Approved Thoughts

The first two stories this week are mindblowing. Huge if true, as they say.

1. Australian Tech Founder Uses ChatGPT and AlphaFold to Design Dog Cancer Vaccine — Tumours Shrink by 75%. Originally seen here and video here:

   A Sydney data engineer with no background in biology has used ChatGPT and AlphaFold to design what researchers are calling the world's first personalised mRNA cancer vaccine for a dog, and the results have stunned the scientists who helped make it.

2. A Fly Has Been Uploaded:

   In 2024, the entire neuronal diagram of the fruit-fly brain–some 140,000 neurons and 50 million connections–was mapped. Later research showed that the map could be used to predict behavior. Now, Eon Systems a firm with some of the scientists involved in the fruit-fly research and with the goal of uploading a human brain has announced that they uploaded the fruit fly brain to a digital environment.

    

   The digital fly appears to behave in the digital environment in reasonably fly like ways–this is not a simulation, the fly’s “sensors” are being activated by the digital environment and the neurons are responding.

3. Some more AI tutorials. So many tutorials, so little time.

Savitha Shan, an undergrad double major here in economics and information systems, who was murdered over the weekend by an Islamist terrorist who started randomly shooting people on Sixth Street, apparently angry about the war in Iran. Two other innocents were also killed. - Scott Aaronson

So senseless. And the 180 schoolgirls Minab, Iran. Did any of them know it was their time? Did they get to live a full life? Will I? It's one thing to know this and another to feel it in your bones. But the worst is when you start feeling it and your self-preservation instinct kicks in - allowing the feelings to only go in so deep and no more. RIP.

Links

1. The Brand Age: Worth reading the whole thing just for this sentence but there's a lot more and it ends in a very different place from where it starts.

   This is an instance of what I call the comb-over effect: when a series of individually small changes takes you from something that's a little bit off to something that's freakishly wrong.

2. The Hidden Advantage of Being Over 50 in the Age of AI: Hope & cope?

   The leaders who win this era won’t just be 22‑year‑olds building AI‑native startups. They’ll also be experienced operators who integrate AI quietly and intelligently into systems they already understand. If you’re over 50 and feeling behind, you might actually be early. Because when the tools get easier, experience becomes more powerful—not less. And this time, that experience may finally be the competitive edge.

4.

5. I wouldn't stand there.

   

I have been following the AI revolution almost since the day ChatGPT launched in Nov 2022. That this was a transformational technology has also been clear to me for almost as long. I even sensed the "vibe shift" in early Jan.

But so far I didn't really "feel the AGI". Last week I did.

AGI stands for Artificial General Intelligence. Here's how Claude defines it:

An AI system capable of performing any intellectual task that a human can — reasoning, learning, and adapting across domains without being specifically programmed for each one." - Claude Sonnet 4.6

It is also worth knowing the related concept of ASI, Artificial Superintelligence.

An AI that surpasses the cognitive abilities of all humans combined across every domain, including scientific reasoning, social intelligence, and creative problem-solving."

Last week we finally signed up for Claude Code at work and I started playing with it.

Claude Code works via the CLI (command line interface) or the terminal - the black screen with white text used by all the movie nerds. It can be a little intimidating if you are not a programmer but really once you set up Claude Code, it works just like the chat window.

It is so much more powerful though. It can manipulate files on your computer and run the code it writes. This means it is not restricted to recommendations or single steps any more. It can generate an entire plan of action and execute and implement the thing by itself. It can be more than a little freaky when you see the output of a particularly complex task.

Let me share a couple of examples that blew my mind.

At work we have a database that stores our financial projections for a company we cover. The investment team can use custom functions in excel to then download this data. This is useful to create reports for analysis - say comparing 5 companies across a few metrics. There are different functions for different types of data and the IT team has created an excel file with about 20 sheets listing the syntax for each function, the list of values that can go in each function etc.

I pointed Claude Code to this help file and asked it to create a "skill" for itself that would allow it to create reports in excel using these formulas. I gave it the same context as the previous paragraph - maybe a little more technical but nothing a lay person wouldn't understand.

With that single command, in maybe 3-5 minutes, the skill was ready. Now when we need to create a report we can just ask Claude Code to use the skill, the data we want in the report and it creates a fully formatted excel file with the (usually) correct formulas. Tasks that would take me or my team members 20-60 minutes, automated permanently. Using English sentences, no technical knowledge.

Second example. We wanted to perform some statistical analysis on 20 year historical performance of 600 stocks to identify specific episodes / time periods and then dig deeper into specific episodes to understand their fundamental causes.

An analyst spent probably 5-6 hours to download the data in excel, process it so we could start identifying the qualifying episodes / time periods in different markets. At this point we were somewhat stumped about how to isolate the relevant episodes from this vast data. Probably a trivial problem for a data scientist but not for us.

In the past, we would have spent probably another 5-10 hours trying to either eyeball the data using charts or some other way to get our answer. Over the last couple of years, we would have asked Claude or ChatGPT to suggest a better way.

But since we had Claude Code, I pointed it to the existing file, with all its messy sheets and structure and explained what we were trying to do and asked it to identify the episodes. It whirred away for about 20 min, an occasional question here or a permission there and then it spat out a report.

But the report didn't just have the episodes identified. It also identified potential causes for each episode (based on web searches presumably), linked patterns across multiple episodes, gave charts and commentary that helped us understand the relevance of each episode, limitations of the analysis, suggested next steps etc.

Now we have all seen more than enough AI slop to not be impressed by the sheer volume of content these tools can spit out. But we spent a few hours verifying the numbers and conclusions. So far it all checks out. You have to take my word for it, but man, this was not slop. If a junior associate had put this out I would be proud of them. There were parts that I would have been proud to create and, of course, large parts that we simply couldn't have created at all.

And those were not the only thing Claude Code did last week that blew my mind.

So, let it be noted on this 8th of March, 2026, I felt the AGI last week. I may even have felt the ASI.

The big news in markets this week was this report from Citrini Research & Alap Shah that apparently crashed the markets and led to a lot of debate in our office. It lays out a "fast take-off" scenario for AI, which causes mass layoffs of white-collar emplopyees as AI replaces intelligence work and starts off an economic downward spiral as demand collapses.

It should have been clear all along that a single GPU cluster in North Dakota generating the output previously attributed to 10,000 white-collar workers in midtown Manhattan is more economic pandemic than economic panacea. The velocity of money flatlined. The human-centric consumer economy, 70% of GDP at the time, withered. We probably could have figured this out sooner if we just asked how much money machines spend on discretionary goods. (Hint: it's zero.)

AI capabilities improved, companies needed fewer workers, white collar layoffs increased, displaced workers spent less, margin pressure pushed firms to invest more in AI, AI capabilities improved…

It was a negative feedback loop with no natural brake. The human intelligence displacement spiral. White-collar workers saw their earnings power (and, rationally, their spending) structurally impaired. Their incomes were the bedrock of the $13 trillion mortgage market - forcing underwriters to reassess whether prime mortgages are still money good.

The report found many believers in markets but I find myself on the skeptical side, much more pusuaded by the many pushback articles which are grounded in conventional economic theory. And they came from many sources.

Here's Tyler Cowen in his cryptic style. Here's Zvi with the inverse. And finally here's Citadel.

And lastly, here's Claude summarizing it all and adding its own perspective:

Why Citrini's Scenario Doesn't Add Up The piece is an excellent thought experiment and a useful sector-level vulnerability map. The macro conclusion — that AI abundance causes a demand collapse and systemic crisis — is built on a fundamental accounting error.

The Core Contradiction: Every Loss Is Someone Else's Gain The entire scenario rests on a demand collapse: AI replaces workers, workers stop spending, the economy spirals. But the same force destroying jobs is also destroying prices. If a Claude agent does the work of a $180K PM for $200/month, then everything that PM helped produce also gets dramatically cheaper. The piece catalogs agents slashing insurance premiums, SaaS costs, delivery fees, real estate commissions, and interchange — then claims displaced workers can't afford things. Which things? The things that just got 80% cheaper?

Every corporate revenue loss in the piece is a gain on the other side. ServiceNow loses $500K in licenses — that's $500K freed for the client. DoorDash loses its 30% take rate — drivers earn more, consumers pay less. Real estate commissions drop from 6% to 1% — that's a 5% stimulus to every home purchase. SaaS fees are a tax on business. That tax went down.

Meanwhile, the piece describes NVIDIA posting records, hyperscalers spending $150-200B/quarter, AI companies thriving. Someone is paying for all of that. You cannot have booming AI revenues and an economy where nobody is spending. The money doesn't vanish — it circulates through different channels. The piece tracks one side of every transaction and ignores the other.

The Timeline Is Physically Impossible The scenario requires mass deployment across every major industry in ~30 months. Enterprise procurement cycles, organizational restructuring, regulatory review, and infrastructure buildout don't move at this speed. More critically: there isn't enough compute on Earth to run always-on agents for every American consumer while simultaneously replacing most white-collar work by 2028. The compute constraint alone paces everything and makes this timeline a non-starter. Even clearly superior technologies (electricity, the internet) took decades to restructure economies.

Zero Policy Response for Two Years Is Ahistorical The US deployed trillions within weeks during COVID. Automatic stabilizers activate immediately when unemployment rises. The piece treats political difficulty in designing optimal long-term policy as equivalent to no response at all.

More importantly: in a world with booming real output and deflation, aggressive fiscal stimulus is straightforwardly correct — you have too much output and not enough demand, so you print money without inflationary risk. During COVID, stimulus risked inflation because real production was down. Here, real production is exploding. This is the easiest macro policy environment imaginable.

What's Actually Useful in the Piece The sector-level analysis of which business models are vulnerable is genuinely good. SaaS pricing power, intermediation platforms, payments, insurance built on inertia, real estate brokerage — anything dependent on friction or information asymmetry is legitimately threatened. The private credit / insurance / reinsurance daisy chain is a real and underappreciated fragility.

The transition will be uneven. Specific sectors, geographies, and demographics will get hit hard, and the speed of disruption concentrated in high-earning white-collar cohorts could cause real dislocations even if the macro aggregates are fine. That's worth positioning around.

But the mechanism for how sectoral disruption becomes a macro demand collapse is where the analysis fails — because it treats every efficiency gain as pure destruction rather than a transfer from incumbents to consumers. Read it as a sector vulnerability map, not a macro forecast.

Going back to the title of my last post from Singapore, there's clearly been a vibe-shift when it comes to what the latest models can do. In early Jan only those focused on AI were feeling it and now it is going more mainstream. But that's no reason to fall prey to half-baked thinking. Let Claude taste it and cook it for you.

Links

1. Mathematics in the Library of Babel: Speaking of vibe-shifts...Daniel Litt is a professor of mathematics at the University of Toronto.

   I think I have been underrating the pace of model improvements. In March 2025 I made a bet with Tamay Besiroglu, cofounder of RL environment company Mechanize, that AI tools would not be able to autonomously produce papers I judge to be at a level comparable to that of the best few papers published in 2025, at comparable cost to human experts, by 2030. I gave him 3:1 odds at the time; I now expect to lose this bet.

2. Andrej Karpathy:

   It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the "progress as usual" way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn’t work before December and basically work since - the models have significantly higher quality, long-term coherence and tenacity and they can power through large and long tasks, well past enough that it is extremely disruptive to the default programming workflow.

   It’s not perfect, it needs high-level direction, judgement, taste, oversight, iteration and hints and ideas. It works a lot better in some scenarios than others (e.g. especially for tasks that are well-specified and where you can verify/test functionality). The key is to build intuition to decompose the task just right to hand off the parts that work and help out around the edges. But imo, this is nowhere near "business as usual" time in software.

3. Best Practices for Claude Code: TBF this looks like an influencer account but I am collecting these guides and this is one.

4. The Claude-Native Law Firm: Another one of the above.

5. Writing about Agentic Engineering Patterns: A more serious work...

   I think of vibe coding using its original definition of coding where you pay no attention to the code at all, which today is often associated with non-programmers using LLMs to write code.

   Agentic Engineering represents the other end of the scale: professional software engineers using coding agents to improve and accelerate their work by amplifying their existing expertise.

6. How will OpenAI compete?: Great read.

   OpenAI has some big questions. It doesn’t have unique tech. It has a big user base, but with limited engagement and stickiness and no network effect. The incumbents have matched the tech and are leveraging their product and distribution. And a lot of the value and leverage will come from new experiences that haven’t been invented yet, and it can’t invent all of those itself. What’s the plan?

7. These Al Prompts Exposed My Biggest Blind Spots: More influencer content but interesting direction.

AI Links

1. The singularity won't be gentle:

   If AI has even a fraction of the impact that many people in Silicon Valley now expect on the fabric of work and daily life, it’s going to have profound and unpredictable political impacts.

2. Rebuilding our world, with reference to strong AI:

   When 2012 passed into 2013, we did not have to rebuild our world, not in most countries at least. It sufficed to make adjustments at the margin.

   After the Roman Empire fell, parts of Europe had to rebuild their worlds. It took a long time, but they ended up doing pretty well.

   After the American Revolution, the newly independent colonies had to rebuild their own world. They did so brutally, but with considerable success.

   After WWII, Western Europe had the chance to rebuild its own world, and did a great job.

   We moderns are not used to having to rebuild our world.

   It is now the case that strong AI is here/coming, and we will have to rebuild our own world. Many of us are terrified at this prospect, others are just extremely pessimistic. It seems so impossible. How are all the new pieces supposed to fit together? Who amongst us can explain that process in a reassuring way?

   Yet we have done it many times before. Not always with success, however. After WWI ended, Europe was supposed to rebuild its own world, but they came up with something far worse than what they had before. Nonetheless, in the broader sweep of history world rebuilding projects have had positive expected value.

   And so we will rebuilding our world yet again. Or maybe you think we are simply incapable of that.

   As this happens, it can be useful to distinguish “criticisms of AI” from “people who cannot imagine that world rebuilding will go well.” A lot of what parades as the former is actually the latter.

   In any case, it all will be quite something to witness.

3. Death of Software. nah.:

   Strap in. This is the most exciting time for business and technology, ever.

4. AI Doesn't Reduce Work - It Intensifies It:

   I think we've just disrupted decades of existing intuition about sustainable working practices. It's going to take a while and some discipline to find a good new balance.

5. Seb Krier: Some of this was weirdly scary.

5. Family deepfakes help people celebrate and grieve in India:

   When the lights dimmed at Jaideep Sharma’s wedding reception in the north Indian city of Ajmer, guests expected to see a cheesy montage of the young couple in various attractive locations. Instead, they saw Sharma’s father — dead for more than a year — on the screen, smiling and blessing the newlyweds.

6. I spent $10,000 to automate my research at OpenAI with Codex

7. My AI Adoption Journey

8. Agent Skills with Anthropic

Other Stuff

1. ‘They All Tried to Break Me’: Gisèle Pelicot Shares Her Story: Words fail me.

   I think we’re going to do great things together. I think we’ll make the most of these beautiful years we have left, and I hope they’ll last very long.

   Amen.

2. Navigating ER / Hostpital in US:

   The most important thing I've learned about hospitals over the last decade: if your loved one needs to be admitted to the hospital, chances are they will get incredible care... as long as that care can be immediately administered in the ED.

   However, if they need to move outside the ED, you must learn as much as you can so you can help expedite the process, advocating to them to get to where they need to go — usually an inpatient floor, as quickly as possible.

   The stakes are probably higher than you think.

3. The Economics of a Super Bowl Ad:

5. Codex:

   No, this is not an AI post. Codex is a NYC bookshop at 1 Bleecker St., at Bowery. It is quite extraordinary in its curation of used books. The fiction section is large, yet you can pick up virtually any title on the shelves and it is worth reading. A wonderful place to go to get reading ideas, plus the prices are reasonable and the used books are in decent shape. Such achievements should be praised.

6. Record Low Crime Rates Are Real, Not Just Reporting Bias Or Improved Medical Care:

   This post will do two things:

   1. Establish that our best data show crime rates are historically low

   2. Argue that this is a real effect, not just reporting bias (people report fewer crimes to police) or an artifact of better medical care (victims are more likely to survive, so murders get downgraded to assaults)

7. Rob Johnson:

8. What it was like to be a bush at Bad Bunny’s Super Bowl performance:

   Some of the biggest stars to emerge from this year's Super Bowl halftime show never even showed their faces on camera. They were the ones who dressed as bunches of grass to transform a football stadium into the sugarcane fields of Puerto Rico.

For the last 2-3 weeks I had been noticing a "vibe-shift" about a jump in the abilities of the leading LLMs. This week that conversation took center stage as many blog posts and tweets raving about the enhanced abilities of Claud Code, especially when using the command line interface (CLI) went viral. I have not had the chance to test it out myself, as I am pre-occupied with the family's upcoming relocation. However, after that, this is top of the list for me now and all links but two below are on this topic. I recommend everyone go down this rabbit hole.

1. A Personal Panopticon (via MR): A great summary.

   A few months ago, I started running my life out of Claude Code. Not out of intention to do so, it was just the place where everything met. And it just kept working. Empires are won by conquest. What keeps them standing is something much quieter. Before a king can tax, he must count. Before he can conscript, he must locate. Before he can rule, he must see. Legibility is the precondition for governance…

   The first thing Claude solved was product blindness. NOX now runs on a cron job: pulling Amplitude, cross-referencing GitHub, and pointing me to what needs building. It handles A/B testing, generates winning copy, and has turned customer support into a fully autonomous department.

   Once I saw this was possible, I chased it everywhere. Email, hitting inbox zero for the first time ever, with auto-drafted replies for everything inbound. Workouts, accommodating horrendously erratic travel schedules. Sleep, built a projector wired to my WHOOP after exactly six hours that wakes me with my favorite phrases. Subscriptions, found and returned $2000 I didn’t know I was paying. The dozen SFMTA citations I’d ignored, the action items I’d procrastinated into oblivion. People are using it to, I discovered, run vending machines, home automation systems, and keep plants alive.

   The feeling is hard to name. It is the violent gap between how blind you were and how obvious everything feels now with an observer that reads all the feeds, catches what you’ve unconsciously dropped, notices patterns across domains you’d kept stubbornly separate, and—crucially—tells you what to do about it.

   My personal finances are now managed in the terminal. Overnight it picks the locks of brokerages that refuse to talk to each other, pulls congressional and hedge fund disclosures, Polymarket odds, X sentiment, headlines and 10-Ks from my watchlist. Every morning, a brief gets added in ~/𝚝𝚛𝚊𝚍𝚎𝚜. Last month it flagged Rep. Fields buying NFLX shares. Three weeks later, the Warner Bros deal. I don’t always trade, sometimes I argue with the thesis. But I’m never tracking fifteen tabs at 6am anymore.

   It feels borderline unfair seeing around corners, being in ten places at once, surveilling yourself with the attention span of a thousand clones.

   A panopticon still, but the tower belongs to you.

2. Claude Codes: The definitive guide to the developments and the conversation, including some useful guides.

   Claude Code with Opus 4.5 is so hot right now. The cool kids use it for everything.

   They definitely use it for coding, often letting it write all of their code.

   They also increasingly use it for everything else one can do with a computer.

3. Toby Lutke, Founder & CEO, Shopify: Do endorsments come any stronger than this?

4. Claude Code Tutorials: Not tried. Saving here for later.

5. Among the Agents: Examples and implications.

6. Andrej Karpathy: Don't worry if you feel you are behind. So does Andrej and he's among the best out there.

7. Self-Driving Cars: The robots are coming too. What a time.

8. The Final Offshoring: More on robots.

   Thus, why should the future be any different? Why should one expect a sudden, dramatic wave of robotics working not just in the coming decade, but the coming handful of years? Why should the curse of Moravec’s Paradox suddenly break?

   The standard answer a savvy technologist would give is that increasingly capable AI video and world models will serve as a “base,” providing real-world understanding, while deployments, whether through teleoperation, data gloves, or egocentric capture, will generate an additional data flywheel. This has already led to interesting emergent behaviors: absorbing egocentric data, tactile sensing, and generalization across environments. And we’re about to scale everything up by 100x. Long robotics. Things will be big soon.

   I think this is mostly correct, but let me add some nuance around both why to be bullish and two of the challenges that robotics faces today.

9. Can Timothée Chalamet Break This Oscar Curse? (NYT): Woke comes full-circle with NYT worrying about unfair treatment of young, white, male actors?

   For nearly a century, Oscar voters have been reluctant to hand the best-actor prize to young men, almost always opting to reward more seasoned performers.

   Though Oscar voters have no qualms about rewarding young actresses, they traditionally want to see more mileage on their men.

10. The Tyranny of the Complainers:

    In 2015, for example, 6,852 of the 8,760 complaints submitted to Ronald Reagan Washington National Airport originated from one residence in the affluent Foxhall neighborhood of northwest Washington, DC. The residents of that particular house called Reagan National to express irritation about aircraft noise an average of almost 19 times per day during 2015.

1. ‘I Was Just So Naïve’: Inside Marjorie Taylor Greene’s Break With Trump: Starting the new year with a revelation - people can surprise you.

   "“After Charlie died, I realized that I’m part of this toxic culture. I really started looking at my faith. I wanted to be more like Christ.”

2. Kazuyoshi Miura, 58, signs with new club to extend record-breaking soccer career - The Athletic:

   He’s known as “King Kazu” in Japan and has played 40 professional seasons dating back to the mid-1980s. He started in Brazil’s Serie A, the country’s top division, with Santos in 1986. He has had brief, varying spells abroad over the course of his career, in Australia, Italy, and Croatia, all before 2000, when he returned to Japan for good, firstly with Kyoto Purple Sanga in 1999.

3. Beginner’s Guide to the Mahabharata and Ramayana:

   Do you desire to know the stories of India’s two great epics, but are intimidated by the massive tomes with hundreds of characters and thousands of pages full of sentences like this: “Ugrasrava, the son of Lomaharshana, surnamed Sauti, well-versed in the Puranas, bending with humility, one day approached the great sages of rigid vows, sitting at their ease, who had attended the twelve years’ sacrifice of Saunaka, surnamed Kulapati, in the forest of Naimisha.”

   Well if so, I’ve got just the guide for you!

4. The Prison Of Financial Mediocrity: I saw this a fair bit on my timeline. The response in the tweet below makes a lot more sense to me though.

   A 25-year-old making $70k is constantly fed content from people their age making $2mn, living in Bali, "working" four hours a day. The baseline for "enough" keeps moving.

   You never catch up. No matter what you achieve, social media will show you what you're missing. The spread between your life and the life you "should" have is maintained algorithmically, forever uncollapsible.

   So you have AI shrinking your timeline AND social media ensuring you never feel like you've arrived. The pressure to escape, NOW, FAST, before it's too late, compounds daily.

5.

6.

7.

A long one to mark a year of link posts. Starting with feel-good stories for the festive season.

1. The best story you’ll read this Christmas. Truly.

2. Your Social Muscles Are Wasting Away. Here Is How to Retrain Them.: Everything old is new again and the search for connection is timeless.

   I’m a married 41-year-old woman who lives with housemates by choice. Rather than trying to acquire as much space and privacy as we could as quickly as we could, my husband and I decided to do the opposite. Parenting in our mid-30s, bursting out of our small London flat, we rented and then bought a London home with another couple.

3. Sisters in Sweat: A couple years ago I played soccer every saturday morning, for about a year. Great memories. I get this. New year resolution.

   SiS has become a lifeline for thousands of women like Almeida in India, helping build a rare space where sport turns into an experience of liberation and camaraderie.

4. How I read: I have stopped reading long form for a while, so I am a sucker for these guides. Not a New Year resolution though.

   One of the many joys of living in New York City is the library system. The Performing Arts Library and Stavros Niarchos Foundation Library (on Fifth Ave across from the main branch) are both delightful places to spend a few hours in Manhattan, and in Brooklyn I spent more than my fair share of afternoons at the Grand Army Plaza main branch. I pick a section and walk the shelves until I get hungry, thirsty, or under-caffeinated.

5. I count AI summarized books as “Read”: Possibly a New Year resolution.

   I upload books to Claude and ask it to “Comprehensively and engagingly summarize and fact-check, writing in Malcolm Gladwell’s style, the book …”. I can read it in an hour instead of twelve. Four bullet points instead of forty. With (this surprised me) roughly the same number of insights I actually do something with.

6. Ruby's Ultimate Guide to Thoughtful Gifts: New Year resolution?? Who am I kidding?

   Give a man a gift and he smiles for a day. Teach a man to gift and he’ll cause smiles for the rest of his life.

7. The Lost Generation: Tough reading.

   At the time, I blamed those women. Of course I did. They’ve since ascended the TV ladder and work as co-executive producers on major shows. On some level, even today I can’t help but think: That could have been me. That should have been me.

   But those women didn’t take our jobs any more than the 50-year-old Hollywood lifers had. The lifers were still there. They’re still there. And I’m not angry at the women and people of color who made it instead of me—people have the right, in most cases the responsibility, to take the opportunities that are offered them—or even at the older white guys who ensured that I didn’t.

8. Paranoia: A Beginner's Guide: Worth reading just for the first line.

   People sometimes make mistakes. (Citation Needed)

9. Chemical hygiene: A good follow up to the previous link?

10. How Did the C.I.A. Lose a Nuclear Device? Where else but in India?

    A plutonium-packed generator disappeared on one of the world’s highest mountains in a hush-hush mission the U.S. still won’t talk about.

11. Castration increases lifespan across vertebrates: Or at least, it feels longer.

12. Pedagogy Recommendations:

    I think the single most thing important I can say is this: Every time you are inclined to use the word “teach”, replace it with “learn”. That is, instead of saying, “I teach”, say “They learn”. It’s very easy to determine what you teach; you can just fill slides with text and claim to have taught. Shift your focus to determining how you know whether they learned what you claim to have taught (or indeed anything at all!). That is much harder, but that is also the real objective of any educator.

13. How Google Maps quietly allocates survival across London’s restaurants: It's amazing the rabbit holes people will go down.

    I needed a restaurant recommendation, so I did what every normal person would do: I scraped every single restaurant in Greater London and built a machine-learning model.

14. I didn't think the current LLMs could solve "out-of-sample" problems, ones that are not in their training set. But I was wrong. And another one. These are hard problems from the looks of it.

15. Automate your life with Claude Code:

16. Copywriters reveal how AI has decimated their industry: It is coming for the white-collar jobs.

    AI is really dehumanizing, and I am still working through issues of self-worth as a result of this experience. When you go from knowing you are valuable and valued, with all the hope in the world of a full career and the ability to provide other people with jobs... To being relegated to someone who edits AI drafts of copy at a steep discount because “most of the work is already done” ...

18. A curated list of the best finance blogs, tools, and webpages.

1. The Reverse-Centaur’s Guide to Criticizing AI:

   Over the summer I wrote a book about what I think about AI, which is really about what I think about AI criticism, and more specifically, how to be a good AI critic. By which I mean: "How to be a critic whose criticism inflicts maximum damage on the parts of AI that are doing the most harm." I titled the book The Reverse Centaur's Guide to Life After AI, and Farrar, Straus and Giroux will publish it in June, 2026.

   But you don't have to wait until then because I am going to break down the entire book's thesis for you tonight, over the next 40 minutes. I am going to talk fast.

2. The Best Philosophy Lectures on YouTube:

   Platforms like YouTube are the home of most slop, but they are also home to some fantastic educational content. I’ve compiled a list of philosophy lectures which you can enjoy, free of charge, to further your philosophical education.

3. Everyone is Gambling and No One is Happy:

   A few weeks ago, Michael Green wrote an article stating that $140k is the new poverty line, that no one can afford to participate in society. It took over the Internet in a fiery storm. There have been many rebuttals, from Tyler Cowen to Jeremy Horpedahl. But the reaction to the piece was very interesting, as John Burn Murdoch wrote about.

   People overwhelmingly agreed with the article (many of the rebuttals to the rebuttals were “who cares if the math is wrong, the vibe is correct!). Both More Perfect Union and the Free Press republished it. People on both sides of the aisle, read the article and said “Well, yes, that is why things feel so bad. This is poverty. My economic pain is justified by the data now. What a relief.”

4. Tangled Parachute Leaves Skydiver Hanging From Plane (NYT): Video at the link.