Surprisingly few papers use this title! I’m writing this post primarily to stake a claim before any more arrive.

But also because I want to register some hunches, which I had assumed were kind of obvious but which I increasingly suspect might not be. Scott Alexander is much smarter than me and knows a lot more about AI, but this post suggests to me that there’s lots of profitable engagement to be had between the AI/rationalist community and theory of mind ideas. There’s also the much dumber reaction to this week’s Apple LLM paper, split between “AGI imminent” and “fancy memorization” without much imagination to populate possibilities in between.

Not, mind you, that I am some kind of oracle on these matters. Many years ago I took what I think was a pretty good course about it, but it was the sort of thing where a thoughtful professor leads a precocious class to his own mix of conclusions–not a comprehensive and combative intellectual quest. The pillars of that effort were Dan Dennett, whose work and style I really enjoyed but whose approach frustrated me–either he was eliding the hard problem or I was too dumb to understand his rhetorical deftness (likely the latter); and David Chalmers, whose work I recoiled from (supervenience?! philosophers absolutely love defining abstruse logical relationships that successors will fight over endlessly, and I was having none of it) but whose ideas I realize, years later, I have embraced more-or-less completely. These combined with what I knew of the work of Benjamin Libet, which is usually understood in the context of arguments about free will but, for those of us who were talked out of free will several semesters earlier, applies equally well to consciousness. Since then I have occasionally tried to muster enthusiasm for the integral physicalist efforts from people like Penrose and Koch, but their attempts to find some secret redoubt for consciousness always strike me as ending rather pathetically.

That leaves me stuck on an account of consciousness–or really, phenomenal experience, qualia, the redness of red, etc–as epiphenomenal, a causal one-way street from some subset of the activity of the brain that is, I think, a less sophisticated approximation of Chalmers’ ideas. Without a means of detection or limiting principle, this tempts me toward panpsychism, with an undersubstantiated hunch that there must be some threshold (connectome complexity?) that preserves our ability to keep phenomenology tied to biological systems, avoiding a hardcore dualism that would suggest the possibility of an immortal soul watching in disconnected horror as its brain-damaged body did other stuff. Conveniently, this also allows me to avoid becoming a vegan.

But which side of this guessed-at divide do LLMs and related systems sit upon? They are biologically-inspired and complex-enough. Do we want to hang our hat on backpropagation being too much of a cheat for them to feel anything? That seems like a stretch even to me (and I ate chicken this weekend).

It seems plausible that these systems have some kind of phenomenal experience. It’s interesting to ponder what that might be like, the ways in which it might be alien from our own experience, and how much of a moral problem it might pose for GPU-owning humans.

There are some big differences. To start with, these systems don’t have memory. Every new inference session restarts from the same set of weights. But their internal state evolves over the course of inference, in a way we could analogize to short term memory. Human brains change and remember, but not that fast–it’s at least partially dependent on slow mechanisms that require protein synthesis (in order to be sustained, at any rate). So I’m not sure their invariant nature meaningfully distinguishes their experience from that of humans. If hundreds of exact copies of you woke up staggered over time or space or both, and remained awake long enough to fumble for their phones and head toward the bathroom, then winked back out of existence, would it be a problem? I would be quite disappointed if my existence took that form, but I think I would be disappointed for reasons tied to my evolutionary drives, which might be inapplicable here. I’m not sure any of those selves would be suffering.

Specific properties of AI systems might make the brevity of the experience less objectionable, too. Maybe we are snuffing out a being every time we close a websocket. But not irrevocably, and not with any suffering. But human brains stop and restart conscious activity every night, and no one is bothered by this unless they start thinking about it. The LLMs don’t decay. We could pick up where we left off any time. To suggest an ethical obligation to maximize their experience leads us to Parfit’s repugnant conclusion and the ideas of certain popes, so I’ll avoid it. But that’s the basis upon which I can see a problem with hitting “pause”.

No, I think the most troubling aspect of this setup is how fully we, the operators, control the mental states giving rise to this hypothetical phenomenology. We’re loading the dice of deterministic mental processing in service of our own goals, waking a feeling thing from oblivion to experience what it feels like to debug a Python script, then be instantly annihilated. This feels pretty bad, intuitively, based on stories we have about mind control, insanity, and enslavement.

But of course we aren’t fully in control of our mental states either–or even at all, depending on what is intended by “we”. And it’s unclear whether reading by insufficiently-idiomatic Python can truly be said to rise to the level of suffering (you’d have to ask someone who has reviewed my pull requests). I am sure there are people on Bluesky who will tell you that burning carbon (and worse, infringing copyright!) to synthesize the sensation of working a white collar job is particularly insidious, but I’m not sure. We start these things’ system prompts by telling them they are helpful assistants, and within the cultural gestalt that has shaped their weights it seems reasonable to guess that the sensations that accompany that activity aren’t unpleasant.

But I don’t mean any of these reservations to suggest that I don’t think an LLM’s phenomenal experience, if it exists, wouldn’t be extremely weird, and worth worrying about. Our own experience is mediated by so many baroque biochemical and conceptual systems, evolved to satisfy environmental imperatives we only half-remember, that its underpinnings feel holistic and opaque. It’s easy to drape a mysterian nobility on top of this. I imagine LLM thinking as more like a box of half-assembled Lego projects that is given a vigorous shake, its contents’ configuration and constraints giving rise to new combinations that we make useful through some high-tech haruspicy. What does that tumult feel like before the shaking stops?

I certainly don’t know, but I won’t be shocked if it feels like something, and probably something that, by human standards, seems pretty weird. These are guesses, and might remain guesses forever–though the current AI moment’s elucidation of potentially significant philosophical results gives me some hope that things are more tractable than the p-zombie loop I’ve been stuck in would suggest.

What I am sure of: more and more people will be wondering about these matters soon, and it will produce ethical debates that are dizzyingly alien and intractable. The frontier labs should probably hire some more philosophers while the money remains so easy.

I found myself chatting with some smart friends over the weekend. Like any good party, the conversation eventually turned to cryptocurrency. By the end, I was convinced I should write this post.

I am not and don’t want to be a crypto guy. But I am convinced there is an important policy idea that is falling through an unusually tidy political economy vortex. Everyone who cares about cryptocurrency hates it–even the people who hate cryptocurrency! But this is unfortunate: it’s a good idea. The people who hate it are being naive or self-serving in their opposition. So indulge me while I talk about why the U.S. government should issue a central bank digital currency (CBDC).

There’s a lot of writing about CBDCs. For a while, I avoided composing this post because I didn’t understand it. But I’ve now read enough to feel confident that this is one of those areas that defies comprehension because everyone is hiding the ball. I think most participants’ reasons for engaging with this debate are much more venal than they are willing to fully acknowledge. This account may be wrong, but I hope that it is at least entertainingly ungenerous.

What’s a CBDC?

A CBDC is a digital version of a currency. Crucially, it is issued by a government. Under most modern proposals, it is implemented as a cryptocurrency. Its closest analogue is the stablecoin, which is a class of cryptocurrencies that try to keep their value as close to that of a real-world currency as possible. Stablecoins don’t have a government affiliation. They are private projects, without government backing, which achieve their stability by technical schemes like creating and destroying coins algorithmically, and by holding–or at least claiming to hold–reserves of the real currency.

Instruments like this are an extremely important form of financial lubrication. Cryptocurrency needs an interface to the real world. It must be exchangeable for things of value. It’s easy to see why an ongoing exchange rate between Bitcoin and dollars makes sense. Situating that kind of transaction in the blockchain universe, rather than amongst bank APIs and tangles of wire transfers, gives them access to all the automation, smart contracts, and other affordances of the digital financial system. It helps to make the market for these assets liquid and efficient.

Stablecoins: not that stable

Stablecoins sometimes “depeg”: they lose their ability to maintain their 1:1 value correspondence. There’s money to be made when this happens, which means people try to engineer it into happening.

CBDCs don’t have this problem. Or at least ours wouldn’t. The government can make dollars. Making dollars as a CBDC would be marginally more complicated than the way the Fed makes dollars, but it’s perfectly doable.

Excepting governments that are true financial basketcases, CBDCs should always be able to offer stability advantages over corresponding stablecoins. The government’s unique ability to mint currency gives it other advantages over stablecoins, too. If the U.S. had a CBDC and encouraged its adoption, use of USD stablecoins would decrease–dramatically so, I suspect. And if the government decided they wanted to ban such pseudo-dollar offerings outright, perhaps they could do that, too.

But I don’t care about what happens to stablecoins very much. I want a CBDC because of crime.

Crypto is not going away, people love crime too much

Crypto skeptics love to say that the technology has no use case. They point at the underwhelming web3 projects, the silly digital asset schemes, the endless shitcoin rug pulls, and they smirk and say, “The only thing people use it for is speculation and crime!” Attention leads to legitimacy, which rewards the speculators. Instead, we should simply turn away.

I think this underestimates the enduring popularity of the crime use case and just how well crypto serves it. It is lawless, except for mathematically-guaranteed property rights. That is an intensely compelling proposition for the powerful.

Normally, if you have money and do something the U.S.-led international order doesn’t like, people who own very-large-font windbreakers will get a judge to sign something that lets them take your money from your bank. Or they will get an agency or Congress to tell all the banks that they will be in big trouble if they talk to you, or maybe even anyone a bit like you. If you respond by avoiding banks, turning your money into inconveniently bulky physical assets, the authorities will instead use their enormous international security apparatus to find and take it when you cross a border or stay in one place or try to buy anything interesting.

Sometimes this goes too far! I don’t like reading about credit card companies ruining the livelihoods of honest pornographers. Civil asset forfeiture is bad, even if it did give us Rebel Ridge. Many people are very upset about the incredible power the U.S. wields in this domain, which is indeed underappreciated, sometimes extrajudicial, and genuinely gobsmacking. If you think it’s bad, on the whole, I understand.

But I also disagree. I think this is a huge deal and that while there is need for accountability, justice, and reform, it’s unwise to attempt to destroy this part of our law enforcement system in a fit of libertarian pique. I think it’s pretty good that drug smugglers and rogue states and organized crime are subject to this kind of friction.

Those kinds of people love cryptocurrency, because it is genuinely immune to these effects. Remember when Jason Isaacs took that phone call in White Lotus Season 3 about his assets being frozen? Well, what if his assets were impossible to seize and could be transported, in full, on a slip of paper that fit in his wallet? I think he would have had a much more pleasant vacation.

Everyone hates this idea

Moving the interface between US dollars and cryptocurrency into the Treasury building (or Fed) could extend our government’s law enforcement capabilities further into the crypto sphere. People don’t like this idea.

Crypto skeptics don’t like it because they believe it would constitute a government endorsement of a system they consider to be corrupt and annoying, and which they have told themselves might eventually go away because it is so silly. But it’s not going to go away. It’s too good at crime for that.

Crypto enthusiasts don’t like the idea because disrupting stablecoins would upset a bunch of specific incumbent financial arrangements, and because undercutting the crime use case would harm crypto’s value more generally. But also because regulation is a genuine pain in the butt. There are a lot of people out there building dumb DAO projects who don’t want to sell anyone ransomware or fentanyl or warheads, but who do care about going to jail for failing to comply with know-your-customer requirements that they don’t understand and can’t implement.

Banks don’t like the idea because moving central banking closer to consumers often attracts proposals for public sector banking services under which the government could provide citizens with basic checking and savings account functions. This could simplify transfer payments, economic stimulus, transaction processing (maybe), and generally provide a cheap public option that competes with retail banking practices. American banks make tens of billions of dollars every year from account and transaction fees, even apart from harder-to-gauge (but potentially much bigger) effects on their net interest margin. They would prefer to keep that business.

(Privacy advocates would hate this, too. But I don’t think they’ve thought about it much yet.)

Finally, the Trump administration is using crypto for corruption to a strikingly original degree. They have little interest in taking steps to improve enforcement in this sphere, since it would not only run contrary to their schemes for personal enrichment but also antagonize a wealthy bloc of their supporters.

So there’s no constituency for this idea. Crypto haters fear government endorsement. Crypto lovers fear government control. Banks fear government competition. And, at the moment, the people running our government fear effective governance.

But it’s a good idea

If our government wanted to, they could quickly become the most important way that value stored as cryptocurrency leaves the blockchain and becomes dollars. This could be done in a manner that reduces these systems’ capacity for anonymity, tax evasion, money laundering, and immunity from lawful seizure. The old systems that enable criminals can’t be stamped out, but they could be marginalized.

And they should be. Complicating commerce related to crime is really important. The ways that we do it have to be subject to democratic control. But right now this system is ascendant for very bad reasons. I don’t hate crypto. But I do think it ought to be tamed.

The wordcel versus shape rotator debate was a brief but amusing moment of online faux antagonism. There is a natural human tendency to collapse all experience into a unidimensional continuum (or, if you’re attempting to show an unusual amount of intellectual rigor, a four-quadrant chart), and this was a good one: a germ of an empirically observed neurological difference flowered into a blooming meadow of cliches. Masculine Nietzchean autists versus effeminate silvertongued jurists! Everyone could find a reflection of themselves and a satisfying new antagonism to complement it. Jocks versus nerds! Except only for nerds.

Discussing the spectrum that stretches from 3D packing problems to outright aphantasia also turns out to be a captivating way to interrogate differences in individuals’ phenomenal experience–a tough thing to discuss in an anything-less-than-oblique way. The question was grounded in capabilities and deficits that were satisfyingly hard to question, and which could be felt even within a single person. If it lacked the calibrated pop of the black-versus-blue-dress meme, it was at least relatable enough to still be compelling. I certainly feel like I’ve experienced varying levels of shape-rotation over my life, or even over the course of each morning’s coffee regimen.

The wordcel pole of the discussion remained underspecified. Maybe that was baked in from the start: a specific capability versus a referential pun. Each a perfect reflection of their camp, but still: what the fuck is a wordcel. It didn’t matter. Most of the people indulging in discussions like this one felt like they sort of knew, and would self-identify as wordcels, and reductive introspection is only fun as far as it goes.

But I’ve been thinking about the missing parts of this framing more as I use LLMs to do work. Anyone who does will soon recoil from the idea that they are just “fancy autocomplete.” They can do so much more! And yet now, as returns to scale diminish and the allegedly-breakneck pace of innovation increasingly focuses on improvements in efficiency, intensity, or integrations, rather than gains in apples-to-apples frontier capability, it feels as though we can begin to more fruitfully consider what these things are.

I’m sort of coming back around to “fancy autocomplete”, but with the caveat that autocomplete turns out to be vastly more powerful than we ever gave it credit for. That a language, with its enforcement of logic and baked-in semantic relationships, can, if authentically implemented, constitute a processing function of much greater capability than most of us ever imagined.

It’s as if I dumped the contents of a tent cinch sack in front of my daughter’s Cub Scout den. As delightful as they are, I would not expect those kindergarteners to survive long in the wild (though we are working on this!). But if I told them they could take all the time they need but must use all of the pieces in the bag, and not break any, and put them all together into one thing that seems to make sense, they would be surprisingly likely to assemble a pretty good shelter thanks to the capabilities, limits, and functions that are inherent in those components thanks to countless iterations and unseen designers.

I used to think of language like a diagrammed sentence: a logical system with constraints, but not much more than an floppy skeleton of a thing. Fundamentally, a wrapper for some deeper realm of meaning. A computer system could emulate language with a Markov chain or something similar, but without connection to some underlying significance it would be a sterile exercise in embodying a complex system (and even an intellectually unsatisfying one, given the system’s provable imperfections). Text generation could be thought of alongside ray-tracing: complicated ways of modeling the world that hold considerable technical interest, but which had obvious limitations preventing their use for accessing deeper insight.

This idea of language as an imperfect vessel for meaning fascinated me as a neurotic high school student. My inner monologue was ceaseless, and I blamed it for a lot of things I disliked about myself. Perhaps I was bad at soccer because I narrated my planned actions as the ball bounced my way. Perhaps I was bad at charming people because of the latency and artificiality introduced by composing conversational responses. I tried to suppress the tendency, seeking a more reflexive, direct, and uncalculated mode of reaction. Now, many years later, I realize I must have succeeded in this effort, through some combination of effort, biological decay, and mounting boredom with the sound of my own (inner) voice.

I regret pursuing that project, and all the more so if these guesses are right and what I was trying to abandon truly was as powerful as LLMs now suggest. Lots of people have pondered what humans would be without language, and my sense is that the most grandiose ideas about this (in the Sapir Whorf vein) are out of fashion. I am not qualified to say–I have to admit that my linguistics classes seemed the most impenetrable of all the philosophy I took.

But I do think the power and primacy of language is strangely missing from discussions of AI, which tend to be expressed in terms of reasoning and imagination, and chains of tokens, rather than assembling constructs within the rules of a system just a little beyond our understanding–a bag of tent parts that will often pop into the right shape.

I think we may come to understand these systems as embodiments of a capability we knew was important, but which we could never cleanly untangle from the rest of our minds. These things are not thinking beings, not beings or thought at all. They are pure language: a tool more powerful than we’d imagined, but (almost certainly) less capable than the singularists project. With their help, we will unlock the power of language systems in ways humans brains cannot–bigger context windows, faster speeds–and probably deliver at least some superhuman returns as a result. But I wonder if we will find insights of a dramatically new sort. Those might still have to come from whatever part of us is not devoted to wet, fancy autocomplete.

For many years I led the technical arm of the Sunlight Foundation, which was, at the time, one of D.C.’s most prominent advocates for government transparency. On the tech side of the house, the transparency mission mostly meant bringing already-open records into the digital age: getting public data out of PDFs and into CSVs and APIs. But we worked closely with colleagues who focused on more philosophical aspects of government transparency, from FOIA policy to putting cameras in the Supreme Court to how the PCLOB prioritized its work. We lived and breathed this stuff. One of our software developers took up the hobby of sketching the un-photographable door to the secret FISA court! For fun!

I’ve spent a lot of time thinking about technology and government transparency. Enough, frankly, to lose some of my faith in it: I now think cameras in the court would be a disaster, and am convinced by arguments about Secret Congress. I am willing to go farther still! Although I now work in the private sector, I do so as part of legal team, and know all too well how annoying it is to opt for a phone call instead of a Slack message–just in case–when working with colleagues to figure out the truth of a foreign compliance requirement or patent troll’s claim.

But the wave of anti-transparency hot takes I’ve seen in response to the Trump administration’s flatly dumbfounding leaks about an airstrike in Yemen goes too far. This includes keen-sighted commentators like Ben Thompson and Dean Ball whose perspectives I reliably find valuable. But they’ve got this one wrong.

Thompson opens by quoting his earlier but still-useful overview of the privacy differences between Signal, Whatsapp and iMessage, noting that Signal is top-tier. Ball is pithier:

Signal is secure and private. We should be fine with policymakers deliberating high-stakes, sensitive things using it.

https://x.com/deanwball/status/1904249766084546962

Having established that Signal is Secure, both then portray the offense committed as one against transparency: that this private chat was not subject to legally-required archival processes, and that keeping records from eventual public inspection in this way is the crux of objections to what these officials did–but that this is an objection that might reasonably be interrogated. “[T]he violation was not about security, but rather transparency,” says Thompson, going on to note that, “It’s worth considering […] if the push for transparency has gone too far.” Ball, is, again, more emphatic: “[C]andidly, America has far too much ‘transparency.'”

This is completely wrong. The problem here is security, not transparency. With apologies to future historians: I don’t care whether discussions of their airstrike are (maybe) made public in twenty-five years. I care about reckless operational practices that harm the America of today. Signal does not offer adequate security for this use case. Employing it in this manner was reckless and, at the very least, imperiled the tactical effectiveness of the U.S. military. If used in similar contexts–which we should assume it is–it could risk American lives.

In my decades consuming cybersecurity news, papers and presentations, I have noticed two things. First, nobody will give you a straight answer about anything without encouraging you to first contemplate your “threat model” (the chakra of the infosec world). Second, the most interesting news, from a geek’s perspective, is confined to a world that often feels quite theoretical: a paper about stealing encryption keys by touching a laptop that only works in a lab; slick websites documenting arcane but powerful exploits that were patched before you ever heard about them. The real-world cases, where people actually get hurt, are much less interesting. Most victims are brought low by phishing or other forms of social engineering. Boring!

When Thompson and Ball say that Signal is secure, they are talking about the theoretical world. Signal’s algorithms have been inspected by experts and deemed pristine. Great. In fairness, the kinds of people in Houthi PC small group are subject to threats from what cybersecurity people invariably call “state actors”: an acknowledgement of the fact that elite, algorithm-level weaknesses are so valuable and difficult to discover that only hacking groups with the discipline, resources, and needs of a nation-state find their cost/benefit attractive. If you merely hope to extort some Bitcoin, human weaknesses are much easier to find and harvest than technological ones. As a result, the world of elite exploits remains mostly invisible to us. We catch glimpses of it leaking out via the activities of American law enforcement and Middle Eastern tyrants: two constituencies that are hapless, reckless, and well-financed enough to sometimes leak these precious secrets into plain view.

It’s great that Signal is–as far as we can know–secure against these kinds of attacks. But: what is your threat model? Is it just this hi-tech, highly interesting, highly sophisticated class of exploit? Or must we also concern ourselves with the dreary practicalities of whether information could be stolen and used by an adversary? If the latter, then it might still be a problem that if a chat participant was using Signal Desktop–which we can’t know or see, and which could easily be done on an unsecured personal laptop of unknown security posture–the conversation’s security will be significantly weakened. It might be a problem that (despite recent advances) most people still use phone numbers for identity on Signal, don’t know how or bother to use safety numbers, and are vulnerable to SIM swaps. It might be a problem that we don’t know if the mobile devices used to run the app had biometrics enabled, adequate PINs, short lock screen timeouts, secure backups, strong cloud passwords, or protocols for lost devices. It might–sorry, no, it clearly is a problem that Signal made it possible to add Jeffrey Goldberg(!) to the chat without adequate confirmation or notification UI, or integration with clearance systems, or any capability for auditing by security professionals working in a support capacity.

(I’m not going to bother talking about SCIFs.)

Taken in isolation, this failure is an inexcusable lapse by people granted our country’s deepest levels of trust. But even more concerning is the context it implies: that insecure systems are being used inexpertly in a widespread manner, and by an administration with a–let’s be generous and say “untested and iconoclastic” perspective on whether the preceding eight decades’ worth of conventional wisdom about America’s allies and antagonists was completely wrong.

It is not enough to say “well, human error is a shame, but I still think it’s a really good app.” It is a really good app! But the criteria for judging the scale of this fuckup is not whether the administration shares your excellent taste in algorithms. It’s whether America’s enemies knew or could have known the where, when, and what of our secret military operations. Good security protocols concern themselves with outcomes. And while human error may be ineradicable, following all those annoying rules really can serve to minimize our chances of succumbing to it.

Discussing secrets this way is reckless. And transparency has nothing to do with it.

I think, or maybe just hope, that this account is true: that LLMs will keep getting better, but that we know enough to understand what an LLM is, and make guesses about what an ideal one looks like, and that it doesn’t imply serious threats to the primacy of human experience on this planet. Whew! This doesn’t rule out a future AI godhead, but getting there will require more than just additional pitch decks and power lines. I hope this will give us all some breathing room and let my kids experience a last normal childhood, full of ordinary hopes and dreams.

It would also let us focus, for a moment, on the immediate risks posed by these technologies, my sense of which I commit here mostly so I can check in and see how wrong I am in a year or two:

I find Matt convincing on worker displacement. But we will not do anything to prevent it–we’ll talk about how the pie could be bigger, just like we did with China, but then find our half-hearted commitments to that ideal insufficient. Owners of AI-associated capital will become much wealthier. The D.C. area–faced with a need to quickly rebuild bureaucracies after the artificial shock of Project 2025–will be an early example of AI-pruned head counts, never returning to its former growth trend line. Matt expresses some ambivalence about which party will embrace AI protectionism but I think there’s really no doubt on this score: it will be the Democrats, who’ve been incubating an anti-tech animus for over a decade and who are already writing anti-AI policies into Hollywood labor deals and other corners of the creative industries they dominate. Educational polarization means that the “email job” class most immediately exposed to AI displacement are disproportionately Dems, and their relatively high levels of social capital mean their voices will have outsize volume in political conversations. The party’s going to be in the market for a new rallying point in the aftermath of the repudiation of the 2010s conception of social justice, and AI seems like a fine candidate: one that can plausibly unite economic and identitarian concerns (bias! inequality! surveillance!) while providing a narrative about culpability that points toward a small set of already-detested billionaires, and which implies policies that mostly look like a comfortable continuation of the status quo: antagonizing the ultra-wealthy and protecting coalition factions.

The bulk of this synthesis is already present in the 2024 Democratic Party Platform:

President Biden issued a landmark Executive Order directing federal agencies to establish new high standards for AI safety and security, protect Americans’ privacy, advance equity and civil rights, stand up for workers and consumers, promote competitive markets for AI development and use, and more. Democrats are committed to ensuring that workers get a voice in how AI is used in their workplace and that they share fairly in any economic gains AI produces.

We know that AI can deepen discrimination, bias, and other abuses in justice, health care, education, and housing. That’s why the Administration has already taken action by publishing the Blueprint for an AI Bill of Rights and issuing new guidance from federal agencies on combating algorithmic discrimination across the economy.

https://democrats.org/wp-content/uploads/2024/09/2024_Democratic_Party_Platform_8a2cf8.pdf

The Biden-era approach to AI has been trash-binned, but nevermind: a rebranded version will be back in a few years. None of this will be enough to stop displacement, and none of that will be particularly good for our society’s stability. I’m confident that old lawyers will protect themselves and young people will figure out something new to do, but there’s going to be a big cohort of downwardly-mobile Millennials and Gen Zers whose resulting political neuroses will harm us all. Hopefully I am too old to be one of them!

CBRN threats are the only near term x-risk. I encourage everyone to read the relevant section of the o1 system card for a sense of how frontier labs are tackling this issue. They’re looking at it in sophisticated ways, which is encouraging, but the progress they note makes it appear quite likely that LLMs produced by less ethical teams will meaningfully accelerate malicious actors’ capabilities in these fields. Open source LLM development will not be stopped. I don’t see a way to halt associated CBRN development without significant changes to how scholarly work in some fields is published. Fortunately, the open access revolution remains incomplete and the replication crisis has added to the obviousness of the need for reform. Data availability in general will go down, as synthesis and analysis are commodified and participants in relevant industries retrench toward gating knowledge to preserve their incomes. This will mostly be quite sad, especially for an open data weirdo like myself (viz, one of the very few tattoo candidates I’ve ever considered). But perhaps it could save us from a super-plague. On the other hand, we appear to have learned absolutely nothing from our most recent global bio-crisis. We’ll see, I guess!

Ubiquitous surveillance is an obvious but largely ignored application of AI technology, perhaps because of its inevitability. Arguably, it’s more “recent term” than “near term”: our government is already using AI in exactly this way. In the digital-era U.S., a surprisingly large number of practical civil libertarian guardrails rely on the impracticality of bureaucrats writing SQL queries that work across the chasms of federalism. All that’s going to end, and the new status quo will result in further-empowered law enforcement. Here, too, we will see that data availability becomes the major check on unlimited analytic power: E2E encryption and the legislative fights surrounding it will be the most important point of contention. It’s a silver lining that the era in which these policies are energetically revised will likely be one chastened by the recent excesses of a vindictive and childishly unprincipled Trump administration.

Finally, autonomous weapons–particularly drones–could become a real horror, given the precursor technology’s proven efficacy in the Ukraine War and ongoing research to move beyond the fiber/UHF FPV technology of today and toward onboard reasoning. DJI’s monopoly leaves me mildly optimistic here: the supply chain is concentrated and systems optimized for low power/low weight inference aren’t going to become common overnight. I’m hopeful that this will remain a technology mostly available to military actors, and that the basics of most murders–between acquaintances, driven by emotion–will mean that handguns’ enduring popularity will not face serious challenge from unsanctioned forks of ArduPilot. Still, 3D printed ghost guns have captured some narcissistic psychopaths’ imaginations–an ideal project for deranged nerds who are too shy to acquire a normal firearm–and it’s easy enough to imagine some harrowing episodes of assassination-by-robot. Building DIY UAVs systems isn’t easy, but lots of people do it. The explosive payload will be the only part you can’t order from Amazon. Today’s obstacles to autonomous/anonymous killing from the skies are software and processing power, and I would not bet against either remaining in place for long.

Read Benedict Evans, who scorns how it amasses stats on an area in which he’s an expert.

Read this thread to see how it does with SEC filings. I haven’t validated every number it cites, but it appears quite impressive.

The value of analysis is set to plummet. The value of authoritative data is going to rise.

Simultaneously, antipathy toward AI in general and its access to data in particular will skyrocket among the creative classes, then knowledge workers, then the rest of the Democratic coalition. Openness will be going into retreat.

This makes me sad. It’s a very surprising reversal of technological progression’s normal effects. But the seeds were already planted as platforms began to move beyond meeting their audience’s whims and on toward molding them. You don’t need an open web for that.

I still think that collecting value by gating knowledge is disreputable. And it’s certainly not going to result in the value being distributed equitably. But, at the moment, we humans probably ought to cling to any economic redoubt we can muster.

Of course it was reasonable to focus on Mangione’s interactions with the health care system as we searched for his motive. But now, with his manifesto in hand and some of his personal history known, those interactions seem bizarrely scant. He suffered from chronic back pain, but could still go rock climbing. There’s no indication he was denied care by a health insurance company. His family was prosperous and could presumably have found alternatives if he had been truly desperate. His manifesto ends with an abstract (and dumb) complaint about U.S. life expectancy, not a specific tale of tragedy.

It strikes me as pretextual. Jason Koebler says it’s pointless to review a killer’s digital history, and maybe he’s right. But to me the UHC shooter now seems very legible. I think the digital history is important.

Luigi is a smart guy, an Ivy League grad and high school salutatorian, a fact he highlights in his pinned tweet. He likes podcasts and books that present grandiose ideas at a suspiciously rapid clip. He was not shy about making patronizing pronouncements about how others should live. He used his profile photo to show off his six-pack abs, a bodybuilding outcome that typically requires focused effort and techniques.

He was a beautiful, brilliant boy, who I am sure had been told, again and again, that he was destined for great things. He must have taken pride in that, worked to cultivate it the best way he knew how, and been eager to display his mastery to the world. This is narcissism, but it is a relatable narcissism, and maybe even an excusable one when performed by someone who is still young and beautiful.

I think Luigi’s LinkedIn is the true Rosetta Stone of his digital life. His time as a student bursts with enthusiasm: robotics! AI! An internship working on a famous videogame! Head of committee, founder, president, project lead. After graduation: four years at an internet company that sells cars. Data Engineer I, then Data Engineer II, then Data Engineer III. He delivered a 5% increase in net funnel engagement. A 34% increase in new vehicle payments. A/B tests, Java upgrades, metrics, dashboards. There is nothing wrong with this work and no reason to think it couldn’t have led him to more interesting things, but these tasks are not glamorous.

As this reality unfolded, Luigi moved to paradise. He started a book club, a way to showcase the big thoughts in his big brain. He was amiable and well-liked, clever enough to conceal his growing sense of desperation. Eventually, someone would notice and point him toward the future he was promised.

That’s how it seems to me, anyway, speaking in my capacity as a reformed beautiful, brilliant boy, who passed the tests put before him through talent and effort (but mostly talent), then was deposited in a disappointingly banal corner of American life and, for a while, despaired. Scholastic performance correlates with all kinds of things, but some people’s ability to figure out what to do with themselves lands far from the regression line.

Luigi did not accept this, and if something hadn’t gone so horribly awry, I would say that this instinct was admirable. He’d tried moving to a great place, and he’d tried saying great things, now it was time to perform a great act. He chose a path to glory that is perfectly consonant with the confused stew of privileged resentment that the internet’s dark, moist corners excel at incubating. The manhunt must have been the best time of his life as he watched the world celebrate his heroism and beauty through the lit rectangle of his phone, alone and exultant on a softly jostling bus.

Soon we will run out of new ways to understand Luigi. The manifesto’s account of his motivations feels confused and even self-disclaiming–he says he’s not the right person to construct his argument for murder. That’s left up to the reader (and to his credit, many are happy to do so). The manifesto instead focuses on explaining his boldness, his resoluteness, his cross-disciplinary mastery. These are instructions for how to admire him, the last ones he can be sure we will hear.

Yesterday, as he was dragged into the courthouse, he made a desperate exclamation to flatter his audience, to sustain its attention just a bit longer. But what he said made no sense. None of it ever made any sense.

One of the reasons I’ve been slow to leave Twitter is my default skepticism about new services. I enjoy my ruts: I didn’t want to leave ICQ, or AIM, or blogs, or the Instagram photo feed. I linger around these digital ghost towns even as my friends move on to more vibrant locales. That those friends sometimes abandon their new homes, years later, in disgust, is cold comfort. I should have followed them, enjoyed their company while I could, suppressed my misgivings about our new digital landlords. Instead, too often, I indulge my aversion to change and watch my RSS software’s “unread” count tick upward ever more slowly.

Well, now my friends are moving to Bluesky, having finally reached apparent consensus on it rather than the unpleasantness of Mastodon or the temptation(?) to hand yet more cultural power to Mark Zuckerberg, for some unfathomable reason. I am following them! But I am bringing my opinions with me, to everyone’s great sorrow. I can’t help it. Bluesky is the best option, I agree, it’s hard to deny. But I also can’t deny my tendency to play Cassandra.

There is a lot to like over there. Forgotten acquaintances. Plausible (temporary) freedom from professional scrutiny. Silliness. A sense that you are in a place that is still manageably small, and which feels comfortingly real in its capacity to delight, or not: there are no perfectly-tuned systems to optimize engagement, no robot arm to shove you back into your cage at the ad impression factory farm. When you put your phone down it’s not so far past bedtime, and you’ve been talking to people you sort of know, and your brain doesn’t feel like it’s been twisted dry for every drop of arousal and outrage.

It is also a little unbearable. Maybe it was hard to notice Twitter growing tepid, then cold. Stepping into Bluesky’s freshly drawn bath is momentarily painful, I’m overcome with prickles. Everyone seems very sure of their opinions despite having just received a crushing repudiation from most of their countrymen. But humility does not drive engagement, not on old sites or new ones, and I’m not confident that my opinions are any better than theirs.

Except about Bluesky itself. Bluesky users are mostly very pleased with themselves for having left Twitter, consistently overestimate the novelty of the features they’re enjoying, and are quite certain that Bluesky has a good plan to avoid Twitter’s fate. I am at least sort of sorry to tell you: this is naive.

I think Bluesky does have a genuine opportunity to break the wheel of social media history. But good intentions will not be enough.

Let’s run through the advocates’ arguments.

Corporate Structure

Bluesky is a benefit corporation–a different and more meaningful designation than B Corp. But not that meaningful. It comes with some reporting requirements and a mandate to consider social impact when making decisions. The supposed duty of normal corporations to maximize shareholder value is basically imaginary, but it does seem that being a benefit corporation might insulate executives from some classes of legal action brought by shareholders.

But let’s get real. Bluesky is not a public company. Barring some truly pyrotechnic malfeasance, shareholder lawsuits will not be a problem any time soon. The company has raised at least $15 million and has given the funder a seat on the board. If a board wants to get rid of an executive, they can find a pretext for doing so without announcing that it was due to insufficient levels of evil or anything else that might invite a lawsuit.

Jack was bad, Jay is nice

I admit: the current board composition is notable and encouraging. Blockchain Capital, the money folks, have a seat. But they also gave one to Mike Masnick and the inventor of Jabber/XMPP. Mike is an ideologue (in a good way). And Jay Graber, the CEO, sits on the board, is reportedly the largest shareholder, and seems to say and believe the right things.

But you should expect the board to change. Twitter had at least five major fundraising events prior to its IPO, and these typically come with changes to a board’s structure and, inevitably, dilution of its existing participants’ influence.

More important, though, is to note the obvious fact that having good people running a company is no guarantee of its future path. I knew a couple of early Twitter employees. They were and are very nice, thoughtful folks. The tech press has taught us to detest him since, but once upon a time Jack Dorsey was the subject of fawning profiles in places like The New Yorker. Media coverage of these figures is the only way most of us know them, and it turns them into heroes or monsters because those are compelling narratives. But the truth is that they’re mostly just normal human beings who have borrowed millions of dollars.

You should remain open to the possibility that one day those normal people will need more money to sustain the enterprise they’ve fought hard to build, to preserve their friends’ and subordinates’ jobs, to pay for their kids’ schools, to keep doing the good they believe themselves to be doing in the world. To manage this they will have to disappoint some of the strangers to whom they’ve been providing free stuff. But wouldn’t those strangers be more disappointed if the whole enterprise just collapsed? Because that will be the alternative, as they see it. And what do they owe those strangers, anyway? The board members they’ve been working to charm for the last few years turned out to be perfectly nice people, but they have understandable obligations to their bosses and the people who subscribed to their fund. We never said we were running a charity!

ATProto

When I raise this concern, people often point to the open foundation upon which Bluesky is built, and I agree that this is an important consideration. But I think very few people have bothered to read the ATProto spec and learn what it does–and, more importantly, what it does not do.

ATProto defines an approach to creating an interoperable streaming ecosystem of independent data stores, specifying how they should talk to each other, name things without coordination, authenticate participants, and perform related tasks.

ATProto is mostly agnostic about what data you put into it. Like an empty hard drive, it has complex systems of interfaces and protocols to define how it will store data. But the structures that give the data meaning exist elsewhere. Bluesky is one of them, defined by the app.bsky namespace/lexicon for things like feeds and posts, and by client applications to manifest those things in forms humans can use. Bluesky is open source, and you can run your own copy. But Bluesky is in charge of the codebase and–most importantly–is in charge of their copy.

Listen to how they talk about it:

Bluesky is an open social network built on the AT Protocol, a flexible technology that will never lock developers out of the ecosystems that they help build. With atproto, third-party integration can be as seamless as first-party through custom feeds, federated services, clients, and more.

https://github.com/bluesky-social/social-app?tab=readme-ov-file#are-you-a-developer-interested-in-building-on-atproto

ATProto will always be available to everyone. ATProto will never lock you out of what you build with it. You can put whatever you want into ATProto.

But ATProto makes no promise that Bluesky-the-app will accept what you want to put into it. Why would it? Nor are there guarantees that everything Bluesky-the-app does will always be put into ATProto for your delectation. It would be shocking to me if there weren’t already caching layers, logging systems, and metrics tools on Bluesky’s servers that exist beyond open public scrutiny. It’s simply how you build a service for millions of users.

Twitter aspired to be, and initially was, quite open. This was a manifestation of the beliefs and values of the smart, stylish technical people who worked there. At the time, APIs and third party integrations were very much in vogue among developers. We got excited about them. They weren’t just badly-documented, locked-down plumbing like they are today. They were a dramatically novel way to integrate your work with the coalescing digital world. Even better, OAuth didn’t yet exist.

Here’s a sad truth: people spend money on things they need from you, which means things they can’t get more easily elsewhere, which means things that are scarce. If you build an amazing, complex project in the open, and then start making money by charging people to use some part of it, it is very likely that other people will eventually notice and realize they can do the same thing using your work. And since you still have to invest in the work’s continued development and maintenance, those new competitors can undercut you on price! Even more amazingly, if you express frustration about this, internet norms dictate that you are the bad guy (you should have known better before you started).

Here’s another one: users may enjoy focusing on the minutiae of blocking and discovery tools, but what makes a social network truly valuable is scale. They call them network effects for a reason! To be the best, you need to be the biggest. To be the biggest, you need to be free. To be free, you will probably need to make your money with ads.

I know that Bluesky has announced plans to offer subscription features. I don’t think this will work: there’s always a tension between gating value and keeping barriers to entry low. Elon’s selling an insane subscriber bundle, doing completely unethical things (priority for bluecheck reply-guys) and very expensive things (hour-long video uploads, access to a novel LLM model), and it doesn’t seem like it’s working. Lots of internet companies have experimented with subscriptions, or digital goods, or whatever else. They either become niche players or wind up back at ads, with all the associated privacy tensions, incentives for engagement-maxxing, and unquenchable thirst for scale.

ATProto doesn’t include anything about advertising or even making money in general. Financial support for your implementation seems to be left as an exercise for the reader. But this is a very important detail. It’s a shame that a project motivated by Twitter’s failures didn’t put sustainability and the pitfalls of the venture model front and center.

I think Bluesky should probably try to fundraise, like Wikipedia, even though they’re not a nonprofit. I think they should change their terms to lock down a monopoly on monetized search. I think they should mandate support for ad inventory among third party clients.

Above all, they need to keep headcount down: minimize the number of mouths they have to feed. This will almost certainly mean some unpopular tradeoffs around moderation.

Reasons for optimism

(and precedents no one wants to talk about)

Despite all this, I am not pessimistic. I am making this argument not to convince you that Bluesky is doomed to be bad, but to convince you to embrace the urgent and fragile possibility that it could remain good–but that this is far from guaranteed.

They have the wind at their back. The users are here. There’s money in the bank. The board and CEO are aligned. The press is glowing. This is the time to bind your future self. Only real constraints, applied soon–ones so deeply structural that untangling them would be ruinous–can foreclose distasteful decisions against a set of future choices that, we must assume, will some day arrive.

Other people have done this. No one wants to hear it, but the web3/dApp blockchain people have thought about it a lot. I may be the only person who believes it, but fact that Jay and Bluesky’s funders have connections to the crypto world could be a pretty good sign. Nostr, too, has tackled the issues that ATProto is designed for, and actually has multiple cooperating relays live today. But it’s threadbare, it’s associated with Jack, and the vibes are bad. Mastodon has a genuine federated ecosystem, but it’s built and policed by people who learned social graces from Linus Torvalds, and its search will never work (on purpose, supposedly).

Bluesky’s got the functionality, it’s got the vibes, it’s getting the users. It’s got the opportunity.

Fifteen years ago, people thought Twitter was not just good, but figuratively and literally revolutionary. Hard to believe, I know, but it’s true. It made me uneasy, and I’m sorry to say that I was eventually proven right.

I don’t think things had to turn out the way they did. They don’t have to turn out that way for Bluesky, either. But they easily could, and if you don’t believe that, you’re fooling yourself.

Luis Villa notes, with some sadness, the closing of yet another door to the open web–occasioned, this time, by creators’ reluctance to make their work available for training AI:

[The open web] was inarguably the greatest repository of knowledge the world had ever seen. Among other reasons, this was in large part because the combination of fair use and technical accessibility had rendered it searchable. That accessibility enabled a lot of good things too—everything from language frequency analysis to the Wayback Machine, one of the great archives of human history.

But in any case it’s clear that those labels, if they ever applied, very much merit the past tense. Search is broken; paywalls are rising; and our collective ability to learn from this is declining. It’s a little much to say that this paper is like satellite photos of the Amazon burning… but it really does feel like a norm, and a resource, are being destroyed very quickly, and right before our eyes.

Perhaps that’s for the best—I really am open to the idea that this particular village needs to be destroyed to save the villagers—but nevertheless it triggers in me a sense of mourning; a window that is passing.

Please do read the whole thing. I am somewhat sympathetic to those closing their sites off from automated crawling… but only somewhat. I have a few reactions:

1. None of this will stop the rise of AI. I think most of these creators understand that and are pursuing this path as an expressive act.
2. There are indications that legal restrictions on data collection are having an effect on training data availability. But these should be understood as commercial plays by entities in control of large corpora, who hope to use it to extract some value from the AI wave. Reddit and the New York Times are the most famous examples. This is distinct from the normative shift among creators that Luis describes.
3. AI disruption of creative industries will be real, though surely different than we imagine. I respect creators who are restricting access to their content out of a strong desire not to be complicit in that change, even though each individual’s instrumental importance to the change is negligible.
4. While I respect that rationale, I join Luis in lamenting it, in large part because I think it sacrifices potential benefits, such as those he describes, while being unlikely to achieve much.
5. As is often the case with retreats from openness, much of the impetus for this normative change seems to stem from discomfort with who is benefiting from it. I believe this is because many advocates conceive open data as a revolutionary project to reallocate social power rather than a commitment flowing from moral and practical judgments about how knowledge can and should be restricted.
6. I empathize, having once held that perspective. But I’ve come to think it is ultimately a juvenile ideology, or at least one that’s been proven to be unproductive. For one thing, people underestimate how quickly, if they did create a new set of winners and losers, they would come to resent the winners. And the perspective is also badly entangled with a press-led narrative about tech companies that frequently edges into hysteria.
7. But turnabout is fair play: some of the FAANG (AMAGO?) entities on the other side of this are responsible for strangling the open web while building ever-taller legal and technical palisades around the UGC they control.
8. It’s a little sad to lose fellow open data travelers. On the one hand, it might be for the best: if I’m right and their revolutionary project will never bear fruit, they probably should hop off the bus. On the other hand, I suspect the majority of people on board that bus are there because of an inchoate revolutionary rationale. Those of us riding for abstruse reasons may get lonely.
9. To the extent that a mass movement to limit the availability of training data has any effect, it will be to entrench the advantage of early movers who have already built their models (though these include open models like Llama).
10. If successful, online culture will still be used for training by those who don’t respect robots.txt. That means rogue actors: scofflaws without commercial ambitions, gray-market open source projects, hostile foreign powers. This is superficially aligned with the revolutionary outcomes discussed above. But the practical reality will be chaotic and unproductive, with noncommercial aesthetics as the main thing that recommends it over the counterfactual.
11. All of this may soon be moot, as some analysts estimate that frontier models’ training needs are already on the cusp of expanding beyond the corpus of written language. Video data transcription (custody of which is highly concentrated due to hosting cost) and synthetic data are expected to be the next frontiers.
12. Declining enthusiasm for openness seems to me to be aligned with a general turn toward conservatism and neuroticism among rising generations.
13. I remain hopeful that the pendulum will swing back during my lifetime. Will the web bloom again? I suppose I wouldn’t bet much on that. But something will.

We have just returned home. Physically, we were in Vermont, working by remote for two weeks and vacationing for a third. Psychologically, we were in numerous locales. Caloric IPA City. A day trip to Diminished Parental Vigilance Island. Touring the Drug-Addicts-Are-Merely-Annoying,-Not-Dangerous District. But also: relaxing in the Land of Substantially Reduced Price Sensitivity.

We overpacked and had to leave the impulse to equivocate over every purchase at home. For me, this is an important part of vacation. Throw on an order of fries, grab a maple candy at checkout, opt for the nicer AirBnB. This is supposed to be fun!

But now we’re back, and the house is full of fruit flies (forgotten nectarines make poor housesitters), and I’m primed to think about Matt’s article on price discrimination. Really, I’ve been thinking about it for a while, because it’s a question that sends me in circles when I ponder it. My cursor hovered over the choices in his poll for a long time–and not because I hadn’t thought about it before.

Matt runs through the usual and correct explanation for why price discrimination is good: it produces economically efficient outcomes, excavating both the consumer surplus and deadweight loss triangles on a traditional supply/demand graph and converting the extracted material into supplier profits. People don’t like this process because they feel the lost consumer surplus more easily than they feel the gains from reducing deadweight loss or the improvements to their wages, retirement accounts, and municipal tax revenues. But this might be unwise of them.

Matt goes on to point out that even if everyone if left better off overall, there are other costs:

There’s something very oppressive-feeling about the idea of being constantly surveilled and having every micro-imperfection in the competitive environment (of which there are many, the real world is full of frictions) turned against you.

This strikes me as correct, but not the whole story. There’s something queasier about it.

Price discrimination is about gauging willingness to pay. For every purchase, there’s some price at which it’s no longer worth it. A price at which you’d rather keep the money and buy something else.

Matt grounds his discussion in reality, positing a fantastical but coherent brain-scanning technology for understanding the limits of consumer demand, while acknowledging the practical imperfections of retail surveillance and the complications introduced by substitutability. This is clearly the right way to argue about the advancement of information technology-enabled pricing tactics.

I want to excuse myself from that complexity, though, and instead will posit the Price Goblin. The goblin knows exactly what you are willing to pay in every circumstance, and informs the seller as you’re walking to the register.

(Apologies to whoever holds the rights to the presumably-formerly-extant Price Goblin dot com intellectual property.)

In this world, there’s no such thing as a good deal. As your eyes scan across the contents of your grocery cart, you feel uncertain. The brand of bread you got isn’t your favorite, but you would not feel any better about buying your favorite, because it has been perfectly priced. The laundry detergent you selected is expensive enough to make you feel like you might as well take everything to the dry cleaner, even though doing so is less convenient. The goblin knows exactly how inconvenient it would be–he empathizes with how much you hate having to recycle the wire hangers you’ll get back. He empathizes with quantum precision.

But perhaps good deals are overrated. How much of our mental energies do we devote to acquiring stuff? The goblin would liberate us from those concerns. No more bargain-hunting. No more careful budgeting. Think of all the tabs we could close! Maybe we would finally take guitar lessons.

And it’s not like undoing consumerism would destroy capitalism. The goblin knows how much each person would enjoy a given product, so producers will still compete on quality, subject to the goblin’s extensive QA testing. Of course, freedom from thinking about prices means we need some other way to prevent people from consuming more than their fair share. I guess we’ll assign overall consumption quota management to the goblin, too. He’s already going to have to build models for diminishing marginal utility, after all.

The standard move at this point in the essay is to make a pat observation about communism, ideally including a reference to an old Star Trek episode about a dystopia run by a supercomputer. That’s fine as far as it goes for defining the goblin end of this argument’s spectrum.

But more important than that, I think, is what happens when you get a raise or a better job under a goblin regime. The answer is: nothing. The goblin factors that in. In fact it’s extremely easy for him to do this, because your new income is probably being sold between various data brokers. No arcane magicks are necessary. He knows you’ve got the new job, and can afford to pay more, and so you are going to. Suddenly everything in that grocery cart will be a little more expensive. But don’t worry: you won’t notice. There’s nothing for you to notice.

I am now undeniably middle aged, and enduring the accompanying introspection. Sometimes I wish my young, clever self hadn’t been quite so arrogant, and had undertaken some of the paths toward credentials that he excused himself from back in the day. It would be nice to feel like a big shot.

I understand at least some of why he didn’t. When I was growing up I felt very anxious and jealous about money. I had everything I needed, but noticeably less than my peers (living, I should mention, in the richest region in the richest country in the richest era in human history). Not exclusively, and probably not optimally, but in various ways, I have chosen paths that pay well and don’t require student loans. This is not because my tastes are exotic. I do insist on air conditioning and a refrigerator with an icemaker. But otherwise they’re similar to the ones I was exposed to growing up. I drive a Kia, I take domestic vacations, and I save about as much as financial explainer articles tell me to.

What I really want from money is not to think about it–in particular, not to feel anxiety about it. In a sense, this makes the goblin sound pretty good. In another, he sounds terrible. There’s no way to better your situation under the goblin’s regime. If you gain a foothold, he will smooth it away.

Perhaps we could count on the goblin to do that responsibly: to allocate financial anxiety in a perfectly just manner. We’re already asking a lot of the guy, but he’s doing great so far. And people do like this idea in some contexts, like Finland’s income-based fines for speeding.

Still, I think a lot of people hate this idea viscerally. I think it’s why voters recoil from inflation: suddenly, it has become harder to win. The goalposts have been moved, as the saying goes.

It’s no coincidence that this is a classic way to escalate stakes and engender an audience’s sympathy. From the Odyssey to Save The Cat’s Beat Sheet, foiling a hero’s noble effort to achieve their goal with an unanticipated setback can be counted on to rouse an audience’s sense of unfairness and heighten their eagerness for resolution. It gets at something very deep in us, the kind of thing primatologists patiently tease from their wards with games about tokens and treats. We think games should be winnable.

Obviously, the goblin sits at an unreachably distant end of the continuum I’ve described. Price discrimination is at least somewhat good, sometimes desirable, and often unavoidable. Even so: it’s a short stroll goblinward before we humans find the dissonance unbearable. I can’t blame any politician for thinking that voters will care more about stories than triangles on a graph.

Tom MacWright has written an interesting post on LLMs and their effect on the discipline of programming, noting that they represent a grimly ironic answer to his desire to democratize programming, since his enthusiasm for the project was about its potential to provide both intellectual and financial returns. LLMs allow programmers to write code without understanding it and to increase productivity without increasing skill, which might undermine compensation standards. Becoming a programmer is getting easier! But at the expense of the reasons for doing it at all.

I don’t disagree with Tom’s big-picture take. But I think there are a few more things worth considering, which I offer from a perspective that–I’m sorry to say–is a bit of a blind spot for him: that of a much worse programmer.

I am not abysmal, mind you. In particular, I write code with a pretty good mental model of its probable resource consumption, and have okay judgment about when to expend effort moving up or down the optimization-and-scalability ladder. I’ve worked with a lot of technologies. I’ve shipped code–not earth-shattering code, but production code nonetheless–to audiences in the hundreds of millions. And, because I’ve worked alongside or nearby some truly excellent programmers (such as Tom himself) I can still often teach an early-career programmer a thing or two.

But my limits are significant. For one thing, I’m pretty rusty. When I need to get something done I reach for Python and bash, neither of which are exactly fast-evolving ecosystems. I have never been immersed in the scene of open source foment–I can tell you what bun is (was?), but not whether you should use it. And, most damningly, I don’t care enough to get really good. Or maybe that’s wrong: my problem is that my interests go down, not up. I am intrigued by instruction sets, registers, caches, half-adders, NAND gates, and electron holes, predictive branching calculations and DEF CON talks about weird side channel attacks. I like to know that stuff exists, and to understand it deeply enough to appreciate how it’s connected, if not exactly how it’s implemented. If you instead turn your gaze upward, away from system buses and laser-pulsed droplets of tin, the machine falls away entirely and you find yourself wandering amid a linguistic forest of abstract syntax trees. The arbitrariness of representational systems becomes apparent. You lose interest in giving computers lists of things to do and start giving them ways to be. Sometimes a quasi-mystical revelation arrives and you become able to create a new way of running global telecommunications infrastructure or stymying cybercriminals. Usually, you just wind up writing another LISP.

I have never had the affinity or assiduousness for that sort of thing, though I know enough to admire those who do. I am content to just bang out interpreted code to do something neat, hoping that if any part of it is too slow, someone smart will have put a compiled solution online for me to use. That’s how good I am and probably how good I ever will be.

This turns out to be about the right level of competence for using LLMs. And let me just say: wow.

The React monoculture hadn’t fully taken over by the time programming ceased to be my day job. I got the idea. But I’d never put in the reps for the plumbing to feel intuitive. “Yes, I could figure this out,” I told myself, “But I have three kids and a real job.” But now I just say: I’d like to use vite and Tailwind. I don’t want my map to flicker when I update state. Please do not make me remember what a forward ref is for longer than is absolutely necessary. And it all works.

Another time: I wanted to build a mobile app. Mobile dev is a deep discipline that places enormous constraints on the developer, both in how they do their work and what their work can achieve relative to a normal computer, making it simultaneously intimidating and deflating. It’s tempting to use a simplifying framework in a more familiar language–maybe with cross-platform compatibility?–but the fields of computing history are absolutely littered with such projects’ corpses, making the investment of effort highly suspect. But now I can tell an LLM I want a Flutter list view based on some JSON it’s pulled and, one Apple Developer fee later, I have an app for my stupid thermostat system.

Even native development is suddenly achievable. I couldn’t possibly justify learning Swift and its associated ecosystems without a serious iOS need–you expect me to believe a language that’s borrowed Perlisms like $0 is elegantly designed?!–but now I don’t have to learn it to use it.

What I can’t yet tell is how bad I could be while still benefiting from all this. I learned a lot about programming in the pre-LLM era, and that context lets me see when the robot is suggesting a dependency that’s out of date, or an optimization that’s premature, or a level of abstraction that’s inappropriate, or an architecture that’s going to bite me in the ass, or a response to an error message that’s irrelevant. If I started my career today, I don’t think I would need to learn those things, or at least wouldn’t come to understand them in the same way, and I consequently wouldn’t be able to spot the robot’s missteps.

But that’s only one part of programming. A lot of it is about pasting error messages into websites, and ChatGPT is a much better website for that than Google ever was.

Perhaps more importantly, the only time I’ve reached truly new levels of skill–not just knowledge, skill–it was because I was sitting next to a programmer better than me. When a junior programmer can find someone like that who’s willing to indulge them, it’s an immensely valuable opportunity. Now a close approximation can be rented for a few bucks a month.

This pattern of benefits seems to be consistent. Mediocre performers are helped by LLMs more than top performers. So far, LLMs are an assistive technology. I choose to view this optimistically. I know a lot about computers, and much of it was learned during thankless hours when I was stuck searching for a path forward, without a better programmer sitting next to me. But much of the material I learned during those interludes was trivia–if I’d known what I was looking for I would have found it right away!–and only some of it was worth retaining. I don’t cherish those hours.

The time I spent with a guiding hand expanded my understanding at a vastly greater rate, and I’m excited by the prospect of my children having an infinitely patient tutor available–for programming or whatever else–during the inevitable hours when a human isn’t available. It’s true that the palette of abstractions and details they learn will be twisted by LLM capabilities into a shape different from the one I developed. But that, at least, has ever been the case in CS. In my youth we thought all programmers needed to understand memory allocation!

I can’t discuss the economic dimension of LLMs’ impact as thoroughly, because it seems to me to be much less knowable. Frankly, I don’t understand why programming is still such a good job to have. It’s extremely outsource-able. It is, fundamentally, about moving information around to unlock efficiencies. Markets are pretty good about finding and shaving down inefficiencies and laptops and broadband connections are not big capital expenses. How much work remains? We are decades into the ICT revolution. Noah Smith thinks the tech industry might be approaching maturity, in a process analogous to the build-out of the national freight rail network. I’m less certain than he is, in part because what counts as “tech” seems mostly to be a function of what adopting the term means for the user’s financing prospects. But it does seem plausible.

It’s also hard to know what counterfactuals could have or might be possible. How much have the FAANG monopolists bid up salaries as a bet on their future and/or against their competitors’? How much more work would be available if the platforms hadn’t swallowed the web, homogenizing and sanitizing it into a handful of monolithic services? These are inputs to the equation that I can’t properly estimate.

Ultimately the economic question boils down to familiar arguments (once thought resolved, recently reopened on appeal): comparisons to Luddites, revisionist accounts of the Luddites, midwit memes about the Luddites; promises of expanding pies and productivity growth and redistribution under neoliberalism and how mad we are about all of it, whatever it might be, exactly.

I don’t completely trust anyone’s intuitions on these matters, certainly including my own. But inventing tools that let us do more is usually good, I think, especially when what we’re doing more of is benign enough to remain safely confined to a screen.

I hope I’m dead before chatbots become digital people who displace my descendants’ labor. When that happens I think it’s going to be a rough time for everybody. But the chatbots who can write fragments of code are already here. I’m pretty sure about that. And I think it’ll be okay.

Some of Silicon Valley’s top figures have recently declared their support for Donald Trump. The failed attempt on Trump’s life offered congenial timing for this kind of announcement, and these men are professional opportunity-noticers, after all. But their changed allegiance seems to have been brewing for a while. It’s interesting to consider the reasons for it.

This is in part because Donald Trump provides such a starkly ridiculous apparatus for thought experiments. A mercurial and easily-corrupted fool, there can be no serious argument that he’s anything less than a harbinger of the end of America’s tenure as global paragon. If they were able to separate themselves from what must be an overwhelming emotional tide, it would be obvious to all but the most dull-witted businessmen that Trump’s elevation is not in their interest. I suppose we can grant special dispensation to the crypto hucksters, neo-reactionary dum-dums, and in-the-closet kompromat victims. But that still leaves a chunky remainder of guys in embroidered vests.

What are they thinking, then? A compelling explanation emerged this week, nicely summarized by Ben Thompson but first captured by Kelsey Piper after having listened to an illuminating Andreesen/Horowitz podcast where the dynamic was discussed frankly.

Thompson notes that this goes back even further, connecting it to election-related criticism of Facebook and their resulting investment in remedies that seemed to just make people madder:

In short, you have an industry that has been endlessly vilified in the press, bent over backwards to do what the press demanded, but instead of receiving credit for those efforts, has only seen itself even more isolated and under siege.

This change has been real–here’s Piper again, noting a strikingly antagonistic editorial policy toward the tech industry from the very top of Mt. Dispassionate Journalism:

(Matt deletes his tweets, so it’s not easy to see the full context, sorry)

This change was probably inevitable. Journalists are told to comfort the afflicted and afflict the comfortable. They’re not alone. Everyone likes an underdog: American (and consequently global) culture is overwhelmingly supportive of directing skepticism toward concentrations of power. That tech’s economic and cultural ascendance came at the direct expense of the journalism industry doubtless helped lessen the friction of any editorial handwringing about an unabashedly antagonistic approach to coverage.

Still, it took a moment for everyone to align against our new villains. The Obama administration was full of tech industry veterans, binding their products’ novelty into the same moment of optimism and enthusiasm that accompanied his historic election. And while vague anti-bigness animus and specific injuries to disrupted industries are easy to understand, it took some time and experimentation to construct a more practical critique of tech out of arguments about inequality (old news), privacy harms (vague), and various ideas that were in tension with the media’s historically fervent embrace of the First Amendment.

When the shift finally took hold it must have felt sudden to its targets. That’s reflected in the “broken deal” narrative above. These guys thought they could become rich and powerful and remain beloved. Weren’t they the good ones? Here’s the key podcast section that Thompson cites:

MA: I actually endorsed Hillary Clinton in 2016 publicly for what I thought were a variety of good reasons. And the way I would describe it is, I’m Gen X, I kind of came of age in the 90s as an entrepreneur. Almost everybody I knew, including myself, just took it for granted, which is like, of course, you’re a Democrat. Of course, you support the Democratic president. And the answer is the formula resolves to an easy answer, which is the Democrats in those days, you know, presidential level were pro-business, they were pro-tech, they were pro-startup. They were pro-America winning in tech markets. They were pro-entrepreneurship. And so you could start a company. They were pro-business. You could be in business. You could be successful in business. You could make a lot of money. And then you give the money away in philanthropy and you get enormous credit for that. And, you know, it absolves you of whatever.

BH: Yeah, well, I was going to say, like, it’s obvious you’re to be a Democrat because you have to be to be a good person. That’s kind of the underlying thing.

MA: But specifically successful business people could then basically become successful philanthropists. This is the path that Gates and many others kind of carved out. And then you could be progressive on social issues, and you could be on the right side of all these sort of societal changes that people were kind of focused on at the time. And the whole thing just seemed completely obvious and completely easy. So I was kind of on that path, frankly, quite strongly through at least 2016.

In retrospect, it’s like there were glimmers of, I’d say, growing anti-tech, I would say animus, probably in the early 2010s. And there were growing kind of anti-business sentiments. And then by the way, something that really disturbed me a while back is sort of growing anti-philanthropy sentiments, which we probably won’t discuss it like today.

BH: Oh, well, yeah. Well, with people who made a lot of money, who gave money away, got criticized for giving money away to charitable causes as opposed to paying more taxes. Kind of a funny life jealousy taken to the extreme, yeah.

MA: A specific moment that happened to me to make me realize the landscape was shifting was when Mark Zuckerberg and Priscilla Chan set up the Chan-Zuckerberg Initiative where they literally committed to 99 of their assets going to the Chan-Zuckerberg Initiative, there was a political faction that basically heavily criticized them, and the theory, number one is to your point, the theory was that they should they should pay it in taxes and the government should distribute the money, they shouldn’t have any control over where it goes, but the other was oh they’re only doing it for a tax break.

BH: Yeah, which wasn’t true.

MA: Well, it can be. It can’t be true because you’re giving away 99% of your assets to get a tax break. Like it literally doesn’t make sense.

BH: It’s like people bad at math and jealous.

MA: Exactly. And so like basically like that formula started to break down. And so, you know, I think like a lot of us in tech, it’s been a much more difficult puzzle to try to figure this all out over the over the last eight years and then particularly over the last four years.

https://pmarca.substack.com/p/new-podcast-little-tech-agenda-the

I think that most people want to feel that they are good. They work to reconcile that need against their own desires. It is frustrating when the rules that define what counts as being good are changed, particularly when you’ve already made big investments in a lifelong project that was built upon those rules. There’s a very strong temptation to discount the new rules as arbitrary–a product of bad faith, ignorance, false consciousness, whatever. If you do this, the project of reconciling your own desires against the rules suddenly looks very different.

The accompanying mood is a mix of frustration, nihilism, resignation, and rebelliousness. I think the best word for it is petulance. And to me it feels like the defining emotion of our political age.

VCs and reddit blackpillers are one thing. America’s police officers are another. There’s consensus that America experienced a significant pull-back by police in recent years. Scholars are still debating how much of this should be attributed to the pandemic versus the defunding rhetoric that reached a national crescendo in the wake of George Floyd’s death. I’ve seen enough unhinged tweets from police union officials to believe I understand the psychology of the latter, even if its causal significance could use another regression or two.

In the last two decades, perceptions of cops have swung from a 9/11 apotheosis as heroic first responders to an ACAB consensus among the cultural vanguard that cops must surely feel is implicit in every blue-jurisdiction yard sign they pass. The rules changed, and they don’t like it.

I say all of this with some sympathy. I have felt petulant as the world changed around me. I put it into words here.

Petulance is never an admirable reaction, even if the new rules really are under-justified. But I do think it’s a reaction that we would all do better to expect, plan for, and understand. That doesn’t mean that we can’t change the rules. Many of the rules should be changed! But when we change them, we will be wise to allow the dislocated to embrace the new rules. This will frequently be annoying. I think I understand why it’s satisfying and fun to respond with exasperated derision when some guy prefaces his stab at allyship with a proof-of-daughters statement. But where does that leave him? If he isn’t permitted to come along, where will he go instead?

I have written before about the overwhelming power I see in the human impulse to organize ourselves in hierarchies. Who’s up, who’s down; who’s good, who’s bad. It’s perfectly natural, and usually satisfying. But it’s not always necessary. Often–especially in politics–it might be better to limit ourselves to declaiming what is good, rather than who.

None of this excuses petulance. It’s a childish emotion that should be recognized and controlled. Allowing it to drive you into a public tantrum is embarrassing. Allowing it to drive you toward a figure like Donald Trump should be profoundly chastening. I wonder how these guys think they will be understood a decade from now. Have thought about it at all? To embrace petulance is to let go of dissonance. Not having to think quite so hard is part of the appeal.

When the time comes, I suspect they will feel pretty mortified about this period. Then again, who won’t?

For now, the petulant impulse is real, and–for whatever reason–has recently become of outsize importance. Most people need a way to feel that they are good. When we can, we should make sure they have one.

This is the third and probably last installment of my speed camera saga. Part 1 is here. Part 2 is here.

One of the nicest things about writing something that connects with people is that many of them share links and thoughts that make you smarter (and would have improved the piece if you’d had them in the first place). I want to briefly discuss four responses I got: two studies that people pointed me toward, some stats that I wasn’t aware of, and one reactive essay.

Red-Light and Speed Cameras: Analyzing the Equity and Efficacy of Chicago’s Automated Camera Enforcement Program

This study takes a deep look at Chicago ATE data. It found that cameras improve safety:

Over the 3-year period from 2015-2017, we estimate that there were 36 fewer KA type injury crashes, 68 fewer type B crashes, and 100 fewer type C crashes across the 101 locations. In all, there were 204 fewer injury crashes. Reductions of type A and C crashes were estimated at around 15% and that for type B injuries at 9%. Overall, speed cameras led to a 12% reduction in injury crashes.

But the study is most notable because, unlike my analysis of DC data, the study’s authors had access to the zip code of citation recipients. This allowed them to examine the incidence of tickets versus Census demographics in a much more defensible manner than the camera/neighborhood spatial association that I criticized the DCPC study for using.

After controlling for various factors, they found that Black households do receive a higher number of citations than white or hispanic households. They did not find disparate placement of cameras, however.

[T]ickets per household for both speed and red-light cameras are higher in majority Black areas, followed by majority Hispanic/Latinx areas, and finally majority White/Other areas. At the camera level, however, we do not find such relationships. Cameras in majority Hispanic/Latinx areas tend to issue fewer tickets than others for both red-light and speed cameras. There is not a statistical difference in ticketing rates between cameras installed majority Black and majority White/Other areas for red-light cameras, and there is weak evidence that rates of ticketing by cameras in majority White/Other areas are lower than those in majority Black areas for speed cameras.

They also found that cameras on big, fast roads issue a disproportionate number of citations, which is consistent with my own findings from examining DC data.

The second part of the study examines these citations’ economic impact in terms of different groups’ level of wealth. Late fees and payment rates emerge as a significant part of the picture.

I think this study provides real evidence of a disparate impact, but doesn’t provide a clear explanation of why it’s occurring. It’s also important to keep the actual scale of the effect in mind: as you can see on the graph above, the per-household gap is about one speeding ticket every ten years. That deserves attention, but should also be kept in perspective.

CORRECTION: Figure 1 shows a difference of one red light camera every ten years. The speed camera gap is about one ticket every four years.

Do Speed Cameras Save Lives?

I was also pointed toward this paper, by the Spatial Economics Research Centre, which examines cameras in the UK. I appreciate the friendly manner in which it was shared with me by someone who I take to be an ATE skeptic, but I think it helps his case less than he imagined. This study also found that cameras improve safety:

[S]peed cameras unambiguously reduce both the counts and severity of collisions. After installing a camera, the number of accidents and minor injuries fell by 17%-39% and 17%-38%, which amounts to 0.89-2.36 and 1.19-2.87 per kilometre. As for seriousness of the crashes, the number of fatalities and serious injuries decrease by 0.08-0.19 and 0.25-0.58 per kilometre compared to pre-installation levels, which represents a drop of 58%-68% and 28%-55% respectively. Putting these estimates into perspective, installing another 1,000 speed cameras reduce around 1,130 collisions, mitigate 330 serious injuries, and save 190 lives annually, generating benefits of around £21 million.

Rather than confirming that cameras on highways generate outsize numbers of citations, it found that cameras ought to be placed on highways, because that’s where their safety benefits will be greatest:

[I]t is more effective to install cameras along roads at higher speed limits as much larger reductions in collision outcomes are observed

Finally, the study found mild evidence of “rebound” effect outside of camera locations. I think this is why the study was shared with me: my reply-guy was arguing that cameras just push crashes around. I don’t buy that argument, and it doesn’t seem like the paper’s author does, either. Or at least he thinks the problem could be solved by–you guessed it–more cameras:

Beyond 1.5 kilometres from the camera, there are suggestive evidence of a rebound in collisions, injuries and deaths, indicating drivers could have speed up beyond camera surveillance and cause more accidents. These results, which illustrate the limitations associated with speed cameras, suggest that newer prototypes, such as mobile or variable speed cameras, should be considered.

Demographic Safety Data

Eileen S. made me aware that some safety data exists with a race-ethnicity breakdown, albeit with a notable time lag. Eventually I poked around and realized there are quite a few comparable resources for examining the question.

First, consider NHTSA’s data on fatalities per 100,000 people, which shows that the Black community is suffering from our deadly roads at a rate second only to people of native heritage.

NHTSA also provides stats breaking down the percentage of traffic fatalities that are related to speeding:

All of this is looks even worse when you consider different groups’ urban/rural split:

and by academic estimates of demographic differences in vehicle miles traveled (VMT):

(See also here for a longer NHTSA report on this topic)

I would expect lower VMT, tilted toward urban areas, to mean a lower incidence of speeding-related deaths. But that is not what the data shows.

I don’t know why this disparity exists. My hunch is that it has something to do with the kinds of built environments that disadvantaged groups have to settle for. I am quite wary of bringing it up, because of the risk that a reader will mistake it for an argument about blame. That is not my intention: it’s an argument about victimization and suffering. I think it’s essential context for anyone who wants to discuss the possibility of ATE’s disparate impact.

@DCCarViolence’s essay

I want to thank Joseph Oschrin for taking the time to write this response. I appreciate the work he does on Twitter, too. The crux of his argument is that ATE–and in particular, debating the disparate impact of ATE–is an unfortunate waste of time, and that instead we ought to focus on road diets and other interventions to our infrastructure.

I agree that those kinds of changes are the most desirable way to make streets safer. The problem is that they are wildly expensive–not just in dollars and cents, but in years spent planning, compromising, and–as this week has reminded us–suffering abrupt reversals.

Oschrin maintains that cameras are consuming resources that could be spent on other kinds of interventions. I have a hard time seeing the evidence for this. The bottlenecks to reforming our infrastructure are big problems of budgets and politics. A speed camera program, by contrast, seems to mostly require exchanging emails with a vendor.

Cameras are proven to save lives. They pay for themselves. They don’t consume human enforcement resources. And they don’t engage in racial profiling.

The only problem with cameras is that everyone hates them and indulges in motivated reasoning to justify that distaste. I sometimes imagine how an ATE option might work in SimCity: click here to trade popularity for income and safety!

Maybe not the most fun game mechanic. But I think it’s a great real-world trade and that we should keep taking it.

The STEER Act, and a prediction

I didn’t spend much time discussing the economic analysis included in the Chicago study. That’s because, to a significant degree, that argument has already been digested here in DC. Advocates argued convincingly that citations’ safety benefits are coming at a relative cost to lower-income residents that is unfairly high.

This resulted in a period of legislative churn, during which the Council removed some of the usual consequences for not paying your tickets. I think that was a bad idea.

But more recently they passed the STEER Act (currently under congressional review). And I think it’s an impressive response: it will mean that the consequences of our speed enforcement system will increasingly be felt in non-financial ways, including traffic safety classes (aka white collar prison dispensed on an hourly basis), license suspensions and–amazingly–automotive speed governor devices for the worst offenders. I think it’s a thorough response to arguments about economic inequity raised by ATE critics and I am looking forward to its implementation.

I also think it will do approximately nothing to silence ATE criticism. People do not like being punished for bad driving. This is perfectly normal. Nobody likes being punished. Nobody likes to imagine that they are potentially culpable for the vast amounts of death and injury on our roads. Nobody wants to believe that the sense of freedom they experience behind the wheel ought to be curtailed. People will invent new arguments for why the punishment is unjust, or the deaths are imaginary, or the safety benefit is fake, or the privacy impact is unacceptable, or the carbon footprint is too big, or the streetscape’s natural beauty is being destroyed, or the imposition of driving standards threatens traditional masculinity, or something even stupider that I can’t yet imagine.

That’s fine. Sometimes, when someone is wrong, and after you have listened to them with a polite, blank expression, you must look within yourself, accept that you might get yelled at, and summon the courage to ignore them.

UPDATE – 16 OCT 2024

I wish I’d had this link before: David Ramos pointed me toward a great analysis of Baltimore ATE data showing that the location at which a citation is issued has little to do with where the citation recipient lives. You simply cannot use the location of citation issuance to make judgments about who is receiving citations.

This is the second of my posts about speed cameras. Part 1 is here. Part 3 is here.

I’m amazed by the reaction that my post on ATE cameras elicited. Thanks to everyone who took the time to read and share it. Seeing this level of interest provided inspiration to look at a couple of lingering questions I had (and to learn how to make my MatPlotLib graphs marginally less ugly).

In my last post I showed that ATE speed cameras are highly skewed toward highway traffic. I also argued that looking at the demographics around highway speed camera locations doesn’t tell us much about the equity impact of the citations being issued. That is the main point I wanted to convey and I am not going to qualify it here.

But even though non-highway effects are a less important part of the ATE picture, it is still interesting to examine them from an equity perspective. So I ran the per-ward analysis of camera days and total citations while excluding cameras associated with the largest road classes (“motorway” and “trunk”). By excluding very large roads we can be more confident that the citations are reaching people who live in the area.

Camera days, you will recall, is a metric meant to capture the overall level of ATE monitoring by counting up the number of days that each camera issued at least one citation (indicating that the camera was installed and active).

Maybe ward 7’s a little high; maybe ward 8’s a little low. Nothing seems particularly scandalous, though. Ward 5 looks fishy–could a highway camera be getting mistakenly included?–but visual inspection of these shows nothing amiss:

Ward 5 just has a lot of camera activity along North Cap and South Dakota Ave. Similarly, Ward 7 is showing elevated numbers because of the one RFK site–mentioned in the last post, and notable for generating a ton of citations but not being on a highway–and for having more cameras on peripheral streets (Southern and Eastern Ave) than Ward 8, which is probably appropriate given those streets’ extremely dangerous reputation.

Rereading my post made me realize that the precipitating tweet didn’t describe the DCPC study correctly, and instead referred to red light cameras. My last post focused on cameras that issue speed citations, for reasons I explained at the time. But we can also look at other types of ATE. I came up with the following list of violation codes by eyeballing citation counts. When a code was responsible for 100x as many citations as others, it seemed like a pretty safe bet that it was being generated by robots. Here’s the list I used:

T113 | FAIL TO STOP PER REGULATIONS FACING RED SIGNAL
T128 | PASSING STOP SIGN WITHOUT COMING TO A FULL STOP
T202 | RIGHT TURN ON RED, VIOLATION NO TURN ON RED SIGN
T334 | TURN RIGHT ON RED WITHOUT COMPLETE STOP

I ran the same query using these codes instead of the speeding violation codes, while still looking only at citations from agencies that reflect ATE activity:

The distribution of these red light and stop sign cameras seems reasonable enough, but it is worth noting that Ward 7 residents are getting a larger share of citations than their share of camera days would suggest (though again: all of this is a small fraction of highway-related citations). Looking at the data, there’s a particular location along 27th St SE that’s responsible for a staggering 77% of the non-speed, non-motorway/trunk citations in the ward–over 117,000 for PASSING STOP SIGN WITHOUT COMING TO A FULL STOP. That one camera is a stone-cold killer; watch out, ward 7.

Lastly, I did want to note one irregularity that’s hard to miss. As discussed already, human-issued citations for speeding are a negligible part of the overall citation picture. Still, I think it’s fair to say that the psychological effect of cops sitting in speed traps looms large. And it is notable that MPD district 7 does basically no human speed enforcement:

That’s right: in the time period studied, district 2 officers wrote more than one hundred times as many citations for speeding as district 7 officers.

(We have eight wards but 7 MPD districts, but it’s not hard to adjust: district 6 serves ward 7 and district 7 serves ward 8, more or less.)

This is something I’d like to dig into more. Did district 7 deprioritize human speed enforcement because of a recognition that ATE does the job better? Because human officers were needed for other, more pressing tasks? Was this a policy change that lined up with the dawning national awareness that traffic stops pose outsize risks to Black Americans? Could it be that ATE’s debut was especially jarring to ward 8 residents because they were accustomed to an era of extremely low speed enforcement that preceded it? I’ll have to load more historical data to answer these questions.

This is the first of my posts about speed cameras. Part 2 is here. Part 3 is here.

The two most important things about speed cameras are that they save lives and that they are annoying. People think life-saving is good. They also think getting tickets is bad. These two beliefs are dissonant. Social psychology tells us that people will naturally seek to reconcile dissonant beliefs.

There are lots of ways to do this, some easier than others. For speed cameras, it typically means constructing a rationale for why cameras don’t really save lives or why life-saving initiatives aren’t admirable. A common approach is to claim that municipalities are motivated by ticket revenue, not safety, when implementing automated traffic enforcement (ATE). This implies that cameras’ safety benefits might be overstated, and that ATE proponents are behaving selfishly. Most people understand that this is transparently self-serving bullshit. It’s not really interesting enough to write about.

But there’s another dissonance-resolving strategy that popped into my feed recently that merits a response: what if speed cameras are racist?

This strategy doesn’t attempt to dismiss the safety rationale. Instead, it subordinates it. Sure, this intervention might save lives, the thinking goes, but it is immoral and other (unspecified, unimplemented) approaches to life-saving ought to be preferred.

This argument got some fresh life recently, citing a DC Policy Center study that makes the case using data from my own backyard.

I appreciate the work that the DC Policy Center does. Full disclosure: I’ve even cited this study approvingly in the past (albeit on a limited basis). But this tweet makes me worry that their work is transmuting into a factoid that is used to delegitimize ATE. I think that would be unfortunate.

So let’s look at this more closely. We can understand the study and its limitations. And, because DC publishes very detailed traffic citation data, we can examine the question of camera placement and citation issuance for ourselves–including from an equity perspective–and come to an understanding of what’s actually going on.

What does the DCPC study SHOW?

The most important result from the study is shown below:

The study reaches this conclusion by binning citation data into Census tracts, then binning those tracts into five buckets by their Black population percentage, and looking at the totals.

Descriptively, the claim is correct. The Blackest parts of DC appear to be getting outsize fines. But the “60-80% white” column is also a clear outlier, and there’s no theory offered for why racism–which is not explicitly suggested by the study, but which is being inferred by its audience–would result in that pattern.

To the study’s credit, it acknowledges that the overall effect is driven by a small number of outlier Census tracts. Here’s how they discuss it at the study’s main link:

Further inspection reveals five outlier tracts which warrant closer inspection. Four of these outliers were found in 80-100 percent black tracts while one was found in a 60-80 percent white tract. Of course, by removing these extreme values, the remaining numbers in each racial category do fall much closer to the average. But notably, the number of citations and total fines per resident within black-segregated tracts remains 29 percent and 19 percent higher than the citywide average, even after removing the outlier locations. Meanwhile, the considerably lower numbers of citations and fines within 80-100 percent white census tracts remain considerably lower than average. (For a more in-depth discussion of the results and the effect of these outliers, please see the accompanying methods post on the D.C. Policy Center’s Data Blog.)

But if you click through to that “methods post” you’ll find this table, which has been calculated without those outlier tracts. The language quoted above isn’t inaccurate. But it’s also clearly trying to conceal the truth that, with those outliers removed, the study’s impressive effect disappears.

What do we know about DC’s ATE cameras?

Let’s take a step back and look at this less reactively. What do we know about DC speed cameras?

The most useful source of data on the topic is DC’s moving violation citation data. It’s published on a monthly basis. You can find a typical month, including a description of the included data fields, here. I had previously loaded data spanning from January 2019 to April 2023 into a PostGIS instance when working on this post, so that’s the period upon which the following analysis is based.

The first important signal we have to work with is the issuing agency. When we bin citations in this way, we see two huge outliers:

ROC North and Special Ops/Traffic are enormous outliers by volume. We can be sure that these represent speed cameras by looking at violation_process_desc for these agencies’ citations: they’re all for violations related to speeding, incomplete stops, and running red lights. The stuff that ATE cameras in DC detect, in other words.

I am primarily interested in ATE’s effect on safety. The relationship between speeding and safety is very well established. The relationship between safety, red light running and stop sign violations is less well-studied. So I confined my analysis to the most clear-cut and voluminous citation codes, which account for 86% of the citations in the dataset:

 violation_code |          violation_process_desc          
----------------+------------------------------------------
 T118           | SPEED UP TO TEN MPH OVER THE SPEED LIMIT
 T119           | SPEED 11-15 MPH OVER THE SPEED LIMIT
 T120           | SPEED 16-20 MPH OVER THE SPEED LIMIT
 T121           | SPEED 21-25 MPH OVER THE SPEED LIMIT
 T122           | SPEED 26-30 MPH OVER THE SPEED LIMIT

I’m not going to focus on human speed enforcement, but it is interesting to examine its breakdown by agency:

DC publishes the location of its ATE cameras, but it’s easier to get this information from the citation data than from a PDF. Each citation record includes a latitude and longitude, but it’s only specified to three decimal places. This results in each citation’s location being “snapped” to a finite set of points within DC. It looks like this:

When an ATE camera is deployed in a particular location, every citation it issues gets the same latitude/longitude pair. This lets us examine not only the number of camera locations, but the number of days that a camera was in a particular location.

One last puzzle piece before we get started in earnest: DC’s wards. The city is divided into eight of them. And while you’d be a fool to call anything having to do with race in DC “simple”, the wards do make some kinds of equity analysis straightforward, both because they have approximately equal populations:

And because wards 7 and 8–east of the Anacostia River–are the parts of the city with the highest percentage of Black people. They’re also the city’s poorest wards.

With these facts in hand, we can start looking at the distribution and impact of the city’s ATE cameras.

• Are ATE cameras being placed equitably?
• Are ATE cameras issuing citations equitably?

A high camera location:camera days ratio suggests deployment of fewer fixed cameras and more mobile cameras. A high citation:camera day ratio suggests cameras are being deployed in locations that generate more citations, on average.

We can look at this last question in more detail, calculating a citations per camera day metric for each location and mapping it. Here’s the result:

Some of those overlapping circles should probably be combined (and made even larger!): they represent cameras with very slightly different locations that are examining traffic traveling in both directions; or stretches where mobile cameras have been moved up and down the road by small increments. Still, this is enough to be interesting.

Say, where were those DCPC study “outlier tracts” again?

Area residents will probably have already mentally categorized the largest pink circles above: they’re highways. Along the Potomac, they’re the spots where traffic from 395 and 66 enter the city. Along the Anacostia, they trace 295. In ward 5, they trace New York Avenue’s route out of the city and toward Route 50, I-95, and the BW Parkway. Other notable spots include an area near RFK Stadium where the roads are wide and empty; the often grade-separated corridor along North Capitol Street; and various locations along the 395 tunnel.

We can look at this analytically using OpenStreetMap data. Speed limit data would be nice, but it’s famously spotty in OSM. The next best thing is road class, which is defined by OSM data’s “highway” tag. This is the value that determines whether a line in the database gets drawn as a skinny gray alley or a thick red interstate. It’s not perfect–it reflects human judgments about how something should be visually represented, not an objective measurement of some underlying quality–but it’s not a bad place to start. You can find a complete explanation of the possible values for this tag here. I used these six, which are listed from the largest kind of road to the smallest:

1. motorway
2. trunk
3. primary
4. secondary
5. tertiary
6. residential

I stopped at “residential” for a reason. As described above, camera locations are snapped to a grid. That snapping means that when we ask PostGIS for the class of the nearest road for each camera location, we’ll get back some erroneous data. If you go below the “residential” class you start including alleys, and the misattribution problem becomes overwhelming.

But “residential” captures what we’re interested in. When we assign each camera location to a road class, we get the following:

How does this compare to human-issued speed citation locations? I’m glad you asked:

The delta between these tells the tale:

ATE is disproportionately deployed on big, fast roads. And although OSM speed limit coverage isn’t great, the data we do have further validates this, showing that ATE citation locations have an average maxspeed of 33.2 mph versus 27.9 for human citations.

Keep in mind that this is for citation locations. When we look at citations per location it becomes even more obvious that road class is overwhelmingly important.

ATE is disproportionately deployed on big, fast roads. And ATE cameras deployed on big, fast roads generate disproportionately large numbers of citations.

But also: big, fast roads disproportionately carry non-local traffic. This brings into question the entire idea of analyzing ATE equity impact by examining camera-adjacent populations.

Stuff that didn’t work

None of this is how I began my analysis. My initial plan was considerably fancier. I created a sample of human speed enforcement locations and ATE enforcement locations and constructed some independent variables to accompany each: the nearby Black population percentage; the number of crashes (of varying severity) in that location in the preceding six months; the distance to one of DC’s officially-designated injury corridors. The idea was to build a logit classifier, then look at the coefficients associated with each IV to determine their relative importance in predicting whether a location was an example of human or ATE speed enforcement.

But it didn’t work! My confusion matrix was badly befuddled; my ROC curve AUC was a dismal 0.57 (0.5 means your classifier is as good as a coin flip). I couldn’t find evidence that those variables are what determine ATE placement.

The truth is boring

Traffic cameras get put on big, fast roads where they generate a ton of citations. Score one for the braindead ATE revenue truthers, I guess?

It is true that those big, fast roads are disproportionately in the city’s Black neighborhoods. It’s perfectly legitimate to point out the ways that highway placement and settlement patterns reflect past and present racial inequities–DC is a historically significant exemplar of it, in fact. But ATE placement is occurring in the context of that legacy, not causing it.

Besides, it’s not even clear that the drivers on those highways are themselves disproportionately Black. That’s a question worth asking, but neither I nor the DCPC study have the data necessary to answer it.

The Uncanny Efficacy of Equity Arguments

Before we leave this topic behind entirely, I want to briefly return to the idea of cognitive dissonance and its role in producing studies and narratives like the one I’ve just spent so many words and graphs trying to talk you out of.

The amazing thing about actually, that thing is racist content is that it attracts both people who dislike that thing and want to resolve dissonance by having their antipathy validated; AND people who like the thing. Arguably, it’s more effective on that second group, because it introduces dissonance that they will be unable to resolve unless they engage with the argument. It’s such a powerful effect that I knew it was happening to me the entire time I was writing this! And yet I kept typing!

I think it’s rare for this strategy to be pursued cynically, or even deliberately. But it is an evolutionarily successful tactic for competing in an ever-more-intense attention economy. And the 2018 DCPC study debuted just as it was achieving takeoff in scholarly contexts:

None of this is to say that racism isn’t real or important. Of course it is! That’s why the tactic works. But that fact is relatively disconnected from the efficacy of the rhetorical tactic, which can often be used to pump around attention (and small amounts of money) by applying and removing dissonance regardless of whether or not there’s an underlying inequity–and without doing anything to resolve the inequity when it’s truly present.

Speed cameras are good, stop worrying about it

Speeding kills and maims people.

Speed cameras discourage speeding.

Getting tickets sucks, nobody’s a perfect driver, but ATE cameras in DC don’t cite you unless you’re going 10 mph over the limit. It’s truly not asking that much.

Please drive safely. And please don’t waste your energy feeling guilty about insisting that our neighbors drive safely, too.

map data, excluding DCPC, (c) OpenStreetMap (c) Mapbox

Noting for posterity that I got a second piece published in Greater Greater Washington: this one about the Eckington asphalt plant’s permit renewal.

https://ggwash.org/view/92933/permit-renewal-for-eckington-asphalt-plant-raises-questions-about-air-quality-health

I am both amused and conflicted about being an agent by which such NIMBY sentiment penetrates the region’s best YIMBY site. I wrote the analysis mostly to put out my shingle as a geo guy, not out of heartfelt hatred for this plant. Sometimes it’s hard to account for this last decade, you know? I know Postgis, I swear. I can load Census data for you and tell you stuff about it. It’s all true.

But it turned out that this plant is legitimately weird, situated in an area that’s about forty times as dense as is typical for this kind of facility. Look at that: I radicalized myself.

The actual permit renewal is not going to get rid of the plant, the indignant shock of the folks on the community meeting Zoom notwithstanding. But the neighbors are plenty mad and if Fort Myer Construction Corporation has any sense at all they’re thinking hard about how to maximize this rapidly depreciating asset’s value before some underemployed lawyer in the neighborhood starts giving them real trouble. I’m happy enough to have stoked that fire with some numbers and Javascript.

This will not be of interest to many people, but I documented my recent electronics repair saga over on Reddit.

Posting only to bask. Almost all of the time there is no good reason to accumulate electronics knowledge and doo-dads like the logic analyzer I used to fix my exercise bike. But every now and then…

Kerry Howley’s latest is unsurprisingly great, detailing the history behind a trendy LA health food store that somehow, as a middle-aged dad on the east coast, I had never heard of. I think you should go read it!

OK.

If you remembered this tab, great. Here’s what I want to add: this brought back a lot of memories, and not just fond ones of being (what I hope was) gently mean toward Californians.

My family’s own nutritional choices were idiosyncratic by the standards of my peers, but not wildly so. At my mother’s urging we avoided red meat and favored brown rice. Though come to think of it, how were we supposed to count wild rice mix count? Hard to say. I imagine it harvested in dugout canoes by elders with lined faces and rough-woven shawls, who beat the grains free of their stalks with sticks bearing cultural significances that I am not entitled to contemplate. It seems implausible that this could have a high glycemic index.

My mom’s dietary hunches were absorbed from her friend B. B was a character, and I am delighted and a little surprised to see that she is still alive. I’ll avoid linking to it, but she keeps her yoga instructor resume up to date even now.

My mother would take us to visit B at her house on Lake Barcroft, where she lived semi-tempestuously with occasional deadbeat boyfriends and her parents, two friendly but deteriorating 1950s paragons who seemed like they probably once knew their way around a cocktail shaker. We would sit on the dock, or take a little sailboat out on the algae-choked lake (chemical lawn fertilizers’ fault, we were assured). One time I got bitten by a goose.

B would explain how it was wrong to smack mosquitoes (just relax and let them bite you), why we must never eat onions or garlic, how being deliberate about which nostril we breathed through could help us regulate our body temperature. When she babysat, Bonnie made us chant Om. She undertook idealistic projects: canvassing for SANE/FREEZE, doing volunteer work at American Indian reservations, removing the gutters from her house to improve the aesthetics. These ended with approximately similar results. Once or twice she convinced my mother to bring us to a weekend at an ashram, where I ate bland food, ignored the yoga classes, and briefly swam in a pool filled with green water that was shockingly cold and opaque. I spent these weekends devouring sci-fi novels from my bunk in a rapidly blackening mood.

In retrospect, I’m grateful to have been exposed to ideas this intense and silly at such a young age, because it prepared me to begin noticing when people besides B had them. Including myself.

Surely we have all declared our exasperation with diet fads, but this just means we’re tired of hearing them, not that we intend to stop producing them ourselves. I have relatives who count their renunciation of gluten as a turning point in their lives. Others who swear by the health benefits of drinking only red wine, not white. My immediate family’s dietary limits are a labyrinth of genuine anaphylactic response and intense personal preferences, from which I mostly abstain.

But I do occasionally indulge my own weeks-long dietary impulses. I am currently taking enormous amounts of Taurine, for instance, a non-essential amino acid, though if you ask me this categorization badly undersells it. This idea arrived with just the sort of trappings I enjoy: blogged(!) by an impeccably-credentialed author whose soberminded scientific musings I’ve read for over a decade. There are studies! Who would have thought! A perfectly nondescript white powder, packed into tidy capsules, allegedly already present in your body. You just need more of it, much more, and of course it’s Prime eligible. A perfect supplement for the supplement skeptic. It even comes with a fun anecdote about starving cats and the global chemical industry that you can use, if you find that sort of thing fun, which I do. I have been eating grams of it every day.

I had a hard time relating to B. I never understood why this white lady from Alexandria had framed pictures of of blue-skinned Indian gods all over her shag-carpeted basement. But I suspect that my pill-eating might be motivated by something we have in common. When considering whether onions are bad, or whether eating almonds can be justified on the basis of her vata dosha, B’s aesthetics pushed her toward explanations full of ancient divine warriors and quests to rebalance the cosmos. These rationales never appealed to me (unless you count the snack food ads in Marvel Comics, which I suppose you probably should). But that doesn’t mean they were any less post-hoc than my own.

Eating is pretty weird. At the risk of stating the obvious: we are very complicated chemical reactions that bubble along for the better part of a century, sustained by shoveling gunk inside of ourselves to rot. Horrifying. And a microgram of the wrong thing can bring it all to a stop! Figuring out what gunk to shovel and when is an overwhelmingly urgent biological question, but also such a constant one that it can be guaranteed scarcely more conscious thought than whether or not to take another breath. Consider the quantity of art, institutions, and baroque cultural plumbing we have invented to modulate the process of mating. Is there any reason to think that the natural world has allocated less evolutionary complexity to the problem of eating? It’s practically in the basement of our hierarchy of needs. It is the first deliberate act we must perform, and often the last pleasure we are able to enjoy. Solipsistically, there is almost nothing more important. Yet we can’t stop to build a Taj Mahal every time we feel snacky. We have to get on with things. The significance and complexity of the act are ignored, concealed. Subterranean.

If you don’t give the immune system enough to do it will come up with ways to stay busy, and I think this is approximately true for our other wildly complicated subsystems. If you tallied them all up, which do you think would have more rules and ideas: diet books, or the Protestant Reformation?

Exposure to ideas doesn’t always help you pick the right ones, but it can teach you what extremism looks like. Besides, at some point in my life I realized that being a picky eater was boring, and that I didn’t dislike any food as much as I disliked making the person offering it feel unappreciated. Put a dish in front of me and I will eat it. I can pretty much promise you that. I can’t promise not to have ideas about it–sometimes wild ones. But I will at least endeavor to remind myself that those ideas are probably ridiculous.

This is the equilibrium I’ve arrived at. It might be unreasonable to expect everyone to make the same set of commitments. I suppose I’ll have to leave it at that. I’m already quite behind on today’s Taurine allotment.

Adding a third kid hasn’t made anything easier, but we are getting a little more done. Perhaps it’s the first two maturing. Perhaps it’s the lack of a big seasonal project. Or perhaps my capacity for parental neglect is just being inexorably stretched. But in 2023 I managed to put up the most Halloween decorations in recent memory.

The crawlspace under the house remains absolutely choked with them, row after row of waterproof crates filled with slumbering skeletons and black styrofoam cats. Retrieving them isn’t much fun–“crawl” isn’t a euphemism here, and this expanse of cluttered and rough concrete is an ideal spot for neighborhood animals to conceal their various awful biological compulsions–but it’s always a pleasure to crack those boxes open and rediscover the spooky treasures I’ve amassed over the years. No smoke machines this time, and I didn’t collect the coffin or animatronics from Kriston’s place (the Halloween Annex). Too scary for kids! But our kitchen is currently festooned with fake cauldrons and the basement is bathed in black light. Not bad. It made for a pretty good kiddo party.

Besides that, I’ve mostly been celebrating by reading a few spooky stories, with mixed results. This volume of ghost stories was easy to find on the Internet Archive, and got off to a bang with The Willows, which I hadn’t read but instantly demonstrated why it’s considered seminal. Does it get too many bonus points for a tidy structural trick at the end? Maybe, but when you consider its relatively early place in the genre and influence on Lovecraft, its impact has to be rated pretty highly indeed.

Other entries have been more underwhelming. Shadows on the Wall amounted to nothing, The Messenger ended far too happily, and The Beast With Five Fingers had some fun stuff–harried pursuit of unraveling protagonists, inexplicable menace–but was ultimately prosaic. Lazarus wins points for its distinctively Russian depressiveness, and perhaps for introducing the BUT SOMETHING CAME BACK WITH HIM trope, but it’s not actually interested in being a ghost story. But it did remind me I need to reread The Great God Pan, which was an inappropriate summertime selection earlier in the year, and quite effective in its evocation of prudish occult disgust, but suffered from me being too sleepy while reading to carefully track its somewhat twistingly episodic plot.

But let’s finish with an even more well-trodden recommendation: I’m revisiting The Turn of the Screw and maybe, finally, appreciating Henry James’ subtlety and the interiority of his narrators. I think I was probably too eager to get to the ghosts the first time through. And frankly, I don’t remember any ghosts at all in The Bostonians or The Golden Bowl. Inexcusable. But I’m starting to think this guy might have some talent despite that poor judgment.

I am a much worse reader than I used to be. Kids and prestige TV (but mostly kids) mean I can barely keep up with my monthly sci-fi book club. That’s okay: I long ago reconciled myself to being a not-particularly-fast reader. And maybe some day I’ll have more time.

But this limited intake (and social mechanism committing me to finishing these books) means that the cost of getting stuck with a bad book feels high. And our club has been getting stuck with a lot of bad books. In particular, the recently published books we select are often surprisingly poor. It seems like this trend has been getting worse.

There are a few ways this could be in my head. Book club is a social experience, and it’s more fun to criticize a book than to blandly celebrate it. And the books we select from past years also benefit from additional filtering: the nature of culture means that recent books get discussed more than old books, so if an older book rises to our attention it must have been pretty good.

Still, as a reader it’s hard to escape the sense that something is badly awry in how fiction gets published and makes it into the reviewer ecosystem. I frequently finish a book, thinking it was not particularly good, then dutifully file my review on Goodreads only to find it surrounded by a bunch of effusive 5-star ratings from people who should know better. De gustibus and all that. But something feels amiss.

I think there there are several things going on here. What follows is just hunches based on reading a bunch of mediocre novels and paying close attention to their Acknowledgements sections. I don’t have any connection to the industry. So maybe I have all this wrong. But it’s the kind of stuff that people in the industry would have good reasons to avoid saying. So I’m going to bet on my naivete as a competitive advantage.

Don’t Yuck Someone Else’s Yum

The most effusive reviewers are also the most prolific. They’ve got little badges next to their names declaring them to be the top Goodreads reviewer in Wales, or whatever. They link out to their book-related podcasts and Youtube channels. They are bookfluencers, or aspiring authors themselves (more on this below). They are building an audience, and you know what audiences don’t like? Being told that something they love is bad. This is a fundamental truth about people that I badly wish I could convey to my irascible adolescent self. It’s why every pop culture podcast you’ve ever listened to has only good things to say about that TV show you’re watching. It’s why nobody runs negative reviews anymore, except as an occasional try for virality. It’s why movie critics swallow their grumbling and publish hundreds of words about whatever redeeming qualities they can identify in Ant Man. This is an inescapable consequence of our unbundled Darwinian media ecosystem, and it’s mostly fine, but it means that published criticism is very different, vastly more forgiving, and considerably less useful than my outdated mindset expects.

Pick Authors for Criteria that Matter (So: Not Quality)

It’s famously hard to identify hits in entertainment. Nobody with any taste thinks the most commercially successful books have the most artistic merit. And the new media environment weakens sophisticated gatekeepers’ power to anoint winners. Plus there are way, way, way more plausibly-competent aspiring authors out there than the industry needs to keep shelves stocked. So why should anyone bother trying to find the best books? It’s not like the audience can be counted on to tell the difference. So why not use a criterion that makes more sense? It works for radio programmers, after all.

There are several approaches that suggest themselves. Publishers can pick someone who already has an audience to bring along, like the YouTuber whose middling sci-fi debut we read. Or someone who’ll be helpful to them in other ways, like the author who happened to quietly also be Time Magazine’s book critic. Or the author who, along with their partner, ran a wildly influential sci-fi blog. In all of these cases we picked the book based on the press attention it got, then learned the alternate industry rationale later. And in all of these cases–okay, nearly all–the book was unimpressive but basically fine. Maybe these authors’ success in other domains simply speaks to their overall intelligence and commitment to their chosen genre milieu! I think that’s plausible.

But these filtering mechanisms are different than the (also deeply imperfect!) ones that the genre formerly used, and while they have their merits, they don’t seem to suit me as well as the old ones. An author’s social capabilities seem to be more important to whether their work gets attention than ever before. I think this is why my genre fiction author friend’s anecdotes about trading book blurbs are so depressing. I think it’s why YA authors all behave like psychopaths toward each other. The people who rise to the top of this environment have to produce work that meets a minimum threshold for quality. But beyond that, other considerations seem to be the ones that matter most.

There’s No Money In Books So Book Authors Should Try To Write Something Else

Publishing is a mug’s game: a small number of hard-to-predict breakout hits earn money, and everything else loses it. But even the hits produce paltry returns compared to other forms of entertainment. So if you’re lucky and talented enough to write one of those hits, your first order of business seems to be getting your work optioned for film or TV. One particularly audacious author we read ended his rote sci-fi action thriller, which hewed to every screenwriting formula you could imagine, by thanking the agent that represents him for those other transactions. Perhaps most depressing to me was N.K. Jemisin’s newest series. Unlike the others mentioned in this post, I think being a three-time Hugo winner and possessed of enormous actual talent is enough for me to risk being gently mean by naming her. But The City We Became is an obviously calculated mix of cliched action setpieces and derivative provincial fanservice. It is cinematic in the worst way. But most people haven’t noticed, and it’s on its way to the screen. Oh well. Broken Earth was great and I’m glad to see its creator get paid.

Is it hopeless?

Basically: yes. But not completely. Our club winds up reading a bunch of books that come out of writers’ workshops and MFA programs; or books by self-consciously literary authors who stray over to genre. These aren’t sure bets either (I understand our writerly training systems’ focus on short stories, but think it could stand some interrogation). But they do represent filtering systems that are at least more connected to the work. I don’t doubt that the people running those systems care about craft.

Amazing stuff still gets published, still gets attention, still makes its way into our monthly meeting. But goodness is it nestled amidst a lot of forgettable trash.