Podcasts about economists

Ukraine's president Volodymyr Zelensky has fired the commander-in-chief of the armed forces, Oleksandr Syrsky. That follows days of protests at his sacking of the defence minister. Our correspondent analyses the fallout. America's murder rate is falling, so why are cops killing more people? And a shift in the Japanese taboo on living in homes where people have died.Guests and host:Shashank Joshi, defence editorDaniel Knowles, US Midwest correspondentMoeka Iida, East Asia reporterRosie Blau, co-host of “The Intelligence”Topics covered: Fedorov, Syrsky, Zelensky, UkrainePolice killings, gun crime, murderJapan, haunted houses, jiko bukkenListen to what matters most, from global politics and business to science and technology—subscribe to The Economist. Hosted on Acast. See acast.com/privacy for more information.

A.M. Edition for July 22. In a landmark agreement, President Trump okays a nuclear deal with Saudi Arabia. WSJ's Laurence Norman explains why the commercial deal has nuclear proliferation experts concerned and could shift the balance of power in the Middle East. Plus, utilities pledge to limit increases in electricity bills caused by the data-center buildout. And in a real-life cybersecurity nightmare, OpenAI admits that a pair of its models escaped the lab and hacked into another company. Luke Vargas hosts. Sign up for the WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Ukraine's president Volodymyr Zelensky has fired the commander-in-chief of the armed forces, Oleksandr Syrsky. That follows days of protests at his sacking of the defence minister. Our correspondent analyses the fallout. America's murder rate is falling, so why are cops killing more people? And a shift in the Japanese taboo on living in homes where people have died.Guests and host:Shashank Joshi, defence editorDaniel Knowles, US Midwest correspondentMoeka Iida, East Asia reporterRosie Blau, co-host of “The Intelligence”Topics covered: Fedorov, Syrsky, Zelensky, UkrainePolice killings, gun crime, murderJapan, haunted houses, jiko bukkenListen to what matters most, from global politics and business to science and technology—subscribe to The Economist. Hosted on Acast. See acast.com/privacy for more information.

Today, Lebanon's president Joseph Aoun visits the White House to ask for Donald Trump's help rebuilding the country. Our correspondent reports from his recent trip to Lebanon on the challenges it faces. Threats to Red Bull's magnificent marketing machine. And why the age gap between the wealthiest men and their wives is shrinking.Guests and host:Gregg Carlstrom, Middle East correspondentVendeline von Bredow, senior Germany correspondentDoug Dowson, data journalistRosie Blau, co-host of “The Intelligence”Jason Palmer, co-host of “The Intelligence”Topics covered: Lebanon, Beirut, Joseph Aoun, HizbullahRed Bull, energy drinks, extreme sportsTrophy wives, age gap, misogynyListen to what matters most, from global politics and business to science and technology—subscribe to The Economist. Hosted on Acast. See acast.com/privacy for more information.

Today, Lebanon's president Joseph Aoun visits the White House to ask for Donald Trump's help rebuilding the country. Our correspondent reports from his recent trip to Lebanon on the challenges it faces. Threats to Red Bull's magnificent marketing machine. And why the age gap between the wealthiest men and their wives is shrinking.Guests and host:Gregg Carlstrom, Middle East correspondentVendeline von Bredow, senior Germany correspondentDoug Dowson, data journalistRosie Blau, co-host of “The Intelligence”Jason Palmer, co-host of “The Intelligence”Topics covered: Lebanon, Beirut, Joseph Aoun, HizbullahRed Bull, energy drinks, extreme sportsTrophy wives, age gap, misogynyListen to what matters most, from global politics and business to science and technology—subscribe to The Economist. Hosted on Acast. See acast.com/privacy for more information.

Patrick Bet-David sits down with economist Steve Keen, one of the few economists to warn of the 2008 financial crisis before it happened. A longtime critic of mainstream economics, Keen debates capitalism, Marxism, climate change, AI, America's debt crisis, Iran, housing affordability, and the future of civilization.Steve Keen explains why he believes economists misunderstand money, why AI is headed for a massive shakeout, and why humanity faces its greatest challenges in the decades ahead.

AI investment is reshaping the global outlook. In part one of this economic roundtable, our panel explores where the momentum is strongest — and where investment still needs to catch up.Read more insights from Morgan Stanley.----- Transcript -----Seth Carpenter: Welcome to Thoughts on the Market. I'm Seth Carpenter, Morgan Stanley's Global Chief Economist and Head of Macro Research. Michael Gapen: And I'm Michael Gapen, Chief U.S. Economist. Chetan Ahya: And I'm Chetan Ahya, Chief Asia Economist. Jens Eisenschmidt: And I'm Jens Eisenschmidt, Chief Europe Economist. Seth Carpenter: And today is going to be our third quarter economic roundtable taking a wide-angle view on the global economy and all the key forces shaping our outlook and the economy. Seth Carpenter: It's Monday, July 20th at 10am in New York Jens Eisenschmidt: And 4pm in Frankfurt. Chetan Ahya: And 10pm in Hong Kong. Seth Carpenter: Since our last roundtable in April, the global economy has continued to face all sorts of shocks, a mix of resilience and friction. Inflation pressures have not disappeared. Energy and geopolitical risks have come up, they've receded, they've come back, they've receded all over the place But there is one underlying source of momentum that we have to talk about. And that is the AI-driven CapEx cycle. Michael, let me turn to you because the U.S. is a real focal point of all of this. Tell me a little bit about where Morgan Stanley Research is thinking about hyperscaler CapEx. How big it is? And then for you, when you think about the U.S. economy, just how big of a driver is it for what we're looking for in the U.S.? Michael Gapen: Yeah, we continue to revise higher our estimates for hyperscaler and AI-related CapEx in the U.S. economy. We were thinking a little over a trillion for 2027. Now we're more like 1.2 - 1.3 trillion, maybe as high as 1.4 trillion in 2028. So, the level of hyperscaler spending continues to keep rising. The growth rate and its effect on the economy is likely to slow. But as you noted, it's still a major driver of momentum in the U.S. You would look at that headline number and think, "Wow, that's, you know, 3.5 percent or so of GDP. Must be a massive source of momentum for GDP growth." But roughly about 60 percent of that hyperscaler CapEx spending goes to items like computers and peripherals, equipment spending categories that have a very, very high import content. We still get a significant number that AI CapEx is probably contributing around 40 basis points to growth this year. Be a similar-sized amount perhaps next year.So, for an economy that's growing somewhere a little bit above 2 percent right now, maybe closer to 2.5 percent next year, that's a non-trivial amount. We just have to remember it's fueling growth around the world, just not here in the U.S. Seth Carpenter: Yeah, that's a really great point because I have seen some estimates where people say, "Well, if it wasn't for AI CapEx, the U.S. economy wouldn't have grown at all." And that's clearly wrong, as you point out. But U.S. imports are necessarily exports from somewhere else. And, Chetan, if I can pull you into the story then, U.S. firms are buying a lot of AI-related equipment from Asia. What does that mean in your part of the world? And in particular, I'm thinking about Korea, Taiwan, and maybe some other economies in Asia. What's the critical story there? Chetan Ahya: So, for Asia, this has definitely been a big boon. If you look at Asia's exports, they have been booming, and particularly for the ones which are exporting semiconductors to the U.S. They are seeing semiconductor exports growing by 90 percent. And when we go back in time and compare Asia's semiconductor exports, it's very tightly linked to the U.S. IT CapEx. And it's not surprising when Mike Gapen mentions about the imports going up. It's on the other side, helping Asia's exports quite meaningfully. So, so far, we've seen this benefiting Korea, number one, Taiwan, and also Japan. All these three are big beneficiaries of U.S. AI CapEx. And of course, also not just U.S., but the other countries which are doing any little amount of CapEx on AI front, that's also helping these three economies in the region. Seth Carpenter: You've been doing a lot of work, Chetan, recently about how much the story can actually broaden out, that the AI CapEx cycle has really contributed to Asian growth, but it doesn't tell the whole story that there's a broader industrial cycle. Can you give us a little bit of a flavor of that story? Chetan Ahya: That's right, Seth. So, we are actually highlighting that there is a CapEx and industrial super cycle that is underway in Asia, and there are four components to this story. AI and semiconductors CapEx, which we just briefly discussed. Number two is energy. Number three is defense. And number four is industrial supply chain onshoring related CapEx. I know that everybody still thinks that AI is the most important part of this story, but when I give you the numbers and the breakup of that... So, for Asia, AI and semiconductor companies CapEx is about $380 billion in 2026, but energy CapEx is going to be $900 billion. So, this is a far broader story than just AI for Asia. Seth Carpenter: Mike, let me come back to you and to the U.S. then. So, isn't the growth story also broader than that as well domestically? So, what's going on in terms of consumer spending in the U.S., and is there a broader CapEx story in the U.S. as well? Michael Gapen: I would say, is it broader than that? I think maybe you could argue also it's narrower than that. Here's what I mean by that. As I noted AI CapEx contributing about 40 basis points to growth, it's certainly underpinning equity valuations in the U.S. and underpinning strong wealth creation. So about [$]180 trillion in household net worth in the U.S. About [$]55 trillion of that has been created in just the last five years alone, underpinned in part by AI-related spending and optimism about future profitability. That's really supported spending by upper income households. So, I think it's both investment-led and consumer-led, but they're inextricably linked. So, the positive for the U.S. is that it's providing a lot of resilience. The negative component of that is it feels like momentum in the U.S. is narrowly driven. Jens Eisenschmidt: Let me maybe jump in here from Europe to provide some perspective from the other side. So, I think it's a fair summary to say that AI investment is not yet, or maybe will never get there, dominating the business cycle. What we do have instead is an unusually consumption-driven expansion. That has to do not so much with an extraordinary strength of consumption, but more of an absence of other factors. Now, prospectively looking forward, we think the fiscal expansion might help lifting us a little bit. And then it is really the debate how much AI investment can arrive in Europe. For now, I would say it's probably a factor of 20 that separates European investment plans from the plans we know that exist for the U.S. Seth Carpenter: Let me stick with you then in Europe because you brought up fiscal as one of the factors going on here and where it's going… You and your team recently wrote a blue paper talking about what the outlook is for fiscal policy in Europe, and in particular, we had this era of cheap debt. Interest rates in Europe were low, at times negative. It was super easy to borrow. Not as much happened then. There's been a shift towards more fiscal expansion at the same time that interest rates have gone up, causing the cost of debt to go up. Feels like there's a lot of push and pull going on. Can you unpack for us a little bit what was in that paper you wrote, what's going on with fiscal policy in Europe, especially in Germany? And what it might mean over time for Euro-area countries? Jens Eisenschmidt: Yeah, so I think fiscal policy in Europe really is looking at a regime shift. So, there is this very famous, probably in the U.S. even more so than here, notion that the Europeans have built a very comfortable welfare state. And that's true if you just look at the accounting from a GDP perspective. It's close to 50 percent that, you know, budgets are actually extended on welfare spending. And now you have three structural headwinds for any type of fiscal spend. So, one is aging related costs, you mentioned it already. Defense spending has to increase significantly, and the interest rate costs will also rise significantly. All of that means there will be very hard choices to be made. The one thing that actually could help here is growth. Growth is the one thing that's, for now at least, missing, at least in comparison to the U.S. It's probably half what we expect, what the U.S. colleagues think is in stake for the U.S., and a quarter or even less than that of what is there in Asia. So, growth is really the key, the solution, the answer to everything in Europe. More growth than just 1 percent, which is potential, would help solving that fiscal challenge. For now, it looks really, really like an uphill battle. Returning to Germany, it's the one country that has a very good fiscal starting position. They are pushing a lot but they're to some extent pushing a string. So, even with the German huge fiscal package, given that private sector investments so far are absent, doesn't get us a ton of growth. Seth Carpenter: Chetan, maybe I'll come back to you before we close part one of this roundtable. The AI CapEx cycle started with AI, broadened out further. How long do you expect this cycle to last? How durable can it be? And how might it compare to previous CapEx cycles? Chetan Ahya: Yeah, Seth. So, we think this will be a multi-year CapEx cycle. And when we are thinking about the duration of the cycle, there are two things that I would keep in mind. Number one is that most of the drivers that we just discussed – the CapEx on AI, energy, defense, and industrial supply chain onshoring related investments – these are all structural drivers. So, we think these are going to continue for some more time. At this point of time, we have the visibility for this cycle to be lasting for three-four more years. And then the second point of framework that I would keep in mind is that the corporate balance sheets are in a pretty good shape. So, when you are thinking about the leverage in the private sector, you can look at both households and the corporate sector balance sheet. But since the cycle is CapEx driven, we are looking at the corporate balance sheets, and they are in a pretty good shape. Across the region, corporate debt to GDP is below where it was in 2019. Seth Carpenter: Mike, let me, let me wrap up quickly with you. We talked about AI, AI CapEx. For now, that's a very strong demand story. When are we going to see a supply side of things coming from AI? Are you already seeing a big contribution to GDP and growth from productivity coming from AI? Michael Gapen: We are, but not outside of the high-tech sectors, and we're seeing limited, what I'll call labor market restructuring of tasks and occupations beyond high AI-exposed occupations. So right now, everything is still very isolated I think maybe as we get into 2029 and beyond, so as Chetan says, we probably have a three to four-year super cycle here around a build-out phase. Then we might see some of that broader-based diffusion to other non-tech sectors in the economy. Seth Carpenter: All right, Jens, for you, let's wrap up here. So, what is the state of play for the build-out in the CapEx cycle for AI in Europe? Jens Eisenschmidt: Yeah, it's very early stages. As I said before, we really; we connected to all the industry experts or analysts covering the sector and the total plans are a factor of 20 below what we see in the U.S. by just the seven hyperscalers. So, I would say very fragmented, very small, in general. Not only AI. I think the one thing I would be looking at for any type of sign of revival, sign of growth is investment. The second would be investment. And you can guess what the third would be… Investments in the core countries. That's really what we need to see, and we haven't seen much in Germany or France on this front. Seth Carpenter:That's a great place for us to stop today. We talked about the real side of the economy, AI, CapEx, trade. Tomorrow we're going to come back, and we'll talk about how that growth outlook affects inflation. And once you start talking about growth and inflation, you got to talk about policy, and that's where we'll be tomorrow. Mike, Jens, and Chetan, thank you for joining today. And for the listeners, thank you for listening. Be sure to tune in tomorrow for Part 2 of our conversation. And I have to say, if you enjoy this show, please leave us a review wherever you listen, and share Thoughts on the Market with a friend or a colleague today.

In the second part of our economic roundtable, Michael Gapen, Jens Eisenschmidt and Chetan Ahya join Seth Carpenter to discuss how central banks are balancing sticky inflation, resilient growth and regional policy trade-offs.Read more insights from Morgan Stanley.----- Transcript -----Seth Carpenter: Welcome to Thoughts on the Market. I'm Seth Carpenter, Morgan Stanley's Global Chief Economist and Head of Macro Research. And once again today, I am joined by Morgan Stanley's chief regional economists: Michael Gapen, the Chief U.S. Economist, Jens Eisenschmidt, our Chief Europe Economist, and on the other side of the world, Chetna Ahya, our Chief Asia Economist. Yesterday, we talked about what's supporting growth around the world, especially AI spending in the U.S. and some government spending in Europe, and Asia's role in making all of this happen. Today, we're going to try to dig deeper and go into policy. It's Tuesday, July 21st at 10 am in New York Jens Eisenschmidt: And 4pm in Frankfurt. Chetan Ahya: And 10pm in Hong Kong. Seth Carpenter: Since the last time we did this in mid-April, I will say the debate around central banks has probably become more complicated. Global growth has held up, probably better than many people expected. And inflation, which picked up a lot, started to recede. But it has not gone away. And some of the forces helping to shape the economy, the AI spending, government spending, that possible upswing in manufacturing, that could keep demand strong, and it might keep pushing inflation higher. So, the question today is, if growth remains resilient, how much room really do central banks have to navigate? Mike, let me start with you because your call for the Fed here in the U.S. is out of consensus, or at least at odds with where the market is pricing things. We talked about the demand going from AI. You pointed out that imports are actually limiting how much domestic demand there is. So, what is the underlying story for inflation in the U.S.? And what does it mean for the Fed? Michael Gapen: So, our view is that inflation will come down in the U.S. So, we think disinflation will be driven by some payback in energy prices. Some payback from tariffs, which have pushed up goods prices over the last year. And some further diminishment in housing-related inflation, namely shelter. So, we think on a broad-based perspective, inflation has already peaked and will start moving lower. And we think we've seen evidence of this in recent inflation prints. A risk to that, though, is from the demand side of the economy and AI-related inflation in two parts. One, higher software prices, chipflation. So, the pass-through of some of the AI pricing components. Fortunately, here, they're about less than 1 percent of the consumer basket. So, we don't think that there's a great risk, a strong risk, a high risk of AI-related inflation in the consumer bundle. I think the real risk is that maybe we underestimate broad-based demand, animal spirits. And so, you might just see a broad-based increase in inflation from stronger demand. That'll be a little bit harder to see in real times. But our expectation is that inflation moves lower to about 3 percent, by the end of this year and closer to 2.5 percent next year. Seth Carpenter: All right. Thanks, Mike. And in fact, the most recent inflation report that we just got confirms your perspective that inflation should be coming down. And so, I guess the question then remains: What would it take for the Fed to hike this year if inflation has come down like we've seen? Michael Gapen: Well, I think that the answer there is that inflation wouldn't come down in line with our expectations. So, if the view is that energy prices, tariffs, and shelter inflation should provide plenty of offset and bring inflation down, I think the answer is you don't get payback. Explicitly, core goods prices stay elevated. Maybe we get ongoing disruptions in the Middle East that push energy prices higher and create second-round effects. So, I think inflation just lingering at elevated levels could mean the Fed gets brought in to raise rates in September or later this year. We think if they're patient enough, they'll see enough disinflation to keep them on the sidelines. But the risk is disinflation forecast is too optimistic, inflation stays firm, the Fed needs to raise rates. Seth Carpenter: All right, Jens, what about for you and the ECB? They've already raised interest rates once this year. I think you've got a forecast for them raising interest rates again in September. What could make you wrong about that forecast? What's going to make you convinced that you're right about that forecast? And is there a similar tension that the ECB is wrestling with that Mike talked about for the Fed? Jens Eisenschmidt: Yeah. I mean, starting with the last part of your question, I think no doubt, very similar tension. Just that, of course, it's less obvious. It's essentially a nuanced European version instead of the loud American version that we always stereotypically think the world looks like. So, essentially, we have here clearly not an AI boom. That, I mean, there's no question. And we have discussed that yesterday. Still, there is certainly the notion that the world demand is not really weak, and some of this will also arrive in Europe. And so, you have that tension between maybe there's more resilience than we had thought, and so inflation will not come down through to slack as much. And so, we might actually add something here in terms of monetary restrictiveness. Now, the other thing that is often forgotten, even though it's blatantly obvious, the starting point is just different. The ECB is running neutral monetary policy by all accounts. I mean, you could say 2 percent is neutral, and now they are 2.25. But, you know, there are ranges of uncertainty around any estimate. And the latest that they published runs – goes from 1.75 to 2;2.5. So basically, even if they were to increase rates to 2.5 in September, you could go with the microphone around the governing council, and you would probably find a lot of people saying, "Well, this is still a neutral policy." That's probably not the case for the U.S. So, I guess this matters here for that debate too. Seth Carpenter: All right. Yesterday we talked about lots of different things, but for Europe, we brought up fiscal policy. How do you think about fiscal policy and how it affects monetary policy? And so, I'm thinking about two channels. One, how much does the ECB care that if they keep pushing up interest rates, they're going to increase the debt service burden for countries that are already facing high debt costs? And second, is fiscal policy going to be the extra impetus for inflation that forces even more rate hikes from the ECB? Jens Eisenschmidt: I guess it depends on who you ask. Certainly, more concerned members in the governing council that would point to exactly that fiscal stimulus as a reason why interest rates have to be increased further from here. The other answer I would give is – probably for now at least, the view on fiscal policy is really model-based. You look at what type of increase in interest rate gets you essentially more fiscal restraint because there's an increase in interest rate bill and so less spending somewhere else. And that gets you basically less stimulus or less growth, I mean, very roughly speaking. I don't think it's a major concern for now. We haven't reached yet interest rates where this would start to play a role. I guess, again, Europe being fragmented as it is, with all the political risk that's around the corner. Think about the elections in France and Italy and Spain next year. That will very likely find itself expressed in spreads. And so, the higher the interest rates are, the larger the spreads could become. Seth Carpenter: So, for each of you, there's clearly a role for inflation. One of the risks we'll talk about maybe is inflation expectations and how maybe there's a big shift in what's going on with inflation. But Chetan, that brings me to you and Asia, because one economy where there unquestionably has been a fundamental shift in inflation and inflation expectation over the past several years is Japan. The Bank of Japan is on this normalization path where they're raising interest rates. Interest rates had been negative and then zero, and now they're gradually raising things up. Inflation has come back to Japan. Markets are looking at what the Bank of Japan is likely to do. Can you tell us a little bit about what our view is for the Bank of Japan this year and next? And what might make them hike interest rates faster than we think? And is there any risk that in fact they hike interest rates slower than we think? Chetan Ahya: Yeah, Seth. So, we are expecting BoJ to hike twice from here. The first rate hike is coming up in December of this year, and then another one coming up in June of next year. And then we think that, you know, the underlying inflation trend in Japan is not really that strong. So, while market pricing is for about three more rate hikes instead of two that we are building in our base case. And some of the macro investors are even talking about four more rate hikes. We think the underlying inflation trend warrants a caution and BoJ to go slowly than what the market is pricing in and what the macro investors are saying in. And the key part of our framework on thinking about Japan's inflation is that bulk of the explanation to inflation rise in Japan lies in currency moves. And secondarily, you can look at also the other drivers are more from supply side, which is higher energy prices or food prices. Whereas it's not driven so much by demand. To elaborate further on why it is not driven by demand, when you look at Japan's consumption trend, and if you index it to hundred at pre-COVID levels in September [20]19 then it's currently about 101; i.e., that it's just about 1 percent up over the last seven years. So that's a very tepid trend of consumption demand. And therefore, we don't think that BoJ needs to rush into hike in a more aggressive pace going forward. Seth Carpenter: So, there is this fundamental shift, but boy, it's not on a tear, and so the BoJ can take its time. You know, Chetan, it's hard to wrap up a conversation about the global economy without talking about China. I get the sense that there's not a lot going on with monetary policy, but we did just see a soft Q2 GDP print. So, against that backdrop, what should we be expecting in terms of policy? Is there any monetary policy coming? Or is there going to be some fiscal expansion? Or is China just sort of stuck in this lower gear? Chetan Ahya: Yeah, Seth. So, we were also surprised by the soft GDP print. But when you look into the data, actually, it was interestingly doing well on exports. And I mentioned earlier about how the global CapEx trend is helping Asia. It's definitely helping China too. But at the same time, China's domestic demand turned out to be quite weak. And particularly in the areas where we think that the policy response can be providing some help, i.e., infrastructure spend, was also very weak. And therefore, we are expecting that in the back half of the year, you will see the government taking up some fiscal expansion. Not new stimulus announcement, but whatever they had budgeted. They have enough room within that to utilize that budget and actually increase that fiscal spending towards infrastructure. We have about 2 trillion RMB worth of funds available for the government to go ahead and spend in the second half. And then lift that growth trend, which has dipped to 4.3 percent in second quarter to back to 4.6 percent in the back half of the year. Seth Carpenter: You know what? Maybe that's a great place for us to leave it. We've gone around the world again today, but this time focusing much more on policy. In the U.S., the Fed is facing this interesting situation. We think inflation is coming down. The last CPI print went in our favor. And so as a result, our forecast is that the Fed doesn't change policy at all this year. But it's going to come down to the data, and in particular, whether or not Mike and his team are right in terms of where inflation is going. In Europe, the ECB has already raised interest rates once this year. Jens and team are looking for another interest rate hike. The ECB really does seem more sensitive to inflation coming from the energy shock, but there are lots of other crosscurrents that they're paying attention to as well. And then the other major developed market central bank, the Bank of Japan, is on this normalization path. They are in the process of raising interest rates, but Chetan pointed out to us that the growth rate is such that they don't have to be in any sort of hurry, and they can take their time. So, with that, Mike, Jens, Chetan, thank you so much for helping us connect all of these dots. And to the listeners, thank you for listening. If you enjoy the show, please leave us a review wherever you listen. And share Thoughts on the Market with a friend or a colleague today.

The Big Tech companies driving the AI boom are expected to spend more than $700 billion on data center infrastructure this year. To finance this spending spree, they're increasingly looking to debt. Hyperscalers like Alphabet, Oracle, and Meta have been issuing corporate bonds at a scale the industry has never seen — nearly a quarter of a trillion dollars this year so far. The bond market could be considered a less risky way than stocks for investors to get a piece of the AI hype. To explain what the flood of AI debt means for the bond market, we're joined by Julie Ask, an independent industry analyst at Ask Advisory. More on this: “The Quarter-Trillion-Dollar Onslaught of AI Bonds Is Testing Investors' Limits” from the Wall Street Journal “AI has taken over the stock market. The bond market is next.” from The Economist

The Big Tech companies driving the AI boom are expected to spend more than $700 billion on data center infrastructure this year. To finance this spending spree, they're increasingly looking to debt. Hyperscalers like Alphabet, Oracle, and Meta have been issuing corporate bonds at a scale the industry has never seen — nearly a quarter of a trillion dollars this year so far. The bond market could be considered a less risky way than stocks for investors to get a piece of the AI hype. To explain what the flood of AI debt means for the bond market, we're joined by Julie Ask, an independent industry analyst at Ask Advisory. More on this: “The Quarter-Trillion-Dollar Onslaught of AI Bonds Is Testing Investors' Limits” from the Wall Street Journal “AI has taken over the stock market. The bond market is next.” from The Economist

We endure the ravages of time to deliver you this exhausted Supplementary Material. Bowed but never broken, the decoders persist.The full episode is available to Patreon subscribers (Full Episode: 2hr 20 mins)Join us at: https://www.patreon.com/DecodingTheGurusSupplementary Material 5300:23 Introduction: Matthew the Succulent's Post-Holiday Report02:15 Pickle Ball Injuries & Bouldering Curses07:40 They Won't Let You Talk about Middle-Aged Sports Injuries08:37 New AI Models09:31 Cory Doctorow, Trevor Noah, and Confirmation Bias23:45 The Glorious Past and the Terrible Present28:28 The Horror of Tinned Asparagus30:26 The Collapse of Industries & Stores36:16 Negativity Bias37:11 AI Prompting 10144:30 AI and Research Applications50:28 Nigel Farage vs Count Binface59:10 Nigel Farage Knighted on Russia Today01:02:53 Hasan's Advice for Ukrainians01:07:51 Russian Apologetics01:13:32 Vlad Vexler and soft Anti-Vaxx rhetoric01:19:35 Guru Red Flags01:23:48 Bret Weinstein in 2020 on Vaccines01:28:53 Gary vs Piers Morgan01:30:58 Piers Morgan being a blustering idiot about Elon Musk01:36:55 Gary's Pitch01:41:55 Alternative Tax Proposals01:46:18 Matt and Chris' Economics01:47:22 Gary's Wealth01:52:07 Piers the sleazy liar01:55:25 Gary is the best inequality economist in the UK01:57:08 Gary's Predictions02:00:40 Gary is Very Tired02:07:14 A message from anyone else to finish?02:14:25 Final Takeaways02:16:55 Matt attempts to wrangle another holidayLinksThe Verge: China delivers a one-two punch to America's AI dominance What Now? with Trevor Noah: Why Google, Apple & Big Tech Keep Making Everything Worse — Cory Doctorow and Trevor NoahBaumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001). Bad is stronger than good. Review of general psychology, 5(4), 323-370.Count Binface on BBC Morning Live — full interviewCount Binface on NewsnightHasan offers Ukraine his advice to “give peace a try”Report on polling from Ukraine in 2025 that does not show what Hasan claimsReport on more recent polling that still does not show what Hasan claimsVlad Vexler tweets about vaccinesBret's Tweets in 2020 on Vaccines (and here)Chris noting Bret's anti-vaccine rhetoric in 2020 (see the comments under the tweet)Kyiv International Institute of Sociology: Ukrainian views on exchanging Donbas for security guaranteesDecoding Academia 34: Matt and Chris' EconomicsPiers Morgan Uncensored: “I'm QUITTING!” Gary Stevenson vs Piers Morgan on Wealth, Economic Growth & Elon Musk

The Consumers Price Index (CPI) has increased 4.1% in the 12 months to the June 2026 quarter, according to figures released by Stats NZ today. As expected, the increase can be partially contributed to the US-Iran war as the largest upwards contributor to the annual inflation rate was petrol, up 27.5%. Finance Minister Nicola Willis is labelling annual inflation hitting its highest level in more than two years a “Trump spike”. Former Reserve Bank senior economist Michael Reddell told Andrew Dickens that economists don't tend to look at the headline numbers but they will be digging through the numbers to find trends. "It's a bit like the famous line about democracy, you know, it's not a perfect system, it's just better than any of the alternatives that have been tried, and inflation targeting is a bit like that as well. It's definitely not perfect." LISTEN ABOVE See omnystudio.com/listener for privacy information.

The alleged murder of Ann Widdecombe prompted a debate about how we should speak about the dead, after some people came under fire for comments they made in the wake of her passing. When is the right time to bring up criticism of a public figure that's died, and how do you accurately reflect someone's life and legacy? To find out, Adam Fleming spoke to Ann Wroe, obituaries editor at The Economist.

Inflation has hit 4.1% - the highest level in more than two years. Economists had expected it surge to somewhere between 3.9 and 4.1 percent. However, the end result is well below some previous forecasts, which reached 4.9%. If we ignored the surging fuel prices, Westpac economists reckon inflation would be about 2.8 percent. So what does this actually mean for the cost of living, for interest rates, and for the story of the New Zealand economy in 2026? Today on The Front Page, NZ Herald business editor at large Liam Dann is with us to unpack how we got to 4.1%, what’s really driving the numbers, and what comes next for households, businesses, and the path back to stable prices. Follow The Front Page on iHeartRadio, Apple Podcasts, Spotify or wherever you get your podcasts. You can read more about this and other stories in the New Zealand Herald, online at nzherald.co.nz, or tune in to news bulletins across the NZME network. Host: Chelsea DanielsEditor/Producer: Richard MartinProducer: Jane YeeSee omnystudio.com/listener for privacy information.

The Consumers Price Index (CPI) has increased 4.1% in the 12 months to the June 2026 quarter, according to figures released by Stats NZ today. As expected, the increase can be partially contributed to the US-Iran war as the largest upwards contributor to the annual inflation rate was petrol, up 27.5%. Finance Minister Nicola Willis is labelling annual inflation hitting its highest level in more than two years a “Trump spike”. Former Reserve Bank senior economist Michael Reddell told Andrew Dickens that economists don't tend to look at the headline numbers but they will be digging through the numbers to find trends. "It's a bit like the famous line about democracy, you know, it's not a perfect system, it's just better than any of the alternatives that have been tried, and inflation targeting is a bit like that as well. It's definitely not perfect." LISTEN ABOVE See omnystudio.com/listener for privacy information.

Andy Burnham faces tough challenges as Britain's new prime minister, both within his party and the country at large. Our correspondent analyses his prospects. After years of strife, how can Syria be rebuilt? And food markets have changed New York.Guests and host:Hugo Gye, Britain political correspondentGareth Browne, Middle East correspondentAnnie Crabill, senior digital editorRosie Blau, co-host of “The Intelligence”Topics covered: Andy Burnham, Keir Starmer, prime ministerSyria, Ahmad al-Sharaa, AssadNew York, street markets, farmers' marketsListen to what matters most, from global politics and business to science and technology—subscribe to The Economist. Hosted on Acast. See acast.com/privacy for more information.

Andy Burnham faces tough challenges as Britain's new prime minister, both within his party and the country at large. Our correspondent analyses his prospects. After years of strife, how can Syria be rebuilt? And food markets have changed New York.Guests and host:Hugo Gye, Britain political correspondentGareth Browne, Middle East correspondentAnnie Crabill, senior digital editorRosie Blau, co-host of “The Intelligence”Topics covered: Andy Burnham, Keir Starmer, prime ministerSyria, Ahmad al-Sharaa, AssadNew York, street markets, farmers' marketsListen to what matters most, from global politics and business to science and technology—subscribe to The Economist. Hosted on Acast. See acast.com/privacy for more information.

SPONSORS: - Go to https://www.plaud.ai/curt and use the promo code "CURT" to get a Plaud device today - I personally subscribe to The Economist. TOE listeners get 35% off the annual subscription. No other podcast has this! https://economist.com/TOE This is a podcast all about parallel universes, and whether we have any right to believe in them. David Wallace, professor of philosophy of science at the University of Pittsburgh and a leading defender of the Many Worlds Interpretation, joins Professor Emily Adlam, his former student, for a rare joint conversation. The central fault line: Adlam argues no one living inside a many-worlds universe could ever rationally confirm the theory describing it, since Everettian probability may undermine the evidence used to support quantum mechanics itself. Wallace disagrees, but takes the challenge seriously. From there they range across the nature of the wave function, relational quantum mechanics, QBism, and scientific intersubjectivity — two sharp, collegial minds disagreeing productively about what kind of universe we actually live in. TIMESTAMPS: - 00:00:00 - Wave Function Ontology - 00:05:35 - Everettian Probability Problem - 00:11:45 - Relational Quantum Mechanics Limits - 00:19:15 - Quantum Gravity Frameworks - 00:24:50 - Subsystem Decomposition Approximations - 00:31:10 - Testing Wavefunction Collapse - 00:38:35 - Scientific Intersubjectivity Crisis - 00:47:55 - Many Worlds Rationality Constraints - 00:58:50 - QBism and Physical Reality - 01:05:05 - Conservative Philosophy of Physics LINKS MENTIONED: - Emily Adlam's Papers: https://scholar.google.com/citations?user=RlTqjZMAAAAJ - David Wallace's Papers: https://scholar.google.com/citations?user=Z35CeHAAAAAJ - Pointer Basis Of Quantum Apparatus [Paper]: https://libertadacademica.com/Cuantica/ModernaHist/Zurek%20b.pdf - Information Is Physical [Paper]: https://arxiv.org/abs/2203.13342 - Relative Information, Relative Facts [Paper]: https://arxiv.org/abs/2510.11349 - What Kind Of Relationality Does Quantum Mechanics Exhibit? [Paper]: https://arxiv.org/abs/2502.06991 - Models Of Wave-Function Collapse, Underlying Theories, And Experimental Tests [Paper]: https://arxiv.org/abs/1204.4325 - On Gravity's Role In Quantum State Reduction [Paper]: https://link.springer.com/article/10.1007/BF02105068 - Real Patterns In Physics And Beyond [Paper]: https://philsci-archive.pitt.edu/23888/ - Learning To Represent [Paper]: https://philsci-archive.pitt.edu/23224/1/learning_to_represent.pdf - Operational Theories As Structural Realism [Paper]: https://arxiv.org/abs/2201.09316 - Relational Quantum Mechanics [Paper]: https://arxiv.org/abs/quant-ph/9609002 - Measurement Outcomes And Probability In Everettian Quantum Mechanics [Paper]: https://www.sciencedirect.com/science/article/abs/pii/S1355219806000694 - Quantum Probability From Subjective Likelihood [Paper]: https://arxiv.org/abs/quant-ph/0312157 More links (past guest episodes) at https://curtjaimungal.substack.com FOLLOW: - Spotify: https://open.spotify.com/show/4gL14b92xAErofYQA7bU4e - Substack: https://curtjaimungal.substack.com/subscribe - Twitter: https://twitter.com/TOEwithCurt - Discord Invite: https://discord.com/invite/kBcnfNVwqs - Crypto: https://nowpayments.io/donation/TOE - PayPal: https://www.paypal.com/donate?hosted_button_id=XUBHNMFXUX5S4 Guests do not pay to appear. #science Learn more about your ad choices. Visit megaphone.fm/adchoices

******Support the channel******Patreon: https://www.patreon.com/thedissenterPayPal: paypal.me/thedissenterPayPal Subscription 1 Dollar: https://tinyurl.com/yb3acuuyPayPal Subscription 3 Dollars: https://tinyurl.com/ybn6bg9lPayPal Subscription 5 Dollars: https://tinyurl.com/ycmr9gpzPayPal Subscription 10 Dollars: https://tinyurl.com/y9r3fc9mPayPal Subscription 20 Dollars: https://tinyurl.com/y95uvkao ******Follow me on******Website: https://www.thedissenter.net/The Dissenter Goodreads list: https://shorturl.at/7BMoBFacebook: https://www.facebook.com/thedissenteryt/Twitter: https://x.com/TheDissenterYT This show is sponsored by Enlites, Learning & Development done differently. Check the website here: http://enlites.com/ Turi Munthe is a journalist and policy analyst turned media entrepreneur and writer. As a journalist and Middle East policy analyst, Turi has written for the Economist, Guardian, TLS, THES, the Nation, Spectator, and many others. He has appeared on the BBC, Fox, CNN, al-Jazeera, NBC and others, and given lectures at universities all over the world - Oxford, Sciences Politiques Paris, CUNY, and elsewhere. He has also advised governments across Europe and the Middle East. He is the author of Why We Think What We Think: The Unexpected Origins of Our Deepest Beliefs. In this episode, we focus on Why We Think What We Think. We talk about what opinions are, and the many factors that influence them. We explore the social aspects that predict people's opinions; climate and geography; why conventionally attractive people are more right-wing; and peanut butter preferences. We discuss a biological basis to political beliefs, the social functions of opinions, and tribalism. Finally, we talk about philosophy and science, and political radicalization and polarization.--A HUGE THANK YOU TO MY PATRONS/SUPPORTERS: PER HELGE LARSEN, BERNARDO SEIXAS, ADAM KESSEL, MATTHEW WHITINGBIRD, ARNAUD WOLFF, TIM HOLLOSY, HENRIK AHLENIUS, ROBERT WINDHAGER, RUI INACIO, ZOOP, MARCO NEVES, COLIN HOLBROOK, PHIL KAVANAGH, SAMUEL ANDREEFF, FRANCIS FORDE, TIAGO NUNES, FERGAL CUSSEN, HAL HERZOG, NUNO MACHADO, JONATHAN LEIBRANT, JOÃO LINHARES, STANTON T, SAMUEL CORREA, ERIK HAINES, MARK SMITH, JOÃO EIRA, TOM HUMMEL, SARDUS FRANCE, DAVID SLOAN WILSON, YACILA DEZA-ARAUJO, ROMAIN ROCH, YANICK PUNTER, CHARLOTTE BLEASE, NICOLE BARBARO, PAWEL OSTASZEWSKI, NELLEKE BAK, GUY MADISON, GARY G HELLMANN, SAIMA AFZAL, ADRIAN JAEGGI, JOÃO BARBOSA, JULIAN PRICE, HEDIN BRØNNER, FRANCA BORTOLOTTI, URSULA LITZCKE, SCOTT, ZACHARY FISH, TIM DUFFY, SUNNY SMITH, JON WISMAN, WILLIAM BUCKNER, LUKE GLOWACKI, GEORGIOS THEOPHANOUS, CHRIS WILLIAMSON, PETER WOLOSZYN, DAVID WILLIAMS, DIOGO COSTA, ALEX CHAU, CORALIE CHEVALLIER, BANGALORE ATHEISTS, LARRY D. LEE JR., OLD HERRINGBONE, DAN SPERBER, ROBERT GRESSIS, JEFF MCMAHAN, JAKE ZUEHL, MARK CAMPBELL, TOMAS DAUBNER, LUKE NISSEN, KIMBERLY JOHNSON, JESSICA NOWICKI, LINDA BRANDIN, VALENTIN STEINMANN, ALEXANDER HUBBARD, BR, JONAS HERTNER, URSULA GOODENOUGH, DAVID PINSOF, SEAN NELSON, MIKE LAVIGNE, JOS KNECHT, LUCY, MANVIR SINGH, PETRA WEIMANN, CAROLA FEEST, MAURO JÚNIOR, TONY BARRETT, NIKOLAI VISHNEVSKY, STEVEN GANGESTAD, TED FARRIS, HUGO B., JORDAN MANSFIELD, CHARLOTTE ALLEN, DAVID TONNER, PATRICK DALTON-HOLMES, NICK KRASNEY, RACHEL ZAK, DENNIS XAVIER, CHINMAYA BHAT, RHYS, ALEX MACLEOD, HAIDAR, JULIEN PORCHER, ROBERT SUNDSTRÖM, JON STEWART, AND JAMES DORLING!A SPECIAL THANKS TO MY PRODUCERS, YZAR WEHBE, JIM FRANK, ŁUKASZ STAFINIAK, TOM VANEGDOM, BERNARD HUGUENEY, CURTIS DIXON, THOMAS TRUMBLE, KATHRINE AND PATRICK TOBIN, JONCARLO MONTENEGRO, NICK GOLDEN, CHRISTINE GLASS, IGOR NIKIFOROVSKI, PER KRAULIS, ADAM HUNT, AND JOÃO BARBOSA!AND TO MY EXECUTIVE PRODUCERS, MATTHEW LAVENDER,SERGIU CODREANU, AND GREGORY HASTINGS!

Economists say households already feeling the squeeze from the cost of living could be about to face another blow. Many are expecting today's inflation figures to be at the highest level in two years, climbing to 4.1 percent in the year to June. Reporter Anna Sargent asked some people in Christchurch how they're feeling.

There's cautious optimism from economists that inflation will start falling after today. Stats NZ is releasing CPI figures for the June quarter later this morning, which is expected to largely reflect the impact of the Iran war, along with other cost pressures like rates and utility costs. Economists expect it surge to somewhere around 4%. BNZ Chief Economist Mike Jones told Mike Hosking that based on their forecasts, this should be the peak. He says next quarter BNZ predicts inflation to come down to 3.8%, before it tails away to be within the Reserve Bank's band in the middle of next year. LISTEN ABOVE See omnystudio.com/listener for privacy information.

Gregg Carlstrom, Middle East Correspondent for The Economist

Join us for a special interview with former TD Bank Chief Economist Don Drummond as he breaks down what the latest Bank of Canada decision means for inflation, industry trends and financial markets.   Recorded on July 16, 2026. At Fidelity, our mission is to build a better future for Canadian investors and help them stay ahead. We offer investors and institutions a range of innovative and trusted investment portfolios to help them reach their financial and life goals. Fidelity mutual funds and ETFs are available by working with a financial advisor or through an online brokerage account. Visit fidelity.ca/howtobuy for more information. For a fifth year in a row, FidelityConnects by Fidelity Investments Canada was ranked #1 podcast by Canadian financial advisors in the 2025 Environics' Advisor Digital Experience Study. -- Soyez des nôtres lors de cet entretien spécial avec Don Drummond, ancien économiste en chef de la Banque TD, qui discutera de la récente décision de la Banque du Canada et de ses conséquences sur l'inflation, les tendances sectorielles et les marchés des capitaux.   Pour une version avec des sous-titres français, veuillez consulter https://youtu.be/QqdeH2JEQeE Date : 16 juillet 2026 Chez Fidelity, notre mission consiste à aider le public investisseur canadien à se bâtir un meilleur avenir et à rester à l'avant-garde. Nous offrons aux particuliers et aux institutions une gamme de portefeuilles de placement innovants et fiables pour les aider à atteindre leurs objectifs financiers et personnels. Les fonds communs de placement et les FNB de Fidelity sont offerts par l'intermédiaire des conseillers et conseillères en placements et de comptes de courtage en ligne. Pour de plus amples renseignements, visitez fidelity.ca/commentinvestir. Les baladodiffusions DialoguesFidelity se sont classées au premier rang pour une cinquième année consécutive lors du sondage 2025 d'Environics sur l'expérience numérique des conseillers et conseillères en placements au Canada.

What impact do hormones have on women with ADHD? A pioneering study by Kings College and Queen Mary University in London is putting the link to the test, by asking 50 women who are medicated for their ADHD to track their menstrual cycle. Report academic Dr Jessica Agnew-Blais and Laura Mears-Reynolds from the charity ADHDAF+ join presenter Nuala McGovern to discuss why the research matters.This weekend Dalia Stasevska conducted the first night of the BBC Proms. The Finnish-Ukrainian Principal Guest Conductor of the BBC Symphony Orchestra also has a new album, Ukrainian Mixtape, recorded with the BBC Symphony Orchestra, showcasing rarely heard works. Dalia joins presenter Anita Rani to discuss her route into conducting and her passion for Ukrainian music.New analysis in The Economist suggests that, after years of progress, fewer women are reaching executive positions and there may also be signs that younger women are becoming less interested in promotion. Nuala speaks to the author of that report, Vinjeru Mkandawire, and Sanchia Neilson who left an executive role to rebalance her life.Australian surf and fashion photographer Cait Miers is photographing 1,000 women globally, bare-faced, for a project she has called Laugh Lines. Anita talks to Cait about her mission to challenge beauty standards and also Alexis Zahner, one of the first women to be photographed for the project.Actor Sarah Hadland will be a familiar face to many, having played numerous roles over the years, but is probably best known as Stevie in the BAFTA-nominated sitcom Miranda. Currently on stage in London's West End, she joins Nuala to discuss her latest role as Alice in The Truth, a fast-paced comedy of infidelity and duplicity.Presenter: Anita Rani Producer: Kirsty McQuire

In 1776 — that same year America declared its independence — Adam Smith published the equally revolutionary The Wealth of Nations, his founding explanation of national economic value. Two hundred and fifty years later, Tim O'Reilly argues in the free-market Economist that Elon Musk and his fellow tech barons are building a monarchical form of capitalism that the proto-democratic Smith would have hated. Musk, O'Reilly reports, believes that SpaceX will become “worth more than the rest of Earth”. The merchants are becoming princes, O'Reilly warns. And the rest of us are becoming peasants. Such is the road to serfdom in our AI age. So who should own the AI in our bewildering age of multi-trillion dollar start-ups like SpaceX, Anthropic and OpenAI? Or as That Was The Week publisher Keith Teare asks in his latest editorial, who should own the “intelligence” of our AI age? Keith uses a bottling plant as a metaphor to describe our dilemma. Since no single entity can own this intelligence — the sum total of our common experience — charging us for it would be like seizing the Earth's water supply and selling it back to us, Coca-Cola style, in plastic bottles. Except that the Hayekian Keith approves of the bottling process. Private companies, rather than governments, he argues, are most suited to doing this. For Keith, this dilemma is also an opportunity to redistribute the ownership of intelligence. He argues for a “Human Wealth Fund” into which every consequential AI company should put a slice of its equity. In the manner of Norway's sovereign wealth fund, this fund would be distributed to all citizens. Rather than Denmark, now we should become like Norway, a tiny homogenous nation with a cultural distaste for Muskian individual wealth. Not very realistic, I fear. On top of that, it's hard to imagine our tech princes collaborating on anything. Musk and Altman aren't on speaking terms while Altman and Amodei, who also loathe each other, are focused on their IPOs. Meanwhile, the Trump administration, which presumably would coordinate this fund, is pitching a $100,000-a-month fast feed of the president's posts. Keith's question, “who owns the intelligence”, is the right one. But the answer won't come from trickle-down funds set-up by our tech princes. Such supposed munificence is about as likely as America becoming Norway. Read the fine print of any “Human Wealth Fund” set up by Sam Altman and Elon Musk. As we should know all too well by now, when a “revolutionary” Silicon Valley gives stuff away, it turns out to be exorbitantly expensive. Free plastic bottles of intelligence, anyone? Five Takeaways •       Intelligence, Not AI. The week's framing shift: the word AI is too small, because AI is merely the tool for harvesting and delivering the thing itself — intelligence, the sum total of our common human experience. Keith argues the renaming is not semantic but political: the moment intelligence sits at the center of the discussion, everyone's opinion has to be shaped by what it actually is, and the idea that any single entity could own it starts to look as bizarre as owning the world's water supply. Andrew's rejoinder: they're still just words — though he concedes intelligence is the better one. •       Bottled Intelligence Is Good — The Question Is Who Benefits. Keith refuses the critic's role: bottling intelligence, like Google's bottling of the world's words into search, is a good thing, because only massively capitalized private companies can innovate at that scale — and between private entities and governments as owners of intelligence, he'll take the companies every time. What's wrong is the distribution of the benefits. Even insiders are complaining: Alex Karp is publicly angry at OpenAI and Anthropic's pricing, while China's Kimi K3 — released the day of recording and, Keith claims, better than Claude Fable — signals that very good models are about to get very cheap. •       Capitalism Adam Smith Would Hate. Tim O'Reilly argues in The Economist that Musk and his type are building a capitalism Smith would despise — founders as monarchs, a point Henry Farrell reinforces with a slide from Peter Thiel's startup class placing the king of a monarchy and the founder of a startup side by side. Keith's response is characteristically unsentimental: Smith would have hated everything since the Federal Reserve, and the founder-king structure — Larry and Sergey's voting shares, Zuckerberg's special rights, corporations bigger than countries with user bases bigger than China — is simply the stage of capitalism we're at. The question is whether there's a path from here to somewhere better. •       The Human Wealth Fund. Keith's path comes in two versions: government-down, a sovereign wealth fund holding AI equity for every citizen; or company-up, the AI companies voluntarily endowing a global fund — and it only takes one to move first, because everyone else would have to react. His proxy is Norway, where every citizen benefits from ownership — not payouts, ownership — in the oil fund; AI revenues, unlike Norwegian oil, could eventually drive most of a doubled global GDP. His critique of the Brynjolfsson economists' much-signed statement is that “must act now” is vacuous: he'd have added a point four naming the actual mechanism. •       The Bet. Andrew's counter-case: Musk and Altman loathe each other, the mob hates AI so thoroughly that no pro-AI politician can survive, the states from Newsom's California to Florida are embracing nothing, New York just enacted the first data center moratorium, and the founders — eyes on their IPOs — are in the pockets of the banks. Hence the wager: 5% of the Teare Wealth Fund says no Human Wealth Fund this year, and none in the twenties. Keith declined the bet, on principle: he's an advocate, and only through advocacy does public opinion change. As Andrew put it: keep fighting the good fight — maybe one of the crazy ideas will stick. About the Guest Keith Teare is the founder and editor of the That Was The Week tech newsletter, and Andrew's weekly co-host. A British-born Silicon Valley entrepreneur and investor, he was a co-founder of TechCrunch and runs the Palo Alto–based venture firm SignalRank. He and Andrew have been arguing about technology — productively — every week for years. References: •       That Was The Week — Keith's newsletter; this week's editorial argues that the word AI is too small, and that the central question of the age is who owns intelligence. •       Tim O'Reilly in The Economist — on Elon Musk building a form of capitalism that Adam Smith would hate, quoting Musk's claim that SpaceX will become worth more than the rest of the Earth. •       Henry Farrell — the big tech critic's companion piece, featuring the slide from Peter Thiel's startup class that plac...

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.There was an issue with this only going to paid subscribers, so sending it again. Apologies to those who get it twice. I appreciate being paid so feel free to upgrade if you enjoy TWTW.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president could still fire commissioners, as the Court now permits, but if those firings broke quorum, the agency would be unable to proceed until replacements were confirmed. The guardrail would

This week, feuding between some of the biggest tech companies spilled into public view. We discuss Apple's accusation that OpenAI tried to steal secrets about Apple's hardware business, as well as share our reactions about OpenAI's new model, Sol, and Anthropic's decision to extend access to its model Fable. Then, we unpack the loudest warning yet about A.I. and jobs. We talk with Erik Brynjolfsson, a Stanford economist, about a statement he helped organize that implores economists and A.I. researchers to “act now” to steer A.I. in a direction that complements humans. And finally, we play a round of HatGPT.   Guest: Erik Brynjolfsson, senior fellow at the Stanford Institute for Human-Centered A.I., and director of the Stanford Digital Economy Lab.   Additional Reading: Apple Sues OpenAI, Accusing It of Stealing Company Secrets OpenAI's First Device Will Be Movable, Screenless Speaker Built as A.I. Companion Nearly 200 Economists and Tech Leaders Warn of A.I. Threats The loudest warning about A.I. and jobs yet OpenAI Is Showing Kalshi's World Cup Odds in ChatGPT New York Enacts Nation's First Statewide Moratorium on Data Centers Brown Professor Suspects Majority of His Class Used A.I. to Cheat MiniMax CEO Vows to Forgo Salary Until Achieving A.G.I. Lorde Speaks Out — With Expletives — Against A.I. Glasses Nearly 6 in 10 Young Women Get Health and Wellness Information from Influencers Meta Removes A.I. Feature on Instagram After Days of Backlash   We want to hear from you. Email us at hardfork@nytimes.com. Find “Hard Fork” on YouTube and TikTok. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Binyamin Netanyahu's government will, unusually, serve out its full term. What comes after October's election will decide what kind of Israel will emerge. Our correspondent asks whether the World Cup will be remembered more for the dramas on the pitch or off it. And a tribute to A23a, the world's largest iceberg, after it melts away at last.Guests and host:Josie Delap, Middle East editorJon Fasman, senior culture correspondentAnn Wroe, obituaries editorJason Palmer, co-host of “The Intelligence”Topics covered: Israeli election, Binyamin NetanyahuWorld Cupicebergs, climateWatch extended clips from Insider here. And listen to what matters most, from global politics and business to science and technology—subscribe to The Economist. Hosted on Acast. See acast.com/privacy for more information.

Binyamin Netanyahu's government will, unusually, serve out its full term. What comes after October's election will decide what kind of Israel will emerge. Our correspondent asks whether the World Cup will be remembered more for the dramas on the pitch or off it. And a tribute to A23a, the world's largest iceberg, after it melts away at last.Guests and host:Josie Delap, Middle East editorJon Fasman, senior culture correspondentAnn Wroe, obituaries editorJason Palmer, co-host of “The Intelligence”Topics covered: Israeli election, Binyamin NetanyahuWorld Cupicebergs, climateWatch extended clips from Insider here. And listen to what matters most, from global politics and business to science and technology—subscribe to The Economist. Hosted on Acast. See acast.com/privacy for more information.

While there are considerable crosscurrents on both the growth and inflation fronts, our views remain squarely focused on resilient global growth (with risks biased to the upside) and a 2H26 global inflation profile that downshifts to a still elevated level.       Speakers: Bruce Kasman Joseph Lupton   This podcast was recorded on 17 July 2026.   This communication is provided for information purposes only. Institutional clients please visit www.jpmm.com/research/disclosures for important disclosures. © 2026 JPMorgan Chase & Co. All rights reserved. This material or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan. It is strictly prohibited to use or share without prior written consent from J.P. Morgan any research material received from J.P. Morgan or an authorized third-party (“J.P. Morgan Data”) in any third-party artificial intelligence (“AI”) systems or models when such J.P. Morgan Data is accessible by a third-party. It is permissible to use J.P. Morgan Data for internal business purposes only in an AI system or model that protects the confidentiality of J.P. Morgan Data so as to prevent any and all access to or use of such J.P. Morgan Data by any third-party.

Gregg Carlstrom, Middle East Correspondent with The Economist, discusses the latest from the region as Iranian state media reported new attacks on Thursday, including on Qeshm Island in the Strait of Hormuz.

“We have a Justice Department which is now 100% the political pawn of the president,” warns Brookings senior fellow Jonathan Rauch. “He points, and they shoot.” Point and shoot. Like an old Kodak camera. Not exactly assuring words, you might think, from a man who begins our conversation looking back at the first six months of 2026 by announcing that he's significantly less alarmed than he was a year ago. Yes, Rauch acknowledges, Trump's approval ratings have sunk, the courts have pushed back, Elon Musk's DOGE rampage has petered out. And yet the pointing and the shooting goes on. Rauch, who only months ago diagnosed eighteen “distinct and unmistakable signs” of an American fascism in a much touted Atlantic piece, now admits he may never crack the Trumpian code. Every time you nail it to the wall, he says, it morphs, creeps or sails away. Like an Iranian gunboat in Hormuz. Slippery stuff for the liberal Brookings analyst. Fascism one month, McKinley-style imperialism the next, then Gilded Age plutocracy — although without those ontologically undeniable Carnegie libraries. Meanwhile, America's 250th birthday party fizzled into what Rauch calls a “damp squib,” its reflecting pool turning an opaque green rather than a clarifying blue. A muddy madness in DC. Still, amidst all the opacity, Rauch remains a defiantly optimistic liberal. In contrast with yesterday's guest, the reality hallucinating Turi Munthe, Rauch believes not only that there is an ontological reality, but that it's good. Frank Fukuyama was right, Rauch insists. Liberalism is not only the only political system that creates wealth, produces knowledge and settles disputes, but also establishes an undeniable reality. Liberals just need to relearn how to clearly tell its story. Perhaps. Though storytelling is certainly simpler when nobody is waving a gun at you. Five Takeaways •       Less Alarmed, Still Scared. Rauch opens with the good news: he is significantly less alarmed than he was a year ago, when the administration was running rampage, putting agencies out of business and demanding Greenland. Approval ratings have dropped, so Trump has less political space; the courts have pushed back, so he has less judicial space; Stephen Miller has vanished from view. And then comes the caveat that gives the episode its title: the Justice Department is now 100% the political pawn of the president — he points, and they shoot — and Trump has shown that as his ratings fall, he becomes more willing, not less, to use those tools. •       I May Never Crack the Code. Only months ago, Rauch diagnosed eighteen distinct and unmistakable signs of a modern American reinvention of fascism in The Atlantic. He doesn't regret the essay — but he has gone back to being confused. The Trump phenomenon is slippery: every time you nail it to the wall, it morphs, creeps or slides away. Fascism one month, McKinley-style imperialism in Venezuela the next, an Iran war with no rationale at all. Trump is such an improviser, and so disorganized, that Rauch concedes there is an element of randomness he may never decode — though he accepts Andrew's suggestion that attention is now the coin of the political realm. •       Not the Gilded Age — No Carnegie Libraries. The new inequality, Rauch argues, is different in kind: a class of people almost superhuman in the wealth they control, and strangely narcissistic and nihilistic toward the broader society. The Gilded Age tycoons did some bad things, but they also built — Carnegie's libraries, Mellon's National Gallery, Rockefeller's University of Chicago, Stanford's university. This group builds rockets and sounds, in the case of Marc Andreessen, like a parody of an Ayn Rand novel — or, as Andrew corrects him, not a parody at all: they simply repeat what they've read. Even so, Rauch is not sorry to see politics reacting to a world where Musk can casually drop $300 million into a presidential race. •       The Gloves-Off Court and the Accelerating Presidency. The Supreme Court term brought the clearest statement yet of the conservative agenda: Humphrey's Executor overturned after eighty years, making it far easier for presidents to fire agency heads at will; what remained of the Voting Rights Act effectively gutted; birthright citizenship surviving by a shockingly narrow margin. The imperial presidency is not new, Rauch notes — what's new is the speed. A president can now simply refuse to run a congressionally mandated agency, and the Senate, forty quietly nixed nominations notwithstanding, remains lacking in spine. The Todd Blanche nomination, he says, is the next test of whether any line exists at all. •       Fukuyama Was Right — and Liberals Should Say So. Rauch sees a moral vacuum and, for the first time, a craving to fill it: the pope's AI encyclical, multi-faith clergy bearing witness in Minnesota, the Episcopalians and Latter-day Saints finding their voices. His prescription for the second half of 2026 is a liberal one, in the nineteenth-century sense — science, markets, constitutions, rule of law. Fukuyama, widely misunderstood, was right: there is only one system that produces knowledge, peace, freedom, and wealth on a global scale, and it's ours. It needs fixing — he cheers the bipartisan housing bill Trump refused to sign — but liberals must relearn how to tell that story, and how to brag. About the Guest Jonathan Rauch is a senior fellow in Governance Studies at the Brookings Institution and a contributing writer at The Atlantic. He is the author of nine books, including The Constitution of Knowledge: A Defense of Truth (2021), Cross Purposes: Christianity's Broken Bargain with Democracy (Yale, 2025), and Kindly Inquisitors: The New Attacks on Free Thought. A recipient of the National Magazine Award, he serves on the boards of Heterodox Academy and Civic Life, and is a longtime friend of the show. References: •       Rauch's Atlantic essay identifying eighteen “distinct and unmistakable signs” of a modern American reinvention of fascism — the piece he stands by, even as he admits the phenomenon keeps morphing. •       His recent essays for The UnPopulist on why liberal societies need grand stories about themselves, and why liberals must relearn how to brag about liberalism. •       Jonathan Rauch and Peter Wehner in The New York Times — the earlier argument, which Rauch says still holds, that the Republican Party is more dangerous to the constitution and the rule of law than the Democratic Party. •       Tim O'Reilly in The Economist — on Elon Musk building a form of capitalism that Adam Smith would hate. •       Francis Fukuyama — whose widely misunderstood The End of History thesis Rauch defends: there is only one system that creates wealth, produces knowledge, and settles political disputes on a global scal...

Long before onchain markets made mechanism design a daily engineering problem, Nobel Prize winner Paul Milgrom was asking how prices actually form — and how better auction rules could reshape actual markets. His work helped transform auction theory from an elegant branch of economics into a practical toolkit for allocating scarce resources, from wireless spectrum to digital ads to financial markets. In this episode of First Principles, Tim Roughgarden, Head of Research at a16z crypto, sits down with Milgrom alongside Scott Kominers — Harvard Business School professor and a16z crypto research partner — for a conversation about auctions, information, price discovery, and the design of complex markets. Together, they explore Milgrom's foundational work on auction theory, the famous Milgrom-Weber paper, the Grossman-Stiglitz paradox and the Glosten-Milgrom model of market microstructure, and why understanding how prices form matters for everything from prediction markets to decentralized finance. They also discuss Milgrom's work designing the FCC spectrum auctions — including the auctions that helped allocate wireless spectrum for technologies like mobile broadband and 5G — and the later FCC incentive auction, a massive market design challenge that combined economics, computer science, policy, and real-world implementation. Highlights 00:00 Intro: economics assumptions that are “just wrong” 02:19 Scott Kominers on the genius of Paul Milgrom 05:35 The price discovery problem economics forgot 07:48 The auction theory breakthrough of the 1980s 17:15 Why market microstructure matters for DeFi 24:17 When math teaches economics something new 29:22 Designing auctions people can actually use 32:40 How theory became spectrum auction design 36:22 The floppy disk that helped convince the FCC 41:05 What changed when auctions moved online 45:28 The auction that reorganized television 57:25 Why the best auctions feel simple 1:07:30 What economics and computer science can learn from each other 1:13:22 Futures markets for compute 1:15:12 Paul Milgrom's advice for builders About First Principles First Principles is a special limited series from a16z crypto about the scientific roots of modern computing — especially blockchains — told through rare conversations with the pioneers who helped shape the foundational ideas behind distributed systems, consensus protocols, economics, mechanism design, cryptography, zero knowledge, and more. People often tell the story of the Bitcoin whitepaper as if it appeared out of nowhere. But the ideas behind Bitcoin — and blockchains more broadly — come from decades of computer science, economics, mathematics, and cryptography. First Principles is a guide to that lineage, as told by the people who helped build it. Subscribe to follow along:https://www.youtube.com/playlist?list=PLjQ9HCQMu_8yIg60YAq67HDdvp7E_T5e8 Hear more from Tim Roughgarden: https://twitter.com/Tim_Roughgarden Scott Kominers: https://twitter.com/skominers Follow a16z crypto X: https://twitter.com/a16zcrypto LinkedIn: https://www.linkedin.com/showcase/a16zcrypto/posts/ YouTube: https://www.youtube.com/@a16zcrypto Substack: https://a16zcrypto.substack.com/subscribe/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Creemos que en otros lugares hay sintaxis, y no. Ni siquiera, lamentó, es vergonzosa la sentencia del Tjue sobre la amnistía; simplemente demuestra la desgana e indiferencia con la que Europa se ha tomado lo que ha ocurrido con el proceso desde 2017. ¡Cosas de los españoles, tan pintorescos!Y no es para menos. La condena al hermano de Pedro Sánchez más el juicio a su mujer darán más razones. Es curioso, observó, porque antes solía decirse que el puesto –¡por ser quien sos!– libraba de la persecución, y ahora desde el Gobierno se impone lo contrario: son perseguidos por estar ahí. Quizá, terció Santos y admitió él, delinquieron –presuntamente– justo porque pensaron librarse por ser quienes eran.Celebró que en Burgos impidan el baño con burkini –si bien burkini le parece una palabra excesiva– y comentó con desconcierto que Cádiz, paraíso, se esté quedando vacío.Es alucinante cómo argentinos, esas gentes que vinieron de los barcos, esos encargados, se convierten con el fútbol en todo aquello que no son. Dicho lo cual, y después de reiterar el punto patriótico que le brotó el martes –nada menos que contra los franceses–, sentenció: no habrá piedad el domingo.Y fue así que Espada yiró.Bibliografía:- "Women's progress at work is stalling", The Economist, 5 de julio de 2026.- Para ver: Gunda.- BSO 1.- BSO 2. Hosted on Acast. See acast.com/privacy for more information.

We take a peek into a quirk of British politics as the country's populist-right polling leader goes up against a man who wears a bin on his head. It is more serious than it sounds. Africans tired of shoddy internet services are turning to Starlink—for a price. And our culture editor reviews an anachronism-laden take on a classic epic.Guests and host:Hugo Gye, British political correspondentỌrẹ Ogunbiyi, Africa correspondentCatherine Nixey, culture correspondentJason Palmer, co-host of “The Intelligence”Topics covered: Nigel Farage, Count Binface, British politics, farceAfrica's internet infrastructure, Starlink“The Odyssey”, Christopher NolanListen to what matters most, from global politics and business to science and technology—subscribe to The Economist. Hosted on Acast. See acast.com/privacy for more information.

We take a peek into a quirk of British politics as the country's populist-right polling leader goes up against a man who wears a bin on his head. It is more serious than it sounds. Africans tired of shoddy internet services are turning to Starlink—for a price. And our culture editor reviews an anachronism-laden take on a classic epic.Guests and host:Hugo Gye, British political correspondentỌrẹ Ogunbiyi, Africa correspondentCatherine Nixey, culture correspondentJason Palmer, co-host of “The Intelligence”Topics covered: Nigel Farage, Count Binface, British politics, farceAfrica's internet infrastructure, Starlink“The Odyssey”, Christopher NolanListen to what matters most, from global politics and business to science and technology—subscribe to The Economist. Hosted on Acast. See acast.com/privacy for more information.

Olivia Fox Cabane reveals the behaviors and practices that help develop your personal charisma.— YOU'LL LEARN — 1) The number one myth surrounding charisma 2) How to amplify your presence in just a few seconds3) The master key to looking and feeling more powerfulSubscribe or visit AwesomeAtYourJob.com/ep1167 for clickable versions of the links below. — ABOUT IAN — Olivia Fox Cabane is a leading authority on the science of charisma and the bestselling author of The Charisma Myth and The Net And The Butterfly, translated into 36 languages. Formerly Director of Innovative Leadership for Stanford StartX, she has lectured on charisma, leadership, and innovation at Harvard, Yale, MIT, the Marine War College, and the United Nations. As cofounder of the KindEarth.Tech Foundation, she supports foodtech companies advancing environmental sustainability. Her clients include the leadership of Apple, Google, TikTok, Deloitte, UBS, and JP Morgan as well as founders of Airbnb, Brex, Paradigm, and Presight. She has been featured in The New York Times, The Economist, and The Wall Street Journal. • Book: The Charisma Myth: How Anyone Can Master the Art and Science of Personal Magnetism• Meditation: The Charisma Myth Exercises: Metta• Website: AskOlivia.com— RESOURCES MENTIONED IN THE SHOW — • Study: “Case Report: Women, Be Aware that Your Vocal Charisma can Dwindle in Remote Meetings” by Ingo Siegert and Oliver Niebuhr• Study: “Organizational Behavior and Human Decision Processes” by Jane M. Howell and Peter J. Frost• Study: “Predictors of leadership: The usual suspects and the suspect traits” by John Antonakis• Researcher: Dr. Oliver Niebuhr• Microphone: Maono HD300T• YouTube: Podcastage• Book: Influence: The Psychology of Persuasion by Robert Cialdini• Book: Radical Acceptance: Embracing Your Life With the Heart of a Buddha by Tara Brach— THANK YOU SPONSORS! — • Shopify. Sign up for your free trial at Shopify.com/awesomepod• Monarch. Get 50% off your first year with code AWESOME at Monarch.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

On June 10, the Metropolitan Museum of Art in New York returned two pieces of artwork to Cambodia: an Angkor statue and a sandstone lintel. It's the latest repatriation effort by the U.S.'s premier art museum, and the third time the Met has had to give up Cambodian artifacts specifically. Matthew Campbell's The Man Who Stole The Gods: A True Story of War, Obsession, and a Global Art Conspiracy (Penguin, 2026) dives into the story of the Cambodian antiquities trade, from looted temples in the Cambodian forests, through dealers in Bangkok like Douglas Latchford, and then into museums and billionaire homes in the West. And he also digs into how this trade fell apart: How the U.S. Department of Justice and activists in Cambodia pressured dealers and museums like the Met to give this art back. Matthew is an award-winning reporter for Bloomberg Businessweek. His previous book, Dead in the Water—co-authored with Kit Chellel—was selected as a Book of the Year by The Economist, the Financial Times, and The Times. Matt has reported from more than twenty-five countries on crime, corruption, terrorism, economics, and the environment. His work has earned some of journalism's highest honors, including awards from the Gerald Loeb Foundation, the Overseas Press Club, the National Press Club, SOPA, and SABEW for both feature and investigative reporting You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books, including its review of The Man Who Stole the Gods. Follow on Twitter at @BookReviewsAsia. Nicholas Gordon is an editor for a global magazine, and a reviewer for the Asian Review of Books. He can be found on Twitter at @nickrigordon. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

In 2008, bankers made reckless bets with your money, burned the economy to the ground, and kept their bonuses while you paid the bill. Economists call it moral hazard: when the person making the decision never suffers the consequences of being wrong, they get careless. They get greedy. And they stop caring whether what they're selling you actually works.We swore never again. We were lied to. Right now, there's an industry embedded in your doctor's office, your child's school, and your own family that runs the exact same scam — except the losses aren't measured in foreclosures. They're measured in your kids. It knows things about its product it will never tell you. It gets paid whether you improve or deteriorate. And when it fails you, that failure doesn't trigger accountability. It generates the next invoice. You've probably already been a customer. You may be one right now.Dr. Roger McFillin builds the case one brick at a time — and by the end, you'll understand why the worst outcomes in American life keep getting rewarded with more money, more power, and more access to your children.Once you see it, you can't unsee it.

On June 10, the Metropolitan Museum of Art in New York returned two pieces of artwork to Cambodia: an Angkor statue and a sandstone lintel. It's the latest repatriation effort by the U.S.'s premier art museum, and the third time the Met has had to give up Cambodian artifacts specifically. Matthew Campbell's The Man Who Stole The Gods: A True Story of War, Obsession, and a Global Art Conspiracy (Penguin, 2026) dives into the story of the Cambodian antiquities trade, from looted temples in the Cambodian forests, through dealers in Bangkok like Douglas Latchford, and then into museums and billionaire homes in the West. And he also digs into how this trade fell apart: How the U.S. Department of Justice and activists in Cambodia pressured dealers and museums like the Met to give this art back. Matthew is an award-winning reporter for Bloomberg Businessweek. His previous book, Dead in the Water—co-authored with Kit Chellel—was selected as a Book of the Year by The Economist, the Financial Times, and The Times. Matt has reported from more than twenty-five countries on crime, corruption, terrorism, economics, and the environment. His work has earned some of journalism's highest honors, including awards from the Gerald Loeb Foundation, the Overseas Press Club, the National Press Club, SOPA, and SABEW for both feature and investigative reporting You can find more reviews, excerpts, interviews, and essays at The Asian Review of Books, including its review of The Man Who Stole the Gods. Follow on Twitter at @BookReviewsAsia. Nicholas Gordon is an editor for a global magazine, and a reviewer for the Asian Review of Books. He can be found on Twitter at @nickrigordon. Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/southeast-asian-studies

“If you can only explain the arguments of the other side because they're mad or dangerous or dumb, the problem is with you.” — Turi Munthe On yesterday's show, the psychiatrist Sally Satel described how Americans imagine their own mental condition differently, depending on their politics and age. Which is a nice segue for today's conversation with the Anglo-French journalist turned media entrepreneur Turi Munthe. It's not just in our mental health self-evaluation, Munthe argues, that we hallucinate reality. Indeed, the French born Munthe often sounds like one of his post-structuralist compatriots in his defiantly slippery notion of ontological reality. In Why We Think What We Think: The Unexpected Origins of Our Deepest Beliefs, Munthe argues that our deepest convictions turn out to be shaped by genetics, brain shape and sometimes even by the agricultural legacy of our distant ancestors. Left and right thinkers, Munthe argues, are different political phenotypes — each hallucinating their own version of reality. Total relativism, then — the full French post-structuralist monty? Not quite. Here's where Munthe's Englishness kicks in. Following the Anglo-Russian philosopher Isaiah Berlin, Munthe insists pluralism and relativism are different. So Turi Munthe doesn't just think what he thinks because of his English or French origins. Borrowing from the cognitive scientists Dan Sperber and Hugo Mercier, Munthe defines thinking as a “contact sport”. So, for example, believing that the 2020 election was stolen is what Munthe calls a social commitment, because humans would rather be wrong together than right alone. Speaking of convenient segues, Munthe's thoughts on thinking set the scene for next Tuesday's conversation with Emily Eakin, author of The Frenchmen. It's her history of seductive post-structuralists like Foucault, Derrida and Lacan who corrupted a whole generation of literary American Ivy Leaguers (including Eakin) into hallucinating reality. Five Takeaways •       Pluralism Is Not Relativism. Munthe opens with Isaiah Berlin's distinction: registering the sincerity and value of opinions across the political, religious, and ethical spectrum does not relativize truth. That Charles Windsor is King of the United Kingdom is a statement of fact; whether you're a monarchist or a republican is where opinion begins. The book confines itself to the second category — beliefs, values, and opinions that cannot be factually proven — and asks what the nonrational influences on them actually are. The answer is humbling: genetics account for perhaps half of political persuasion, and the rest is shaped by everything from brain anatomy to the agriculture of our ancestors. •       Different Political Phenotypes. At the margins, left and right differ neurologically: right-leaners are on average more readily startled by loud noises and more attentive to threat, while left-leaners carry a slightly larger anterior cingulate cortex — the brain region where we process ambiguity and split hairs. That anatomy, Munthe argues, explains the ideological capture of academia and media better than any conspiracy: hair-splitters go where the hair-splitting is, and a conservative 22-year-old doesn't volunteer for a newsroom where 80% of colleagues think differently. We are, in his phrase, different political phenotypes, each hallucinating a different version of reality. •       Thinking as a Contact Sport. Drawing on Dan Sperber and Hugo Mercier's research, Munthe argues that reason didn't evolve for solitary contemplation — Rodin's Thinker is the wrong image — but for argument: to convince you to hunt the buffalo with me, I need reasons that look objective to you too. The evidence is everywhere, from the most impactful academic papers being written by pairs and groups to the creative density of small university towns. The implication is political: the people we disagree with are not obstacles to good thinking but the condition of it — the loyal opposition that helps us get out of ourselves. •       Wrong Together Rather Than Right Alone. Munthe's reading of January 6 and the stolen-election faith is social rather than psychiatric: an enormous number of our beliefs matter more for what they do than for what they say, and professing them is a commitment to a group. From an evolutionary perspective, believing what your village believes — even about the god who is a giant rock at the end of the field — is intelligent, because the ostracized lose the protection of the group. The terrifying data point is the marriage test: in the 1950s, around 4% of families would have objected to a child marrying across party lines; today it approaches 45%. That is affective polarization, and it can pull societies apart. •       The Problem Is With You. Munthe spent his twenties unable to fathom American gun rights — supporters had to be bought, dumb, or morally corrupt — until he did the work and found a tradition he now calls beautiful and heroic, whether or not he shares it. His rule of thumb: if you can only explain the other side's arguments as madness, danger, or stupidity, the problem is with you. This is not centrism — there was no middle ground on slavery or the Holocaust — but a defense of the clash itself: societies need the left to fix inequality and the right to defend the village, and we think best when the two are, in his words, continually bashed against each other. About the Guest Turi Munthe is a journalist and policy analyst turned media entrepreneur. He founded Demotix, which became the largest network of photojournalists in the world before its sale to Corbis in 2012, and Parlia, an encyclopedia of opinion. He has written for The Economist, The Guardian, the TLS, The Nation, and The Spectator, has sat on the boards of Index on Censorship, openDemocracy, and the Bureau of Investigative Journalism, and is a board member of the Italian media group GEDI, publisher of La Repubblica and La Stampa. He studied Arabic and History at Oxford. Why We Think What We Think: The Unexpected Origins of Our Deepest Beliefs (Penguin/Hutchinson Heinemann) is out now in the UK, with US publication early next year. References: •       Why We Think What We Think: The Unexpected Origins of Our Deepest Beliefs by Turi Munthe (Penguin/Hutchinson Heinemann, 2026). Timothy Garton Ash: “Thinking is a contact sport.” •       Isaiah Berlin — the Anglo-Russian philosopher whose insistence that pluralism and relativism are not the same thing frames the whole book. •       Dan Sperber and Hugo Mercier...

The sketch of a deal to end the war is all but dead; oil is up as strikes rain down. We look at the options available to America—all of them bad. India's government websites are almost universally impossible to use; we look at the structural reasons behind the bad IT. And the growth of summer camps for adults. Listen to our “The Weekend Intelligence” episode on kidulting.Guests and host:Gregg Carlstrom, Middle East correspondentLeo Mirani, Ashoka columnistRachel Mayman, senior audience editor Jason Palmer, co-host of “The Intelligence”Topics covered: Iran war, Strait of HormuzIndia's government, ITsummer camps, kidultingListen to what matters most, from global politics and business to science and technology—subscribe to The Economist. Hosted on Acast. See acast.com/privacy for more information.

The sketch of a deal to end the war is all but dead; oil is up as strikes rain down. We look at the options available to America—all of them bad. India's government websites are almost universally impossible to use; we look at the structural reasons behind the bad IT. And the growth of summer camps for adults. Listen to our “The Weekend Intelligence” episode on kidulting.Guests and host:Gregg Carlstrom, Middle East correspondentLeo Mirani, Ashoka columnistRachel Mayman, senior audience editor Jason Palmer, co-host of “The Intelligence”Topics covered: Iran war, Strait of HormuzIndia's government, ITsummer camps, kidultingListen to what matters most, from global politics and business to science and technology—subscribe to The Economist. Hosted on Acast. See acast.com/privacy for more information.

Gregg Carlstrom, Middle East correspondent at The Economist, discusses his latest reporting on what the Trump administration could potentially do to reopen the Strait of Hormuz during the ongoing war with Iran. Photo: This picture shows ships sailing near the Strait of Hormuz off the eastern coast of the United Arab Emirates at Khor Fakkan on July 13, 2026. The United States is "taking over" the Strait of Hormuz and will be paid for protecting it, US President Donald Trump declared on July 13, as Washington and Tehran once again fought over the vital waterway. (Photo by AFP via Getty Images) /     Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

A new report from the Health and Social Care Committee says customers are being pushed towards products high in fat, sugar and salt which are typically cheaper than more nutritious food and that the government's food policy needs overhauling. The chair of the committee, Liberal Democrat MP Layla Moran, talks to Nuala McGovern about their recommendations.New analysis in The Economist suggests after years of progress fewer women are reaching executive positions and there may be signs that younger women are becoming less interested in promotion. Nuala McGovern speaks to the author of that analysis and a woman who left an executive role to rebalance her life. We hear from Sarah Ruggins who recently broke the world record for cycling the length of Europe - a distance of over 6,000 kilometres – double the length of the Tour de France. She only started cycling three years ago having spent a decade recovering from a condition that saw her bedridden and immobile – Complex Regional Pain Syndrome, one of medicine's most painful conditions. Sarah now holds three ultra-cycling records.Known at one point as the Diana of the Middle East and the rose of the desert, Asma al-Assad, the wife of Syrian President Bashar al-Assad, quickly became a well-known face around the world. But just how much do we know about the London-born former First Lady and the role she played in her dictator husband's oppressive dictatorship where peaceful protests against his regime were violently repressed, eventually leading to 13 years of civil war that saw more than half a million people killed. Nuala speaks to investigative journalist Chloe Hadjimatheou about her new podcast, We Call Her Emma. Presenter: Nuala McGovern Producer: Kirsty Starkey

Myanmar's jailed leader has not been seen since 2022. We ask if she is in fact alive, and what might happen if the military junta acceded to demands for her release. We examine Turkey's plan to turn the war in Iran to its advantage. And delving into the research on just how much sleep is enough—and too much.Guests and host:Aaron Connelly, Asia diplomatic editorCerian Richmond-Jones, international economics correspondentSam Wikeley, science correspondentJason Palmer, co-host of “The Intelligence”Topics covered: Aung San Suu Kyi, MyanmarTurkey, economics, Iran warsleep, scienceListen to what matters most, from global politics and business to science and technology—subscribe to The Economist. Hosted on Acast. See acast.com/privacy for more information.

Myanmar's jailed leader has not been seen since 2022. We ask if she is in fact alive, and what might happen if the military junta acceded to demands for her release. We examine Turkey's plan to turn the war in Iran to its advantage. And delving into the research on just how much sleep is enough—and too much.Guests and host:Aaron Connelly, Asia diplomatic editorCerian Richmond-Jones, international economics correspondentSam Wikeley, science correspondentJason Palmer, co-host of “The Intelligence”Topics covered: Aung San Suu Kyi, MyanmarTurkey, economics, Iran warsleep, scienceListen to what matters most, from global politics and business to science and technology—subscribe to The Economist. Hosted on Acast. See acast.com/privacy for more information.

The longtime South Carolina senator died suddenly at the weekend. His political arc mirrored that of his Republican party in the Trump era. We ask what his death leaves behind. Frontier AI models opaquely embody a worldview and set of values; we poke the big ones, asking what they believe. And why Old Master paintings are again so popular. Guests and host:Adam Roberts, foreign editorSondre Solstad, senior data journalistAlexandra Suich Bass, culture editorJason Palmer, co-host of “The Intelligence”Topics covered: Lindsey Graham, Republican party, American foreign policyAI frontier models, valuesart market, Old MastersListen to what matters most, from global politics and business to science and technology—subscribe to The Economist. Hosted on Acast. See acast.com/privacy for more information.

The longtime South Carolina senator died suddenly at the weekend. His political arc mirrored that of his Republican party in the Trump era. We ask what his death leaves behind. Frontier AI models opaquely embody a worldview and set of values; we poke the big ones, asking what they believe. And why Old Master paintings are again so popular. Guests and host:Adam Roberts, foreign editorSondre Solstad, senior data journalistAlexandra Suich Bass, culture editorJason Palmer, co-host of “The Intelligence”Topics covered: Lindsey Graham, Republican party, American foreign policyAI frontier models, valuesart market, Old MastersListen to what matters most, from global politics and business to science and technology—subscribe to The Economist. Hosted on Acast. See acast.com/privacy for more information.