the data unreliable, you open the door to your own personal bias. Your politics, career ambitions, or even just your good hearted desire to observe people being more decent than our own pessimissim might otherwise allow for. To allow yourself to manipulate the potentially fraudulent data is to potentially make a bad situation worse.

Replicability and transparency of analysis was important before, but now we’re entering an even more tedious and slow landscape because critics aren’t just going to want to adjudicate your analysis, they’re going to want to adjudicate every observation in your data set. Or perhaps I am being too negative. There is a genuine upside. As people look to distill and correct for systemic measurement error, they’re going to create greater demand for 1) parallel analysis of similar questions using different techniques on the same data and 2) great forensic analysis of data and the institutions that create it. Never forget that sovietology was a genuine research career. More work to be done, but it can be done.

More work that has to be done. Sigh. My stomach hurts.