there are three big categories of barriers. The application barrier, the interview barrier, and the document barrier. And that’s what we spent most of our time iterating on and building a system that could slowly learn about those barriers and then intervene against them.

The application is asking, “Are you convicted of this? Are you convicted of that? Are you convicted of this other thing?” What is that saying to you, as a person, about what the system thinks of you?

Often they’ll call from a blocked number. They’ll send you a notice of when your interview is scheduled for, but this notice will sometimes arrive after the actual date of the interview. Most state agencies are really slammed right now for a bunch of reasons, including Medicaid unwinding. And many of the people assisting on Medicaid are the same workers who process SNAP applications. If you missed your phone interview, you have to call to reschedule it. But in many states, you can’t get through, or you have to call over and over and over again. For a lot of people, if they don’t catch that first interview call, they’re screwed and they’re not going to be approved.

getting to your point about how a website can fix this —  the end result was lowest-burden application form that actually gets a caseworker what they need to efficiently and effectively process it. We did a lot of iteration to figure out that sweet spot.

We didn’t need to do some hard system integration that would potentially take years to develop — we were just using the system as it existed. Another big advantage was that we had to do a lot of built-in data validation because we could not submit anything that was going to fail the county application. We discovered some weird edge cases by doing this.

A lot of times when you want to build a new front end for these programs, it becomes this multiyear, massive project where you’re replacing everything all at once. But if you think about it, there’s a lot of potential in just taking the interfaces you have today, building better ones on top of them, and then using those existing ones as the point of integration.

Government tends to take a more high-modernist approach to the software it builds, which is like “we’re going to plan and know up front how everything is, and that way we’re never going to have to make changes.” In terms of accreting layers — yes, you can get to that point. But I think a lot of the arguments I hear that call for a fundamental transformation suffer from the same high-modernist thinking that is the source of much of the status quo.

If you slowly do this kind of stuff, you can build resilient and durable interventions in the system without knocking it over wholesale. For example, I mentioned procedural denials. It would be adding regulations, it would be making technology systems changes, blah, blah, blah, to have every state report why people are denied, at what rate, across every state up to the federal government. It would take years to do that, but that would be a really, really powerful change in terms of guiding feedback loops that the program has.

when you start to read about civic technology, it very, very quickly becomes clear that things that look like they are tech problems are actually about institutional culture, or about policy, or about regulatory requirements.

If you have an application where you think people are struggling, you can measure how much time people take on each page. A lot of what technology provides is more rigorous measurement of the burdens themselves. A lot of these technologies have been developed in commercial software because there’s such a massive incentive to get people who start a transaction to finish it. But we can transplant a lot of those into government services and have orders of magnitude better situational awareness.

There’s this starting point thesis: Tech can solve these government problems, right? There’s healthcare.gov and the call to bring techies into government, blah, blah, blah. Then there’s the antithesis, where all these people say, well, no, it’s institutional problems. It’s legal problems. It’s political problems. I think either is sort of an extreme distortion of reality. I see a lot of more oblique levers that technology can pull in this area.

LLMs seem to be a fundamental breakthrough in manipulating words, and at the end of the day, a lot of government is words. I’ve been doing some active experimentation with this because I find it very promising. One common question people have is, “Who’s in my household for the purposes of SNAP?” That’s actually really complicated when you think about people who are living in poverty — they might be staying with a neighbor some of the time, or have roommates but don’t share food, or had to move back home because they lost their job.

I’ve been taking verbatim posts from Reddit that are related to the household question and inputting them into LLMs with some custom prompts that I’ve been iterating on, as well as with the full verbatim federal regulations about household definition. And these models do seem pretty capable at doing some base-level reasoning over complex, convoluted policy words in a way that I think could be really promising.

caseworkers are spending a lot of their time figuring out, wait, what rule in this 200-page policy manual is actually relevant in this specific circumstance? I think LLMS are going to be really impactful there.

It is certainly the case that I’ve seen some productive tensions in counties where there’s more of a mix of that and what you might consider California-style Republicans who are like, “We want to run this like a business, we want to be efficient.” That tension between efficiency and big, ambitious policies can be a healthy, productive one. I don’t know to what extent that exists at the state level, and I think there’s hints of more of an interest in focusing on state-level government working better and getting those fundamentals right, and then doing the more ambitious things on a more steady foundation.

California seemed to really try to take every ambitious option that the feds give us on a whole lot of fronts. I think the corollary of that is that we don’t necessarily get the fundamental operational execution of these programs to a strong place, and we then go and start adding tons and tons of additional complexity on top of them.

Money became increasingly fake-seeming as it diverged more and more from a hard day’s work and most conventional wisdom.

The growing number of people taking chances that they barely believed in (starting an Onlyfans, going all in on a memecoin, becoming a performative racist for clicks) reflected a rational response to seeing absurd and/or conventionally shitty ideas have outsized success (Bored Apes, Trump, the Babyccino).

bucking conventional wisdom in any direction became the order of the day. Contrarianism became incredibly popular. Taking the diametrically opposed position to consensus as a shortcut to standing out in a crowded and volatile field was a key Clown Town strategy.

As a subset of contrarianism, Hot Sauce Behavior became especially popular. Hot Sauce involves taking something basic or mid and applying a socially forbidden or mysterious spice to it (in place of, or to function as, the X factor or the je ne sais quoi). This element had to be shocking, bad, atavistic, or otherwise “not normal”—it could be Nazism, grooming, the Occult, Catholicism, outright aggression, the threat of violence, or the attitudes of obscure-to-you political groups—but in smallish amounts. It made peoples’ hearts race and adrenaline pump while they consumed something otherwise bland. (This was the Tension Economy as the new Attention Economy.)

If the 2020 degen was a gambler willing to go all in on a whim… …the 2023 degen is a sophisticated risk manager We have found ourselves in a new cultural era in which multiple overlapping crises and rising interest rates have led to an emergent reckoning. It is now widely understood that it was very stupid to play crazy games with tons of excess money instead of actually improving material reality. But certain questions remain: What the fuck is anything worth today? What’s the best way to manage risk while it all comes falling down?

In chess, today’s average player is more skilled than the one from yesteryear because online exposure of advanced theory has led to regular players making the moves of masters. As Virgil once said, “One kid does a new skateboard trick, then hundreds more can do it the next day around the world.”

Everyone should be able to use their increased intelligence and awareness to better navigate the world. In reality, the irony is painful: When everyone gets smarter, things get harder. If everyone is reassessing the most-effective-tactics-available all the time, it gets harder and harder to win, even though you’re smarter and “should be in a better position.” The Yale admissions office realizes thousands of applicants have watched the same obscure how-to-get-into-Yale TikTok, and decides to change the meta: Leadership is no longer a valuable quality.

Max Pain means, even when you’re right, you’re wrong; it describes a climate in which everyone’s opinion is right at some point, but never at the right time.