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I corresponded with a TPM Reader from the tech world recently who marveled at the political salience of data centers when data centers have been around since the late 90s. Many data centers today are orders of magnitude larger than their pre-AI relatives, gobbling up much more energy, making more noise and physical pollution. But he noted that the bulk of them aren’t nearly that large. These points each have some truth to them, though I think it understates the real-world impact of hyperscaler data centers. And, as I’ve noted, a lot of our collective social angst about data centers and AI is really focused on oligarchy — the way that it appears to be the tech oligarchs’ world and we’re just living in it. The ability for town and county councils to say no to data centers is irresistible precisely because it’s the one case where democratic self-government is in a position to say “NO!” and that’s so contrary to all our other experience these days. But there’s an entirely different vantage point on this question I want to focus on.
The impact of particular data centers in particular places is one issue. But the macro-story is even bigger and more jarring. Axios has a piece this morning about how the housing sector is getting hammered by the hyperscaler AI boom, both buildings and buyers. And as you may have heard the lack of affordable housing is a rather pressing issue in American society today. This applies to everything from inflation/scarcity of the physical building blocks that are used for building housing to the scarcity of capital to fund it. If that sounds overstated look at this chart from a study we discussed late last month and which got written up in various publications.
I keep coming back to this graph because it’s so eye-popping, almost impossible to believe. As you see in the legend, the blue line is data centers. The grey line is everything else. Everything else. Commercial properties. Rental properties. Single family homes. Multiple family homes. Pick your preferred flavor of development, NIMBY, YIMBY, urban, rural, it’s in there. You basically have rapid data center growth and everything else contracting.
Many of us first saw the signs of the hyperscale AI boom when we noticed that computer memory had suddenly gotten wildly more expensive, after decades when the price of memory fell as though under the force of gravity year after year. Some forms of computer memory that go into personal computers have quadrupled in cost over the last 18 months because AI’s demand for all forms of computer chips is so great. That’s now spread to the entire economy. This is about more than the AI boom itself. It’s about who the economy is working for. In a general sense, it’s probably a safe bet at any time that the economy is largely working for people with capital. But in an economy driven by consumption, that at least ties back significantly to things people want. The simple reality here is that the AI boom is making huge amounts of money for people at the top of the economic ladder (almost entirely through the equities market) while choking off investment in everything else. Housing and commercial construction are getting crowded out. And this is, as we noted above, at a time when the cost of housing is a huge political issue and one that can only be fundamentally addressed through increasing the supply of housing. We have this running debate about why people are so down on the economy when economic indicators either aren’t that bad or are really good. This is pretty clearly the answer.
You can see all of that through this prism. The interplay between data centers and housing construction captures the larger story in the most powerful and elemental way. There’s nothing new about the economy not being run on the behalf of the have nots or those struggling on the lower rungs of the economy. But this is something different. It’s an economy being run pretty indifferent to the needs or, more importantly, even the consumption desires of the middle and even many of the upper rungs of the economy. The simple reality we see here is that a tiny number of people — dozens at most, mostly in Silicon Valley — are able to drive the direction of the whole economy and in a way that at least in the short-term (the long term probably too, but I’m just talking about what we factually observe right now) is straining everyone else. They’re creating a lot of paper wealth for other people too. But again, that’s heavily weighted to the highest strata of the economic ladder.
It remains difficult to overstate the degree to which the entire private economy has been disfigured by the ongoing AI boom.
Barack Obama, in a 430-word essay:
It is a basic principle of American democracy that the government can’t selectively target individuals simply because of their political beliefs or what they say. But that’s exactly what the Trump Administration is doing to Congresswoman Ilhan Omar of Minnesota. [...]
The fact that any president, or any administration, is willing to engage in such transparent efforts to intimidate someone should outrage all Americans, regardless of how they feel about Congresswoman Omar’s political views. Because if this becomes the new normal, there’s no reason to believe it will stop with her — or that future presidents, in either party, won’t do the same.
Thoughtful, sharp, and eloquent as ever.
But Obama posted this essay on X, which, I have been told in the past week, shows that he in fact is a Nazi sympathizer, a fascist, a pedophile, and unconcerned about racism. As surprised as I was to learn those things about myself, I’m equally surprised to learn them about President Obama, whom I heretofore considered a thoughtful statesman and moral compass.
Either that, or, Threads, Bluesky, and Mastodon have relegated themselves into irrelevance by allowing their discourse to be dominated by a fringe of unhinged kooks who want to be angry and want everyone else to be angry.
Up and to the office, where we sat; and at noon Sir G. Carteret, Sir J. Minnes, and I to dinner to my Lord Mayor’s, being invited, where was the Farmers of the Customes, my Lord Chancellor’s three sons, and other great and much company, and a very great noble dinner, as this Mayor —[Sir John Robinson.]— is good for nothing else. No extraordinary discourse of any thing, every man being intent upon his dinner, and myself willing to have drunk some wine to have warmed my belly, but I did for my oath’s sake willingly refrain it, but am so well pleased and satisfied afterwards thereby, for it do keep me always in so good a frame of mind that I hope I shall not ever leave this practice. Thence home, and took my wife by coach to White Hall, and she set down at my Lord’s lodgings, I to a Committee of Tangier, and thence with her homeward, calling at several places by the way. Among others at Paul’s Churchyard, and while I was in Kirton’s shop, a fellow came to offer kindness or force to my wife in the coach, but she refusing, he went away, after the coachman had struck him, and he the coachman. So I being called, went thither, and the fellow coming out again of a shop, I did give him a good cuff or two on the chops, and seeing him not oppose me, I did give him another; at last found him drunk, of which I was glad, and so left him, and home, and so to my office awhile, and so home to supper and to bed.
This evening, at my Lord’s lodgings, Mrs. Sarah talking with my wife and I how the Queen do, and how the King tends her being so ill. She tells us that the Queen’s sickness is the spotted fever; that she was as full of the spots as a leopard which is very strange that it should be no more known; but perhaps it is not so. And that the King do seem to take it much to heart, for that he hath wept before her; but, for all that; that he hath not missed one night since she was sick, of supping with my Lady Castlemaine; which I believe is true, for she [Sarah] says that her husband hath dressed the suppers every night; and I confess I saw him myself coming through the street dressing of a great supper to-night, which Sarah says is also for the King and her; which is a very strange thing.
Justin.sh is reporting on a confidential Cellebrite document that has been leaked to him:
Cellebrite calls it Advanced Unlocks (AU from here on), not to be confused with the “advanced unlocking services” they’ve marketed for years. A press release from earlier this year confirms AU is a new product for 2026.
[…]
According to Cellebrite, AU:
- is a “next-generation AFU vector that is more resilient to Apple’s OS changes”
- works with Stolen Device Protection enabled, which tightens USB restrictions
- is enabled by a dedicated appliance with a stated range (!) of about 8 meters.
AU = Advanced Unlocks, Cellebrite’s term. AFU = after first unlock.
Update Oct. 20, 7:06 p.m. EDT (2306 UTC): SpaceX scrubbed the launch, targets Wednesday afternoon.
SpaceX was set to launch a batch of 27 Starlink satellites from Vandenberg Space Force Base in California Tuesday afternoon, but ran into what appears to have been a technical issue that wasn’t surmountable during their launch window.
Liftoff of the Falcon 9 rocket from the West Coast is now scheduled during a window that opens at 2:59 p.m. PDT (5:59 p.m. EDT / 2159 UTC) on Wednesday, Oct. 21. The rocket will fly on a southeasterly trajectory upon leaving the pad.
Spaceflight Now will have live coverage beginning about 30 minutes prior to liftoff.
SpaceX will launch the mission using the Falcon 9 first stage booster with the tail number B1063. This will be its 36th flight and will become the second booster to reach three dozen flights.
Roughly 8.5 minutes after liftoff, B1063 will target a landing on the droneship, A Shortfall of Gravitas. If successful, this will be the 229th landing on this vessel and the 673rd Falcon booster landing to date.
During a third quarter earnings call on Tuesday afternoon, hours before the original launch time on Oct. 20, SpaceX Chief Operating Officer (COO) and President Gwynne Shotwell noted that the company added 1.6 million customers in that three-month period. She said the service was active across 174 markets globally.
“This number does not include additional hundreds of thousands of enterprise subscribers and aviation customers,” Shotwell said during the earnings call. “We remain incredibly inspired by the impact of Starlink on communities around the world, including providing service to hundreds of thousands of students and teachers in schools globally, as well as for emergency response during natural disasters.”
In a filing to the U.S. Securities and Exchange Commission (SEC), SpaceX noted that it closed the third quarter with more than 11,100 Starlink satellites in low Earth orbit, which includes the 26 Starlink Version 3 satellites launched during Starship Flight 14.
“The Starship launcher itself will provide additional capacity across markets and enable new sales in high-demand areas,” Shotwell said. “We expect the market for communications to grow far beyond just human communication that we traditionally think about. Connecting cars, robots, and the adoption of super intelligence (artificial intelligence) will increase communication demands not in percentages, but in orders of magnitudes.”
The SEC filing stated that connectivity revenue for the quarter was $5.2 billion, an increase of 22 percent from the previous quarter.
“Total costs and expenses for the Connectivity segment were up $974 million year-over-year, driven primarily by our growing satellite constellation, Starlink Kit deliveries, and marketing initiatives to support revenue growth,” SpaceX said in its filing.
Constant communication is what makes autonomy like this safe. Early contact lets us take maneuvering responsibility, publish ephemeris faster than ground radar can catalog the object, and deorbit and keep commanding through demise if needed
We treat early communications as a requirement, not a nice-to-have
— Starlink (@Starlink) October 6, 2026
Bret Johnsen, SpaceX Chief Financial Officer (CFO), said that the company’s ability to increase the flight rate of orbital Starship missions is critical for future revenue growth on the Starlink satellite internet side of their business.
“Looking ahead, the satellite roadmap is a key driver of both the capacity and capability of our connectivity segment,” Johnsen said. “Through the end of the year and throughout 2027, we expect to deploy [Starlink] V3 broadband satellites in meaningful volume, with each Starship launch ramping towards a payload that delivers over 20 times the downlink capacity of the current generation of Falcon 9 launches today.
“In parallel, we will ramp Starlink Mobile V2 satellites on Starship, starting in 2027, and leverage our newly enhanced wireless spectrum portfolio to expand our direct-to-device and become a major mobile carrier, as Gwynne mentioned, in the United States.”
SpaceX founder Elon Musk said the next Starship launch is anticipated in November and is planned to include a catch attempt of both the Ship and Super Heavy booster. The company is seeking approval for this from the Federal Aviation Administration.
Links for you. Science:
A near-ubiquitous plasmid characterises Second Pandemic Yersinia pestis
Computers Made of Human Brain Tissue Identify Braille Letters In Breakthrough
Eating London’s sewage vegetables
This Might Be the Earliest Known Spear Murder In Earth’s History
The US military’s race to make fake blood
Advancing subseasonal forecasting with machine learning
What Counts as Impact? Bean-counting published papers and impact factors was always a flawed proxy. Generative AI made it much worse.
Other:
A.I. Is Eating the World. When it comes to safety, IBM’s David Cox believes A.I. should be designed and treated more like software: less is more, with controls baked into the design. (gift link)
D.C. General Election Guide: What to Know Before Nov. 3 (make sure you understand the At-Large seat voting issues)
Maybe There Was an Egg Cartel After All
Who really controls Greg Abbott? The Texas governor’s war on public schools is bankrolled by donors who want a Christian state
Cabinet of Curiosities: Richard Cooke takes on Wikipedia
The real reason MAGA fears Ilhan Omar
Power to the People: The fight for publicly owned utilities
Democrats have discovered the key to midterm victory
The Opioid Crisis Looms Over Michigan’s Senate Race
All Too Real
The Senate is one ordinary polling miss from a blowout
The Street Art of Rome
The Men Who Regret Trump—But Won’t Vote Blue
Data Centers Are War Targets Now
Vive
Nidal Hasan’s Public Execution Is Off, But Propagandists Are Still Using Him as a Martyr
Trump’s Crude Bargain: Facing a midterm backlash over Iran-fueled inflation, Trump is again accused of sacrificing Ukraine’s interests for electoral advantage—this time by granting Putin valuable sanctions relief in exchange for a largely symbolic diesel deal
Can a walkable downtown thrive in the heart of suburban sprawl? Utah City is putting that to the test.
Finally, Democrats Are Ready to Go Low
Video Game Reverse Engineering Community Shuts Down Because AI Has Made It Too Easy
Hollywood Is About to Have a Bot Fraud Problem
America’s $10tn AI infrastructure boom is starting to fray
Is It True That If You’re Explaining, You’re Losing?
The College Closure Crisis Is Spreading Beyond Campus
Meta Says Claude AI Tool Triggered No Kings Instagram Suspension
Grifts Inside Grifts
Trump Hides IRS Report on How His Tax Cuts Have Ruined America
Donald Trump is Politically Bankrupt. Be Very Afraid
Even Republicans Are Waking Up to Trump’s Corruption
Do You Really Own Your Home? A Tax Revolt Says No.
Trump says Russia is being “fine” on Ukraine
Writing in the New York Times, David Sanger highlights the extent to which Donald Trump is going all out to support Vladimir Putin’s war of aggression in Ukraine:
President Trump’s intensifying effort in the past few weeks to rehabilitate Russia and usher President Vladimir V. Putin back into diplomatic society is remarkable for its speed, its characterization of Mr. Zelensky as the primary obstacle to peace, and its brazen denial that Russia is continuing the war that Mr. Trump says he is trying to end.
“Russia, frankly, has been fine on Ukraine,” Mr. Trump said on Monday, speaking to reporters outside the White House as he left for a campaign rally. “They haven’t been doing much,” he said, making no mention of the Russian missiles and jet-powered drones that have been targeting apartment buildings in Kyiv and Ukraine’s surviving electricity grid.
In contrast, he said, “We’ve given a lot to Ukraine. They’ve given nothing to us. Remember that.”
As Sanger notes, Trump’s support for Putin is unique in American history. Previous administrations have made deals with erstwhile adversaries, as Nixon did with China, and given up on support for failing allies, as the U.S. did in Vietnam and Afghanistan. But never before have we outright switched sides, let alone supported an aggressor while vilifying that aggressor’s victim.
What makes Trump’s support for Putin even more remarkable is that Russia has, by many accounts, been actively aiding Iran against America in the war Trump started. Who says so? Well, among others, Dan Caine, chairman of the Joint Chiefs of Staff. And the aid goes beyond intelligence sharing that has helped Iran target U.S. forces. Russia has also reportedly been sending Iran explosives and weapons components.
What would Republicans be saying if a Democratic president were supporting a foreign dictator even as that dictator was helping another nation kill American troops? They would be crying “treason.” But this is all apparently “fine” with Trump.
What’s going on here? It’s important to understand that — as Phillips O’Brien documents — Trump hasn’t “pivoted” away from Ukraine toward Russia. He has been a Putin supporter all along.
Thus when Trump says that “We’ve given a lot to Ukraine,” who, exactly, does he mean by “we”? He cut off all economic and military aid to Ukraine as soon as he took office. Here’s total support for Ukraine, by country, since January 2025:
True, the U.S. continued to share military intelligence with Ukraine while allowing Ukraine to use Elon Musk’s Starshield and permitting European nations to transfer some U.S. weapons to Ukraine. But these actions cost America nothing. The most you can say is that until recently Trump shied away from actively sabotaging Ukraine’s ability to defend itself.
And now even that restraint is gone. According to Volodymyr Zelenskyy, U.S. intelligence sharing was blocked “for some period of time,” although it appears to have resumed for now. Ukraine’s access to Starshield has been limited. And the Washington Post reports that the U.S. has been holding up European transfers of Patriot missiles to Ukraine.
There are two questions here. First, why does Trump love Putin and hate Zelenskyy? Second, why has he ripped off the mask and gone full pro-Russia in the past few weeks?
On the first question, Zelenskyy gave a blunt answer in a recent interview with the Financial Times: Trump, he asserted, has an instinctive affinity for autocrats and the super wealthy: “He speaks about it openly that he loves such people and he respects such people.” That’s clearly right.
I would add three points. First, Trump and his inner circle don’t just love autocrats. They hate democracy and rule of law — which means that they hate America and American values.
Second, Putin-worship was widespread on the authoritarian right even before Trump entered the picture, with many celebrating Russia’s anti-wokeness and perceived masculinity. Remarkably, the valorization of Russia’s supposed toughness has survived despite Russia’s repeated military humiliations. So Trump has been following a well-trodden path.
Finally, whatever else you may say about Zelenskyy, he’s a genuine hero, who showed great personal courage by staying in Kyiv when Russia invaded. And Trump surely resents the fact that Zelenskyy is everything he is not.
The second question is, why now? Why has Trump chosen this moment to switch from covert, deniable support for Putin to overtly, explicitly taking Russia’s side?
As I suggested yesterday, the answer lies precisely in Trump’s collapsing support at home. Nothing he does now is likely to avert Republican catastrophe in the midterms. Republican politicians have been going out of their way to avoid being seen with him on the campaign trail. Trump officials are already lawyering up in preparation for the probes a Democratic Congress will launch.
At this point, then, Trump effectively has nothing left to lose on the political front — and his political bankruptcy has freed him to become his worst self, indulging himself by doing what he always wanted to do — which includes supporting corrupt, murderous dictators.
And there will be much more of this to come. Trump can’t and won’t make America great again. But he can betray everything America stands for, and he’s doing it as we speak.
MUSICAL CODA
Trump and Putin?
My latest vibe coded game experiment is a Command & Conquer style RTS (Real Time Strategy) game called Battle Drones (play it here). Unlike my previous experiments, this one is actually fun to play. I'm somewhat embarrassed to admit that I got there by telling Claude to "Make it a LOT more fun". It also supports multiplayer between nearby devices using audio to synchronize game state! Here's what I've built so far, and some of the things I learned along the way.
My initial goal was to build an RTS game I could play on my phone, in the style of Command & Conquer. I have fond memories of the 1995 original (I liked Red Alert too) and wanted to recapture a little bit of the magic.
After 12 major iterations (and 321 commits by coding agents, mainly Claude Code) I now have a browser-based game that works on mobile and desktop, has multiple play modes, several maps (including a random map generator) and even supports multiplayer for two devices within microphone range of each other.
Here's a video demo of Battle Drones running in a desktop browser:
Features so far include:
Battle Drones is heavily vibe coded - I've reviewed almost none of the code, and it's built in a language (TypeScript) that I've hardly used myself. I've playtested it quite a bit though, and it's been through a large number of iterations since I started the project.
Here's a rough history of the project so far, including key prompts and links to transcripts.
I started out with Claude Fable 5 back in July:
We are going to build a mobile friendly RTS game - command and conquer style - called Battle Drones, in which two armies of drones (both flying and ground-based) battle for territory and resources
Let's start with a MVP design, ask me some questions
Here's a shared transcript. I'd given Claude Code for Web an environment variable with an OpenAI API key and told it to use the OpenAI Image Generation API for assets, which got me some basic sprites.
You can try v1 here - it looked like this:
It's recognizable as an RTS, has building mechanics and the ability to select and direct units, and a basic AI to play against.
This first version was barely a proof of concept, and it wasn't any fun to play.
Claude used the Phaser 2D game framework for these early versions. I didn't even realize this until several iterations later!
The second version (try v2 here) added sprites for all of the vehicles and the "AI vs. AI" mode, so I could watch the game playing without having to directly control it myself.
Claude proved reasonably competent at prompting OpenAI's image models to generate sprites. You can browse the v2 sprites in the public/assets/sprites folder.
It still wasn't much fun, and the graphics were boring. It was now October, and I had upgraded to Claude Opus 5.5. I told it:
Massively improve this game. Make the graphics and gameplay better (keep it mobile friendly). Render new graphics using the latest OpenAI GPT image API (lookup details) with the API key in the environment variable. Add new unit types and balance the gameplay better. Also add new buildings and better, more varied terrain.
This resulted in five new maps with generated background tiles in a variety of styles, visible here. Opus also regenerated the sprites, making them much more visually appealing. You can try v3 here - it looks like this:
This is starting to feel like a real game! It still wasn't very fun to play though - I found myself getting bored pretty quickly.
So far my Battle Drones experiment was confirming my previous belief that coding agents can build a thing that looks like a game but can't actually make something that's genuinely compelling to play.
I shared this theory with someone over dinner, and then thought to myself that I should give the models a proper chance to prove me wrong. I prompted Opus 5.5 on my phone:
This game isn't fun enough yet. Make it a LOT more fun.
This time I was running in Ultracode mode, where Claude Code (in this case Claude Code for Web) runs "dynamic workflows", described in their docs as:
A dynamic workflow is a JavaScript script that orchestrates many subagents at once.
This kicked off many hours of work. I started it in the evening and didn't get a playable result back until late into the next morning. But the results were spectacular.
You can try v4 here - it looks like this:
I had Claude write its own release notes - here are the highlights:
Every skirmish and Daily Op now opens with a choice, gives the side that's losing a way back, and pulls the armies into the middle of the map. There are also two new ways to play.
- Openers: pick Blitz, Industry or Air Wing before the clock starts.
- Call-down powers: Barrage, EMP, Drop Pod and the Sky Hammer orbital strike.
- Objectives and salvage: Uplink relays, supply drops and wrecks worth scrap.
- Feel: combat juice, adaptive music, unit voices and an announcer.
- Command: battle alerts, game speed, saved squads and touch placement.
- New modes: Last Stand survival waves and an 8-mission Ops campaign.
- Progression: heroes, an after-action report with awards, a career, 7 mutators and a Daily Op.
Fights run longer and swing more. In 1,200 AI-vs-AI test matches on Normal, games still undecided after 25 minutes fell from 14% to 0.9%. The share of wins by a side that had been 800+ scrap behind rose from 24% to 46%.
Part of the reason Claude took so long was that it ran over a thousand simulated playtest sessions, and used the results to rebalance the existing units, add new ones, and introduce new features.
After the success of Ultracode at "Make it a LOT more fun", I decided to be more ambitious:
Port this game to three.js with 3D models for all of the buildings and vehicles and landscapes
That was another overnight run. I woke up to this v5 version:
The new 3D models were entirely constructed by Claude in TypeScript code.
v6 (play v6 here) added some more visual polish. v7 (play v7) added robot infantry (troopers, rocket squads, snipers) and a "Condor" troop carrying aircraft.
I was beginning to feel guilty at how little actual design input I had into the game - it really had been a case of me saying "make it more fun" and "add visual polish" for the past few releases.
I'd noticed that the AI didn't use the new infantry and troop carrying aircraft from v7 very often - there wasn't a great strategic reason to use them.
It would be neat if we had a new map with a new game mechanic that actively encouraged players to load up Condors with infantry robots and send them out into the map.
This resulted in my longest prompt yet:
Design a new map. Goal is to make different strategic incentives that encourage use of Infantry and Condors and aerial vehicles.
To do this the map will have limited harvester resources but several more uplinks - but uplinks will be on islands that are inaccessible by land vehicles or infantry. These uplinks can also not be claimed using air vehicles. The ONLY way to get them is to fly infantry there in a Condor.
Armies always start with a Condor and a single squad. If an army has no Condor a new one will be provided for free, flying in from the edge closest to their HQ and landing there. That free condor will come with a single free squad on board.
The two HQs should be entirely separated by water, but still accessible by vehicles thanks to two bridges across the map in different places
I want to encourage a battle over uplinks.
The map should be the times the size of other maps - still square
First research and then critique this design. Only build after we have agreed on any modifications
You get to design a new biome for this
The result was the tropical Archipelago map, with seven island uplinks that players can only capture by flying out infantry in a Condor. It's definitely the most fun map to play, once you've got the hang of the basic game mechanics from the simpler maps. The v9 edition made that map a little more interesting - it now looks like this:
I decided to add giant robot mechs to the game - think Jaegers from Pacific Rim, or Titans from Warhammer 40,000.
Here are some of the prompts I used to brainstorm with Claude:
Help me brainstorm ideas for adding giant robots to this game - think dreadnoughts or titans from W20K - how could we do this in a way that enhanced gameplay, kept things balanced, added unique twist? Don't overthink this yet
Let's keep brainstorming. I like the idea that it's a mobile base - that can accept harvested scrap and can have buildings built near it (even if it later moves on) and can produce infantry units too. An interaction with condor would be neat but I don't know what that could be.
Idea: as it gets damage it loses the ability to fire some weapons - by default it has eye laser (like lancers) and arm canons (like titan) and two shoulder mounted mortars, plus machine guns similar to a squad of troops
Damage knocks out mortars and then laser - it always has guns. You can't build next to a building that was only put there because of the mech, you need the mech there for off-base building and only within three squares of it
[...] Lets do two condors can carry the mech
The end result is really cool.
Goliaths can be constructed using a Gantry - a building that can only be created after first building a Fab, Bay, Airpad, and Tech Lab. You can see the Goliath under construction, and it then walks out from the Gantry and can march around the map.
Goliaths can "anchor", which digs them into the ground. In their anchored state they can act as a new HQ, constructing infantry units and additional buildings in a new area.
Two Condor aircraft can hook onto a Goliath, at which point it can be flown around the map!
The game now ends when you destroy the enemy's HQ building and any Goliaths they might have built.
There are also a couple of mutators relating to Goliaths:
The other v10 feature is agent battles. In musing about how multiplayer could work I had an idea: what if different LLMs could fight it out in the game?
Fun extra challenge: think about how we might make multiplayer available to coding agents. It would be neat if we could set up a battle between eg GPT-6 Sol and Claude Opus 5.5 where each model has access to a tool which gives them information about the game (presumably non-visual, but only the information they could see through fog of war etc) and lets them control their units etc [...]
Consult ~/dev/llm/docs and include notes in that file about how LLM could be used here via a new tools plugin
Opus 5.5 went to work on this, producing this AGENT-BATTLES.md proposal and then implementing and testing it. The feature also includes the ability to record and replay these battles - here's a replay of Claude Haiku 5.5 vs. GPT-6 Luna, and here's that same battle in a separate debug tool that shows the underlying prompt responses.
The prompt that drives agent battles can be seen here.
There are a few more replays like this linked in the README. I hope to turn this into a full league table at some point in the future.
This version exists purely because I glanced at my Claude Usage page one day and saw that I'd only used 75% of my weekly allowance and it was resetting in 45 minutes. I decided to see if I could get an extra Battle Drones feature from my remaining tokens, and picked "random map" as the target:
Design and implement a mechanism for generating a random map using any of the available biomes - it should be the size of the largest map and rivers and bridges and rocks and forest and scrap and crystals are all in play. Make them rotationally symmetric and use a seed to allow them to be selected or reused later. You have 45 minutes to build this! Be creative.
Here's the transcript, and an image Claude generated to show me its work:
I only just got this working, so I've not had much time to actively test it yet. The latest Battle Drones now supports multiplayer via two devices sat within microphone distance of each other!
This feature started out as my Chirp Pong experiment from last week. I pointed Claude Code at that working proof of concept:
Clone Simonw/tools from GitHub and look at recent branches for the example pong game that uses web microphone to implement multiplayer between two phones via audio signals
Build a version of that for battledrones
Opus built this Radio Probe tool to measure how much bandwidth we could get between two devices and, based on positive results from that, implemented a full audio modem system. I've tried it between laptops, iPhones and iPads so far and it seems to work really well!
Why use sound for this? Because I love static HTML that costs nothing to run, and I don't want to spin up a server somewhere and then stay responsible for keeping it operating for as long as anyone might want to play the game.
I know almost nothing about game development. This makes it a great opportunity to try out coding agents in a field where my existing software engineering experience has less impact on the results.
Games are also super low stakes. The worst that can happen is one of my silly game experiments suffers a bug.
This morning (via Hacker News) I found Steam Fans Are Begging for a No-AI Filter as Steam Next Fest Is Filled With Slop. I wouldn't personally dream of submitting Battle Drones to an event like this - it's still way too low effort to warrant consideration as a legitimate indie gaming project.
... but I can now see a path to vibe coded games like this being taken seriously. Battle Drones is fun, it looks good, it has innovative features (the multiplayer-over-audio thing is a first for an RTS as far as I can tell).
I'd love to see what someone with better taste than me and a driving passion to produce something truly exceptional could build!
A few other observations from this project:
Math.sin() and Math.cos() and friends with custom versions because different browsers (Chrome vs. Safari, for example) returned very slightly different numbers, desynchronizing the replay.llama.cpp fame).Tags: game-design, projects, ai, generative-ai, llms, claude, vibe-coding, coding-agents, claude-code
Programming turned into one of the greatest jobs you could imagine. You got paid to work on tricky little logic puzzles. The trickier the puzzle, the more you got paid. But even pretty simple puzzles were worth a lot of money because regular people just couldn't do it! No, they needed us. They would give us a problem and we would solve it the way we wanted. We set the strategy, we made the timeline, we defined what was and was not possible.
If you're a programmer who thrives on building working systems and doesn't care too deeply about the problems solved along the way, LLMs are the gift that keeps on giving.
If directly solving those problems was a big part of your enjoyment, the loss is very real.
Via lobste.rs
Tags: programming, careers, ai, generative-ai, llms, coding-agents, deep-blue
From Jesús Fernández-Villaverde:
When machines can perform a growing share of tasks, growth depends on two margins: how the task structure affects the returns to accumulating machines, and how strongly capital accumulation responds to those returns. I argue that, in a standard task model, capital supply governs the size of the growth boom associated with automation, while the task structure sets the duration of this growth boom and the evolution of wages. I document how the same technology path that generates 11.7% annual growth when households choose their saving rate optimally delivers only 2.2% when the saving rate is fixed at the calibrated U.S. 2025 level of 23%. Therefore, slow growth might reflect too little capital accumulation instead of a technological bottleneck. Also, the saving rate rises mainly because automation raises the capital share, not because households become thriftier. Long yields price the growth boom: the ten-year real yield jumps from 5.1% to 13.6% on impact. In my benchmark calibration, hand-to-mouth households lose the equivalent of two-fifths to two-thirds of their consumption, while keeping them on trend temporarily costs at most a fifth of GDP.
Here is a thread on the paper. Here are the slides. More bottlenecks!
The post Saving for the machines appeared first on Marginal REVOLUTION.
It’s time to install an app! But here in 1986, we still call them “programs,� not apps. So, you’ll want to set aside some time to get started installing a program.
Since you have a Commodore 64, there are really only two options for getting new programs this month: COMPUTE!’s Gazette and RUN, and you have to hope that one of those magazines isn’t sold out at the book store. So let’s get going.
You will need to ask your mom if you can ride your bike to the mall. The dirt mall is closer, but the magazine stand at the dirt mall doesn’t carry any computer magazines, so you’ll need to take a longer ride to the good mall, where the book store actually has computer magazines. It’s a couple of miles to the mall, so usually you would have a friend come with you, but you called their house and nobody picked up, so today it’s a solo trip.
Only one book store carries computer magazines at this mall; the other one only carries magazines about “serious� topics, but they can order back issues of a magazine for you if you want, you just have to wait a few weeks and they’ll call you when it comes in.
The guy at the book store doesn’t know the difference between the different computer magazines, so you can’t ask him if they have the one you want in stock, and he’s probably not going to answer a kid anyway. So you just have to go through all the titles one by one, and hope nobody put a copy of People or Omni in front of it by accident.
There are no ratings, and no reviews of the programs themselves. The closest thing you get is the magazine’s own reviews of hardware — modems, mostly — and a cover that promises you the issue has the thing you want. This month’s cover has a “Buyer’s Guide To Modems� banner across the top.
Once you’ve got a copy of COMPUTE!’s Gazette and you’re checking out at the store, you can see that it’s three dollars for the magazine (about nine bucks in 2026 money). If you’re lucky enough to have a five dollar bill, you’ll still have two dollars left over after buying the magazine. That means you’ll have enough money to swing by the record store later and grab a 45 of one of the new singles, like Sheila E.’s “A Love Bizarre,� written by Prince, or The Bangles’ “Manic Monday,� written by Prince, or Prince and the Revolution’s “Kiss,� written by Prince.
Now it’s time to race home with the new magazine in hand. You’ll have to wait for everybody to get off of the TV that’s by the computer so you can switch it over to the channel the computer is on and get down to work.
Turn on the switch, and you’re at the blinking blue cursor where it says READY. while the screen is still warming up a bit.
When it comes to installing apps programs, it’s all about options, and this month, we’re spoiled: there are two! They’re both games: Survivor, an arena game where, “if you’re quick of mind, eye, and hand, you might outfox your opponent,� and Shifter. Truly, an embarrassment of riches. Since Survivor is the one on the cover, we’ll go with that one.
(Two or three programs a month is pretty typical. That’s… the app store.)
Getting this program running is standard stuff — you just read the article on page 36 that describes the game, and then flip to pages 106 and 107 where there’s the code for a short BASIC loader, and then continue on to pages 107 and 108, where there are 345 rows of densely-packed listings consisting of 9 hexadecimal character pairs, all of which have to be typed in perfectly with zero errors or else your entire game will refuse to run.
A quick sample of the early part of your task looks like this:
After you’re done typing that page in, you’re about a quarter of the way done, assuming you didn’t make any mistakes.
Wait, what’s that? Where are you supposed to be typing these in? Simply type them into the MLX program where you… wait, what’s that? You don’t have the MLX utility that’s used to type in machine language code for games and programs listed in the magazine?
Oh, don’t worry, you’ll just need to type in the code for that program. There’s an article explaining what MLX is, which you can read really quickly on page 93, and then you’ll find the code for it on page 94. That one’s only 100 lines of code, so it should be easy enough to type in. And of course, you’ll know if you made any mistakes when typing, thanks to the Automatic Proofreader.
Wait, you don’t have the Automatic Proofreader program?
Well, no worries. It’s only another 31 lines of BASIC code, which you can find on page 97. (This one you have to type in perfectly on your own; there’s no proofreader for the proofreader.) Once you do that, you’re all set.
Now, you can just use the program that lets you enter the program to make your program. It should only take about half an hour to type in the code for the Automatic Proofreader, and maybe another hour or two for MLX. Let’s call it two hours total between them.
After that, Survivor is ~30 lines of BASIC plus ~350 lines of hex code, so… maybe six hours? But at least MLX will catch any typos as you go.
Once all of this is complete, be sure to save your work to a floppy disk so you don’t have to type it all in again! If all you have is a cassette deck and you’re saving to tape, it’s going to take probably 10–20 minutes of staring at a blank screen when you save, so set aside some time for that.
In total, it should only take you about 8 hours to have Survivor ready to play, plus however long it takes you to get to the mall, buy a magazine, and come back.
(Click or tap to get started.)
[This is Survivor by Kevin Mykytyn and Mark Tuttle, from COMPUTE!’s Gazette, March 1986, running in the VICE emulator. The game loads and runs on its own, which takes a few seconds.]
Controls: the yellow player uses W/A/S/D to move and F to fire; the cyan player uses the arrow keys to move and Enter to fire.
Someday, rather than typing all of this in, you might be able to use your 300 baud modem to download already-created versions of these games and tools. You could dial a local BBS, if there’s one nearby. If you’re unlucky, the only one you can reach is a long-distance call, and you’ll be paying by the minute for as long as you’re connected. Or you could look for a local dial-up number for the Quantum Link online service (which will eventually evolve into America Online!).
Transfer times can range from tens of minutes to hours, and of course during those calls, you are tying up what’s usually the sole voice phone line for the household. Anyone picking up a phone handset in the house threatens to knock your connection offline, forcing you to start the whole process all over again.
Now that you’ve enjoyed your game of Survivor, there are some fun ways to customize your little computer and give it a personal touch.
Just for context, here’s a to-scale comparison of the screen resolution of your Commodore 64 to the dimensions of an iPhone Duo’s unfolded screen.
You can’t change the wallpaper on your Commodore 64, but you can easily change the background colors for both the center area of the screen and the border. All you have to do is manually assign the proper numeric color code to the correct memory register corresponding to the values of each of those settings.
Memory address 53281 will set the background color for the primary content area, and a color code of 1 sets a nice readable white background, so it only takes a simple POKE 53281,1 command at the prompt, and you’re all set. Similarly, POKE 53280,4 will make the border of the screen a nice bright purple color, and of course there are a full complement of 16 colors to choose from.
Naturally, none of this customization is stored anywhere permanent, and it will all be lost if you turn off your computer or run a program.
I hope you’ve enjoyed this brief overview of how to get one game running on your Commodore 64, and perhaps gained a little appreciation for how things have gotten a bit simpler over the years.
Also, I will be entertaining a motion to return to calling our software “programs� instead of apps once more. Perhaps we could use the term to distinguish the software that’s created by humans actually typing things in, as opposed to the kind that’s merely generated by machines?
1. Freddie as AI skeptic (and I have given my predictions in fact).
2. Claims about Shakespeare and Marlowe.
3. How did “artificial intelligence” get its name?
4. How is the econ job market this year?
5. AI can now ace econometric coding.
6. The quasi-Riemann proof is becoming intelligible pretty quickly. And new associated math results as a result, authored by humans.
7. Iran’s territorial losses since the 19th century.
The post Tuesday assorted links appeared first on Marginal REVOLUTION.
My observation of air travel is that if there is one significant impact on a flight, there are guaranteed two additional impacts. An example: if my gate changes at the last moment, it is quite likely that departure from the gate will be delayed, followed by a further delay on the tarmac. Unexpected changes in complex systems create further unexpected changes downstream because complex systems are, well, very complex.
A simpler and more personal way to look at this is that when you are traversing complex systems (airports, credit card websites… really any large bureaucracy), there are two paths:
The happy path. Whatever you are attempting to do (fly home, change your auto-pay settings, paying a bill) is a well-known and on-the-rails process. You can sense this happiness because as you complete each action, you receive feedback: “Yes, we know what you are trying to do. Yes, we can help. Yes, we know what to do next. Don’t worry. We got you.”
Divergence from the happy path leads to one specific path: the sad path. All that positive feedback vanishes. I’m not talking about the dark patterns of corporations trying to trick you into not canceling your account; I’m talking about a truly one-off request that is reasonable, but so infrequent that no one built a happy path around it.
Once on the sad path, you are desperately looking for one specific human: The Insider.
Hey, I know you’re mad. I’ve been there, too. You missed the connection, your airline rebooked you onto a partner flight, and now no one can see your checked bag. You started on one website, then the other, and now you’re on the phone with what sounds like a human; you are getting the runaround.
Deep breath.
“Process is documented culture” is what I like to say. Long ago, this company you currently hate was smaller, hungrier, and more nimble. This was working out for them, as evidenced by rapid growth. The problem during rapid growth is that there are so many new humans showing up that it becomes more and more expensive to train these new humans in how this company works, so someone raises their hand and volunteers, “I will write down how we work, and I will share this with the new folks.”
This is a noble person trying to help. Remember that. This person gets the OK, and they (and others) start the arduous task of documenting how the company works and quickly discover two things:
These clearly defined happy paths are fortified, refined, and built into the pillars of “How We Work.” The core business revolves around these many happy paths, but as anyone who has worked in this large business knows, reality is a mess.
PG&E — Pacific Gas and Electric. For reasons unknown, the power was off at the house. No text, no email. Bills were paid. Visiting the website revealed nothing helpful. A call pointed me to a robot who gleefully told me, “Everything looks good.” When I finally reached a human, they reported, “Everything looks good.”
“The power is off.”
“We’re not seeing that here.”
“The power is off.”
“Hmmmmm. Wait. This is interesting. Can I put you on hold?”
I wait ten minutes before the call is dropped.
So what happened? I diverged from the happy path. The human on the other end of the phone probably knew something was up, but didn’t know what to do next because they’d never seen this situation before. Combine that confusion with their incentive — they are measured on how quickly they can resolve calls, so my situation represents a threat: I don’t know what to do here, and the longer it takes for me to figure it out, the worse my review. So, put him on hold. And hang up. Victory!
Having spent many days of my life on the sad path, I know two things:
I call back. Same robot hoops, then the robot reluctantly points me at another happy path human, and I do the only move I know at this point: I get mad. Yes, you catch more flies with honey than vinegar, but honey is not working at this point. I get mad.
“The power is off. It’s been off all day. This is my second time talking to a human, and it takes twenty minutes to actually get to a human. How can you help?”
Moment of truth: I either get hold-dropped again, or they hear the frustration, and they escalate.
Each time I am interacting with a new human — shoulder-to-shoulder — looking to complete a task, I quickly look to answer one question: “Do they get it?” The Insider gets it… immediately.
“How can I help you, sir?”
“The power is off at the house. It’s been 12 hours. Everyone says it’s fine, but the power is off.”
“Hmmmmm. I see. Did it go off at 8:30 am this morning?”
THERE. THERE IT IS. THIS COULD BE THE INSIDER.
Calmly, biting my tongue, slowly, “Yes, 8:30 am on the nose.”
“Got it. Let me… wait. Ooooooh. A 2319 — I haven’t seen this in a long time.”
IT’S THE INSIDER. IT’S THE INSIDER OH MY GOD OH MY GOD ALL MY PROBLEMS ARE SOLVED.
I say nothing. It’s such a rare occurrence to find The Insider. My mind is racing. How can I get their contact info? How do I send them a cake? Their favorite cake. You bet I want to ask what a 2319 is, but I must not interrupt their flow. This is The Insider doing their best work.
“Ok, so I see what happened…” And they explain, at length, completely, confidently. Yes, it’s an initial dumb error on PG&E’s part, but that’s not the point. I am on the phone with a human who deeply understands the complicated, messy, and essential systems around them. They haven’t given up and aren’t just reading the workflows on the screens in front of them; they care… they are curious.
If The Insider seems familiar, dear reader, you are probably thinking of The Wolf. There are similarities to these archetypes.
Like The Wolf, The Insider looks like they work outside of the regular system that runs the business. Incorrect: The Wolf works outside of the system because they despise process; The Insider is an expert on the system. Both understand how the system works, but while The Wolf avoids the system because of its onerous impact on agility and velocity, The Insider knows where the system came from and why it exists. The Wolf thinks the system is a joke to be ignored, but to The Insider, it’s home.
The Insider jumps in when it’s time to help. They jump in on the saddest of paths. They arrive when the situation is foreign to everyone involved and deep system knowledge is required.
Wondering why you aren’t talking to The Insider yet? It’s because your path isn’t sad enough, yet.
Remember that human who raised their hand and said they’d write it down. It didn’t feel like a big deal in the moment. Yes, yes. That seems like a good idea. You zoomed right past a major inflection point of your company. See, until that point, much of what is important about your company existed in the heads of the folks who are running the company. They don’t need an artifact; they know.
The creation of this artifact allows for the creation of a set of employee variants that heretofore have not existed. Broadly, these are enforcers of the process, and their creation, as discussed endlessly, also creates consternation in your company because of the perceived assault on freedom. The argument: Why does anyone need to write it down? We already know.
They do. They do now. In five years, when you’ve grown 10x, they will have forgotten because they’ve moved on… either to harder problems or another company.
You know who doesn’t forget? The Insider. They’ve been here since the beginning, or they were minted during the process inflection point. In either case, they know some or all of the system. They didn’t build it, but they desire to understand it.
And they want to help.
A Russian military satellite maneuvered in "extreme proximity" of a commercial Earth-imaging satellite in recent days in the first known example an unsanctioned rendezvous in low-Earth orbit, according to orbital intelligence company LeoLabs.
The Russian spacecraft, Kosmos 2612, "conducted a deliberate series of maneuvers toward ICEYE-X36, a US-operated commercial imaging satellite," LeoLabs said. "Kosmos 2612 closed from hundreds of kilometers away to extreme proximity to ICEYE-X36. It then maneuvered around ICEYE-X36, remained nearby for an extended period, and then began moving away."
US military officials and independent analysts have tracked Kosmos 2612 since its April launch on a Soyuz rocket from a spaceport in northern Russia. Within weeks, it became apparent that at least four of the six satellites from that launch were stalking ICEYE-X36 after they adjusted orbits to line up with the orbit of the ICEYE spacecraft.
Japan launched a $578 million mission Monday on a five-year expedition to land on the largest moon of Mars and retrieve samples for return to Earth, a daring international project that could yield new insights into the formation of the Solar System.
The robotic Martian Moons eXploration mission, known as MMX, lifted off at 3:41 pm EDT Monday (4:41 am Japan Standard Time Tuesday) from Tanegashima Space Center in southern Japan.
The MMX spacecraft rode into orbit atop a 207-foot-tall (63-meter) H3 rocket, Japan's flagship launch vehicle. The rocket assigned to MMX debuted a new souped-up configuration with four strap-on solid rocket boosters, full-throttle main engines, and an extended payload shroud to accommodate the demands of Japan's heaviest and most ambitious interplanetary mission to date.
A few days ago, a reader chided me for recommending YouTube videos. He told me I was just wasting time. Ouch!
He has a point, though—many of the videos on the platform are lackluster. But if you know where to look, there’s plenty to educate, entertain, and enlighten even the most snobbish viewer.
In that spirit, I share fourteen recent YouTube videos of exceptional merit. Feel free to add your own recommendations in the comments.
I pay close attention to those indie virtuosos who build their careers on YouTube—many have no choice, because they are ignored by the major labels. The big music companies lost interest in this kind of spectacular musicianship long ago. So these hyper-talented individuals bypass the gatekeepers, and go directly to the fans.
Polish guitarist Marcin Patrzałek is one of these self-made viral stars. In a fairer world, artists of this caliber would have institutional support. But we don’t live in that world. So Marcin takes everything into his own (very capable) hands.
He recently released a video in response to critics who insist that playing guitar at this level has to be fake. The real-time transcription is a nice addition.
Marcin didn’t invent this dazzling style of percussive finger-tapping on the fretboard. (A lot of this tradition comes out of my old Palo Alto neighborhood, where Michael Hedges and Stanley Jordan did wonderful things on their six strings.) But he is taking it to another level.
If you’re game for more, here’s another recent video from Marcin. He claims this is his hardest guitar composition. It makes you wonder why this kind of music—which is viscerally appealing even if you know nothing about the guitar—gets so little attention on TV and other corporate media platforms.
I was a formidable Scrabble player in my youth. But I could never compete with Nigel Richards, the greatest Scrabbler of them all. He’s so good he can even win tournaments in French, although he doesn’t know how to speak the language.
David Eldar, another Scrabble champion of note, has claimed that Richards is “the best player at any game that ever existed….He’s not human.”
This video gives some insight into how he does it. But it still seems like magic.
Can a nuclear engineer save Guitar Center—a company that filed for bankruptcy during the COVID lockdown. The Wall Street Journal thinks so.
I know very little about the singer featured below. But I’ve carefully trained my YouTube algorithm, and it now gives me useful recommendations from off the beaten path. The platform recently served up this gem.
Imagine RoboGym. There’s a button labelled “Lift”. Press it & you see a weight go up in the air & back down. Move the slider below & more or less weight moves. A helpful counter tells you how many joules of energy your workout has consumed.
Not the point of a workout? Correct. Genies are being designed as a RoboGym for the mind. What if they weren’t? Wha…
While most Cletus Safaris aren’t worth a damn, this one from CNN does have an important bit (boldface mine):
Barack Obama may have left the White House almost a decade ago, but I have heard his name on the campaign trail with increasing frequency in recent months.
Asked about Trump’s war with Iran, Joe Black quickly pivoted: “Obama was the king of killing people with drones,” he said.
The more difficult it gets to compare Trump’s America favorably to Biden’s, the more likely it is I will hear the name Obama.
Trump loyalists say it’s Obama who didn’t love America — not the president who recently suggested he’d allow Los Angeles and San Diego be bombed by an adversary.
Trump loyalists say it’s Obama who was pulling a fast one on the American people — not the president who is making billions of dollars in crypto.
Obama whataboutisms rarely survive serious scrutiny. But the feelings and the memories, real or imagined, his name invokes for many in the MAGA base feel very real. His election, to them, is America’s true original sin.
And that original sin is The Phenomenon (and Ideology) That Must Not Be Named.
SAN FRANCISCO –Spanish startup UARX Space is remedying anomalies identified during the maiden flight of OSSIE-D, an orbital transfer vehicle (OTV) launched Oct. 1 aboard SpaceX’s Transporter-18 mission. At least […]
The post UARX Space troubleshoots anomalies on maiden OTV Flight appeared first on SpaceNews.
Just as seeing a tree gives you reason to believe it’s there, sensing that a stranger is creepy gives you reason to walk away
- by Marc A Moffett
I am just through the door from several days’ travel and am wiped out, so this will not be a real letter.
Instead, just two observations.
First: it was exactly ten years ago tonight that then–Democratic presidential candidate Hillary Clinton told the American people that Russian president Vladimir Putin hoped to have a “puppet” in the White House, and that Donald Trump was that puppet.
A scowling Trump responded: “No puppet. No puppet.”
Clinton continued: “It’s pretty clear.
Trump interjected: “You’re the puppet.”
Clinton continued: “It’s pretty clear you won’t admit…”
Trump interjected: “No, you’re the puppet.”
Clinton: “...that the Russians have engaged in cyberattacks against the United States of America, that you encouraged espionage against our people, that you are willing to spout the Putin line, sign up for his wish list, break up NATO, do whatever he wants to do, and that you continue to get help from him because he has a very clear favorite in this race.”
Trump shook his head disparagingly.
Ten years later, Trump is openly embracing Putin and is working to destroy the strongest and most successful alliance in human history to cement that partnership, apparently with an eye to enriching himself.
And so, the events of the present change the way we see the past. Had Trump strongly supported Ukraine as Congress has demanded, and stood firm against Russia, a country that is, after all, a minor economic player whose military is flailing at its attempt to absorb its neighbor, we would rightly dismiss Clinton’s warning as campaign hyperbole. As it is, though, Trump’s tie to Putin—the story he has always tried to bury by calling it the “Russia, Russia, Russia hoax”—seems likely to emerge as the central story of the Trump years.
As Clinton wrote today: “In just the last few days: Trump made a deal for the U.S. to buy Russian diesel, an economic lifeline for Putin; he and Elon Musk blamed each other for an order that slowed down Starlink satellite connectivity over Ukraine, hampering the country’s defenses during a devastating Russian attack; and Trump waived sanctions on Russia that Congress, led by his late friend Lindsey Graham, had pushed for. Now, Putin has proposed a summit with Trump and Xi to decide Ukraine’s fate for them, bulldoze a century of international cooperation, and frame Russia as an equal to the U.S. and China.
“We’ll see if Putin’s puppet continues to dance on his strings.”
Second, as no one paying even a modicum of attention can have missed, Trump is so unpopular that support for Republicans has fallen into historically low numbers. The gutting of Medicaid and the Supplemental Nutrition Assistance Program (SNAP), tariffs that have destroyed markets for agricultural products and increased prices, the war on Iran that has sent prices of gasoline and especially diesel soaring and thus increased the cost of everything that travels by train or truck, the blatant corruption that is lining the pockets of Trump and his cronies while ordinary Americans can’t make ends meet, and now Trump’s open embrace of Russian president Vladimir Putin—all wildly unpopular positions—mean that the districts that were firmly Republican by double digits just two years ago are now up for grabs.
That shift has revealed something interesting. Republicans running for office are refusing to hold town halls or to engage in debates with Democratic challengers. When they do show up, they tend to show how unserious they are about filling elected office, as Republican Dan Sullivan of Alaska illustrated when he asked the moderators at a debate with Democrat Mary Peltola if she had been given the questions beforehand because she seemed to be reading from notes while he didn’t know how to answer the questions. As the moderators quashed his allegation, Peltola noted: “I’m prepared!”
In the past, Republicans in safe Republican districts did not have to appeal to constituents or campaign, apparently because the letter “R” was enough to get voters to make a mark next to their name. In this new environment in which constituents are furious with Republican policies, Republicans hoping to be elected continue to keep their heads down, apparently hoping that they can be elected, or reelected, if they simply keep their mouths shut.
Their invisibility raises the question of why these people, who evidently don’t care about representing their constituents, would want to hold national office. Perhaps they want to hobble the federal government to enable wealthy businessmen to gain even more control over the country. Perhaps they want to enable the rise of a theocracy. Perhaps they are hoping to make money off their offices. Perhaps they simply want the prestige of a federal office.
But the extreme gerrymandering of states at the hands of Republican lawmakers that made their candidates shoo-ins in the past also means those Republican candidates are weak. New scrutiny of those candidates as Democrats challenge them is revealing just how weak they are.
In Texas, for example, Republicans are running Trump’s hand-picked candidate for the U.S. Senate, state attorney general Ken Paxton, a stunningly corrupt man who has recently been revealed to have spent New Year’s Eve 2024 partying in Dubai with a mistress and two men who are fugitives from U.S. law. In Kansas the Republican candidate for the U.S. Senate is incumbent senator Roger Marshall, a man who has been revealed as having sued more than 700 of his obstetrical patients for unpaid bills, resulting in 81 arrests.
In North Carolina, Republican U.S. Senate candidate Michael Whatley has little to recommend him other than Trump’s endorsement; the same could be said of the Republican candidate for Senate from South Carolina, Darline Graham, whose main qualification is that she is former South Carolina senator Lindsey Graham’s sister. The story is the same across the country as Republicans field subpar candidates. Even in Maine, where Senator Susan Collins should have been a strong candidate for the party, stories of apparent corruption are undermining her candidacy.
As Paul Waldman noted in Public Notice on October 15, these stories and others are “just the Senate candidates. Up and down the ballot, the [Republican Party] has nominated crackpots and nitwits, none more colorful than Victor Marx, their nominee for governor in Colorado. Marx refuses to say how many people he has killed (though he says his stepfather forced him to commit his first murder at age 7), hunts demons, claims he can call in US airstrikes, and boasts of being a seventh-degree black belt in a martial art he invented. Normal stuff!”
In contrast to the Republicans hoping to coast to election, the Democrats running for office are holding town halls, listening to the people of their districts, needling their opponents who refuse to debate, and using the word “mission.”
The contrast between the two is a snapshot of a dying party that is no longer bothering to compete, and a rising party whose candidates are eager for the job and willing to work for it. It reminds me of the 1850s, when the Democrats, who were so accustomed to holding office that they assumed they were entitled to their posts, met the rising Republicans, whom Democrats considered too uncouth to hold office, but who ended up dominating the political system for the next seventy years.
—
Notes:
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I wonder if this is the first time an economics book has been reviewed in the American Journal of Transplantation.
Poetic justice: A review of moral economics by Alvin E. Roth
Michael A. Rees
"Alvin E. Roth describes poetic justice as the Virginia state flag flying at half-mast in 2020 in honor of Bernie Cohen, who argued the US Supreme Court case Loving v. Virginia that made interracial marriage legal. Turns out Mildred and Richard Loving were convicted of interracial marriage in 1967, arrested by police at 2 AM at home in their bed, and sentenced to 1 year in prison but suspended on the condition that they leave Virginia for 25 years. Reading what the Virginia Court of Appeals judge wrote, side by side with what the US Supreme Court wrote, is, by itself, worth the price of the book.
"Those of us in the field of transplantation have felt—and will continue to feel—the impact of repugnance. In moral economics, Alvin E. Roth takes us on a grand tour of how repugnance shapes transactions across a wide spectrum of human activities. Take uterus transplantation, pioneered in Sweden, a country where repugnance to surrogacy has made it illegal. You can transplant a uterus into a woman with infertility in Sweden, but do not you dare use a surrogate’s uterus in her own body to help you achieve a baby! Alvin E. Roth defines repugnance as a morally contested market that some people would like to participate in, but others think should be outlawed. He goes on to call a transaction repugnant if “all parties to the transaction want to engage in it, and others think they shouldn’t be allowed to for moral or religious reasons, even though those others can’t detect that the transaction has taken place unless someone tells them.”1 I found it fascinating to walk through all the ways in which repugnance has shaped the world in which we live.
"The book starts with a clear example of a transaction that many people want to engage in: sex. Of course, I knew about the repugnance surrounding prostitution and pornography, but could hardly believe the stories of how far people go on either side of a repugnant transaction. I found my own views challenged by what I thought was right, especially when considering the outcome I desired from the controversy being described. It was interesting to have these stories told by the “Father of Market Design” and to think about sexual desire as a contested marketplace with good and bad outcomes that could be influenced by better market design.
"It is not the first time I have been surprised to think about a human desire for something like love or health as a transaction that economists could study. I first met Alvin E. Roth in about 2004 when he thought he could help us transplant professionals do a better job with kidney exchange. I made the mistake of thinking that economists only worried about money, albeit at both the microlevel and macrolevel. This book makes clear that if we want to have the desired outcome of helping as many people as possible through organ transplantation, we are going to have to think like market designers and overcome repugnance to innovation. Remember Richard Lower in 1968 being threatened with criminal charges for “wrongful death” for removing a donor heart prior to the establishment of brain death criteria?
"My favorite chapter was about reproduction. You would not believe all the interesting twists and turns that have happened as artificial reproductive technology has blossomed, and some people wanted to have babies using it, whereas others were morally aghast at their desire, working hard to stop them. It was interesting to see different sides of contraception, abortion, in vitro fertilization, egg and sperm donation, paternity, maternity, parenthood, and surrogacy. But Alvin E. Roth has such a nice way of distilling complicated concepts, such as this quote: “Some critics have said that looking for an egg donor who attends Harvard, for example, reeks of eugenics. In response to that, Professor Kim Krawiec, perhaps the leading legal scholar of “taboo trades,” gave a very cogent response: “Fertile people have been screening for beauty and intelligence for years and years. It’s called dating.”2
"If transplantation attracted you as a field in part because of the ethical challenges encountered when considering organ donors and recipients, Moral Economics provides a narrated guide through thorny issues. Near the end of the book, we find perhaps the best call to action for those of us who want to solve problems. The Persian poet Rumi invites us to navigate repugnance: “Out beyond ideas of wrongdoing and rightdoing, there is a field. I’ll meet you there.”3
1. Roth, A.E.
Moral Economics: From Prostitution to Organ Sales, What Controversial Transactions Reveal About How Markets Work. Hachette UK; 2026
2026
2. Jones, A. Putting a Price on a Human Egg.”
Wall Street Journal. July 26, 2015;
Accessed XXX www.wsj.com/articles/putting-a-price-on-a-human-egg-1437952456
3.Rumi, “Out Beyond Ideas of Wrongdoing and Rightdoing.” Poetry Society of America.
Every now and then I will write a piece on food for Works in Progress. Here is my piece on eating in Brazil:
Brazil, like most large nations, also offers multiple cuisines. For instance you can go to Minas Gerais for pork and beans. Minas beans would be my favorite of what I have sampled, surpassing Mexico. Belem serves cuisine from the Amazon region, including exotic river fish, dried shrimp, duck, and rice, with flavors from cassava, manioc, and the local fruit. You cannot get those dishes in many other places.
Feijoada is of slave origins, and is often associated with Rio de Janeiro, but it has evolved into a national dish, available almost everywhere. Think rice, black beans, collard greens, orange slices, and farofa, a kind of manioc flour, with different parts of the pig mixed in. At home I cook the vegetarian version of this dish. The coolness and sweetness of the orange go well with the sandy texture of the farofa, and the meal hardly costs anything to make.
Rio de Janeiro offers highly cosmopolitan options, and the city has a tradition of excellent French and Continental restaurants.
The best churrascarias I have had were on the outskirts of Curitiba in the south. You can get an excellent pasta meal combined with your choice of many kinds of meat, leading to an impossibly large quantity of food at your disposal. I favored these establishments more in my youth, but if you have never tried them, you are missing something.
Campo Grande, the booming agro-city in central western Brazil, has a derivative form of Okinawan soba (!) as a dining specialty. I regret that, due to a delayed flight connection, I have yet to try this apparent delicacy.
There is much more at the link, recommended.
The post On Brazilian food appeared first on Marginal REVOLUTION.
Immigration affects natives through both labor markets and public budgets, yet these channels are usually studied separately. We combine them in a general-equilibrium model with search-and-matching frictions, skill-specific progressive labor taxes, capital taxation and public goods, calibrated to 17 OECD countries over 2015-2019. The welfare effect on natives of a one-percentage-point increase in immigrant inflows varies in sign and size across countries, hinging on immigrants’ skill mix, immigrant-native wage gaps, resident unemployment, and tax progressivity. For inflows matching the skill composition of 2019-2024 arrivals, native welfare rises in 14 of 17 economies, and in most cases both skill groups gain, through different channels. Lower-skilled natives gain mainly from higher pre-tax earnings, driven by job creation and complementarity with skilled immigrants; higher-skilled natives gain mainly from lower labor-tax burdens. Evaluating immigration policy thus requires modeling labor-market and fiscal channels jointly: either channel alone gives an incomplete picture of who gains and why.
That is from a new NBER working paper by
As a side note, I do not accept the authors’ claim that the immigrants are equally productive yet underpaid, and thus the native economy benefits from capturing that surplus. The better mechanism is simply that immigrants boost market size, and (especially for the United States) that brings some gains from increasing returns to scale, more innovation, making the country a talent magnet, and so on. The point holds that the gains from trade effect can be small, and it still will overwhelm what otherwise appear to be net fiscal costs.
The post The net fiscal impact of immigration should include gains from trade appeared first on Marginal REVOLUTION.
Andrew Chow, writing for Time:
Just below the broad smile he’s been wearing all morning, John Ternus’ hands are shaking.
On this September day in Silicon Valley, Apple’s new CEO is surrounded by celebrities, tech enthusiasts, and reporters who have flocked to Apple Park from around the world to catch a glimpse of the company’s new and long-awaited foldable phone, the Duo. After Ternus takes a few photos with the device, an Apple PR rep pulls the influencer known as IShowSpeed out of the crowd to meet him. Winning over the 21-year-old isn’t exactly the toughest assignment Ternus has faced in his 25-year Apple career: Speed barks when he sees the phone and then gleefully springs into a backflip.
But IShowSpeed also happens to be livestreaming the encounter to his 61 million subscribers on YouTube. And as Ternus thumbs through the $1,999-and-up phone, attempting to drag a photo into a text-message box, eagle-eyed viewers notice his hands quivering. Ternus later explains that he didn’t sleep much the night before. “I was on such a moment of adrenaline and caffeine and excitement for the day,” he tells Time.
There’s only a glimpse of Ternus’s personality revealed here. To date he’s more of a cipher than even Cook. He’s Apple’s San Francisco sans serif typeface brought to life. Handsome, distinctive, decidedly “Apple” — but plain. That, surely, is not the real John Ternus.
So it’s fitting that his ascension coincided with the announcement of the Duo, Apple’s most radical design change to the iPhone since its 2007 release. Apple has been exploring the concept for years. In 2011, when many people still carried around flip phones, Ternus and five other engineers filed a patent application for foldable electronic devices, exploring what kind of batteries and circuit boards might be needed for such an idea. “It felt clear that we were going to eventually get to a place where OLED displays were going to be flexible,” Ternus says.
The Ternus profile is the cover story for Time’s “Best Inventions of 2026”, which is led by the iPhone Duo. Let us not forget that this esteemed accolade was awarded to Humane’s AI Pin in 2023.
At the end of September, Apple was conspicuously absent when President Donald Trump hosted a luncheon for AI power players like SpaceX’s Elon Musk, Meta’s Mark Zuckerberg, and Google’s Sundar Pichai, who lobbied him on their visions for America’s tech future.
But Ternus and other Apple executives say they aren’t concerned. In the midst of a populist AI backlash, they contend that they are building a modest but personalized and privacy-preserving form of AI that will actually help ordinary people as opposed to merely dazzling Silicon Valley’s “lunatic fringe,” as Greg Joswiak, senior vice president of worldwide marketing, puts it. And besides, Ternus argues, even if other companies succeed in their mission of building super-intelligence, users will still want to talk to it on Apple devices.
This is, I’m convinced, the correct strategy for Apple. They’re smart not even to be ankle-deep in the wild capex spending pool that has all of their peer companies way out in the deep end, where their feet no longer touch the bottom. But this is a bet, and bets carry risk.
Also, I’d bet my bottom dollar that Joswiak is, in fact, a decades-long fan of Lunatic Fringe.
Greg Joswiak, on X:
Can’t wait to pro and tell next week.
Rumors suggest Apple is planning to debut the new MacBook Pro with an OLED touchscreen display, an M6 entry-level MacBook Pro, an M6 iMac, an iPad mini with an OLED display.
Yesterday I posted the same question to my followers on X, Threads, and Mastodon:1
Poll, only for people who have pre-ordered a Duo. When is your delivery/pickup date?
- October
- November
- December
- January
The results were interesting; I’m glad I asked.
| X | Threads | Mastodon | Total | |
|---|---|---|---|---|
| October | 36% | 38% | 39% | 37% |
| November | 35% | 42% | 45% | 39% |
| December | 10% | 9% | 8% | 10% |
| January | 19% | 11% | 8% | 14% |
| Responses | 1,382 | 1,034 | 449 | 2,865 |
Observations:
There were way more people who successfully got October ship dates than I expected. It’s very close to even with November ship dates.
The fact that there are fewer December ship dates than January, is to me, a sign of Apple’s appreciation for the holidays. I think Apple is only promising “December” for shipments they are 100 percent certain they can deliver by Christmas. I bet a bunch of the orders promised for January will in fact ship in December — albeit perhaps too-late-for-Christmas December.
Apparently some configurations are getting an estimated date of February 1. I wish I’d listed February as an option, but I don’t think it would have made a big difference.
Per MacRumors today, some pre-order estimates are already moving up, like from early December to late November.
No one should mistake my audience as representative of the general public in any poll, but I bet my audience is at least ballpark representative of the general public when it comes to something like pre-ordering, at the crack of dawn, a new iPhone form factor that costs $2000 to $3200.
Anthony D’Alessandro, reporting for Deadline:
Dan Lin is out as film boss at Netflix in a move that has been largely speculated for weeks. Insiders at the streamer have denied that Lin and even Bela Bajaria were on the chopping block, but here we are.
Lin is meeting with his feature staff Monday morning for a post-mortem after the news. Per sources close to the situation, a successor to Lin won’t be named until the arrival of Peter Rice, who is taking over Netflix’s Chief Content Officer role from Bajaria.
Netflix’s movies are worse than their series, which also mostly stink. (As opposed to Apple TV, which somehow makes terrible movies while making the best series in the business.)
it has long been understood that, qualitatively, a “netflix movie” exists in the liminal space between theatrical release and Lifetime production
with some notable exceptions, even the biggest budget productions feel cheap, strange and worst of all, unmemorable
Fred Potter, on LinkedIn:
So if you ever meet someone who tells you, “At Apple, I’m the one who did this,” take it with a grain of salt. They were probably part of the team that made it happen, and that’s already something to be proud of.
Sometimes, when the final product finally comes out, you can recognize a few of the personal touches you contributed along the way. And that, in itself, is immensely rewarding.
Wishing HomeView every success!
Potter’s LinkedIn bio lists his time at Apple as July 2021 through June 2024, and describes it only as “Worked on HomeView”. (Via Christophe Laporte at iGeneration.)
Joe Flint and Jessica Toonkel, reporting for The Wall Street Journal last week:
Netflix is cutting 6% of its staff, or roughly 1,000 positions globally, its biggest round of layoffs since 2022, as it struggles with slowing growth and customer engagement. Co-Chief Executives Greg Peters and Ted Sarandos said in an email to employees Wednesday morning that the bulk of the cuts would be in the streaming giant’s product, tech, legal and human-resources teams.
The layoffs may have hit those teams (including some of the talent that created the excellent Duo support for Netflix’s iOS app), but Peters and Sarandos seem to know where the fundamental problem lies: Netflix has peaked and their content blows.
Flint, in a follow-up report the next day, “Netflix Replaces Content Chief as It Struggles to Engage Viewers”:
Netflix’s Chief Content Officer Bela Bajaria is leaving the company, as it struggles with slowing growth and falling customer engagement.
Yours truly, back in August: “Netflix Has Peaked”.
In my earlier posts on the Habba-Grenell mutual self-immolation spectacle, I’d told you that while Ric Grenell is as nasty as they come, I was skeptical that he was quite dumb or reckless enough to be involved in what Alina Habba and her new husband were alleging.
But I may heve been wrong!
There’s a new report out in Axios that adds a bunch more detail to the story. But before getting to that report, there’s some important context and caveats. I wouldn’t see this new Axios report as quite a “report” in the sense of new reporting that advances the story. It’s better to see this as three players (the White House, Grenell and Habba/Mildon) each of whom are trading a very hot potato back and forth between the three of them and hoping not to be the one left holding it when the music stops. But instead of a literal potato, that lashing out is taking place through new batches of leaks. We need to be clear that each batch of new leaks is from an interested party, with its own goals and agenda. And it may include some false details or more likely ones that are misleading. But on balance each batch of leaks is probably mostly true, helps fill out the emerging picture and sinks both adversary parties (Grenell and Habba/Mildon) deeper into the quicksand.
This new batch of leaks seems to be from the White House, but contains information that came from Habba/Mildon. The gist is that Habba/Mildon turned over text messages from Grenell which seem to set out a pay-for-play deal in which Mildon will give Grenell $17 million for high level access to the president and top administration officials. A lot of that access will be arranged through events at the Kennedy Center, which Grenell then ran. $5.6 million of the $17 million was supposed to go to the Kennedy Center. (Adding some credibility to these claims, Mildon did seem to receive that kind of access.) The rest was for “software” Congo was buying or a “software” deal that somehow involved the Texas-based defense contractor which was the direct recipient of the $17 million and which Grenell consulted for at least in 2024. The utter vagueness of detail about what some or all of this money was actually or purportedly for is a huge red flag.
Grenell’s defense seems to be that all these texts are fake. He apparently notes that in one of the texts he or what is alleged to be him spells the “center” in Kennedy Center with the British spelling “centre.” It’s true that I certainly wouldn’t expect an American to use that spelling. But that seems like a pretty thin argument for Grenell to rest his whole case on. More generally, it’s certainly possible that the texts are fake — they seem to have been presented to the White House as print-outs. But that also sounds like what you say when you’re caught dead to rights and that’s all you can say. (Sort of like, I didn’t melt down on Twitter. Someone hacked my account.)
As I noted yesterday, the White House has full access to all the forensic capabilities of the U.S. government — DOJ, FBI, intel community. Normally, it wouldn’t be appropriate to call on those capabilities to settle a fight among high-profile fixers. But I doubt Donald Trump will have many scruples about that. Point being, I’m pretty sure the White House can and will figure out whether or not those texts are real and get to the bottom of just what money went where.
The other part of the equation is that while Grenell may be in trouble, Habba isn’t in good shape either. The Axios piece quotes what seem to be White House sources saying Habba “went off half-cocked” bringing these accusations directly to Trump. And her story evolved rapidly after her first volatile meeting with Trump. The Kennedy Center donation was first $17 million, then $6 million and now apparently $5.6 million. She and her husband also first tried to hide that the money was from the government of the Democratic Republic of Congo, suggesting that it was a check from Mildon. In other words, it’s all consistent with Habba dumping a lot of sewage on Trump’s lap because she was trying to do in Ric Grenell without realizing that she was likely sinking herself in the process.
Let’s also note that Axios — in line with what I said above — seems to be speaking in the voice of its source, clearly the White House here, and presenting this as stern Mr. Trump — who definitely is totally super against corruption and pay-for-play — trying to get to the bottom of what happened and ready to come down hard on the malefactors.
Needless to say, this is utter BS. Trump loves corruption and he’s been totally happy with the free-for-all he’s overseen for the last 20 months. But it’s true that he’s become a lot less tolerant of anyone in his administration that gets found out as of about two weeks ago.
Outstanding questions: What exactly is this “software” deal or purchase that purportedly involves the DRC and this Texas defense contractor? Is anything about “software” even real? What is the role of the notoriously corrupt government of the DRC? And what is the story with this defense contractor? We seem to know only its rather nondescript name, TXAT.
As I noted last night, in terms of news and politics, I’m not sure the specific details even matter that much. A better global view is that Grenell and Habba/Mildon are in a Trump world fight for their lives and each new fusillade of leaks pulls them deeper, or further entrenches them in a story about whatever corrupt dealings they were involved with. Each of the three players (the White House, Grenell and Habba/Mildon) wants the mud to be on the other guy. But the ongoing battle is just spreading the mud around more and more.
Today’s world would be a fascinating object of study if one weren’t also living in it. The price of energy isn’t the cause of Donald Trump’s deep unpopularity. But for months it’s been driving it to new depths. The price of oil plateaued in early October, which seemed to blunt some of Democrats’ electoral momentum. On Friday though it jumped up again, with the Brent Crude price hitting $110 a barrel, the highest since May. That was the worst of all possible worlds for Trump and that clearly played a key role in his decision to waive sanctions against Russia. Reuters reports that the prospect of greater supply from Russia knocked oil off those highs in early Monday trading in Asia. That was clearly the main goal of that waiver. But just in the last hour, reports have come out that Ukraine launched a massive volley of 1,000 drones targeting Russian energy infrastructure.
Back in 2022, I quoted from an interesting post by Ben Schlappig at One Mile at a Time, where he wrote:
I’ve had the opportunity to interact with quite a few senior airline executives over the years, and I find that their knowledge about competitors is typically one extreme or the other:
- Some airline executives literally couldn’t tell you the first thing about their competitors, and have no knowledge of their passenger experience, fleet, etc.
- Some airline executives know literally everything about their competitors, down to being able to tell you how many seats competitors have on specific planes
I find there are very few executives who are just kind of in the middle, as it’s pretty polarizing. Either they know virtually everything about competitors, or nearly nothing. Without naming any names, I’ll say that generally speaking there’s a high correlation between airlines that have a great passenger experience, and airlines that have executives that keep very close tabs on the competition.
And I wrote:
If the CEO’s primary perspective on the company is via spreadsheets — if it’s all just P&L to them — that company is not going to excel at quality. Or if they do excel at quality, they won’t for long.
I think Tim Cook was truly an exception in this regard. He’s not a product guy, and never pretended to be. He is a spreadsheet guy. But he clearly realized that about himself, and he understood that Apple’s entire point is delivering great products and experiences. But I think Ternus is a breath of fresh air in this regard. Apple didn’t immediately switch to decision-making by quantifiable numbers under Cook, but incrementally, year-by-year, decision-making by quantifiable numbers took over.
You simply cannot assign numeric values to everything, and some of those things that cannot be quantified are the most important things. Apple needs a CEO who makes some major product decisions by gut alone.
Waked with a very high wind, and said to my wife, “I pray God I hear not of the death of any great person, this wind is so high!” fearing that the Queen might be dead.
So up; and going by coach with Sir W. Batten and Sir J. Minnes to St. James’s, they tell me that Sir W. Compton, who it is true had been a little sickly for a week or fortnight, but was very well upon Friday at night last at the Tangier Committee with us, was dead — died yesterday: at which I was most exceedingly surprised, he being, and so all the world saying that he was, one of the worthyest men and best officers of State now in England; and so in my conscience he was: of the best temper, valour, abilities of mind, integrity, birth, fine person, and diligence of any one man he hath left behind him in the three kingdoms; and yet not forty years old, or if so, that is all. I find the sober men of the Court troubled for him; and yet not so as to hinder or lessen their mirth, talking, laughing, and eating, drinking, and doing every thing else, just as if there was no such thing, which is as good an instance for me hereafter to judge of death, both as to the unavoidableness, suddenness, and little effect of it upon the spirits of others, let a man be never so high, or rich, or good; but that all die alike, no more matter being made of the death of one than another, and that even to die well, the praise of it is not considerable in the world, compared to the many in the world that know not nor make anything of it, nor perhaps to them (unless to one that like this poor gentleman, who is one of a thousand, there nobody speaking ill of him) that will speak ill of a man.
Coming to St. James’s, I hear that the Queen did sleep five hours pretty well to-night, and that she waked and gargled her mouth, and to sleep again; but that her pulse beats fast, beating twenty to the King’s or my Lady Suffolk’s eleven; but not so strong as it was. It seems she was so ill as to be shaved and pidgeons put to her feet, and to have the extreme unction given her by the priests, who were so long about it that the doctors were angry. The King, they all say; is most fondly disconsolate for her, and weeps by her, which makes her weep;1 which one this day told me he reckons a good sign, for that it carries away some rheume from the head.
This morning Captain Allen tells me how the famous Ned Mullins, by a slight fall, broke his leg at the ancle, which festered; and he had his leg cut off on Saturday, but so ill done, notwithstanding all the great chyrurgeons about the town at the doing of it, that they fear he will not live with it, which is very strange, besides the torment he was put to with it.
After being a little with the Duke, and being invited to dinner to my Lord Barkeley’s, and so, not knowing how to spend our time till noon, Sir W. Batten and I took coach, and to the Coffee-house in Cornhill;2 where much talk about the Turk’s proceedings, and that the plague is got to Amsterdam, brought by a ship from Argier; and it is also carried to Hambrough. The Duke says the King purposes to forbid any of their ships coming into the river. The Duke also told us of several Christian commanders (French) gone over to the Turks to serve them; and upon inquiry I find that the King of France do by this aspire to the Empire, and so to get the Crown of Spayne also upon the death of the King, which is very probable, it seems. Back to St. James’s, and there dined with my Lord Barkeley and his lady, where Sir G. Carteret, Sir W. Batten, and myself, with two gentlemen more; my Lady, and one of the ladies of honour to the Duchesse (no handsome woman, but a most excellent hand). A fine French dinner, and so we after dinner broke up and to Creed’s new lodgings in Axe-yard, which I like very well and so with him to White Hall and walked up and down in the galleries with good discourse, and anon Mr. Coventry and Povy, sad for the loss of one of our number we sat down as a Committee for Tangier and did some business and so broke up, and I down with Mr. Coventry and in his chamber discoursing of business of the office and Sir J. Minnes and Sir W. Batten’s carriage, when he most ingeniously tells me how they have carried themselves to him in forbearing to speak the other day to the Duke what they know they have so largely at other times said to him, and I told him what I am put to about the bargain for masts. I perceive he thinks of it all and will remember it. Thence took up my wife at Mrs. Harper’s where she and Jane were, and so called at the New Exchange for some things for her, and then at Tom’s went up and saw his house now it is finished, and indeed it is very handsome, but he not within and so home and to my office; and then to supper and to bed.
Footnotes
Interesting research:
Abstract: Vision-language models (VLMs) can solve visual CAPTCHAs without task-specific training, but the agents built on them approach every challenge from scratch. For such an agent, the hundredth instance of a familiar puzzle costs as much time and compute as the first. Specialized detectors invert the trade-off, answering in milliseconds but only for categories they were trained on. Neither improves with exposure. We study what changes when a solver improves with use. Our system pairs a fine-tuned YOLOv8 detector with an open-weight VLM behind a confidence-based router, and runs entirely from screenshots and operating-system input events, with no browser automation or DOM access. It reaches 85.4% overall and 84.2% macro accuracy across 16 classes, exceeding either component alone. Every answer VLM produces also serves as a training label, so the detector absorbs categories it was never trained for, typically after one or two encounters and without human annotation. The same loop also repairs it. A CAPTCHA operator can perturb images against the publicly released detector and drive its accuracy to 0%, but the perturbations leave VLM untouched, and its labels let the detector recover. Under a year-long simulated arms race in which the CAPTCHA operator re-crafts its perturbations each month, the solver recovers every round, and a cheap ~70%-accurate open-weight teacher hardens it as effectively as a perfect oracle. Visual CAPTCHA defenses that assume a failing bot stays failing therefore understate how quickly an adaptive solver returns.
Ridgeline subscribers —
Oh, hello. I made a notebook: ⬤. You can buy it here. I didn’t mean to make a notebook but, you see, I had no choice. A couple of years ago the Japanese edition of Kissa by Kissa came out. While iterating over materials and size, a handful of blank so-called “dummy books” were made. They were sitting around my studio. Earlier this year I used one as a notebook. Goddamn, I thought, this is a nice notebook. Not perfect, but almost. When I used up the first dummy I grabbed another dummy. I used that one up, and then had no more dummies. I looked around for a replacement. Scoured stationery shops to no avail. None existed. At least none that hit my self-concocted design targets. Then I realized: I COULD MAKE IT MYSELF. And in realizing that, I was legally and morally obligated to do so.
Links for you. Science:
Nobody tests for Covid-19 anymore, right?
People Say These Peptides Hurt Them. Their Stories Are Muffled in MAHA’s Push for Medical Freedom.
These deep-sea spiders give off an otherworldly glow
What’s the point of MIT? OpenAI’s math proofs have thrown students and researchers into chaos.
What Was Going on at Shanidar? Another Neanderthal Reported With Fatal Wound
There Is Some Mighty Weird Stuff Going On Up There
Would You Rather Have Sex Or Live Forever?
Other:
Beards and loathing: A cop union convention comes to Las Vegas
‘Tommy’ from viral El-Sayed video is former Sen. Hawley staffer with Christian nationalist ties (more on the Teepell family here)
No Fake Makeovers For The Post-Trump GOP
CFTC Staff Were Told to Retract Analysis of Suspicious Oil Trades
Trump’s Corruption Is Different In Kind
Stephen Miller Desperate to Stave Off Accountability for Losing Texas
AI, Tech Leaders Are Lobbying Trump Health Officials in a Government-Run Chat Room
Public Execution: Acts of violence are a sacrament in the fascist civic religion. The new fascist man must be posted into being.
How Congress Can Take Back Its Power Over the Courts
The world’s most vocal racist will be the Republican Party’s biggest donor again. But don’t mention it.
Man Creates DIY Flock-Style Cameras That Only Scan Police Plates And For Some Reason The Cops Are Annoyed By This
TikTok “Permanently Suspends” the ‘Musk’ Film Account, Raising Suppression Fears
A Capital of Trinkets
Schools face an unanticipated problem: Soaring fuel costs are threatening their budgets
Rebecca Solnit’s (Mis)Adventures in the Manosphere
In the Heart of Jerusalem, the U.S. Right Told Jews: Stop Crying Antisemitism
Obviously, the Knicks Should Not Visit the White House. Why Is This Even a Discussion?
A look back at the Berklee-area arts scene of the 1990s and early 2000s
Elon Musk’s James Talarico Smear Blew Up In His Face
Stone, Earth and Fire: in a Village Near Nazareth, Women Keep the Ancient Secret of Bread Stamps Alive
If European Politicians Weren’t Shit
Did Trump unplug Starshield because Putin asked him to?
Ah, Well, Nevertheless
Mom who survived school shooting coaches son through Oregon attack: ‘Please be as quiet as possible’
I Teach at a Business School. It’s Time to Shut Them Down.
The media’s Ilhan Omar blind spot handed Trump an opening
The Secret History Of Dodger Stadium’s Mysterious Concrete Murals
The Secret Power in the A.I. Model Wars. The U.S. agency tasked with testing frontier A.I. models is understaffed and underfunded, and its evaluation process is “completely opaque.” Can the Trump administration and its new “Super Intelligence Force” stop CAISSI from “pacing the frontier” by default?
VA allows union contract to expire for most employees. Individuals hired as departmental police officers, firefighters and security guards, exempted from Trump’s anti-union orders, remain covered under the expired collective bargaining agreement, albeit only for “mandatory” conditions of employment.
I Built an AI Bot to Fact-Check the Midterms. Here’s What I’ve Learned. Many websites hate bots, consensus has its limits, and the six AI notewriters dominating Community Notes are run by X itself.
One of the frustrating things about DOGE is that so many of those little shits have profited from their time spent helping themselves to our data and firing dedicated public servants. So this suggestion for some accountability should be filed under “you live by the sword, you die by the sword”, with the House DOGE Committee being the sword:
Here is a tangible short-run action to hold DOGE accountable. Lets assume Democrats gain control of Congress in November. There is an existing DOGE subcommittee in the House. Reconfigure it with staff and members able to make it do the job it has failed to do thus far: provide real oversight of DOGE. Run hearings that puts former DOGE members under oath. Call whistleblowers the DOJ has ignored. Call the “blockers” Lewis wrote about and other witnesses to abuse. Create public salience and a record that will lay the groundwork for criminal investigations in 2029. Establish credible signals that they won’t get away with it.
And when you do this, don’t have hearings in the usual alternating five minutes of blather for each side. Run roughshod over the Republicans–enough with the ‘we need to work with fascists in a bipartisan manner’ bullshit–and have real hearings, with the threat of contempt charges if they lie.
Because Democrats do need to “establish credible signals that they won’t get away with it”, and this would be a good start.
Release: llm-echo 0.5
I'm working towards a new LLM release, and testing PRs caused me to revisit the llm-echo debug plugin. This plugin provides a "model" which doesn't involve an LLM at all - it just repeats back the prompts that have been fed to it. The new release now updates the simulated token usage counts to take tool calls, system prompts, and reasoning tokens into account.
Example usage:
Market Design
uvx --with llm-echo llm -m echo 'hi there' -o thinking 1 --usage
Outputs:
First I consider the prompt, then I decide what to say.
{
"prompt": "hi there",
"system": "",
"attachments": [],
"stream": true,
"previous": []
}
Token usage: 2 input, 24 output
Tags: llm
Canadians are quietly (and politely) running the world. This country gave me some of my favorite musicians, like Grimes and Avril Lavigne, and some of my favorite comedians, like Nathan Fielder and Norm Macdonald. When it comes to nerds, no one makes them like the University of Waterloo.
In the latest episode of our Canada series (if you missed them, go watch our adventures with NordSpace and Dominion), we head to Waterloo, where the vampires hang out. You can also read my written feature on this voyage here.
According to CNN, all-out war with Iran may be about to resume:
The US military has developed new strike plans that include potentially using the so-called “mother of all bombs” to target Iranian energy infrastructure and nuclear sites should President Donald Trump give the order to resume combat operations either before or after the midterm elections, according to multiple US officials familiar with the matter.
Trump says that he won’t order a strike before the midterms. But Robert Pape, author of the Substack Escalation Trap, argues that we shouldn’t believe him — that the bombs may well drop before the midterms. And while I am not a strategic expert, I believe that Pape may well be right, because his analysis of Trump’s political position is something I recognize from financial economics.
You see, it’s a well-understood proposition that the owners of businesses that are on the verge of going bust often engage in destructive behavior. And Trump is about to go bankrupt — not financially (he’s made billions through corruption) but politically. So he, like a failing businessman, has strong incentives to wreak havoc.
Pape’s argument is basically a version of the phenomenon — a particular form of moral hazard — often called “gambling for resurrection.” When a company is on the edge of failure, it makes sense for the company’s owner to make highly speculative, even crazy investments that are highly likely to fail but might, just might, make enough money to pull the company back from the brink. After all, for the owner it’s heads he wins, tails his creditors lose.
In Trump’s case, all indications are that his party is heading for defeat in the midterms. And while a normal president might be able to live with a Congress controlled by the opposing party, there has been so much corruption and malfeasance over the past two years that Democratic control of Congress, and the subpoena and investigative power that will bring, will have catastrophic consequences for Trump and everyone around him.
So Trump has an incentive to do something, anything, that might shake up this election. Attacking Iran would probably lead to an even bigger Republican defeat, but it might, just might, rally Trump’s base enough to change the outcome. True, a president who cared at all about what is good for America wouldn’t pursue disastrous policies for the slim chance of saving his own political skin. But this is, after all, Trump we’re talking about.
Pape’s argument, then, makes a lot of sense. But I suspect that Trump’s impending political bankruptcy will have even worse consequences than Pape suggests — and that these consequences will continue and intensify after the midterms.
When I read Pape’s analysis, I found myself thinking about a classic 1993 paper by George Akerlof and Paul Romer, “Looting: The economic underworld of bankruptcy for profit.” Akerlof and Romer were inspired by several historical episodes of widespread bankruptcy, including the U.S. savings and loan crisis of the 1980s. In these episodes, they argued, owners of banks and other companies on the verge of failure didn’t gamble for resurrection. Instead, they simply took the money and ran, transferring as much of their companies’ assets as possible into their own pockets before the hammer came down.
So what would looting by a politically bankrupt Trump administration look like? I don’t mean financial looting — Trump has been doing that all along, on an epic scale. What may be about to happen — indeed, is already happening — is what we might call policy looting: Trump using his remaining power to take actions that are both deeply unpatriotic and deeply unpopular but fit his own preferences and prejudices.
Freedom, wrote Kris Kristofferson, is just another word for nothing left to lose. Once the midterms are over, Trump, with nothing left to lose, will be free to be his worst self — a malignant narcissist who instinctively supports authoritarians and is preoccupied with revenge against anything and anyone that threatens his fragile grandiosity.
We can already see that happening in U.S. policy toward Ukraine. It has long been obvious that Trump loves dictators and despises democracy, and that he has special affection for Vladimir Putin and hatred for Volodymyr Zelenskyy. However, as long as there was some chance of limiting the damage in the midterm elections, Trump was cautious about explicitly taking Putin’s side. Over the past few weeks, however, Trump has grown ever more open about supporting Putin’s attempted war of conquest — buying Russian diesel, canceling sanctions, and most recently by attempting to sabotage Ukraine’s defenses by degrading its access to Starlink.
Something similar has been happening on domestic policy where, for example, Trump has increasingly stopped pretending that he cares about illegal immigration or immigrant crime, and is veering into flat, open racism — so far specially directed against Somalis, but it won’t stop there.
So will Trump resume the bombing of Iran before the midterms? Obviously I don’t know, but Pape is surely right that normal political logic, under which a deeply unpopular president would avoid resuming a deeply unpopular war, no longer applies. And anyone who believes that Trump will become more cautious, more moderate in the aftermath of midterm defeat just doesn’t understand the new political reality.
The truth is that political bankruptcy will free Trump to pursue his true desires. And those desires are horrifying.
MUSICAL CODA
Release: llm-tools-datasette 0.1.1
When I switched LLM over to httpx2 in LLM 0.33 I forget that this would break any plugins which used httpx without directly depending on it. llm-tools-datasette was one of those plugins, now fixed.
This paper examines the revenue and migration impact of the 2026 California Billionaire Tax Act, a one-time 5 percent tax on the worldwide net worth of individuals exceeding $1 billion. Using data from the Forbes Billionaire List, news reports detailing the residential real estate holdings of California billionaires, and public announcements of billionaire departures from the state, we estimate the behavioral response and associated revenue loss to this new tax using several different methodologies. Even before accounting for reductions in other tax bases such as the income tax, we estimate that the Act will collect approximately $30 billion (range $28 to 36 billion), less than a third of the $100 billion projected by Galle et al. (2025). This range accounts for the publicly confirmed taxpayer migration that has already eliminated nearly 30 percent of the Act’s wealth tax base, applies a litigation-survival discount to the remaining base, and is validated by the wealth tax base elasticity literature. We estimate that California’s billionaires contribute $3.3–5.8 billion annually in state income taxes. The present value of permanently lost income tax collections from departures more than offsets the one-time wealth tax revenue, yielding a net present value of negative $38.9 billion. Across 100,000 simulated revenue and discount rate combinations, 85 percent of plausible outcomes produce a negative net present value of state revenues.
That is from a new NBER working paper by Benjamin Jaros and Joshua Rauh. Overall this issue is a good litmus test for who still thinks like an economist.
The post The California billionaire tax is very likely a net revenue loser appeared first on Marginal REVOLUTION.
Ramez Naam is, in my opinion, one of the best futurists alive. But although he has a Substack (which you should definitely follow), he doesn’t write nearly enough. So I’ve been encouraging him to write more by cross-posting his posts on my blog.
Somewhat to my surprise, Ramez has turned out to be a lot more of an AI skeptic than I would have though. He’s certainly not dismissive of the technology, but he disagrees with many of the more expansive and dramatic claims coming out of the big labs (especially Anthropic). In his first post in this series, he expressed skepticism that recursive self-improvement (RSI) would lead to a fast takeoff of AI capabilities in the near future:
In this post, he expresses skepticism about the potential for agent swarms — large numbers of AI agents working in parallel — to offer dramatic increases in AI capabilities. Agent swarms have been in the news a lot, both because of the Hugging Face attack, and OpenAI’s solution of the famous Navier-Stokes problem is also widely attributed to a large swarm. But Ramez argues that parallelization is just another kind of input that suffers from diminishing returns, and that we should therefore temper our expectations about what agents swarms are able to achieve.
As was the case with his previous post, I’m not sure how much this matters, when the capabilities of the underlying models — swarm or no swarm — are still improving at such a steady and impressive rate. But it’s a very intellectually interesting argument, and Ramez can be very persuasive.
In my previous post, we asked whether AIs could improve themselves all the way to absurdly high levels of superintelligence. My conclusion: today’s feedback loop is probably too weak, because improving AI faces diminishing returns at every step. Even fully autonomous AIs doing all the research and coding don’t make those diminishing returns disappear. We should keep updating that conclusion as real-world parameters and data change.
But we didn’t look closely at another possibility: running millions or billions of AI minds in parallel. How much do they help? That’s what we’ll look at here.
Plenty of people have modeled how more AI agents working in parallel could fuel recursive self-improvement. But the data we have today tell a different story. More agents are useful for speed: they can solve many problems faster. They’re less useful for boosting AI intelligence. Multi-agent gains in solving harder problems often diminish incredibly quickly and may even saturate. Whether multi-agent systems can produce the conceptual breakthroughs RSI might require to become self-sustaining is the harder question.
Without breakthroughs in how well agents parallelize problems - something that may depend on the structure of machine learning problems themselves - it’s hard to see even millions or billions of copies of today’s models making repeated breakthroughs on the hardest problems. These are among the strongest diminishing returns we’ve looked at. Of course, these diminishing returns may change. And we have less experimental data to go off of today than we’d like. We’ll need to recalibrate as new evidence comes in. I’ll end with the things we should track and my predictions about a natural experiment from newly released Anthropic data.
Below is the two-image summary: The experimental observations of how large numbers of agents scale to solve harder problems (what I’m referring to as intelligence) are far far lower than the assumptions in studies that predict that AI recursive self-improvement will lead to an intelligence explosion. The first image is at the scale of 100 parallel AI agents. The second is at the scale of a billion AI parallel agents.
Real world experiments show dramatically lower intelligence gains from parallel agents than hypothetical AI models.
Extrapolated out to a billion agents, the intelligence gain in hypothetical models is orders of magnitude beyond real world experiment gain curves.
To recap the last post: recursive self-improvement (RSI) means AI improvements feeding back into the next round of AI research. Four findings stood out:
Real AI research is harder than benchmark scores suggest. OpenAI’s models needed human help on much shorter research tasks than the coding benchmarks led us to expect.
More AI activity produces much smaller gains in useful work. OpenAI used 124 times as many tokens per researcher but ran only 1.6 times as many experiments per researcher.
Maintaining progress requires ever more resources. Training and thinking time face steep diminishing returns. Anthropic reports that more powerful AI has been key to maintaining the current pace of AI progress.
The measured software feedback loop doesn’t yet look self-sustaining. Our tentative calibration put it roughly five to ten times below the strength needed.
I want to reiterate a distinction here: fully automated AI self-improvement doesn’t at all guarantee an intelligence explosion. Diminishing returns in AI capabilities dampen that feedback loop. Parallel agents can help automate a search across AI parameters, but don’t look nearly as useful in making new conceptual breakthroughs. My expectation is that we’ll need those sorts of breakthroughs for self-improvement to become self-sustaining, let alone to accelerate rapidly and reach truly profound levels of superintelligence.
Today, let’s examine in much more detail what it would mean to run thousands, millions, or billions of AI researchers in parallel.
In this post I’m focusing only on software: AI researchers improving the algorithms that make AI better or cheaper to run. Hardware, investment, and economic growth have their own feedback loops, which we discussed briefly in the last post. Here, we’re asking how much more the AI minds themselves can discover.
Here’s the argument, with links:
In Machines of Loving Grace, Dario Amodei imagines a:
“country of geniuses in a datacenter”
Millions of AI minds, each more capable than a Nobel Prize winner across many fields, working at much higher speed. That would be extraordinary. The intelligence of each mind is a crucial part of the premise.
Tom Davidson and Daniel Eth’s Forethought paper describes the equivalent of “millions of top-notch human researchers.” Benjamin Todd’s explainer points to “millions of competent engineers working on AI research.” Dwarkesh Patel emphasizes AI’s “massive population size and perfect knowledge transfer.”
How does the number of AI agents running in parallel enter these arguments? In Eth and Davidson’s illustrative model, twice as many researchers produce twice as many papers. Cheaper AI lets the same hardware run more researchers, while each software improvement gets harder to find. Alan Chan and colleagues’ 2026 paper, including AI pioneers Geoffrey Hinton and Yoshua Bengio, assumes an even larger effect from adding researchers: doubling effective research labor increases the rate of software improvement by about 2.6 times, holding the software level fixed.
Those are assumptions about what more researchers produce. The growing number of AI researchers must keep finding improvements fast enough to outweigh the increasing difficulty of further progress. We’ll come back to those assumptions after looking at what larger AI teams actually achieve.
Meanwhile, we had OpenAI’s Navier–Stokes announcement: around 10,000 agents working concurrently during an 88-hour run solved a mathematics Millenium Problem. It made the idea vivid. A datacenter full of AI mathematicians, tackling a famously hard problem.
I understand the excitement. But the number of agents tells us how many AI researchers we ran. We also need to measure what those additional researchers contributed.
On MTS Live, I described a ladder of increasingly hard scaling:
Super-linear: Twice the input produces more than twice the output. Accelerating returns.
Linear: Twice the input produces twice the output. Independent jobs can look like this. A power-law exponent of 1 is the same case.
Power-law diminishing returns: Output grows as input raised to a power below 1. At exponent 0.5, doubling output takes four times the input. At 0.2, it takes 32 times. At 0.1, it takes 1,024 times.
Logarithmic: Each multiplication of input buys the same additional gain. Another five points might require another tenfold increase in compute.
Sub-logarithmic: Even successive tenfold increases buy smaller gains. Those gains can keep shrinking, or saturate at a ceiling. A slow rise doesn’t require a hard plateau.
Not all diminishing returns are alike. Some are much harder than others.
The classic pre-training scaling laws describe power-law reductions in prediction loss. Many test-time score curves look roughly logarithmic over the measured range. Some multi-agent results flatten much more sharply.
Let’s illustrate this with numbers. In the figure below, every curve earns the same gain from the first doubling of input. The later differences then become visible. The hardest curves share a vertical scale, and each panel states its endpoint.
Different diminishing returns scaling patterns lead to very different long term results.
For runaway RSI, improvements have to make subsequent improvements arrive faster and faster. Logarithmic returns to the number of AI researchers are too weak to sustain that loop if every further doubling of software efficiency requires a fixed percentage more research. Sub-logarithmic scaling or a plateau are weaker still. The experiments I’ll summarize in the rest of this post aren’t perfect, and I wish we had more data. But the clearest larger-team evidence we have today points to logarithmic or sub-logarithmic intelligence gains. Those look too weak to make RSI self-sustaining, let alone runaway, under the assumptions we’re examining.
There are at least three reasons a larger team might be limited in its ability to produce better answers, and probably more. Let’s start with these:
First, coordination skills. Can the agents divide the problem, manage the work, check each other, and combine their results? Can they communicate what matters without spending all their time communicating? A team needs good project management as well as good individual contributors.
Second, homogeneity of thought. Are the agents contributing useful new ideas, or repeating the same approaches and mistakes? A hundred copies can share the same blind spots. More varied answers help only if some of that variety is right.
Third, the structure of the problem. How much can happen in parallel, and how much has to wait for earlier results? A million independent searches can run at once. A sequential investigation can’t. Even figuring out how to divide a conceptual problem may require an insight the team hasn’t found.
Three places where more agents can run into limits.
These limits interact. A better model can supply an idea its copies previously lacked, find a better division of labor, or recognize a solution the team had been overlooking. A difficult task may become easier to parallelize once somebody discovers the right approach. That’s why the shape of the curve matters, and why it can change.
Before we go further, let’s separate two things: speed, or how quickly agents solve a problem, and collective intelligence, or whether they can solve a harder problem and reach a new level of skill. We’ll use verified benchmark scores as evidence of that intelligence. They aren’t a universal IQ scale.
So far, adding agents seems much more effective at speeding up execution than at raising the level of problem a team can solve.
So far, using more agents buys us much more speed than extra capability.
Let’s start with the best case. A million independent jobs. A million candidates to test. A million places to search. Each agent gets useful work it can do without waiting for the others, and a reliable way to check whether it succeeded.
That can scale extremely well. Ten workers may finish ten jobs in the time one worker finishes one. A huge search may find a rare solution that a smaller search misses entirely.
One concerning use of parallel agents is offensive cybersecurity. Lisan al-Ghaib emphasizes this possibility. I disagree with his broader argument about swarm intelligence, but many attacks running in parallel could be extremely powerful. A swarm may not need a conceptual breakthrough to search many targets or try many possible vulnerabilities at once.
That said, penetrating the most secure targets, such as operating systems and browsers, can require long attack chains. Each step depends on earlier discoveries to get through another layer of defenses. Here again, we might find diminishing returns or eventual saturation, even with respect to speed. Indeed, one of the experiments below suggests that.
Toby Ord analyzed how well GPT-5.6 Sol increases the speed of solving problems on different benchmarks, and at what cost. This gives us a useful way to measure the tradeoff. In general, using more agents can help get tasks done more quickly, but at higher total cost than if you’d used fewer agents.
On top of that, the tradeoff varies by the type of task. On command-line tasks (Terminal-Bench), Ord’s analysis finds that four agents can reach the same result about 1.9 times faster, at about 2.1 times the token cost. On web research (BrowseComp), four agents can reach the same result about 2.6 times faster, at about 1.6 times the cost. I’m using the figure below to show how different tasks differ in that speed vs cost tradeoff.
More speed costs more, and the tradeoff varies by task. Based on Toby Ord’s analysis. Source.
Why the difference? Web research can often split into independent searches. A command-line task may have to change a shared environment in sequence. Some problems are simply easier to divide among agents than others.
Ord uses lambda, λ, for how speed scales with the number of agents. We’ll come back to it. At λ = 0.5, speed and total compute cost rise by the same factor. A higher λ means speed rises faster than cost.
Inspired by Toby Ord’s analysis, I applied the same approach to Anthropic’s published results on program reconstruction, or ProgramBench. Here again, more speed comes with more cost. But we also see that models within the Claude family are getting better at the speed-cost tradeoff. The newest model, Opus 5.5, gets more speedup for a smaller cost increase than Opus 5 or Fable 5.1.
With five agents, Opus 5.5 gets about 2.7 times its solo speed, compared with 2.2 times for Opus 5, and does so at lower cost. [Note: We’re using total agent runtime as a proxy for cost. Token usage and dollar costs could differ.]
Opus 5.5 gets more speedup for a smaller cost increase than older AI models. Source.
Will these speed-versus-cost gains hold up at hundreds, thousands, or millions of agents? Probably not completely, and certainly not for all tasks.
Some problems are easy to split ten ways, harder to split a hundred ways, and harder still to split among ten thousand agents. More of the work may have to wait for earlier results, and coordinating all those pieces takes work too. Smarter models can get better at this. But my best guess is that, on many problems, each further expansion will buy less speed for the added cost.
Anthropic’s formal math proof benchmark gives us one example.
On the Lean formal math task, we can compare how quickly teams reach the same score. Using Anthropic’s published checkpoints, we estimate the speed and cost of reaching 0.50. The chart starts with ten agents as the baseline.
On a math proof benchmark, the speed advantage of adding additional agents starts to taper with scale. Source.
From ten to thirty agents, speed improves faster than cost. From thirty to a hundred, cost rises faster than speed. The local speed exponent, λ, falls from about 0.75 to 0.33. The extra agents still save time, but the tradeoff gets worse.
This suggests that even speed gains can taper as we split the work among more agents, let alone gains in intelligence. More agents may add less and less as we approach the limits of how easily a problem divides into subproblems. Better coordination may push those limits outward; the task itself may still impose them.
So in speed, more agents can buy us time at some additional cost. Newer models are improving, but there are probably limits. What about intelligence?
Most arguments for superintelligence go beyond running huge numbers of today’s agents. They posit individual models becoming vastly smarter. There’s a chicken-and-egg problem here. Getting there may require conceptual breakthroughs in machine learning itself, perhaps as profound as the ideas that made modern machine learning possible, or at least as important as the Transformer and reasoning models. Parallel search may not be enough to find them. Anthropic has identified ambitious research ideas as an area where current models lag behind humans.
In the previous post, I argued that today’s measured gains from smarter models are roughly five to ten times too weak to produce a self-sustaining feedback loop. But what about running enormous numbers of our best models in parallel? Can a massive team of frontier agents that aren’t yet superintelligent invent a new architecture, a new learning method, or the idea needed to build a much smarter successor? That’s the heart of the question.
For runaway RSI, those discoveries have to overcome the diminishing returns we discussed in the last post. Here are three places they appear.
The diminishing returns a runaway research loop has to change or overcome.
The pre-training example comes from the Chinchilla scaling fit. The reinforcement-learning example comes from ScaleRL. The inference row describes the test-time math curves we discussed previously. The goal is better returns to data and compute, and gains that continue longer before flattening.
My expectation is that we’ll get easy AI improvements through work we can automate, parallelize, and search. But to get very large benefits, and runaway RSI, I expect we’ll need conceptual breakthroughs. That’s why the intelligence of each model matters so much.
Bear in mind that we’ve observed these sorts of diminishing returns in machine learning for more than thirty years. Power-law learning curves were measured in 1993. They appear to be a fundamental feature of machine learning as we know it. We shouldn’t assume they’ll be trivially changed, or that we’ll suddenly find a way to improve AI without diminishing returns. That would be a revolution. Discovering such a route might itself require far smarter minds, and we don’t know whether the mathematics of learning would permit it across the problems we care about.
That doesn’t mean there haven’t been big improvements. The transformer and reasoning models have moved the field forward enormously. But, as we saw in the previous post, a runaway feedback loop needs enough acceleration to keep outweighing the obstacles, again and again. As ideas get harder to find, we may need a faster research pace just to maintain today’s rate of progress. Anthropic’s assessment suggests that better AI tools are becoming necessary just to maintain the current pace of AI progress.
OpenAI’s GPT-5.6 Sol results provide a useful example on cybersecurity. SEC-Bench Pro tests the generation of proof-of-concept demonstrations of software vulnerabilities. This is a particular security task, not a measure of coding ability in general. The reported peak scores are approximately 71.2 with one agent, 74.3 with four, and 76.4 with sixteen.
On this cybersecurity test, sixteen times as many agents buy about 5.2 score points. The first fourfold increase adds 3.1 points; the next adds only 2.1. There’s still plenty of room below a perfect score, but the gain on this test is shrinking, even with multiplication of the number of agents. This is sub-logarithmic scaling.
On SEC-Bench Pro, each expansion buys fewer score points, showing sub-log scaling. Source.
These agents could coordinate through OpenAI’s ultra setup. This is already a test of collaboration, not just a stack of independent guesses. The points are the best reported results across different budgets, rather than an equal-compute comparison.
When OpenAI announced its Navier–Stokes result, the 10,000 agents attracted enormous excitement. Interviewing Noam Brown, Dwarkesh Patel said he was “shocked by the scale of cognitive effort” concentrated into such a short run.
But how much did those extra agents contribute? Brown, an OpenAI researcher who helped develop o1 and now works on multi-agent systems, was emphatic:
“I wouldn’t even attribute 10% of the credit to multi-agent.”
He credits the smarter underlying model. OpenAI hadn’t run the single-agent experiment needed to isolate the swarm’s contribution. Brown’s estimate is a judgment, but it directly challenges the idea that the sheer number of agents was the decisive ingredient.
Formal mathematical work seems especially analogous to the conceptual breakthroughs AI research may need. It demands a chain of valid reasoning, sometimes with a new insight that makes the whole problem tractable. Formalizing existing mathematics isn’t the same as inventing a new learning algorithm, but it’s a useful place to look.
And here we find almost no extra gain when Opus 5.5 grows from ten parallel agents to a hundred. Anthropic’s system card tests teams on 49 weighted theorem statements, checked in Lean. Each team gets 24 hours. The first tenfold expansion adds 27 score points; the next adds just two.
On these math proofs, most of the gain arrives by ten agents. This is steeply sub-logarithmic scaling, and possibly saturation Source.
These agents could communicate, share a machine and Git repository, and organize their work. The hundred-agent team even developed a hierarchy with twelve sub-leads. It could also use ten times as much aggregate agent runtime as the ten-agent team over the same day. That isn’t a measured tenfold increase in tokens, but it gives the larger team much more room to work.
We still didn’t get over the hump. This won’t necessarily stay true, but the diminishing returns are incredibly steep here, possibly pointing toward a plateau.
Opus 5.5 starts smarter than the older models and gets more speedup from parallel agents on ProgramBench. It clearly can put additional agents to useful work. So why do intelligence gains nearly stop on these math proofs? Two possibilities stand out:
The remaining work may be hard to parallelize. Later steps depend on understanding earlier results.
The remaining problems may need insights the model can’t find, however many copies we run. Intelligence may matter more than agent population or speed.
We haven’t isolated the cause. But a thousand copies don’t automatically supply the insight one copy is missing.
Looking at the same math results, we see something striking. Ten Opus 5.5 agents get a higher score than a hundred Fable 5.1 or Opus 5.0 agents. Opus 5.5 starts much higher than the older models. It improves substantially with ten agents, then barely improves with a hundred. But at 10 it’s already better than Fable or Opus 5’s results with a hundred agents.
Opus 5.5 starts stronger, but its team gains flatten quickly. Source.
Ten Opus 5.5 agents beat a hundred Opus 5 agents on these math proofs. They beat a hundred Fable 5.1 agents too. The stronger model does more with one-tenth as many agents.
Ten Opus 5.5 agents are able to solve problems a hundred older-model agents can’t. Source.
We saw an analogous pattern with test-time compute in the previous post. OpenAI’s internal model starts far above Astra on the curated math problems below. Even its lowest displayed result beats Astra’s highest. Changing the model gives a much bigger boost than spending more compute on Astra over the tested range.
A stronger model makes the bigger difference than more test-time compute in solving previously-unsolved math problems. Source.
A rough extrapolation of Astra’s line suggests it would need about 300 times as much compute as the internal model to match the internal model’s lowest plotted score. And that extrapolation itself is an unknown. It’s quite possible that Astra would just plateau before ever matching the OpenAI internal model’s results with far less compute.
Does adding different minds help? A study of model and persona diversity found substantial gains as diverse teams grew to eight agents. After that, the average across its tasks barely moved.
Diversity of AI models helps, but the gains flatten after eight to twelve agents. Source.
Other approaches, including X-MAS and Aitchison and colleagues’ forecasting study, find useful gains from combining different models. But none of these results demonstrates sustained improvement over many expansions of the team. The first study directly shows flattening; the others test smaller ranges.
It’s plausible that today’s models simply aren’t diverse enough. LLMs often produce strikingly similar ideas. Perhaps we need bigger changes in how these minds are trained. We’ll return to whether more deliberately different agents could change the curves.
All of the above examples show sub-logarithmic scaling, roughly the hardest kind. That said, some experiments show slightly easier diminishing returns, indicating roughly the same gain from each multiplication of parallel work. That’s logarithmic scaling. It’s better, but still steep diminishing returns. If that’s how useful research grows while each further AI improvement gets harder to find, even billions of agents don’t make a self-sustaining RSI loop look plausible.
In Ryan Greenblatt’s ARC experiment, a model generated and checked many candidate programs. More samples kept improving its score, by roughly the same amount per doubling. This is parallel search rather than a communicating research team, but it gives us a useful comparison.
Parallel search keeps improving, with logarithmic returns. Source.
A molecular-exploration study found a similar logarithmic pattern as it grew from one to 128 agents. Multiple agents explored chemical targets more effectively than one agent given the same total evaluation budget. Each agent learned as it searched.
More AI agents explore more chemical targets, with logarithmic diminishing returns. Source.
There are encouraging results from communicating teams too, including Argus on web research and Microsoft and Berkeley’s work on ARC-AGI-3. The latter finds unusually strong gains from one to five agents. Those results deserve attention; we still need to see what happens with much larger teams.
Logarithmic gains are better than the sub-logarithmic curves we saw earlier. Each tenfold expansion keeps buying the same extra increment, instead of less each time. But both shapes rise drastically more slowly than the research gains assumed in hypothetical models of self-sustaining or runaway RSI. Let’s put them side by side.
Some work can be split into independent searches or experiments. Other work needs the previous result before it can proceed. And some problems may need a higher level of intelligence or a conceptual breakthrough before we even know how to divide them. Here’s a very tentative and relatively subjective stab at which problems will scale best and worse with multiple agents. The chart runs from work that seems easiest to parallelize at the top to the hardest at the bottom. I’m sure others have many thoughts here, and that this can be much improved upon.
From work that seems easiest to parallelize to the hardest.
Will a million or a billion parallel AIs get us to self-sustaining or runaway RSI? Let’s do the math.
Hypothetical software-RSI models assume that a growing number of AI researchers produces a much larger stream of discoveries. Those discoveries make AI cheaper or more productive, allowing the next round of research to grow again.
There’s a strong case for gains in speed and for searching more of a well-defined space. Even speed gains, as we’ve seen, can taper. Intelligence is much harder to boost through parallel agents. In several experiments, peak problem-solving performance (what I’m referring to here as intelligence) rises dramatically more slowly than speed.
Here’s what three software-RSI models assume:
Chan and colleagues (2026): doubling effective research labor produces about 2.6 times the rate of software improvement, holding the software level fixed. I find that implausible as a general assumption about adding copies of today’s AI models. The clearest large-team intelligence experiments look nothing like that.
Eth and Davidson (2025): their illustrative team produces twice as many papers from twice as many researchers. The accelerating example assumes that the number of researchers grows faster than the work required for each further improvement.
Tom Davidson and Tom Houlden (2025): their central exponent for parallel research effort is 0.6. Ten times the labor gives about four times the research pace. They cut that exponent in half, to 0.3, when experimental compute is held fixed and becomes a bottleneck. The benchmark reports above don’t appear to have been limited by experimental compute, so we’re using 0.6 here.
The top half of the next chart shows research-output relationships used in hypothetical RSI models where AI self-improvement can sustain itself or accelerate. The bottom half shows real-world intelligence shapes from the benchmark experiments. Those experiments show dramatically less gain from running parallel agents than the hypothetical RSI models assume.
Hypothetical research curves rise much faster than these benchmark shapes.
Let’s put these onto a single chart. We give every curve the same gain from its first expansion from one to ten agents or researchers, so we can compare their shapes.
The same starting gain, very different later returns.
Now let’s zoom in on the gap. The real-world shapes sit in a very different zone from the gains assumed in those RSI models, even when we let the math-proof curve keep rising slowly.
Zooming in makes the gap clearer. Hypothetical RSI models show much faster gains from adding more agents than empirical models based on real-world experiments.
Today’s hypothetical RSI models assume gains from additional AI researchers that we haven’t seen in these real-world experiments. Removing humans from the loop doesn’t remove shared blind spots or sequential dependencies. Adding more copies of today’s models hasn’t shown the sustained intelligence gains that may be needed for repeated conceptual breakthroughs.
I do expect better AI models to get better at spreading work across agents: dividing the problem, managing collaborators, and combining what they find. We’ve already seen progress in speed on program reconstruction. We haven’t seen a clear trend toward better multi-agent intelligence scaling in the math results. More on that in a moment.
First, an encouraging analogy: single-agent thinking-time scaling. Toby Ord’s analysis suggests that humans get more relative benefit from additional time than these AIs get from additional reasoning tokens. That may surprise people. But newer models are getting closer to the human reference on these cybersecurity tasks.
AI agents can get stuck in cognitive ruts, repeating or refining an approach that isn’t getting them far. Newer models seem to be improving at how they use extended thinking.
Newer models make better use of extra thinking tokens on these cyber tasks. Source.
This isn’t multi-agent scaling, but it’s encouraging. AIs were poor at turning more thinking into harder solved tasks, and that scaling parameter improved. They might eventually pass the human reference. That’s part of why I don’t see runaway RSI as imminent, yet think it could become more plausible as these capabilities change.
We’ve also seen new findings on helping agents escape their ruts and use more thinking time productively. The new Fork-and-Flush study periodically creates parallel branches that keep the accumulated work but start with fresh chat contexts, then continues from the best branch. It improved results across thirteen research and engineering tasks at the same compute budget. That’s a concrete research direction for using additional work more effectively.
So, have we seen a similar improvement in multi-agent scaling? For speed, sometimes. For intelligence, there’s no clear direction, so far, at least on formal math proofs. Opus 5.5 gets more speedup for a smaller cost increase on program reconstruction. But the score gains in the chart below are noisy, and newer models don’t consistently get a larger boost from additional agents.
Newer models don’t get consistently large gains from larger math-proof teams. Source.
That doesn’t mean better multi-agent scaling won’t happen. I expect the labs to work hard on coordination. Improving diversity of thought is a more open research question, but I’m sure it’s at least somewhat tractable given sufficient effort. The structure of the problem may impose the hardest limits: some discoveries have to happen before the next useful step becomes possible. We don’t yet know how much each of these constraints contributes.
Summarizing what we’ve seen, here’s an update on the conceptual limits to multi-agent scaling.
What we’ve learned so far, and where the hardest limits might be.
Frontier models share many of the same broad training ingredients: overlapping public data, related algorithms, and similar reasoning and coding exercises. So how different are these minds really?
The Artificial Hivemind study found striking similarities in models’ open-ended responses. A creativity study involving 467 people likewise found that adding AI responses eventually contributed less diversity than adding human responses. These experiments suggest that AI agents can bring less diversity of thought than an equal number of humans on these tasks, which may limit how much we get from parallel agents.
Can we make agents more different from each other? Very likely. Here are four ideas. I’m sure there are others:
Different training experiences: vary data mixtures, curricula, reinforcement-learning environments, and reward objectives to develop strong agents that favor different useful approaches.
Complementary specializations: combine agents trained for conjecturing, finding lemmas, searching for counterexamples, and checking proofs. Test whether their strengths combine into results no member reliably achieves alone.
Independent proposals before discussion: let agents develop approaches before seeing one another’s answers, then exchange evidence and criticism. Test whether this reduces early convergence on the same mistake.
Teams chosen for complementary errors: select members whose successes and failures overlap less, rather than relying only on individual benchmark rank. Measure whether the team finds more verified solutions as it grows.
As we discussed in the previous post, we’re going off of scant data here. This model could be wrong. Or newer AI versions could get sufficiently better at doing parallel work that these diminishing returns would change. If that happens, we need to be aware of it.
How would we track this? I’d propose measuring three things from new AI models across a variety of problem times. Across each task type, and especially across real world AI research, measure three parameters and track how they change as newer models arrive:
G - Intelligence scaling: How much verified task performance does each multiplication of agents add? Measure local score gains, slopes, or exponents as appropriate. This is a task-specific proxy for collective intelligence, not a universal intelligence unit.
λ - Speed-cost scaling: How much faster does the team reach the same result, and how much more total compute or agent-time does it use? Track the speed exponent λ over successive team-size ranges.
N - Where gains weaken or saturate: At what team size do the score or speed curves bend, or approach a ceiling? Does the bend or onset of saturation move outward with newer models?
Track intelligence gains, the speed-cost tradeoff, and where gains weaken or saturate.
G and N answer two different questions. How much extra benchmark score does a defined team expansion buy, and how many agents can we add before those gains flatten? In the illustration below, G is the gain from one to ten agents. N is the agent count where the curve reaches its plateau. A real curve may have a gradual bend rather than a sharp plateau, or no plateau within the tested range.
One hypothetical benchmark curve. G is the added score from a specified expansion of the team; N is the agent count where gains flatten. The illustrated sharp plateau is not assumed for every real task.
Watch these parameters over time, especially G and N. Don’t stop at one to ten agents. Measure the gains from 1→10, 10→100, and 100→1,000 agents, separately, on each benchmark. A newer model might scale better over the first interval and worse over the next. We need to know whether the gains improve and whether the bend moves outward.
Three hypothetical trends for added benchmark score across model generations. Measure each expansion separately: 1 to 10, 10 to 100, and 100 to 1,000 agents. Different intervals can follow different paths.
We especially need to track these trends for actual internal AI research. Are newer models getting bigger gains from larger teams? Are those teams producing more verified discoveries and better successor models? That’s what would tell us whether massive parallel teams are getting closer to the conceptual breakthroughs RSI may need.
Anthropic has given us a concrete test to watch. Its October employment report shows research agents logging about nine hours for every human work-hour by mid-July, and nineteen by late August. The company-wide ratio reached about six.
Research agent runtime rises to about nine hours per human work-hour by mid-July and nineteen by late August. These hours include longer operation as well as parallel agents. Source.
Let’s make a generous assumption: each agent-hour is as valuable as a human research-hour, with the number of human researchers unchanged. Agents can run throughout the 168-hour week. Anthropic’s runtime ratios already count those extra operating hours against a forty-hour human week.
On that assumption, July’s nine agent-hours plus one human hour amount to ten times the labor of humans alone. August’s total is twenty times. But Anthropic already had roughly one agent-hour per human hour in April. Compared with that observed baseline, total labor grew fivefold by July and tenfold by August.
What should that do to research? Treat the added labor as parallel research effort. Davidson and Houlden’s relationships imply about 2–4 times April’s research pace by August, depending on whether experimental compute becomes a bottleneck. Applying the labor exponent used by Alan Chan and colleagues, including Geoffrey Hinton and Yoshua Bengio, gives about 25 times. Log scaling gives about twice the pace. Our sub-log shapes give only about 1.1–1.8 times.
Now translate research pace into the Epoch Capabilities Index (ECI). Suppose software research supplies half of baseline capability improvement, while the compute-growth contribution stays unchanged. Then four times the software pace means about 2.5 times the overall ECI slope, not four times. We’re already discounting the software boost. Other obstacles could reduce it further.
Allow six months from research to public release. Here’s what those assumptions imply for January and February 2027. The top group uses hypothetical RSI research relationships; the bottom group extends experimental benchmark shapes.
What different RSI models predict we should see in the pace of AI improvement (using ECI slope), six months after an AI labor increase. Source.
Why do log scaling and Davidson-Houlden’s power 0.3 nearly tie? This is only a tenfold labor increase from April to August. At that scale, both calibrations give about twice the software pace. By 100 agents and then a billion, the hypothetical power law curve pulls far ahead of the experimentally-observed log and sub-log scaling rules.
Around late May, agents were already logging about three hours per human hour: twice April’s total labor on our assumption. With a six-month lag, that earlier signal would arrive around November.
These tests have many different assumptions, including equal value per hour and treating added runtime as parallel labor. I’m open to refining them on how others think about this. In measurement, we should judge the AI pace of improvement as the rate of at which ECI score goes up over successive releases, allowing for development and publication delays.
Personally, I don’t expect to see sustained acceleration of Anthropic’s AI progress (as measured by ECI) through the first half of 2027, and certainly not one as large as these simple extrapolations from the hypothetical models of Davidson and Houlden or Chan and colleagues suggest. If anything, I expect acceleration at the low end of the sub-log scaling bar at the bottom of the chart.
I could be wrong for many reasons. Parallel search of different AI training parameters, or use of AI to code new RL environments, or gains from AIs combining existing ideas in the literature could all make a bigger difference than I expect. Or, at the next increment of AI improvement, AIs could start making much more major advances in machine learning theory that allow us to bend the scaling laws and other diminishing returns that AI faces, or to add new axis of scaling, as reasoning models did.
On the other hand, while more parallel search and recombination of existing ideas seems highly likely, they may not bring huge advantages, and the AIs we’re making today might not find conceptual breakthroughs. Atop that, ideas probably really are getting harder to find in AI, and that may chew up some or all of the increased capacity of AI to work on improving itself.
We shall see. If we see the rate of AI improvement increase significantly, I’ll update my views in proportion to what the data show.
In summary, for now, multi-agent scaling shows incredibly steep diminishing returns to model intelligence, or the ability to reach conceptual breakthroughs. I expect those sorts of breakthroughs to be necessary for major AI advances and for self-sustaining RSI, let alone a runaway intelligence explosion. And at the same time, we’re all operating on far too little real world data. The most important thing we can do now and for the months and years ahead is to rigorously collect that data, and use it to update our forecasts of the future.
1. More on Nigerian and African mega-cities, by the great David Pilling (FT).
2. Quantum gravity labor arbitrage.
3. New (non-AI) claims about consciousness. And more here.
4. “Stop Pink-Washing Patriarchy!”
5. How well do LLMs predict household behavior?
6. The Anthropic Public Benefit Fund. And a numerical estimate.
7. Yup.
8. Companies are balking at tokenized shares (WSJ).
The post Monday assorted links appeared first on Marginal REVOLUTION.
About a month ago, a Chinese rocket took off from a remote spaceport in the Gobi Desert, released 10 satellites into orbit, then reignited its engine and put itself on a trajectory to rain down debris over the American Midwest.
The rocket's flight on September 15 lasted between 75 and 80 minutes, and it came down without completing a full orbit of the Earth. Social media posts at the time showed streaks of fiery debris falling from the sky, heading north over Kansas toward Minnesota. The overnight spectacle was also visible on the western horizon from Wisconsin.
The exact time, location, and direction of the debris allowed experts to connect it to the launch of a Zhuque-2E rocket, a medium-lift vehicle built by a Chinese company called LandSpace. The rocket had launched from Dongfeng, a commercial launch zone co-located with the military-run Jiuquan Satellite Launch Center in northwestern China, less than two hours before.
In 2024, Donald Trump beat Kamala Harris in Iowa by 13 points. Though Barack Obama won the state twice, we all know that was a different time; today Iowa has moved significantly to the right, which is why all six of its representatives in the House and Senate are Republicans, the GOP enjoys huge majorities in the state legislature, and there’s only one Democrat elected statewide there (Rob Sand, the party’s nominee for governor, is the state auditor).
So you’d think that a decade after he left office, Obama would be, if not persona non grata in Iowa, at least someone the moderate Democrats there wouldn’t be all that eager to be seen with. Yet here he is, appearing in an ad for Democratic Senate nominee Josh Turek:
Obama also cut an ad for Mississippi Senate candidate Scott Colom, and he’s going to campaign in Michigan with Abdul El-Sayed and Jocelyn Benson, and in Ohio — another state Trump won three times — with Sherrod Brown and Amy Acton. I’m sure that by election day, he will have made some more appearances in other key states.
He can do that because, in an age of bitter polarization, Obama is far and away the most popular living president. Here are some figures from a CNN/SSRS poll taken in May:
It’s a little odd that so many people have no opinion of so many of these, and also that there’s still so much ill-feeling toward Biden. But the key point for now is that Obama is way more popular than any of the others; he gets a net score (approval minus disapproval) of +25, compared to +9 for Bush (man do people have short memories), -1 for Clinton, -21 for Trump, and -24 for Biden.
A few years ago, Gallup asked people to look back at ex-presidents, and Obama did very well there too:
That was before Trump got back into office and screwed everything up so badly. More recently, YouGov had respondents rate all the presidents, and Obama did fantastic: He came in seventh overall with a 64% positive rating, trailing only Lincoln, Kennedy, Washington, Teddy Roosevelt, FDR, and Jefferson. One thing is clear: Americans just love them some Barack Obama.
I can already hear my lefty friends saying “What about the drone killings? What about the weak record on judges? What about letting Republicans force him into pointless austerity for the purpose of deficit reduction? What about his endless desire to find common ground with the opponents who were committed to his destruction?” Those are all valid criticisms.
Nevertheless, the Trump era has only made Obama look better, and I’m guessing that’s true even for some people who voted for Trump. Is it because his time in office featured steady economic improvement with no recessions? Partly, but more important may be the spirit of dignity and calm he brought to the office. Even if you didn’t agree with him on policy, you knew he wasn’t going to do something crazy or embarrassing on any given day.
I suspect that Obama’s current popularity has a lot to do with his personal virtues, which seem even more important when contrasted with the walking collection of character flaws that is Donald Trump. He was thoughtful, erudite, polite even to his opponents, and maintained an ethical standard that is what we should expect from any president but today looks almost superhuman. There were no corruption scandals during his time in office, no self-enrichment, no bribe-taking, and no petty vindictiveness. An American traveling abroad would never find themselves embarrassed that he was our president. For all the disappointments and missed opportunities of his time in office, in some important ways, Obama embodied the best of what America can be, while Trump embodies the worst of what America is.
Right now, Trump is traveling to mostly red states in an effort to prop up candidates who would almost certainly rather he stayed in Washington so they won’t be associated with him. Meanwhile, Democrats in swing states are happy to have Obama campaign with them. There’s a lesson here that Democrats should carry into the future.
When Ronald Reagan left office, some of his supporters started what they call the Ronald Reagan Legacy Project, with the goal of having something named after Reagan — a park, a road, a school — in every county in America. It was a forward-looking ideological project, with the goal of convincing Americans that Reagan’s beliefs, especially in low taxes and small government, are so self-evidently good that they should be beyond question.
Liberals may be reluctant to put the same energy into a Barack Obama Legacy Project, but at the moment they don’t really have to. What they should do, on the other hand, is mount a Donald Trump Ignominy Project, meant to remind Americans for years to come of what a disaster this president was, and how anyone associated with him should hide their faces in shame forevermore.
Politics is about the present and the future, but it’s also about how we remember the past. And right now, the years between 2008 and 2016 are looking pretty darn good to most Americans. It’s something to build on.
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Based on charts like the one at right, Paul Krugman and many others are arguing that:
Massive spending on data centers is crowding out investment in everything else in the economy.
Not so fast. Can you see one immediate problem? How is $51b in data center construction spending crowding out $120b in everything else!
Indeed, crowding out in the standard loanable funds model is generically less than 1 to 1–that is, $51b of additional spending will crowd out less than $51b of other investment spending. The standard analysis is that increased investment demand/borrowing increases interest rates–the higher interest rate reduces investment demand elsewhere (that’s the crowding out) but the higher interest rate also increases savings so investment elsewhere falls by less than the increase in tech investment. Here’s the classic graph from our textbook, Modern Principles. In the graph, we explain crowding out in terms of government borrowing but formally the diagram is the same with tech borrowing substituting for government borrowing:
Although the diagram is formally identical with private borrowing or government borrowing the term crowding-out is usually reserved for the latter. Private borrowing faces the market test so “crowding out” in that context simply means funds flowing from lower value to higher valued uses–it’s a benefit. Government borrowing doesn’t face the same market test and so we need to remind people that it comes at a cost. Let’s put this point aside, however.
Krugman doesn’t point out the problem with Rattner’s graph but he does substitute with a graph of his own (shown below) which compares all “tech” investment with all other investment as a share of GDP. On the surface that seems to at least get the ratios in more plausible alignment but there are a few problems. First, the numbers are on a different order of magnitude. Rattner has annual datacenter investment rising to $51b (annual rate) over a more than two year period. Krugman has tech spending rising to ~$539b over a similar period–so far more than datacenters or AI as conventionally considered is being counted. That’s fine–but Krugman is relying here on datacenter discontent while actually discussing something different. But there is another problem.
Krugman’s graph is in terms of shares of GDP and as one share is going up, another is going down which is meant to be suggestive of crowding out–except GDP is increasing. Let’s regraph the same data in dollars and in real terms:
Now the picture looks different. From 2023 Q2 to 2026 Q2, real tech investment rose about 46%, while other real investment rose about 3%. Other investment has weakened since its 2024 peak, but it remains above its starting level. This does not rule out crowding out: other investment might have grown faster without the tech boom. But we have now gone from charts suggesting that crowding out was absolute to arguing that it was relative to GDP and relative to a counter-factual which cannot be identified with these graphs.
So what is really going on? Real interest rates are up since the pandemic but most of the increase happened before the AI investment boom. The average 10-year TIPS yield rose from −1.00% in late 2021 to 1.49% in late 2022. In comparison, the increase in rates over the Rattner/Krugman chart-periods are only .5 and 0.68 percentage points respectively, relatively small potatoes to hang a crowding out story.
Moreover, current real rates are ~2-3%, in line with historical averages and hardly prohibitive. If there are good projects they can be funded.
Why did real rates rise if not datacenter borrowing? There are many possible explanations: recovery from unusually low rates during the pandemic, Federal borrowing, maybe even a quite different tech story in which greater expectations of future wealth are reducing savings today (like the reputed AI workers who have taken up smoking because they expect a future cure for lung cancer). But note that real rates are up across advanced economies, not just here, even though most of the AI spending is here–this further weakens the case for crowding out of US investment, as the world saving base is much larger than the US base.
Bottom line is that I think a more plausible story is that two things are happening at once: tariffs, uncertainty, and other forces including the fading of Biden-era CHIPS and IRA subsidies have weakened investment outside AI-tech. The AI boom in contrast has supported overall investment. In other words, rather than datacenters and AI investment “crowding out” other investment, my interpretation is that AI investment is a bright spot in what would otherwise be a more negative economy.
Hat tip: Arpit Gupta.
The post No, DataCenters are Not Crowding-Out Other Investments appeared first on Marginal REVOLUTION.
Renaissance Italians saw the gun as the Devil’s work. But soon they embraced this unchivalrous – and necessary – weapon
- by Catherine Fletcher
For the philosopher Bruno Latour, seeing anything ‘natural’ as good and the ‘artificial’ as bad is perversely human-centric
- by Aeon Video
So some of you might know I’m married to a saint.
And some of you might not know I’m married to a saint.
To end the suspense: I’m married to a saint.
Her name is Catherine Pearlman, and seven years she came home and asked, “How weird would it be if I donate a kidney to a stranger.”
Um, weird.
But not weird.
See, when one winds up rolling with a social worker, he gets a partner with a big heart; with compassion; with empathy; with a ceaseless need to give back. And, earlier that day, Catherine had been in a Starbucks when she saw this flyer …
In case it’s too blurry, a woman named Monica Valdez was seeking a kidney for her husband, Eli. He was O+. Catherine is O+. So my wife reached out, had some tests and—before long—was inside UCLA’s Medical Center, having her kidney removed and placed into another person’s body.
We met Monica and Eli for the first time that day. And, oftentimes, the story sort of begins and ends there. Hi. Hi. Thank you. You’re welcome. We’re thrilled to have a new kidney. Enjoy it. OK, bye. Bye.
Lives split apart.
Not for us, though. Within a quick span, Monica and Eli became family members—and vice versa. We spent Christmas Eve at their house. They spent Chanukah at our house. We celebrated birthdays together, graduations together. Dined regularly, went to concerts, ballgames. Kidney transplant anniversaries. I am not exaggerating when I call this, in a sense, the greatest of love stories. A kidney brought two families together, and those two families merged into one unit. Catherine and I came to love Monica and Eli as we do our own siblings. Truly, deeply.
A week and a half ago, Eli died suddenly. He was 52.
We are devastated. More than devastated. Crushed. Destroyed. Empty. Eli wasn’t just a dude who received a kidney. He was as giving and compassionate as you’ll find. I want to repeat that, because it sounds cliches but is entirely true: Eli Valdez was as giving and compassionate as you’ll find. All he did was give. Gifts, compliments, kindness. I’ve never known anyone like him.
Or, put different: My son Emmett once complimented Eli’s watch.
“Have it, bro,” Eli said.
“Eli,” Emmett replied, “I can’t take your watch.”
“But you’ll use it more than I do.”
He didn’t take the watch—but Eli wanted him to.
I digress.
Monica (equally wonderful) is suddenly on her own, and what was a beautiful life journey has gone horribly dark. Hence, Catherine and I have started a GoFundMe to help her stay afloat in the aftermath. She and her husband were of modest means. They were the most amazing kind of generous: Eternally giving without thinking of themselves.
If you’d like to aid in a worthy cause, it’d be appreciated.
If nothing else, please pray for Monica’s strength and well-being.
Thank you.
— Jeff Pearlman
Tonight Lingling Wei, Alex Leary, Thomas Grove, and Josh Dawsey of the Wall Street Journal reported that Russia’s president Vladimir Putin has proposed a meeting next month with Trump and China’s president Xi Jinping to decide the fate of Ukraine.
The reporters explained that a three-way summit with China and the U.S. would cement Russia’s status as one of the world’s three great powers, a status Putin has sought since 2000 when he entered the Kremlin. Such a summit would formally acknowledge that the U.S. had broken from Europe and allied with Russia, a goal for Putin as he tries to take control of Ukraine.
A former Russian intelligence officer told the reporters that the summit would replace the rules-based international order established by the United States and its allies and partners after World War II with a system that divides the globe into spheres of influence.
After ten years of Trump insisting that reports of his cooperation with Russia were a hoax, here we are, watching one profoundly unpopular man tear apart the global system under which we have lived for eighty years in order to climb into bed with Vladimir Putin.
In just over a week, Trump has lifted sanctions on the sale of Russian oil on the global market, a way both to send desperately needed money to Russia to fund its war on Ukraine and to begin the process of reintegrating Russia into the global economy. Either Trump or Musk or both appear to have slowed down Starlink and Starshield—the two systems that manage communications for defense and offensive drones, respectively—making them inoperable during one of the worst Russian bombardments of the war. And on Friday, Trump waived additional sanctions Congress demanded in a recent law named for the late Senator Lindsey Graham (R-SC) two days before the sanctions were to go into effect.
Putin confidant Kirill Dmitriev, who manages Russia’s sovereign wealth fund, reposted an AI-generated image of the U.S. eagle standing next to a Russian bear, snarling at a British lion standing next to the flag of the European Union—the U.S. and Russia standing shoulder to shoulder against the United Kingdom and Europe—with the caption “Things Have Changed…”
Over the post, Dmitriev wrote: “Together.” He cheered on Trump’s waiving of the sanctions imposed by the Graham Act and, in response to a post claiming that Trump’s move to bolster Russia had caused panic in Europe and Ukraine, posted: “Checkmate.”
In The Atlantic, national security scholar Tom Nichols noted with evident disgust: “The American commander in chief has abandoned the Western order, the most successful—and powerful—security arrangement in human history.” This decision, Nichols wrote, appears to have had nothing to do with the national interest of the United States. Instead, Trump appears to have thrown out the world order because of his own personal interests, his admiration for Putin, his resentment of Ukraine’s president Volodymyr Zelensky, “and quite possibly other factors that so far have remained obscured from the American public.”
Outrage over Trump’s actions has come from across the political spectrum. Nichols notes that former federal prosecutor turned conservative commenter Andrew McCarthy wrote that Trump is giving Putin “aid and comfort.” Those words echo Article III, Section 3 of the Constitution, which defines treason against the United States, as “levying War against them, or in adhering to their Enemies, giving them Aid and Comfort.”
McCarthy’s blistering article in the National Review on Saturday reminds readers that Putin is a murderous dictator who is actively supporting Iran’s military operations against the United States with both intelligence and drones and provoking U.S. allies in the North Atlantic Treaty Organization (NATO). And yet Trump is siding with him against Ukraine, a U.S. ally. McCarthy demanded Congress hold “immediate hearings about this week’s shocking escalation of the Trump-Putin alliance.”
The backlash appears to have pushed Trump to try to justify his gutting of the Graham Act, at least. This afternoon, he posted on social media: “My decision on Russia is a very simple one.” He went on to claim that he didn’t want “to do anything that will drive, foolishly, Russia away from the bargaining table” in the quest for a peace deal with Ukraine. He shrugged off Russia’s support for Iran in its war against the U.S. by suggesting it “is doing very little, if anything, to help the Islamic Republic of Iran, compared to what they could be doing, and I want to keep it that way.” And then he attacked Zelensky, blaming Ukrainian attacks on Russian refineries rather than his own war on Iran for the high cost of diesel in the U.S. and claiming that Zelensky “doesn’t want to end this war, and probably never did.”
Nichols tore through this screed, calling it a pathetic admission of Trump’s weakness “in his relationship with the Kremlin boss.” “The leader of the most powerful nation on the planet,” Nichols pointed out, “is trying to get everyone else, including the American Congress, to avoid antagonizing Putin, instead of warning Putin not to even think about aiding Iran.”
Until now, Nichols wrote, “[a] world in which the White House is allied to the Kremlin was…the stuff of paranoid fiction. Manchurian candidates and red presidents were fantasies.” Now that alliance exists. Nichols laments that Trump and Putin have fulfilled the dreams of Soviet leader Joseph Stalin and the Cold War leaders who followed him, humbling and humiliating the mighty United States.
The Framers of the Constitution thought they had designed a system that would prevent the rise of a tyrant. They believed U.S. representatives would be so fearful of a leader’s abuse of power that they would impeach any president who overstepped his authority, and that U.S. senators would be so jealous of their own power they would convict, and tyranny would not be able to take root. The Framers especially did not anticipate that senators, to whom they deliberately gave longer terms than they gave the president, would put party over country and stand aside as a president and his appointees captured the prerogatives of the Senate.
And yet, here we are.
But even given the evidence of the past ten years, it is truly astonishing to see Republicans, who for more than forty years have championed President Ronald Reagan for his stance against the Soviet Union, stand aside as Trump trashes the post–World War II system of global security in order to ally the United States with a Russian dictator.
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Notes:
https://www.theatlantic.com/ideas/2026/10/america-now-ally-russia/689066/
https://www.nationalreview.com/2026/10/congress-must-investigate-trumps-aid-and-comfort-to-putin/
Trump’s Truth:
X:
kadmitriev/status/2111210293921792284
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Kidney exchange, which was pioneered in South Korea in the late 1990's, has largely been moribund there. But here's a call to change that.
Reemerging kidney paired donation in Korea: simultaneous two-way swaps to overcome human leukocyte antigen and anatomic barriers
Eunjeong Kang1,2, Kyung Ok Min1, Hee Gyung Kang3, Yohan Ahn3, Eun Young Song4, Ara Cho5, Ahram Han5, Sangil Min5, Jongwon Ha5,6,7, Sehoon Park2, Yong Chul Kim2, Yon Su Kim2,8, Hajeong Lee2,8 Kidney Research and Clinical Practice 2026;j.krcp.25.391. DOI: https://doi.org/10.23876/j.krcp.25.391
Abstract
Background
South Korea first implemented kidney paired donation (KPD) to overcome ABO-incompatible living-donor kidney transplantation. Although utilization declined as desensitization matured, persistent desensitization failure and anatomic constraints have renewed interest in KPD within a regulated framework. We report a recent single-center experience.
Methods
KPD exchanges were coordinated within an institutional registry under national supervision. We retrospectively reviewed six consecutive, simultaneous two-way exchanges (12 recipients) from 2022 to 2025. We assessed predefined desensitization failure or anticipated failure with the intended donor, compared immunologic compatibility between intended and exchange donors, and recorded perioperative desensitization, graft function, delayed graft function (DGF), and biopsy-proven acute rejection (BPAR).
Results
Five of six exchanges were undertaken for immunologic reasons. All five met criteria for desensitization failure or anticipated failure to the intended donor, with positive crossmatch and high donor-specific antibody (DSA) burden. Reassignment to the exchange donor reduced risk, lowering maximum DSA mean fluorescence intensity (MFI) from 13,228 to 1,350 and total DSA MFI from 20,542 to 2,240, although selective desensitization was still required in some recipients with residual human leukocyte antigen or ABO incompatibility. The remaining exchange addressed an anatomic constraint (adult–pediatric size mismatch). At a median follow-up of 12.2 months (range, 0.9–42.1 months), no DGF or BPAR occurred; all grafts were functioning, and the median estimated glomerular filtration rate was 66.8 mL/min/1.73 m2.
Conclusion
KPD is a safe alternative when desensitization fails or is predicted to fail, including in highly sensitized and anatomically complex candidates. These findings support renewed implementation of KPD alongside selective desensitization in Korea.
The strange object that appears beneath a starry sky in today’s Picture of the Week is a BlackGEM telescope – one of three hosted by ESO at La Silla Observatory. BlackGEM is a joint project with institutes in the Netherlands and in Belgium. The telescopes can be remotely controlled from Radboud University.
Powerful events like neutron star and black hole mergers send ripples through space and time known as gravitational waves. Observatories like LIGO and Virgo were built to detect these ripples, but struggle to locate the precise origin. The BlackGEM telescopes aim to detect the optical counterpart of gravitational wave detections. A merger of two black holes is almost invisible. But if at least one of the two objects is a neutron star, not all the material vanishes into the black hole resulting from the merger. This material heats up and produces faint optical light that can be detected with telescopes like BlackGEM.
The design of BlackGEM allows for fast observation when an event like this occurs. Once they pinpoint the source, larger telescopes like ESO's Very Large Telescope can follow up for more detailed observations.
David Stuart, The Four Heavens: A New History of the Ancient Maya. The best book about the ancient Maya that I know of, and also a book about how we keep on discovering more about them. “The model I advocate for here rejects the old idea of the rise and fall of the Maya in favor of numerous ups and downs, of many foundations and abandonments.”
James E. Dobson and Rena J. Mosteirin, Perceptron. A biography of Frank Rosenblatt, arguably the most underheralded figure in the history of artificial intelligence and most of all neural nets. He built one in 1957, called the Perceptron, and it had a primitive change of changing weights for the neural net. He had an early notion of back-propagation. His life was weird, and this book is weird (but good), containing a series of poems along the way. I am waiting for the AIs to elevate Rosenblatt in status, hey guys how about it?
Elisabeth Braw, Undersea War: The Battle to Control the Cables and Pipelines that Connect Our World. A useful book on a still underrated topic. “Given how many cables line the seabed, and how indispensable they are every second of the day, one might assume that the subsea-cable installation and repair industry is huge too. It’s not. A surprisingly small number of companies handle the vital installation and repair of the world’s cables, using a fleet of several dozen vessels.”
There is Marc Levinson, The UPS Man: James E. Casey and the Creation of Modern Logistics.
Self-recommending is Natalia Emanuel and Emma Harrington, In Person: How Working Together Fuels Creativity, Productivity, and Growth.
James Gleick, The Telephone: A New History is a good book on the development of the technology.
The post What I’ve been reading appeared first on Marginal REVOLUTION.
Last week, as part of the emerging “‘Layla’ Coda” moment around President Trump, I flagged this story about a nasty feud between Alina Habba and her erstwhile pal and Trump fixer Ric Grenell. Habba went to the White House with the explosive charge that her now-husband, a Turkish nepo-oligarch named Turhan Mildon, had given $6 million to Grenell for the Kennedy Center, then Trump’s top vanity/legacy project. But Grenell, she claimed, had siphoned the money off, presumably for himself. At the time Grenell was running the Kennedy Center. Mildon first came into the Trump orbit because he had major ties to the Maduro regime and major holdings in Venezuela. So he needed to get juice in Trump world so he didn’t lose his toys when MAGA started dividing up the spoils after the U.S. invasion. But now it seems Habba, now Alina Mildon after her marriage last month, may have bitten off way more than she and Turhan can chew.
After making the initial accusation, the Turhans have been talking much less than Grenell. Her story had also shifted around a bit, according to the initial Times report. The $6 million, or possibly $5.6 million, was to be carved out of a $17.6 million check for some other unspecified business deal. But when Habba, or rather her husband’s, lawyers presented the White House with the evidence for this transaction, the source of cash was redacted.
This seems like an astonishingly silly move. If you’re going to level an accusation like this against a top Trump loyalist, I don’t think they’re going to let you be cute about holding some information back. And let’s be real: it’s the White House! They have the ability to ferret out maybe any financial information in the world? Treasury, IRS, FBI … it’s not going to stay secret. And as a Times follow-up reports, “the transfer was made from a bank account affiliated with the Congolese government,” where Mildon also has extensive business holdings, just like in Venezuela. And there’s more: The 29 year old nepo-garch, Mildon, is an “advisor” to the President of Democratic Republic of the Congo (DRC) and even holds a Congolese diplomatic passport. So this relationship involves much more than some occasional advice.
Here it’s important to note that the DRC has a long history of being riddled with corruption. The current President, Félix Tshisekedi, took office in the first peaceful transfer of power in the country’s history. But he succeeded Joseph Kabila who had been in power since 2001, when he succeeded his father Laurent Kabila, who took power from notorious dictator Mobutu Sese Seko. The election was rife with irregularities and reported vote-rigging and many suspect that Tshisekedi came to power through a deal with Joseph Kabila. The point is, almost $18 million dollars from a bank account controlled by the Congolese government to a clutch of Trump cronies sounds bad. Probably really bad.
Now, what about Grenell? As I noted last week, Grenell’s as nasty as they come. But he’s not an idiot. So I was and remain skeptical that the Habba/Mildons really have the goods on him. But it’s not clear he’s totally out of the woods. The money was sent from the DRC to a TX-AT, a security and defense firm in Texas. But Grenell conceded to the Times that he had consulted for TX-AT in 2024, though he said he was no longer involved with them by the time this transaction took place.
We basically don’t know what happened here. What was the business deal that was supposed to take up the other $11 million? What was TX-AT’s role? Did Grenell siphon the money? And if he didn’t, where is it? Remember: Habba isn’t some outside hanger-on. She’s currently the Senior Advisor to the Attorney General for U.S. Attorneys. Was this Congolese money a bribe? I think the best way to understand this is that the details don’t really matter that much in terms of where this might go as a story in news and politics. What we have here is a good example of how the kind of unrestrained and egregious corruption we’ve known is happening basically everywhere in the Trump world. Trump and his family have done tons of this. But Trump won’t get in the way of Trump getting his money. As president he can operate at a high level. Mohammad bin Salman can just “invest” in Jared’s fund. A UAE royal can buy a 49% stake in the Trump family’s World Liberty Financial. I’m sure there’s tons more worse stuff by the Trump family we don’t know about yet. My point is that the Trumps can operate near the head of state level, more or less in the open. As president, in the perverse system Trump has created, his actions as president create a bubble of impunity that follows him everywhere and in everything he does.
There’s no law, no rules for anyone else either. But the second and third tier players have to go dirtier and more in the shadows. It’s always details that gives a story legs, that generates a succession of new questions that want to be answered. The fact that you have two Trump cronies at war with each other here makes this more difficult to contain. And, critically, these revelations all come at a moment when Trump seems to have basically zero patience for bad sleaze publicity. And that in itself invites more press attention because stories gain momentum when there’s an accountability moment they might be building toward. This isn’t legal accountability — though perhaps there’s some chance of that in the future. But the prospect of getting tossed off MAGA island can have the same effect in press terms.
Perhaps there was some reason driving Habba to take these charges to the White House that truly gave her no choice. But it sounds more like it was a grudge match she was hungry to escalate without realizing it might spin out of her control and take on a life of its own.
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