Adam Thies

Key to note that AI scientists are not experts on labor. Some other economists active on X doing work on AI & labor: @alexolegimas, @danielrock, @joshgans & @robseamans (among many others) But worth noting that economists don’t have a consensus either: aleximas.substack.com/p/how-will-ai-…

INTERESTING 🗞️ A coalition of Midwest unis (including @UChicago @NorthwesternU @UofIllinois) partnered to create a physical space for Midwest founders to SF My thoughts: our top priority should be reducing brain drain. But strengthening the pipeline to / from the coasts doesn’t mean talent is gone for good You can’t ignore the density of $ and networks on the coasts. At the same time, more coastal investors are spending time in the Midwest looking for founders. For ex YC has hosted 5x events in Chicago just in the past year. Tech is no longer strictly regional 🪃 🪃🪃 Part of my bull case for Chicago is the boomerang effect. Founders leave to learn and raise $ then come back to build closer to their customers Many of those customers are here. The Midwest sits on trillion dollar industries now being rebuilt for an agentic driven future. The rails / infra / applications will follow Build the pipelines. Just make sure they run both directions

What do the oldest experimental farm fields in the Western Hemisphere - have to do with Artificial Intelligence and how we're learning to work with it? More than you'd think. I dug into it - along side some family history - in the inaugural post of The Morrow Plots.

My latest Tribune piece argues Chicago's economic diversification and skew towards physical industries provide relative insulation from AI disruption risk and create opportunities to lead. But structural advantages don't convert themselves. Governance and policy have to deliver.

This means there's actually a huge productivity boom underway, btw. It's AI.

This essay “The Last Temptation of Claude” by Harry Law is excellent. This part stuck out: de skilling doesn’t just happen. People dont make one choice—it’s hundreds and thousands of little choices. Off load a memo, off load a section of the paper, etc. they all seem small but then you wake up one day and hey that thing that used to be easy is now quite hard, or even impossible. The temptation is real, so as with all temptation, it’s important to be deliberate and have rules. Everyone will be different, I have mine (write everything on my own, read old fiction)—but they might sound dumb or not work for others. open.substack.com/pub/cosmosinst…

Glad to have this important conversation on 𝗔𝗜'𝘀 𝗹𝗮𝗯𝗼𝗿 𝗺𝗮𝗿𝗸𝗲𝘁 𝗲𝗳𝗳𝗲𝗰𝘁𝘀 with @davidautor, @NatashaSarin, and @DLeonhardt in today's @nytopinion "Are we facing another Industrial Revolution—or something far more profound?" nytimes.com/2026/02/04/opi…🔗#econtwitter

A few random notes from claude coding quite a bit last few weeks. Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent. IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits. Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased. Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion. Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage. Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building. Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it. Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements. Questions. A few of the questions on my mind: - What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*. - Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro). - What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music? - How much of society is bottlenecked by digital knowledge work? TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.

Terrence Shannon Jr. unveils an upside-down banner shirt, making the most of last weekend's mishap 😂👏 All proceeds from the shirt go to a local Boys and Girls Club in Champaign 🙌 (📸: @Sn1per_T)

Couple thoughts on AI, DOGE, and the future of government. 1. Musk will probably succeed in finding waste, fraud, and abuse (it's not that hard), and finding *some* ways to improve govt efficiency & eliminate redundancy. But....

@TomOrlik and @D_W_Wilcox have a piece out in Bloomberg this weekend on the AI productivity paradox. As @erikbryn @ChadSyverson and I discuss, we should expand implementation lags (aeaweb.org/articles?id=10…). Organizations take a long time to change! Link: bloomberg.com/news/articles/…

Thank you 🙏 @BigTenNetwork 💙🧡

@jsnnsa This seems like a bad reading of Gattaca

having kids in the next 5 years might be a tragic mistake every smart bio founder/scientist i’ve talked to seems to think embryo editing for things like short sleeper, reduced cancer risk, etc is possible on a near term horizon imagine having two kids a couple years apart, one is a super human and the other isn’t “sorry jim, little timmy won’t get cancer, only needs 4 hours of sleep and you’re normal you were just born in the wrong order 🤷🏻 “

Down goes Chegg. Couldn't have happened to a better company. #sorrynotsorry wsj.com/tech/ai/how-ch…

I'm excited to reveal an experiment I've been working on for the past month, which be published in the first issue of the @ClunyJournal tomorrow. It's our Trialogue Series—a series of experimental conversations where we push the boundaries of form. For the first one, I invited three interesting thinkers to participate in an email correspondence with me over five days. On the first day, I asked each of them the same provocative question. I followed with a probe into their answer on day two. On days three and four, I asked each person to react to the responses of the other interlocutors. On day five, I revealed the full correspondence to all and invited each person to give a closing remark. To protect anonymity, all emails were with me. I referred to the participants as “A”, “B”, and “C”. In the published text, we reinserted their real names. There are many reasons for doing it this way. Among them: I'm interested in facilitating conversations between people who might not normally be natural conversation partners, and the anonymity and written form makes it feel completely different than a 'panel' or a regular interview. Each person is forced to focus solely on the substance of what is being said. I hope you enjoy the experience of reading it as much as I did facilitating.

Researching my book has me convinced that Champaign-Urbana has the greatest historical music scene in the world. My challenge is to convince absolutely anyone else to care

@emollick … Depends if you count the ecosystem surrounding Taylor Swift a company.

OpenAI is the only company whose corporate communication strategy consists of obscure hints, Delphic pronouncements, and riddles that could fit into adventure games. (Strawberry is rumored to be an advanced reasoning approach in their new models: reuters.com/technology/art…)

(adjacent 😊) happy 20th to achilles heel 💙

Our team at @praxislabs has published our working redemptive thesis for modern AI — six assumptions and six redemptive directions for venture builders (including our AI entrepreneur-in-residence Mark Sears). Here's a thread with a summary. journal.praxislabs.org/a-redemptive-t…