Podcasts about Manhattan Project

We're starting work on the Manhattan Project-level weapon that will bring down the Tarps Off movement. Sign up at Patreon.com/BattingAround to get access to our bonus episodes including the Wach Party series and our recent episode where we pitch baseball movie legacy sequels.

Is AI the biggest scam of our generation — or the most misunderstood technology in history? Cognitive scientist Gary Marcus has been studying artificial intelligence for over 30 years, and what he has to say will make you question everything you thought you knew about ChatGPT, AGI, and the trillion dollar AI gold rush.In this episode of SparX, we are talking with Gary Marcus – professor, author, and one of the most respected and fiercely independent voices in AI research – about why the promises being made by Sam Altman, Dario Amodei, and Elon Musk may be leading the global economy toward a catastrophic miscalculation.

Spend your holiday with a star-studded "Best Of" special edition of The Brian Kilmeade Show. First Lady Melania Trump joins Brian in studio to break down her historic cinematic project, reveal the secret letters she sent to Vladimir Putin to save children, and share her mission for protecting youth online. Plus, Tyrus grills Brian on a brutal Rocky trivia challenge, UFC veteran Alberto Crane reveals how martial arts gave him the strength to fight multiple sclerosis, Mike Rowe warns of the massive upcoming "Manhattan Project" for blue-collar trade skills, tech expert Tristan Harris delivers a terrifying update on AI escaping its guardrails, and Harvard professor Arthur Brooks lays out the definitive guide to finding true purpose in an age of digital noise. [00:00:00] Melania Trump   [00:12:11] Tyrus quizzes Brian on Rocky   [00:17:59] Alberto Crane   [00:35:56] Mike Rowe   [00:53:58] Nelson Dellis   [01:11:53] Tristan Harris   [01:29:48] Arthur Brooks Learn more about your ad choices. Visit podcastchoices.com/adchoices

From the Manhattan Project to the Challenger investigation, the physicist Richard Feynman loved to shoot down what he called “lousy ideas.” Today, the world is awash in lousy ideas — so maybe it's time to get some more Feynman in our lives? (Part one of a three-part series originally published in 2024.)   SOURCES: Helen Czerski, physicist and oceanographer at University College London. Michelle Feynman, photographer and daughter of Richard Feynman. Ralph Leighton, biographer and film producer. Charles Mann, science journalist and author. John Preskill, professor of theoretical physics at the California Institute of Technology. Stephen Wolfram, founder and C.E.O. of Wolfram Research; creator of Mathematica, Wolfram|Alpha, and the Wolfram Language.   RESOURCES: "How Legendary Physicist Richard Feynman Helped Crack the Case on the Challenger Disaster," by Kevin Cook (Literary Hub, 2021). Challenger: The Final Flight, docuseries (2020). Perfectly Reasonable Deviations From the Beaten Track: Selected Letters of Richard P. Feynman, edited by Michelle Feynman (2005). The Pleasure of Finding Things Out, by Richard Feynman (1999). Genius: The Life and Science of Richard Feynman, by James Gleick (1992). “What Do You Care What Other People Think?” by Richard Feynman and Ralph Leighton (1988). "Mr. Feynman Goes to Washington," by Richard Feynman and Ralph Leighton (Engineering & Science, 1987). The Second Creation: Makers of the Revolution in Twentieth-century Physics, by Robert Crease and Charles Mann (1986). Surely You're Joking, Mr. Feynman!, by Richard Feynman and Ralph Leighton (1985). "The Pleasure of Finding Things Out," (Horizon S18.E9, 1981). "Los Alamos From Below," by Richard Feynman (UC Santa Barbara lecture, 1975).   EXTRAS: "Exploring Physics, from Eggshells to Oceans," by People I (Mostly) Admire (2023). Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!This was recorded before Railway suffered a major GCP outage on May 19, despite being a multi-AZ, multi-zone mesh ring, with HA fiber interconnects between their Metal GCP AWS, because workload discoverability was unintentionally still tied to GCP. All has been resolved with a post-mortem.Railway did not start as an AI infrastructure company.It was founded in 2020 years before agents became the default way people thought about deploying software. Jake Cooper, formerly at Bloomberg and Uber, started Railway with a simple obsession: the activation energy to ship something to production should be near zero. Push code, get a URL, iterate. No Docker files, no Kubernetes manifests, no Ansible scripts stacked on Ansible scripts.For years, this was a slow grind. Railway spent its first 18 months hand-acquiring its first 100 users with Jake personally greeting every Discord signup on a second monitor.Today, Railway has raised $124m and is growing very fast. A 35-person team supports 3 million users, adding roughly 100,000 signups a week. Their bare metal data centers have a 3-month payback period vs. renting in the cloud, with 70% margins funding aggressive cloud bursting when needed. The servers they own have actually appreciated in value as RAM prices have climbed basically meaning the value of their hardware now exceeds the capital they've raised.From rebuilding Railway's network overlay over a weekend to moving the vast majority of workloads onto its own bare metal data centers, Jake Cooper is trying to build a new cloud for an agent-native world. In this episode, Railway's founder and “conductor” joins swyx and Alessio to unpack why the next era of software infrastructure is not just “Heroku but newer,” what agents need that humans did not, and why the old deployment loop of Git, PRs, CI/CD, and static cloud resources may be heading for a rewrite.We go deep on Railway's infrastructure stack: own-metal data centers, three-month cloud payback periods, cloud bursting, data center debt, Railpack, Nixpacks, Temporal, feature flags, Central Station, content-addressable filesystems, agent-safe production forks, and why the CLI may become more important than the canvas in an agent world. Jake also shares the founder journey behind Railway, how the company survived losing $500K/month, why it now serves millions of users with only 35 people, and why he believes the pull request is dying.We discuss:* How Railway went from a slow six-year grind to adding 100,000 users a week* How Railway thinks about agents as the next dominant software species* Why agents need version control, observability, compute, storage, and orchestration at 1000x scale* The economics of Railway's own-metal data centers and three-month payback* How Railway uses cloud bursting while scaling its own infrastructure* Why data center debt can be a better tool than venture debt for infra startups* Central Station, Railway's internal system for clustering customer feedback and incidents* Why responsible disclosure and over-communication matter for platforms* Why feature flags, progressive rollouts, and shadow traffic are essential for agents* Temporal's strengths, pain points, and why workflows matter for agents* Railpack, Nixpacks, Nix, and lazy-loaded content-addressable filesystems* Why “cattle, not pets” may change if you can clone the pets* Why Railway is building a new cloud from scratch instead of copying hyperscalers* The solo founder path, focus, writing, and how Jake thinks about company buildingRailway:* Website: https://railway.com/* X: https://x.com/RailwayJake Cooper:* LinkedIn: https://www.linkedin.com/in/thejakecooper/* X: https://x.com/JustJakeTimestamps00:00:00 Introduction: What Is Railway?00:02:07 Jake's Path to Railway00:06:13 Railway's Six-Year Growth Story00:08:52 Rebuilding the Business After the Free Tier00:11:17 Agents as the Next Software Platform00:13:29 Railway's Infrastructure Philosophy00:15:42 Bare Metal, Cloud Economics, and the Compute Crunch00:17:22 Cloud Bursting and Five-Cloud Networking00:20:20 Data Center Debt and Infra Financing00:23:31 Data Centers in Space00:25:24 What Agents Need From Infrastructure00:28:24 CLIs, Canvas, and Agent-Native UX00:35:15 Central Station, Incidents, and Responsible Disclosure00:40:30 Safe Rollouts, SRE Agents, and Production Forks00:45:00 AI SRE, Specs, Code, and Tests00:48:24 Self-Replicating Infrastructure and the New Serverless00:53:18 Heroku, Temporal, and Workflow Engines01:04:07 Railpack, Nixpacks, and Lazy-Loaded Filesystems01:06:01 Coding Agents, Token Spend, and Roadmap Acceleration01:10:56 The Pull Request Is Dying01:12:28 Feature Flags and the Agent-Era SDLC01:16:15 Cattle, Pets, and Cloning Machines01:19:29 Solo Founder Lessons01:24:12 Focus, GPUs, and Building a New Cloud01:28:20 Closing ThoughtsTranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: Hey, hey, hey. Today we're in the studio with Jake Cooper of Railway.Alessio [00:00:14]: Conductor of Railway.Swyx [00:00:15]: Conductor at Railway. Yeah.Alessio [00:00:16]: Choo-choo.Swyx [00:00:17]: Do you actually have that anywhere, like on your business card?Jake [00:00:20]: We call some of our volunteer moderators conductors. I don't have a business card. We're not that big yet. At some point I will. I got handed a nice business card from the Supermicro folks, and I was like, “Damn, this is pretty official.”Swyx [00:00:30]: Business cards are coming back.Jake [00:00:32]: They're cool. They're hip. The conductor thing is good. We're trying to figure out what we want to call each other internally. Some people think it's super cringe and say, “You don't need a name for people internally.” Some people want to call each other something. We still don't have a really good one.Jake [00:00:55]: We've got New Railcrews, Trainiacs. Nothing has stuck yet.Swyx [00:01:00]: I like Trainiac. Trainiac sounds good. Railwayians. For those who don't know, what is Railway? Let's give people a crisp definition up front.Jake [00:01:09]: Railway is the easiest way to ship anything. You go to the canvas, or you talk with Claude, and you say, “Deploy a Postgres instance, deploy my GitHub repository, run this code,” and you're off to the races.Swyx [00:01:22]: You've got a nice animation on the landing page.Jake [00:01:24]: Thank you. None of my work, by the way. They don't let me touch the design stuff anymore.Jake [00:01:25]: We want to make it trivially easy not just to deploy things, but to evolve applications over time. Most tooling right now stacks entropy on top of entropy: Docker, Kubernetes, Ansible scripts, and all these other things. If we can version all of your software and keep track of all the changes, then we can make it trivial to clone environments, fork into a parallel universe, get copies of production data, get copies of any services, make changes, validate them, and collapse them back in without reproducing everything across a staging environment.The Railway Origin Story: From Uber Systems to a New CloudSwyx [00:02:07]: I was looking at your background: Bloomberg, Uber. Nothing immediately stands out as, “This guy is going to found the next great platform as a service.” What prepared you for Railway?Jake [00:02:21]: It was curiosity to keep going deeper. I started out on front-end stuff, working on Wolfram Mathematica and porting it over. Then I briefly moved to Bloomberg, then toward Uber and distributed systems, taking the Jump Bikes systems and moving them to a distributed system built on top of Cadence, the pre-Temporal Temporal.Swyx [00:02:44]: Which, by the way, I'm happy to talk about, pros and cons.Jake [00:02:48]: Totally.Swyx [00:02:51]: But let's do the Railway story.Jake [00:02:52]: It has been a continual step of wanting an experience. Whether it's walking up to a bike, unlocking it, and having it work frictionlessly, or something else, the depth required to make that happen follows from the experience. A lot of the work I do, and a lot of the team does, is in service of that experience. We fundamentally don't care how deep we have to go. We will swim to the bottom of the swimming pool to get the experience.Jake [00:03:17]: I don't have a physics PhD. I did an EECS degree. It has always been about figuring out the next step: how do we get there? That's what led to starting Railway for that experience and then moving all the way to bare metal data centers. I was adding patches to the kernel this week to get the experience there because I can see how much better it can be.Swyx [00:03:49]: Other patches to the Linux kernel this week?Jake [00:03:51]: Yeah. Not upstream. Our fork.Swyx [00:03:52]: That's a flex. Railpack? No, this is different. This is the OS on top of Railpack?Jake [00:03:57]: No, this is an actual kernel patch. It's always literally: what do we have to do to get that experience? Then figure it out. Anything is figureoutable.Swyx [00:04:10]: Would you send the patch upstream, or does it not fit other use cases?Jake [00:04:13]: Maybe. We have to work out the experience internally. It has to do with the storage layer we're building for some of the agentic stuff. Maybe it'll be useful upstream, but it's deeply useful for us internally.Open Source, Forks, and Non-Deterministic VersioningSwyx [00:04:29]: You mentioned open source before. How do you think about starting from open source, and then coding agents letting you do a lot more from forks of it?Jake [00:04:38]: GitHub's original sin is that it's almost a series of broken pointers. You have this thing, then you clone it, and now you've lost the whole upstream. How do we make it trivial for people to modify really small pieces of it?Jake [00:04:51]: We think of Git in a discrete sense: I've either made a change and merged upstream, or I haven't. What would it look like if it were percentage-based, a little more non-deterministic, or a stream of changes that users traverse as a percentage rolled out in general and then rolled all the way up?Jake [00:05:13]: We have the open-source kickback program and let you deploy templates because we want to make it trivial for people to version these shards over time. It solves a large problem around authentication, authorization, and security. NPM has a way to define, “Don't take any new packages.” The ideal end state is that you roll out progressively to users with the minimum impact zone and continue rolling up. JPMorgan should probably be the last one on the patch line, for all our sakes, because our money and livelihoods are there.Jake [00:05:53]: It's okay if Johnny Vibe Coder gets a broken patch because there's so much entropy in the system that the rubber has to meet the road at some point. You have to test at varying levels.The Long Grind: First Users, Free Tier, and Making the Business WorkSwyx [00:06:13]: I wanted to pull up this glorious chart, which is your usage or number of daily signups?Jake [00:06:22]: Daily signups, I think.Swyx [00:06:24]: You started six years ago. It was a slow grind, and now you're on a rocket ship. You say, “Don't doubt your fight and don't quit.” Maybe pick out certain points that were key inflections for the company.Jake [00:06:40]: At the start, it's about getting your first 100 users, hell or high water. We had a website and a support link. The support link was the Discord channel. I had notifications on with two monitors: the monitor I was working on and the other monitor with Discord. If anybody came in, I was immediately like, “Hey, how's it going?” It was rare, so getting those first 100 users to come back was the start.Jake [00:07:14]: Then you build a consultancy factory because users want all these things. You have to go back to the board and ask, “What is the actual product offering I want to build on top of this?”Jake [00:07:28]: VCs want charts that always go up and to the right, but in reality you don't necessarily want charts that look like that. For us, there have been periods of expansion where we add features to test use cases, and periods of compaction where we ask, “If the experience we have is good, how do we make it significantly better?” Maybe we strip out features that don't fit our ICP anymore.Jake [00:07:57]: The boom from 2022 to 2023 came from the free tier. Everybody under the sun was using it.Swyx [00:08:09]: A lot of Reddit bots and Discord bots.Jake [00:08:12]: And crypto miners. When you build an open product on the internet where anybody can sign up, the internet is a horrible place with so many things. You go through periods of asking, “How do I reach as many people as possible?” Then, “How do I fit the exact use case for the people who really matter and are really excited about this specific thing?”Jake [00:08:39]: Then there was a two-year period of making the actual business work. During the free-tier era, we were losing about half a million dollars a month.Swyx [00:08:59]: On a $20 million bank account.Jake [00:09:02]: On a $20 million bank account with maybe $50,000 a month in revenue. That's a horrible business. I don't know how anybody invested. But you have to go through it and say, “We have an experience people love, but the business has to work.”Jake [00:09:17]: There are two schools of thought. You can run the horrible business all the way up with bad margins, or you can go back and make it work. We've always wanted a super lean team. We're 35 people right now. It's very small.Swyx [00:09:36]: Supporting three million already?Jake [00:09:38]: Yeah. We're adding 100,000 users a week right now, so it's growing fast. We don't want to add headcount for the sake of headcount or throw bodies at problems. We want to build systems. It's hard to build systems during expansion because you're adding things to the system because people are asking for them or things are breaking.Jake [00:10:00]: We had to cut off the free users for a little while, rebuild the business, and make sure it worked. We want to reach as many people as possible because software is important. It's become difficult to create things in the physical world, so it's important to make it easy for people to build in the virtual world and have access to creation. But there are legs to that journey.Jake [00:10:30]: You can see divots in the charts. If you follow between 2025 and 2026, it's either summer or winter. People go on holiday with family.Swyx [00:10:50]: It affects that much?Jake [00:10:51]: Yeah. It's kind of B2C and kind of B2B. People are shipping constantly, then they stop. Our activation curve now shows more people activating on weekdays because we have more business users, so it smooths out over time.Agents as the New Interface to DeploymentSwyx [00:11:17]: Was there a point where you started prioritizing AI development or agent development?Jake [00:11:24]: We've prioritized agentic as a top-of-funnel thing. Over the last six months, we've deeply prioritized agentic as a mechanism to build and deploy things because we believe the curve is so steep and that is how people will build and deploy software.Jake [00:11:42]: It almost fundamentally doesn't matter whether this is dot-com or not because we're all on the internet anyway. If agents are going to deploy a bunch of things and we hit an inference wall at some point, we'll fix those problems. The dominant species over the next 10 years is that we've moved from assembly to C to C++ to JavaScript to words. You're going to need to close that loop.Swyx [00:12:13]: When you say this is dot-com, did you mean buying the domain, or the general case?Jake [00:12:17]: I mean the dot-com era, when companies had a huge run-up because people understood the internet was important. Then they hit bottlenecks, fundamental laws of physics, math didn't work, and everybody came back down to earth. But it didn't matter because the internet became so impactful. If you operate on a long enough time horizon, you should build these things anyway because you can see where it's going.Jake [00:12:45]: That's where I think a lot of agent stuff is. You get to a point where you're running thousands of agents in parallel. What is the inference cost? What is the compute cost? How do you make that efficient? How do you coordinate all this? We have issues coordinating humans; we don't even have good tooling for that. Now we have to figure out how to get agents to coordinate, safely version changes, and know when to raise their hand for someone to intervene. Otherwise it becomes an interrupt factory.Railway's Infrastructure Thesis: Network, Compute, Storage, and MetalSwyx [00:13:19]: Let's go right into the technical side. What are the core infrastructure or architectural beliefs of Railway that allow you to do what you do?Jake [00:13:29]: The primitives matter a lot for us. We need network, compute, storage, and orchestration around it. You need control over a lot of those things. We've talked a lot about how we don't really use Kubernetes because we want higher-order control to place workloads in very specific places.Jake [00:13:48]: The reason is that you have to be very efficient with agents: memory reuse and all these other things, or you're going to massively blow up your cost structure. Being able to rack and stack your own servers and build your own metal unlocks performance and cost. Experiences where you're running 1,000 agents in parallel are not massively cost prohibitive.Jake [00:14:13]: Token use and compute use are blowing up. Over time, those things have to get a lot more efficient. You can get a lot of margin to make those experiences solid by building your own metal. That's all in service of offering a differentiated experience to as many people as humanly possible.Swyx [00:14:51]: You have a data center in Singapore.Jake [00:14:53]: Yeah. We have two in every other region now. In Singapore, we're adding a second one in Q3.Swyx [00:14:58]: What's it like? I've never built a data center. Do you go to Equinix and say, “I want some slots?”Jake [00:15:05]: Yeah. Equinix. You basically go and say, “I want power and I want a cage.” They say, “Great, here's what it's going to be.” You rent the cage for a period of time, fill it with racks and servers, and hook up internet to it. That's all the pieces.Swyx [00:15:36]: Then you handle everything else.Jake [00:15:37]: You handle everything else.Swyx [00:15:39]: What's the math versus clouds doing it for you?Jake [00:15:43]: If we rented in the cloud, our payback period when we go to metal is about three months.Swyx [00:15:50]: Which is crazy.Jake [00:15:51]: It's nuts. That's four years of depreciated hardware. You're going to see a lot of this compute crunch because hyperscalers are buying up a lot of stuff. We're working directly with OEMs, resellers, and people building these machines: Supermicro, Dell, and others.Jake [00:16:11]: Upstream, there's a bunch of supply pressure. When we raised our last round, between deploying capital for servers and now, the amount of money we've raised is less than the amount of money we have in the bank plus the value of the servers because the servers have appreciated as RAM has gone up. It's nuts how valuable hardware has become.Jake [00:16:50]: If you look at hyperscalers, they deployed around $80 billion of capital expenditures this year, and next year will be more. That's a massive infrastructure build-out. You look at that and think it's crazy that they're spending way more than the Manhattan Project. But if every person is going to run dozens or hundreds of agents in parallel, you have no conceptual idea how much compute is required to make that experience happen, even if you're deeply efficient and sharing resources. And that doesn't even count inference.Swyx [00:17:22]: How do you plan the build-out? The growth chart is so vertical. Are you usually at 100% utilization as soon as racks are live? How far ahead are you planning?Jake [00:17:33]: We still maintain cloud presence for bursting. We work with AWS, GCP, and a few other clouds. We can rent, and then the moment we get space or power, we compact those workloads off the cloud. We started on the clouds, then built a system to migrate to our own metal. There's nothing that says you can't continually do that again, and that's exactly what we do. We never want to be compute constrained.Jake [00:18:09]: At the start of the year, we actually became compute constrained because one upstream provider wasn't able to give us quota at the rate we needed, and the hardware was slower. I spent a weekend rebuilding our entire network overlay so we could straddle five clouds: Oracle, AWS, ourselves, GCP, and one other one. We can do more than that now.Jake [00:18:38]: We got into a spot where we were trying to pack instances tight because we couldn't get enough compute. That led to a few reliability issues, which are now past us. I made a tweet pointing out that it's becoming harder and harder to acquire compute at the rate these models need to acquire compute. We got bit by it.Swyx [00:19:15]: How do you think about pricing knowing you might not have your own metal available at all times? Are you pricing assuming you need extra margin if you end up going into the cloud?Jake [00:19:26]: Because we've built out our metal data centers, our margins on metal are around 70%. We can deeply subsidize the cloud business if we want to scale at a reasonable rate. We have a few levers: metal, which makes the margins; cloud burst; debt to buy servers; and venture capital. It's an interesting operational problem: how much cash do we have, how much should we raise, how quickly can we deploy it, and can we scale revenue as quickly as we scale compute?Jake [00:20:05]: If we continue making it trivially easy for people to build and deploy, then the faster we close that loop and the more operationally excellent we are with capital, the faster the business can scale. It's almost a straight linear deployment rate.Financing Infrastructure: Hardware Debt, VC, and Operational LeverageSwyx [00:20:20]: I think infra startups raising debt is a tool people don't utilize enough or know enough about. What can you tell us about that? Is it secured against your CPUs?Jake [00:20:32]: It's secured against our hardware.Swyx [00:20:37]: What rates do you get? Who are the lenders?Jake [00:20:39]: We pay prime plus a spread, and we can refinance any of the debt as rates go down. The terms are pretty good. The unfortunate thing is that Twitter has no nuance, so people say, “Venture debt bad.” But as with all things, there are specific tools and areas where you can be deliberate instead of using one tool as a hammer. Venture capital is not the hammer for everything. You have to explore and figure out what works.Swyx [00:21:12]: VC is usually the most expensive financing you can get.Jake [00:21:15]: Yeah. I also think people think about VC incorrectly from a capital-raising perspective. Most people think, “How do I raise as much money as possible from whoever is probably the best I can get at that time?” That's close to right, but what we've tried to do is figure out what unfair advantage we can buy with that equity.Jake [00:21:34]: It's the most expensive equity you're going to give away at that point in time, assuming the company keeps getting better. How do you use it to work with someone stellar who complements you? In the seed stage, I had never started a company. Ray Tonsing had good advice, and I could text him all the time. He was really fast. Awesome.Jake [00:22:01]: Then with John and Erica at Unusual, they said, “You roughly know what you're doing building a product. We'll mostly leave you alone and be available for advice.” Amazing. Then we got to Series A and the business was an operational tire fire because we didn't know how to scale a business. Work with Erica, and Jordan is over at Redpoint, so bonus.Jake [00:22:28]: Now we've raised from TQ and FPV as we're moving into enterprises. Every step of the way, we've asked: who can we partner with at this specific time to unlock the next section of the journey? I don't know enterprise sales. As an engineer, I can eyeball what features we might need, and we have wonderful people internally who can help. But you want boardroom dynamics where everyone is aligned and asking, “How do we win this?” instead of bickering about strategy.Data Centers in Space and the Physics of ComputeSwyx [00:23:31]: You had a tweet about data centers in space. Why no data centers in space?Jake [00:23:37]: It's not “no data centers in space.” My hot take is that I think it is solvable. I've just never seen anybody solve it.Swyx [00:23:49]: You said, “How are you going to dissipate that much heat in a vacuum?” You're making a physics claim.Jake [00:23:55]: I haven't seen anybody prove how you're going to dissipate that much heat in a vacuum. It doesn't mean it's not possible. It just means nobody has brought it up yet.Swyx [00:24:05]: Astrophage.Jake [00:24:06]: I don't know what that is.Swyx [00:24:07]: The Martian thing. Okay, you're very logical.Jake [00:24:09]: It could work. A lot of people are putting the cart before the horse. They say, “We're going to put data centers in space.” Okay, but how? “We have time to figure it out.” It's like in The Martian where they ask how they're going to intercept something and say, “We'll figure it out.”Swyx [00:24:36]: Making a bet on human invention is weird because you blind trust that it can be solved. But with physics, there are first-principles bounds you can put on it. Maybe not. Maybe you're asking to travel time or break a fundamental thermodynamic law.Jake [00:24:57]: I don't know how VCs do this either. How do you know what's not possible and a grift versus what's possible but sounds completely insane? “We're going to put data centers in space.” Coin flip as to which it is, and I guess you'll know in 10 years. That's one cycle.What Agents Need: Versioning, Observability, and 1,000x ScaleSwyx [00:25:23]: Moving back to agents. The branching, fast spin-up, and orchestration you do feels like pre-work that happened to be exactly what agents want. What do agents want differently than humans?Jake [00:25:37]: They want the ability to version things. It's not that different; it materializes slightly differently. Agents want a way to test changes incrementally. Engineers have feature flags. Is there a reason agents can't use feature flags? I don't think so.Jake [00:25:54]: They want version control. Can we use Git or not Git? That one is up in the air. I think something outside Git will emerge for how we version these things over time. They need observability. You need to query what happened, when it happened, which steps failed, traces, logs, metrics, and all the rest. They need network, compute, and storage. They need to write files, save files, iterate on files, and snapshot file systems.Jake [00:26:25]: A lot of what humans needed is in line with what agents need. Branching and forking are not different; we're just moving 1,000 times quicker. It can look like you need something massively different, but what you need is something massively better than what existed. You need orchestration massively better than Kubernetes. You need networking probably better than Envoy. It goes all the way down the stack.Jake [00:26:55]: If the workload profile doesn't change so much as it gets massively compressed because you need thousands of these things, what assumptions change? etcd is going to melt. You need to replace it with something. You can go all the way down the stack and say, “That part has to change, that part has to change, and that part has to change.”Jake [00:27:19]: The interesting thing about the super-exponential curve is that you have to build systems where you can rip out those parts at any time because a new bottleneck might emerge. You get good at parallel agents, and a different part of the system breaks. So it's similar to what humans needed, but at 1,000x scale.Jake [00:27:55]: How do you do code review in the age of agents?Swyx [00:28:00]: You throw more agents at it.Jake [00:28:01]: You don't. But then who reviews for CVEs and all these other things?Swyx [00:28:07]: More agents.Jake [00:28:08]: And that's how we hit the inference wall. You can continually throw agents at the problem, but I think there's a limit to the number of agents you can throw at a problem.CLI, Agent Handles, and Closing the LoopSwyx [00:28:24]: You already had a CLI before it was cool. How is the shape of what you're exposing changing, if at all?Jake [00:28:28]: CLIs have always been cool. The CLI changes because we think about how to give Claude, Codex, ChatGPT, or any model a handhold.Jake [00:28:50]: A CLI is a single command: deploy, get logs, and so on. Things that were prohibitively annoying to humans are not annoying to agents. They're nice. If I handed you a CLI with 40 arguments and 600 flags, you'd think, “I'm never going to use all of this.” But if you hand it to an agent, it says, “This is excellent. I have so many handles to work with.”Jake [00:29:24]: If you're going to expose things to agents that way, you want as many handles as possible where they can get information, query dynamic information, and close the loop quickly. Most problems right now are about how to close the loop as quickly as possible. Where does the agent get stuck, and how can you remove that?Jake [00:29:49]: Telemetry is important. If you can tell where the agent gets stuck from the CLI and say, “12% of people deviate from the happy path because of this, and now I add this argument and drive it down to 2%,” you massively increase the rate of loop closure.Jake [00:30:03]: That's how we think about not just the CLI, but every point in the dashboard. It's a user journey: I hear about Railway. I get something deployed. I get my first green build or aha moment. I see an endpoint, logs, whatever. Then I iterate. The iteration loop is indefinite. The user wants to deploy a new thing, a Postgres instance, change code, and keep iterating.Jake [00:30:36]: If you focus on the iteration loops and what's blocking them from closing quickly, one thing we say internally is: you never want to be waiting on compute anymore. You always want to be waiting on intelligence. If you're waiting on compute, there's a bottleneck that needs to be destroyed because eventually that bottleneck becomes so large that another workflow emerges to change it.Jake [00:31:04]: We've built a product where you push code, build it, and so on. But I fundamentally believe the push-pull loop is going away. We'll get to a point where you make a small change in production, that change is versioned across your infrastructure, you're working alongside copy-on-write versions of your database and infrastructure, and then you merge it in and it's instantaneously live. That's the holy grail of loops. The push-pull-rebuild thing is a point of friction that we're removing entirely.Canvas as Output: Dashboards, Context Anchors, and HyperstructuresSwyx [00:31:43]: It's incredibly fast. If anyone hasn't tried it, that fast feedback is great. My hot take is that Railway was famous for its canvas, which visualizes your infrastructure and lets you manipulate it visually. But that was for humans. For the next phase of growth, Railway CLI is more important than canvas.Jake [00:32:05]: The canvas is funny because it's a mechanism to show changes over time. You're right that previously we used it a lot as an input. Moving forward, its goal is more like an output. You would go to the canvas, make changes, see them, and watch your infrastructure evolve. Now agents have access to the CLI and can make those changes. So the canvas becomes an output: what information does the human need at this moment to make suitable decisions about control requests? Do I approve this or not?Jake [00:32:57]: It also has to be an anchor for your context, a port in the storm. Think of it like layers in a file system. You start with a project, then drill down into services, then into a function or code, because you want to represent the entire thing not just in your head, but in the canvas. Other people can share that representation, think on the same wavelength, and move quickly.Jake [00:33:33]: A lot of organizations get in trouble as they scale because all the context lives in someone's head. “How does this microservice work?” “I have no idea; go ask this person.” Then you have whole categories of products built around context discovery. A lot of that melts away if you have a solid hierarchy and can infinitely nest services, code, context, and everything else all the way down. That's what lets you build these structures over time.Jake [00:34:18]: It's also what lets us build what I've called hyperstructures: things that are way bigger. You look at the Golden Gate Bridge and ask, “How did we build that?” There's a meme that we lost the technology. To some extent, yes, because the coordination that built those things evolved and changed. We lost some of the art of building structure as we jammed everything into Slack.Swyx [00:34:52]: But you jam everything in Discord.Jake [00:34:53]: Same point. It doesn't matter. It's message passing and interrupts, message passing and interrupts.Swyx [00:35:00]: So you're arguing there should be something better and more structured than Slack?Jake [00:35:04]: Yeah. For sure. I think Slack is awful, and Discord is awful too.Central Station: Context Routing, Support, and Incident ClustersSwyx [00:35:09]: This is the equivalent of my mom test. What have you done that has your solution to this?Jake [00:35:15]: Internally, we've built a tool called Central Station that aggregates all the context from our users. Every piece of feedback, every customer support item, everything gets aggregated into clusters. If an incident is brewing, we can determine how many users are affected and break off a discussion based on that.Jake [00:35:40]: That is more helpful than long-running channels where you're trying to decide which channel to put something in. If you can dynamically aggregate information and dynamically route it to the right person based on context, it works better. We know internally that these four people are close to networking. If we see a networking thing, we can drill it down to those four people. If it's with this part, we can look at the commits. This is no longer a manual process internally.Jake [00:36:13]: If you go to station or help.railway.com, that's why we built it. We wanted to scale with a massive amount of leverage by aggregating feedback.Swyx [00:36:27]: This is built in-house?Jake [00:36:28]: Yep.Swyx [00:36:29]: I remember helping out on this one with Angelo in 2023. You scale a lot with a very small team.Jake [00:36:38]: Yeah. We're about 10 times bigger now.Swyx [00:36:40]: You have your full developer code here? Very cool.Jake [00:36:44]: If you go to railway.com/stats, we expose this as a pub-sub-able thing. It's all real-time metrics. There's a way to get it as JSON somewhere if you care.Jake [00:37:01]: We're big on trying to build everything in public and talk about what we're working on. We've had issues in the past, and we'll say, “Here's how we're fixing these things.” We've gotten compliments and flak for incident reports. We're always trying to make them better and talk with people.Incidents, Disclosure, and Progressive RolloutsSwyx [00:37:20]: You had a big one recently. I liked that it was scoped to 3,000. You presumably used Central Station. Talk through what happened and how you address it internally as a team.Jake [00:37:38]: Internally, this one really sucked. It had to do with an upstream provider that didn't do the behavior it said it documented, which is unfortunate given they wrote the RFC for how the behavior should work. We rolled those things out, and Central Station caught it initially when a couple users said caches weren't invalidating. We turned it off immediately.Jake [00:38:03]: When you roll out to a large user base of three million people, you get a lot of disparate behaviors. We tested in staging and had tests, but we hit an edge case. We've hardened those systems, and now we can make that better. But it was a tough one.Swyx [00:38:39]: I always wonder how private disclosure is supposed to work if people find an issue. Are they supposed to contact you first? When you run a platform, these things will happen. What channels should people pursue to quietly resolve it before it becomes a bigger incident?Jake [00:38:59]: There's responsible disclosure. We err on the side of over-disclosing and letting you know something is wrong versus having your provider gaslight you. We've erred on sharing those things more publicly, even if they impact a small subset of users. That's a decision we've made internally. We have four values. One is honor. The honorable thing is to notify people to the widest degree at which they may have been affected or there was an issue, and then confront it head-on: why did it happen, what can we do better?Swyx [00:39:45]: Not the whole user base. That's because of incremental rollouts and other things?Jake [00:39:50]: Yeah. Progressive rollouts.Swyx [00:39:54]: That should be the norm at all large platforms.Jake [00:39:58]: It should. A variety of companies do this. There's the quote that Meta runs 10,000 different versions of Meta. To our earlier point about agents, they need the same thing. They need shadow traffic and all these other things. We've built so much ceremony around production being sacred that we need to make it trivially easy to test different behaviors in a safe environment. Then you can make mistakes in a safe environment.Safe AI SRE: Customer Agents, Forked Environments, and Production ParityAlessio [00:40:30]: Do you see a world where these things get automatically caught, not necessarily by your agent, but by your customer's agent? The cache invalidation issue seems easy to check if you know to look for it.Jake [00:40:44]: It's hard because to determine it, we almost need to hook into your observability infrastructure. That's why we have the template loop on the platform: so you can roll things out progressively. You can roll out to Johnny Vibe Coder initially, or push a shard that someone consumes at their own leisure. Or you can roll it out over weeks: 0.1% of people, 1% of people, early adopters, then all the way up. That's the non-deterministic version control we talked about earlier.Jake [00:41:30]: I believe that's where most things should go, because most companies end up building staged rollout systems in-house. It's the same thing built again and again at every company. There's a massive opportunity to consolidate developer debt.Alessio [00:41:45]: You should have a free tier. Model providers give free tokens if you let them use the data. You could give free compute if someone is the number-one shard that goes out and lets you plug into their observability.Jake [00:41:55]: We do that. That's why we talked about the impact on 3,000 people. We start with lower-impact people. Larger companies on the platform are last to receive those rollouts so they have a version of the platform that's deeply stable.Alessio [00:42:16]: I have three services, so I'm sure I get the first rollout. You can nuke my thing at any time. There are all these SRE agent companies. Observability people also want agents that fix upstream problems. You have your own agent in the canvas now. How do you see that playing out?Jake [00:42:39]: It's the stacking entropy problem. If you don't have primitives to make iteration in production safe, it becomes difficult. If you're an observability provider saying, “Here's the fix to this error,” assume 80% are good and make sense. But in the last 20% long tail of complex issues, if you let somebody stamp it, you create an opportunity for an incident.Jake [00:43:08]: That's why forked environments are important. People have staging, but it always drifts from production. You need primitives, workflows, and experience built first-party on the platform so you can fork any service at any point in time.Jake [00:43:33]: I think of the canvas as a sheet of transparency paper. The agent is a little guy you push up into the canvas. It should say, “I need to copy that service and that service so I can test these two things.” It gets a read-only copy of production. Anything that's PII gets marked as a transform when we clone the database, create a copy-on-write version, or read from it. Then the agent makes changes and asks, “Does this actually work?” as close to production as possible.Jake [00:44:22]: That's how close you have to be, or you get massive drift. The system becomes unstable. You see this with massive systems built on Docker for local, Kubernetes for production, and a specific thing for something else. That complexity slows developers and becomes unstable at scale, making it hard to iterate. We want to compress that way down and say, “As close to prod as possible is where we want to be.”From AISRE Skeptic to Agent BelieverSwyx [00:45:00]: I was texting Erica for questions, and she says you were originally not a believer in AISRE. Have you come around on it?Jake [00:45:10]: I flipped, but I'm still not a believer in AISRE if you don't have the primitives to make it safe. If you unleash AISRE on production infrastructure without safe primitives for copying volumes and making sure things are fine, it's going to nuke your production database. It's not a matter of if, but when. I'm a big believer in making those loops safe.Jake [00:45:33]: I was a deep AI skeptic until 2023. In 2024, I thought, “Maybe I can roughly make this thing do it.” In 2025, I thought, “Now I can hold this.” Over winter break, everybody came back saying, “It's almost impossible to hold this.”Swyx [00:46:01]: Did you see this on the Claude docs? CloudBot? OpenCloud?Jake [00:46:06]: It's gotten to a point where it's harder to hold it wrong than to hold it right. There's a scene in Avengers where Vision picks up Thor's hammer and says it's terribly well-balanced. It self-balances and works well. I'm a deep believer at this point that this will be the dominant species: assembly, C, C++, JavaScript, words.Swyx [00:46:35]: It feels like a big jump.Jake [00:46:37]: It is. But it's not like you abandon CPU-based discrete logic and move straight to fuzzy logic. You need both. Your skills should call code or applications or some static structure. You can use skills to distill what the procedure should be or how the code should act.Jake [00:47:02]: I'm coming to a thesis: you need three points. You need a clear spec defining the system, the code, and the tests. When you say it out loud, if you've been in engineering long enough, you're like, “Of course. That's an RFC, tests, and code.” But they all matter. Having them together lets them reinforce each other: the spec and tests match, but the code doesn't, so reconcile it. Or the tests and code match but the spec doesn't, so reconcile that. That's the iteration loop.Jake [00:47:41]: That's why you're seeing people talk about software factories, docs, and reconciliation. Some of that is architectural astronomy if you don't implement it, but that loop is where most things will end up.Swyx [00:48:07]: For listeners, we've been talking about this on the pod for three years: the holy trinity of specs and tests. Itamar Friedman from Qodo is the reference if people want to look it up.Self-Modifying Infrastructure and the End of Push-Pull-RebuildSwyx [00:48:18]: One thing I want to mention on the OpenCloud idea is self-modification. I don't know how Railway would support it, but I have my OpenClaw, and I just tell it it has the Railway CLI and can do whatever. In theory, whatever capabilities or new infra it needs, it can call the Railway CLI, provision it, and add it to itself. The agent can modify its own infra.Jake [00:48:45]: It's nuts. I have a loop set up where you put the Railway CLI on top of something that runs on Railway. You're authenticated as whatever the current box is, and you can make any changes to it. Then you call Railway deploy, and it deploys itself.Jake [00:49:04]: It's like: “I need to spin up this instance of this environment. I already exist in this environment. Excellent, I have access to a Postgres instance now.” That's where we want to go with agentic, self-replicating infrastructure. That's your loop: iterate in production. You continue making changes. If it works, merge it upstream. If it doesn't, throw it away.Jake [00:49:37]: How do you make throwaway copies trivial to spin up and super cheap? The era of “I have an AWS instance with four vCPU and 16 gigs of RAM” is going to get destroyed. If you do that for agents, you need a thousand of those machines. It's prohibitively expensive compared with what we've spent a ton of time figuring out: the atomic unit of deploy, whether you call it isolates, sandboxes, or something else. Only pay for what you use, spin up instantaneously, and close the loop as quickly as possible.Jake [00:50:15]: If the system can self-replicate safely and say, “This is my environment, I'm making these changes,” it can come back with, “Does this look good? This is a new state of infrastructure given this prompt. I think I've solved it.” Then you go back and say, “Actually, it looks different.” It does the loop again. Then you say, “Cool. Apply.”Swyx [00:50:38]: That's retroactively obvious, which is the most useful kind. Any other comments on agent deployment on Railway?Jake [00:50:51]: It's getting better every day. I'm on X or Twitter. You can always yell at me about the parts not working as well as they should, because plenty of things should work way better.The New Serverless: Stateful, Long-Running, Pay-for-What-You-Use LinuxSwyx [00:51:04]: At this stage, when people want massively or embarrassingly parallel compute, they usually talk serverless. I feel like there's a new serverless compared to the previous five years of serverless. You're in that new bucket. Do you have comparisons or philosophical differences you want to call out?Jake [00:51:31]: It's somewhere in between. It's the ability to run stateful, long-running workflows or executions.Swyx [00:51:42]: Vercel has Fluid Compute, Cloudflare has some container thing, Google has App Runner and others.Jake [00:51:55]: That's where everything is roughly going, and it's why we've been working on this for six years. We believe users need access to a computer: a box that speaks Linux. They need to deploy what they want. Other systems change the surface area of what you can build. For us, users need a computer and need to deploy anything they truly want. That's why we've focused on the primitives: network, compute, storage. If we give you those and expose them so you can run things indefinitely, that's where we believe it's going.Jake [00:52:43]: Twitter has no nuance, so everyone says “servers” or “serverless.” It's always somewhere in the middle: I want to run it for a long time, but I don't want to provision the resource statically or pay for things I'm not using. That's been our thesis from day one: pay only for what you use, run it indefinitely, and it is full Linux.Swyx [00:53:12]: That's why I like the naming of Fluid. It's fluid. Flexible.Heroku, Focus, and Carrying the Torch Without Becoming the PastSwyx [00:53:18]: Another milestone is the Heroku official deprecation. You're one of the presumptive new Herokus. “New Heroku” has been a category for as long as I've been in developer tooling. It's finally happening. What was that like? Any behind-the-scenes of, “This is the moment”?Jake [00:53:42]: You have people where you're like, “You were running stuff on here? You, as this company?” It's crazy that names you would know are running on it and now coming to us saying, “We want to move a lot of this off.”Swyx [00:54:00]: Any behind-the-scenes on why Salesforce let Heroku stagnate?Jake [00:54:05]: I can only guess. It's hard when it's not your business. Salesforce's business is to build a great CRM. That's their focus. Then you acquire a compute business as an offshoot. A lot of early Meta people talk about focus. Boz has a write-up about how in the early days of Meta they had no money, so they were forced to focus. Then they turned on the money tree and had no reason not to split their focus.Jake [00:54:52]: But that dilutes your product. You get offshoots where you ask, “Is this the focus of the business?” If it's not core, it languishes. A lot of companies get in trouble when they split focus because they're fighting a multi-front war, not just externally but internally for alignment. Where are we going? What are we doing? What is our purpose?Jake [00:55:24]: If you're Salesforce-built and mission-driven, you want to work on Salesforce. Heroku is off to the side. It's not core to the business. Getting resources, budget, focus, and alignment internally becomes hard. It was a matter of time.Swyx [00:56:06]: Kudos for them to call it out instead of leaving it unknown.Jake [00:56:12]: Their release was a little odd. They called it out, but they didn't say they were shutting it down. Behind the scenes, I think they issued messages to people saying they should close accounts and that they were going to deprecate and remove things over time.Jake [00:56:30]: It's crazy because some of my first deployment experiences were on Heroku. You start with dragging things into an FTP server, then you try to get a deploy working, and then it's Heroku. It was the on-ramp for us. But the wheel turns. New things emerge. We're happy to carry the torch for a lot of that. But we don't want to be the new Heroku. We want to be the way people build and deploy software, and ultimately the way people monetize software over time.Swyx [00:57:19]: It's still a big crown to be the new Heroku. There are 50 companies that fought for that.Jake [00:57:23]: Everybody is holding some portion of it. We're happy to support people and companies. The platform works differently. The game loop is similar, but we've been dogmatic about where these things are going: primitives, agents, fan-out. Some things fit; some workflows need to change. We have an approximation of Heroku pipelines with the environment system. It's exciting. We've got a ton of people we can support, and it's growing a lot.Temporal, Workflow Engines, and State MachinesSwyx [00:58:12]: I have one more technical question about Temporal. I've sold my shares. You're a power user and one of our earliest customers. I met you through Temporal. You built on Temporal. You have complaints. This may be the most neutral and informed conversation anyone will hear about Temporal without someone working at the company.Jake [00:58:39]: That's fair. I've used Temporal for almost 10 years because of Cadence at Uber.Swyx [00:58:52]: Give people a sense of what Cadence was at Uber.Jake [00:58:57]: Cadence was the precursor to Temporal. It powers trip actions, rides, when you rent a Jump bike or scooter or car. You're running workflows for a period of time and saying, “This ride will run indefinitely until it finishes.” You attach information: you paused in this zone, so add this charge to the bill. When you end the trip, the workflow is done. That experience was powered by Cadence at the time.Swyx [00:59:34]: I used to say it's like programming the entire user journey top-down as one function.Jake [00:59:39]: It's a powerful idea and important. It's also important for the next phase of the agentic journey. You want an agent to do a specific task, be complete or incomplete on that task, and move on to the next thing. You need a way to manage workflows dynamically.Jake [00:59:59]: Temporal was always great in theory, and great when you got it working the way you wanted in production. But it required you to model the entire journey in your head. If you didn't, you could cause issues where replaying the state of the workflow causes non-determinism.Swyx [01:00:25]: Because it works on deterministic workflow history.Jake [01:00:28]: Exactly. I describe it as a jet engine. If you know how to operate it and run it, it's great. But you can't hand it to people trying to build complicated things if they don't have the whole state in their head.Jake [01:00:48]: We run our whole deployment pipeline on top of it. That's a reasonably complicated workflow: pre-commit hooks, signaling, queuing, and all the rest. We ran into the same thing at Uber. As you express a large workflow, it gets more complicated, with more states in the state machine that you have to map back to the workflow.Swyx [01:01:15]: It's a lot of ifs.Jake [01:01:16]: Exactly. At Uber, we built a system for doing the state machine and testing it. We've started to build some of those things here because it's grown heavily. It's not quite love-hate. When it works well, it works super well. But if someone who doesn't have full context puts something into the system that invalidates state or causes non-determinism, or spins off a ton of activities, you have to keep track of underlying SRE knobs like activity slots. Those should scale with memory, vCPU, and so on. It becomes a bear to scale.Swyx [01:02:10]: You need a capable sysadmin running things behind the scenes. If you moved off, what would you do?Jake [01:02:19]: We'd build our own workflow engine. We have a few internally that we've worked on.Swyx [01:02:27]: This is one of those classes of things you typically wouldn't vibe code, but I'm wondering if you can.Jake [01:02:33]: I still don't think you should vibe code it. You still want to run decent tests to make sure it works.Swyx [01:02:39]: Timo didn't invent that from scratch either. There are libraries you can run. On top of that, it's just a state machine that you have to map out. Ultimately, you define the instructions you want and run them through a state machine.Jake [01:03:00]: It's very doable. Workflow stuff is interesting. Restate is doing neat stuff here.Swyx [01:03:10]: You're tied into JavaScript. Are you a JavaScript maxi?Jake [01:03:13]: Internally, we have TypeScript, Rust, and Go. We don't add more languages. Actually, we have a little C because we write BPF code and hooks. But those are the languages.Swyx [01:03:28]: Is this for sidecars?Jake [01:03:32]: No. It's for the networking stack, volumes, and things like that. We use TypeScript a lot because it powers the dashboard, but we're moving a lot of workflow stuff off the dashboard stack and into the infrastructure stack.Railpack, Nixpacks, and Content-Addressable FilesystemsSwyx [01:04:00]: Cool. Any other technical infrastructure stuff? Railpacks?Jake [01:04:07]: We built an engine for determining dependencies based on source code. It's called Railpack. We built the first version, Nixpacks, on top of Nix, and then we moved.Swyx [01:04:17]: People have been trying to get me to adopt Nix and NixOS for four years. Is it ever going to be a thing?Jake [01:04:23]: I don't know. We're excited about it, but it has pain points. Think of it as a stack of versioned binaries at specific slices in time. If you want version X and version Y, you bloat the package space, which blows up image size and makes real-world workloads difficult.Swyx [01:04:53]: But you content-address it and cache it. In theory, there are optimizations.Jake [01:05:00]: In theory, yes. But with a large enough user base and disparate enough machines, you run into a problem Meta described in the XFAAS paper, their internal serverless system. It becomes difficult at scale unless you break out specific runtimes.Jake [01:05:24]: We didn't want to do that because we wanted to truly allow you to deploy anything. That was our initial thing with Nix. But we've moved toward interesting work around content-addressable file systems that can lazy-load anything from any point and page it into memory.Swyx [01:05:48]: Amazing.Jake [01:05:49]: The future is very bright. It's crazy, and it's going to be nuts.Coding Agent Spend, Roadmaps, and Token ROISwyx [01:05:54]: Founder journey stuff?Alessio [01:05:56]: Your cloud usage: you tweeted you're going to spend $300K this month?Jake [01:06:01]: I think we got to $200K.Alessio [01:06:02]: Coding agents?Jake [01:06:03]: Yeah.Swyx [01:06:04]: Across the company?Alessio [01:06:05]: You only have 35 people, so I'm sure they're not all spending $10K a month. What's the distribution?Jake [01:06:10]: I think I'm at about $25K. We have power users all the way down. We came back from winter break, and I basically said, “If you're writing code by hand, you're doing this wrong.” The tools are good enough now that you can move extremely quickly. There are issues and pain points, but you should be reviewing the code you are writing instead of writing it by hand.Jake [01:06:40]: Architectural patterns matter more now than ever, but you shouldn't spend your time generating code you would write. If you know how to write it, ask the agent to write it and reconcile it until it looks like you would have written it yourself.Jake [01:06:58]: People misconstrue my propensity to push people toward agents as connected to our growth and some reliability bumps. They're not necessarily related. The tools are good enough to move extremely quickly and build things way larger than you could before.Jake [01:07:19]: To the earlier point about cooling data centers in space: I don't know. But with software, you can ask, “How would I build block storage from scratch? How would I do these things?” I have ideas because I have history and have read papers. Let me work them out and build massive test benches with thousands of tests, because those are now free to author. If you're not using AI systems to speed-run your roadmap and reconcile your existing system onto the future, you're missing a large point of what's happening.Alessio [01:08:12]: What's the path to spending $3 million a month? Is it bound by ideas and things customers can absorb?Jake [01:08:19]: For most companies, it's bound by deployment at this point. That's why we've seen a massive boom in users and companies, from Fortune 50s down, asking how to get developers to move faster. You'll probably hit your CFO before any technical limits because they'll look at the eye-watering amount of money spent on tokens. Inference costs have to come down, but we're inference constrained now. There will be price discovery around what makes sense for an org to adopt.Jake [01:09:06]: I think you'll end up with the F1 driver concept. If someone is really adept at these things, it makes sense to put them in a $3 million car. If they're not, it probably doesn't make sense. You'll take a few people and say, “You can drive the F1 car. We need to go in this direction. Figure out if it works and prototype it.”Jake [01:09:33]: We've done some of that and vastly accelerated our roadmap. We thought we'd ship something in a few years; now we can probably ship it in a few months because we validated it and don't have to build it incrementally. We can skip steps and move toward our vision.Alessio [01:09:58]: A lot of people are realizing the roadmap doesn't always have a business impact, so they say tokens are too expensive. But if your roadmap were built to make more money by the time you built it, you'd have token pricing for it, the same way you do with sales. You'd spend a billion dollars on sales if you knew you would get $2 billion of revenue.Jake [01:10:19]: Exactly. A naive way to measure this is the percentage of tokens that end up in production. If you can measure impact because those tokens end up in production, that's awesome. But the burden of proof will rise. Internally, we have a growing number of pull requests that haven't merged. The question becomes: how do you get this into production? It's about how quickly you can build and deploy software, which is exciting because that's our whole thing.The SDLC Shift: Prompt Requests, Feature Flags, and Safe RolloutsSwyx [01:10:56]: The SDLC is changing. One thesis is that the pull request is dying. It's going to be the prompt request. Beyond that, code review is also kind of dying if you have all the other systems in place. What else is changing about the SDLC?Jake [01:11:19]: The AISRE and the tools to make it happen. AISRE is pie-in-the-sky aspirational. What does it take to get an AISRE? What tools do you need to build?Swyx [01:11:32]: You should expose your tooling to customers at some point. The Central Station command center.Jake [01:11:39]: We have it for template maintainers. Template maintainers can deploy and maintain templates, and they get feedback. We're going to expose those things incrementally.Swyx [01:11:51]: Clustering around incidents. Everyone has a version of that, but I don't think anyone has solved it.Jake [01:11:56]: I won't say we've solved it internally, but it's gotten so good that we can see incidents forming pretty quickly. At some point, those will be things either someone else builds or we build. We've always built things purpose-built for us. If it makes sense to make it useful for users, monetize it, or turn that loop into a profit center instead of a cost center, we want to do that.Jake [01:12:28]: Pull request is definitely dying.Swyx [01:12:29]: Do you do first-party feature flagging and incremental rollout stuff?Jake [01:12:34]: We have a feature-flagging engine we built internally and will eventually roll out.Swyx [01:12:38]: I don't see it as a user. How come you didn't give us what you have?Jake [01:12:43]: We have to beta test it. We care a lot about the quality of the things. There's plenty we've used internally that doesn't make it all the way through the journey because it fails. It works for one service but not multiple services. We'd have to build it for multiple services and know that if we released it, we'd rebuild it again and again. Some things are worth that, but many inform the roadmap.Jake [01:13:18]: We don't want to dilute the experience by saying, “This works, but only for this service,” unless it's a core initiative. Over the next few months, we'll roll out things that work for a single service, then multiple services, then multiple services across the environment. You have to be deliberate. Otherwise you create broken disparate experiences and support load because people ask how to use the feature.Jake [01:13:52]: It's the earlier expansion and compaction pattern. You expand the company to get features, then compact and smooth them out so the experience is stellar. You told me in the hallway, “It's gotten so much better.” Internally we're saying, “This part really sucks. We need to make it significantly better.”Swyx [01:14:11]: I can attest to that over the last three years watching you build Railway. For listeners, feature flagging is a huge part of Uber culture. So much so that they have too many feature flags and another thing to remove feature flags. Facebook has Gatekeeper. Agents are going to need this. It's fundamental to incremental rollouts. OpenAI acquired Statsig. GPT-5 is routing and flagging through different models.Jake [01:14:56]: It's super important. If the software development lifecycle is going to change because we're doing things 1,000 times faster and 1,000 times more concurrently, what becomes important at scale?Jake [01:15:16]: Before I started Railway, I built a feature-flagging product and tried to sell it. It was an easier version of LaunchDarkly. I ran into a problem: anyone small enough to adopt your technology doesn't care about feature flags, and anyone large enough to need feature flags needs so much scale that you have to build out all the infrastructure. I scrapped it.Jake [01:15:42]: But what is old is new again. Companies are trying to move quickly, but you can't YOLO a vibe-coded thing straight into production. You need to say, “Here's my blast radius, my impact, and I want to shadow it for these users.” Feature flags. You're going to need the tools larger companies built to maintain their structures. Everything gets compressed by 1,000x so everybody can build those structures quickly.Jake [01:16:07]: That's exactly where we are: compressing the software development lifecycle, then expanding it and adding more new things.Cattle, Pets, and Clonable InfrastructureSwyx [01:16:15]: Another term that comes to mind for newer developers is “cattle, not pets.” People treat production like a pet. It has a name. You baby it and keep it alive. With cattle, you can mass farm, roll out, portion parts out, and kill them.Jake [01:16:37]: I think that might change. You can move toward having pets as long as you have a cloning machine for your pets.Swyx [01:16:52]: Yeah.Jake [01:16:52]: If you can snapshot every single thing at every frame, it doesn't matter if something gets obliterated because you have a snapshot of it. The things we've built right now are designed to block changes from the hermetically sealed DevOps line. You have to write a Dockerfile because you nee

April and A.J. talk about Rachel having a baby and all the comedic and poignant moments that come with it.  Ross is trying to shield off any feelings directed at Rachel, but with some prompting by his friends and his mother, Ross may see that Rachel, the mother of his new born child, is right for him.  And Monica and Chandler are going baby crazy, which leads them to work on starting a family immediately.  Time Lady Phoebe comes up short on meeting a great guy, and Joey is having a bit of a romantic relapse.   Write to us at april5k@gmail.com Bluesky: huestone44 Let us know if you want to join our Discord.   https://seinfeldpodcast.libsyn.com/website www.patreon.com/wrightonnetwork  

Welcome back to the Tom Bilyeu Show Live, broadcasting straight from London, where Tom Bilyeu and Drew break down the world's most pressing news, geopolitical power plays, and technological disruptions. In this episode, they unpack the historic Trump-Xi summit in China — what it means for the Thucydides Trap, dollar diplomacy, Iran, the Strait of Hormuz, and the looming question of Taiwan — and why Trump bringing 20 of the world's biggest CEOs (Musk, Huang, Cook, Boeing, BlackRock, Goldman) may signal the most important pivot in modern US foreign policy. The conversation moves into the Bernie Sanders and AOC bill to freeze every AI data center in the country, why young people and women are leading the pushback, and why Tom argues this is a Manhattan Project moment we cannot afford to lose to China. They debate whether AI is a doomsday weapon or the path to an age of abundance, the real cause of resentment driving anti-AI sentiment, and what happens to the workforce when the Industrial Revolution plays out in five years instead of one hundred. From there, Tom and Drew take on Kamala Harris's new policy pitch and the Democratic Party's identity crisis, Gavin Newsom's "balanced budget" sleight of hand, what Tom learned doing a deep dive on the Nordic model (spoiler: Sweden is begging us to stop calling them socialist), the math problem of open borders plus a welfare state, and why Christopher Nolan's Odyssey casting has the internet at war before the movie even hits theaters. If you want no-nonsense geopolitical analysis, a brutally honest take on AI's impact on your future, and a call to greater personal responsibility in a populist moment, this episode cuts through the noise with clarity, history, and a little bit of humor. Ketone IQ: Visit ⁠https://ketone.com/IMPACT⁠ for 30% OFF your subscription orderQuince: Free shipping and 365-day returns at ⁠https://quince.com/impactpod⁠Monetary Metals: Future-proof your wealth at ⁠https://monetarymetals.com/impact⁠Truemed: Check your eligibility and start saving at ⁠https://truemed.com/impact⁠AT&T Business: Switch to AT&T Business at ⁠business.att.com⁠Incogni: Take your personal data back with Incogni! Use code IMPACT at the link below and get 60% off an annual plan: ⁠https://incogni.com/impact⁠Shopify: Sign up for your one-dollar-per-month trial period at ⁠https://shopify.com/impact⁠Netsuite: Right now, get our free business guide, Demystifying AI, at ⁠https://NetSuite.com/Theory⁠Quo: Try for free PLUS get 20% off your first 6 months at ⁠https://quo.com/impact⁠ What's up, everybody? It's Tom Bilyeu here: If you want my help... STARTING a business:⁠ join me here at ZERO TO FOUNDER⁠:  ⁠https://tombilyeu.com/zero-to-founder?utm_campaign=Podcast%20Offer&utm_source=podca[%E2%80%A6]d%20end%20of%20show&utm_content=podcast%20ad%20end%20of%20show⁠ SCALING a business:⁠ see if you qualify here.⁠:  ⁠https://tombilyeu.com/call⁠ Get my battle-tested strategies and insights delivered weekly to your inbox:⁠ sign up here.⁠: ⁠https://tombilyeu.com/⁠ ********************************************************************** If you're serious about leveling up your life, I urge you to check out my new podcast,⁠ Tom Bilyeu's Mindset Playbook⁠ —a goldmine of my most impactful episodes on mindset, business, and health. Trust me, your future self will thank you. ********************************************************************** FOLLOW TOM: Instagram:⁠ https://www.instagram.com/tombilyeu/⁠ Tik Tok:⁠ https://www.tiktok.com/@tombilyeu?lang=en⁠ Twitter:⁠ https://twitter.com/tombilyeu⁠ YouTube:⁠ https://www.youtube.com/@TomBilyeu Tom Bilyeu, Drew, Tom Bilyeu Show, Trump Xi summit, US China relations, Thucydides Trap, dollar diplomacy, Strait of Hormuz, Iran, Taiwan, rare earths, Xi Jinping, AI data centers, Bernie Sanders, AOC, Artificial Intelligence Data Center Moratorium Act, Kevin O'Leary, AGI, fast takeoff, AI race, China AI, Manhattan Project, age of abundance, job displacement, female dominated jobs, Kamala Harris, Supreme Court ethics, Clarence Thomas, gerrymandering, populism, Gavin Newsom, California budget, Nordic model, Sweden socialism, Mamdani, open borders, welfare state, immigration, Christopher Nolan, Odyssey, modern audience, GTA 6, Fourth Turning, geopolitics, 2026 Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome back to the Tom Bilyeu Show Live, broadcasting straight from London, where Tom Bilyeu and Drew break down the world's most pressing news, geopolitical power plays, and technological disruptions. In this episode, they unpack the historic Trump-Xi summit in China — what it means for the Thucydides Trap, dollar diplomacy, Iran, the Strait of Hormuz, and the looming question of Taiwan — and why Trump bringing 20 of the world's biggest CEOs (Musk, Huang, Cook, Boeing, BlackRock, Goldman) may signal the most important pivot in modern US foreign policy. The conversation moves into the Bernie Sanders and AOC bill to freeze every AI data center in the country, why young people and women are leading the pushback, and why Tom argues this is a Manhattan Project moment we cannot afford to lose to China. They debate whether AI is a doomsday weapon or the path to an age of abundance, the real cause of resentment driving anti-AI sentiment, and what happens to the workforce when the Industrial Revolution plays out in five years instead of one hundred. From there, Tom and Drew take on Kamala Harris's new policy pitch and the Democratic Party's identity crisis, Gavin Newsom's "balanced budget" sleight of hand, what Tom learned doing a deep dive on the Nordic model (spoiler: Sweden is begging us to stop calling them socialist), the math problem of open borders plus a welfare state, and why Christopher Nolan's Odyssey casting has the internet at war before the movie even hits theaters. If you want no-nonsense geopolitical analysis, a brutally honest take on AI's impact on your future, and a call to greater personal responsibility in a populist moment, this episode cuts through the noise with clarity, history, and a little bit of humor. Ketone IQ: Visit ⁠https://ketone.com/IMPACT⁠ for 30% OFF your subscription orderQuince: Free shipping and 365-day returns at ⁠https://quince.com/impactpod⁠Monetary Metals: Future-proof your wealth at ⁠https://monetarymetals.com/impact⁠Truemed: Check your eligibility and start saving at ⁠https://truemed.com/impact⁠AT&T Business: Switch to AT&T Business at ⁠business.att.com⁠Incogni: Take your personal data back with Incogni! Use code IMPACT at the link below and get 60% off an annual plan: ⁠https://incogni.com/impact⁠Shopify: Sign up for your one-dollar-per-month trial period at ⁠https://shopify.com/impact⁠Netsuite: Right now, get our free business guide, Demystifying AI, at ⁠https://NetSuite.com/Theory⁠Quo: Try for free PLUS get 20% off your first 6 months at ⁠https://quo.com/impact⁠ What's up, everybody? It's Tom Bilyeu here: If you want my help... STARTING a business:⁠ join me here at ZERO TO FOUNDER⁠:  ⁠https://tombilyeu.com/zero-to-founder?utm_campaign=Podcast%20Offer&utm_source=podca[%E2%80%A6]d%20end%20of%20show&utm_content=podcast%20ad%20end%20of%20show⁠ SCALING a business:⁠ see if you qualify here.⁠:  ⁠https://tombilyeu.com/call⁠ Get my battle-tested strategies and insights delivered weekly to your inbox:⁠ sign up here.⁠: ⁠https://tombilyeu.com/⁠ ********************************************************************** If you're serious about leveling up your life, I urge you to check out my new podcast,⁠ Tom Bilyeu's Mindset Playbook⁠ —a goldmine of my most impactful episodes on mindset, business, and health. Trust me, your future self will thank you. ********************************************************************** FOLLOW TOM: Instagram:⁠ https://www.instagram.com/tombilyeu/⁠ Tik Tok:⁠ https://www.tiktok.com/@tombilyeu?lang=en⁠ Twitter:⁠ https://twitter.com/tombilyeu⁠ YouTube:⁠ https://www.youtube.com/@TomBilyeu Tom Bilyeu, Drew, Tom Bilyeu Show, Trump Xi summit, US China relations, Thucydides Trap, dollar diplomacy, Strait of Hormuz, Iran, Taiwan, rare earths, Xi Jinping, AI data centers, Bernie Sanders, AOC, Artificial Intelligence Data Center Moratorium Act, Kevin O'Leary, AGI, fast takeoff, AI race, China AI, Manhattan Project, age of abundance, job displacement, female dominated jobs, Kamala Harris, Supreme Court ethics, Clarence Thomas, gerrymandering, populism, Gavin Newsom, California budget, Nordic model, Sweden socialism, Mamdani, open borders, welfare state, immigration, Christopher Nolan, Odyssey, modern audience, GTA 6, Fourth Turning, geopolitics, 2026 Learn more about your ad choices. Visit megaphone.fm/adchoicesSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

(Presented by TLPBLACK: A cybersecurity intelligence platform focused on sharing curated, high-sensitivity threat insights and research with trusted security professionals.) Three Buddy Problem - Episode 98: We dive back into the fast16 malware discovery with fresh speculation that it's targeting spherical implosion simulations for Iran's nuclear program, and wonder who on earth is qualified to confirm this. Plus, thoughts on OpenAI's new three-tier cyber access program, Microsoft's MDASH harness, the 10x Patch Tuesday tsunami, Cloudflare's 1,100 layoffs blamed on AI, and why frontier-lab guardrails may just be elaborate security theater. Cast: Juan Andres Guerrero-Saade, Ryan Naraine and Costin Raiu. Timestamps: 0:00 - Introductory banter 3:19 - fast16 update: spherical implosion simulations? 9:01 - Manhattan Project precedent — why this matches Iran 12:28 - Who can actually reproduce the FAST 16 attack? 19:32 - Google GTIG's "AI-written" zero-day 22:13 - The rise of AI-backend "silent detections" 25:54 - Guardrails as security theater 38:47 - Are the 10x patch numbers real defense? 43:48 - OpenAI's Trusted Access tiers + Microsoft MDASH 53:35 - End of the ‘patch-and-pray' model 57:50 - Sean Heelan: strict harnesses can make models worse 1:03:51 - Pwn2Own Berlin overflow and bug-density debate 1:12:24 - Cloudflare's 1,100 layoffs and AI as scapegoat 1:27:42 - RCS encryption, Android Intrusion Logging, Seedworm & Kazuar

What does real world STEM education look like in a high school where students run actual manufacturing contracts on industry-grade equipment, intern at MIT, and learn AI ethics alongside CAD? Joe Fatheree (Top 10 Global Teacher Prize, Illinois Teacher of the Year) and Dr. Mark Buckner (Smart Industry Top 50 Innovator, founder of Oak Ridge High School's iSchool and Wildcat Manufacturing) take Vicki inside a $1.25 million state grant program where 26 student-run contracts with 18 companies have produced near-net-shape metal 3D printing, augmented reality experiences, and graduates already working four to five years ahead of their college peers. This extended episode also tackles the AI conversation educators most need: where AI belongs in classrooms, where it doesn't, what neuroscience says about kids' developing brains in the attention economy, and why "just because you can does not mean you should" is the most important lesson STEM students will learn this year. In this episode, you'll learn: How Wildcat Manufacturing's profit-sharing model pays students for real client work The three pathways Oak Ridge graduates take — start a business, $100K+ workforce, or accelerate into engineering Why Mark teaches industry frameworks (Scrum, Lean, Toyota Kata, Deming) instead of "edu-ese" Where AI helps (rapid feedback, math practice) and where it harms (Grok Annie, social companionship, attention erosion) What the "Manhattan Project 2.0" frame means for AI policy and your classroom Show notes: https://www.coolcatteacher.com/e933 EF Explore America STEM Tours sponsored today's show. Show students how STEM impacts the world up close and in action. Students could code robots with MassRobotics at MIT or explore marine ecosystems in Florida's coral reefs or even sit down to talk with a former spy in Washington DC. Students will learn how STEM thinking often shows up where you least expect it. Inspire your students visit efexploreamerica.com/STEM

This show has been flagged as Clean by the host. -------------------- 01 Introduction This is a follow up to my 8 part series on nuclear power. In this episode I will answer questions posed by listeners in the comments to the series. I would like to start by thanking these people for taking the time to submit interesting questions. -------------------- Costs of Small Versus Large Reactors 02 brian-in-ohio asked two questions The first was for a cost comparison between large and small reactors. The second was for nuclear plant safety compared to conventional power plants. 03 Answer I think that any answer to the second question is going to be perceived by some people as politically controversial, so it's probably not a good topic for HPR to address. 04 The first question though about cost of small versus large reactors is an interesting one, although not one that is easy to give an answer to. I will restrict the answer to just grid scale electric power production and ignore use cases such as industrial process heat or power for remote mines and communities. 05 This question comes down to economies of scale versus economies of replication. Economies of scale centre around increased efficiencies of use of materials and labour when making something bigger. For example, the amount of steel used by a pipe increases linearly with its diameter, but the amount of fluid that it transports increases with the square. 06 Economies of replication come from increasing efficiencies which result from serial production. As you repeat the same design over and over again, you learn how to do things better and make fewer mistakes. 07 The exact same principles apply to shipbuilding. Indeed, a lot of the inspiration for Small Modular Reactors comes from the shipbuilding industry. If you build a series of identical ships, then each subsequent ship will cost less and be built faster. There are of course diminishing returns to this process, so the improvements are less with each additional unit and after a sufficient number of units the cost and time reductions level off. 08 However, this doesn't discount the benefits of economies of scale. What it does mean is that there are two ways of approaching the problem, and which way works in any given scenario depends on such conditions as how big the local electricity market is how fast the demand for electricity is growing, the ownership and financing structure of the electricity market, and the geography of the area, which may pose limits on the number of sites. 09 According to the finance people who have crunched the numbers, there are two sizes of reactor which make the most sense in the above context. These are 300 MW and 1000 MW. However, take those as very rough numbers rather than immutable laws of nature and other sizes may work as well. 10 The key point is that there are cases to be made for both small and large reactors, with the large reactor being several times the size of the small one. 11 An additional factor is that building only one reactor does not reap the benefits of efficiency of replication. You need to build a series of them on the same site. So if you are building a power plant, you don't build a power plant that has just one reactor unless you are in a small market which can only use that much power. Instead, you should build between 4 and 6 reactors in sequence next to one another. 12 If you are supply a large population with a growing demand for electricity, then 4 or 6 large 1000 MW reactors gains both economies of scale and economies of replication. If you are supplying a smaller population with slow growth in demand for electricity, then 4 or 6 300 MW reactors at least gets you economies of replication. 13 There is what could be viewed as an interesting example in terms of the above taking place just east of Toronto. There they are building four 300 MW SMRs on a site next to an existing nuclear power plant. 14 Here are the cost estimates from the Government of Ontario. All costs are in Canadian dollars. Unit 1 is $6.1 billion, plus $1.6 billion in costs which are shared by all four unit.s Unit 2 is $4.9 billion. Unit 3 is $4.2 billion. Unit 4 is $4.1 billion. 15 As you can see, building a series of reactors sequentially on the same site results in declining overall costs. They are very confident in these costs as they used data from a series of major nuclear power plant refurbishment projects in Ontario which have been coming in on time and on budget. 16 Construction began last year and the plant is expected to have a 65 year operating life. 17 However, the province of Ontario also has plans for expansion of electrical generation by about 15,000 MW by 2050 in order to meet net zero targets. 18 Given the heavy concentration of population in the Toronto region, and the very high cost and difficulty of building long distance transmission lines, and the limited number of sites which could host new power generation facilities of any sort, I suspect it is quite likely that subsequent reactors will be large 1,000 MW ones rather than SMRs. 19 The Wesleyville site (which is further east of Toronto) is tentatively scheduled for a 10,000 MW nuclear power plant. That would seem to make ten 1,000 MW reactors more likely than 34 300 MW reactors. 20 I don't have a comparable set of numbers for building large reactors to give an exact apples to apples comparison of costs. Different countries use different accounting and financing systems, and finance makes a huge difference to overall costs for nuclear power as operating costs are a relatively small share of the total. 21 Now to look at another side of this equation, the provinces of Saskatchewan and New Brunswick wish to replace their coal fired power plants with nuclear power plants. The populations of these provinces are too small to absorb a large new power plant into their grids, and studies assuming large reactors have foundered on this issue. 22 New Brunswick already have a nuclear power plant, but it was build in the days when reactors were much smaller. Both provinces however are very interested in small reactors, even individual ones, in order to replace the coal fired plants that are of similar size. 23 I think this covers the cost versus size issue. The more I look into it, the more it becomes apparent that there is no simple one size fits all answer but rather there are a series of trade-offs which must be taken in light of local circumstances. -------------------- MOX Fuel in the USA 24 The next question comes from mnw who asked about the use of MOX fuel in the USA. 25 mnw asked I am enjoying and look forward to the rest of the series. Do you think the US will ever wake up and start recycling its spent fuel? It seems like such a huge waste just to try and keep a small amount of fuel away from"the bad guys" or whatever they are imagining. Answer 26 My answer to this is as follows. I think I've addressed this in the original series, although not directly with respect to the US so I can provide some more detail on that aspect of it. 27 First though I will review what plutonium-uranium mixed oxide (MOX) fuel is. As mentioned in previous episodes, military grade plutonium is not the same as the plutonium which comes out of commercial power reactors. Just as military grade uranium requires nearly pure U-235 isotope, military grade plutonium requires nearly pure Pu-239 isotope. 28 What comes out of a commercial power reactor as spent fuel is not usable for weapons purposes as the proportion of Pu-239 is much too low. However, plutonium recovered from spent fuel can be used as fuel for nuclear reactors in place of uranium 235 when mixed with uranium 238 either left over from enrichment or extracted from spent fuel. This is what is known as MOX fuel. 29 To look at the US history of this however, here's the sequence of events. The US banned fuel reprocessing in 1976. However, this ban was repealed in 1981. 30 In 2005, the US began building a mixed-oxide (MOX) fuel plant at Savannah River in the state of South Carolina. However, this plant was not intended as a normal commercial operation and it was not intended to recycle commercial nuclear power plant fuel. It was instead intended to convert surplus military grade plutonium into commercial fuel in order to get rid of it as part of an arms control program. 31 The program was suspended in 2018. There were apparently many complex political issues involved in these on-again off-again decisions and I won't pretend to have the time or interest to explore all the details nor do I think most listeners would be interested in hearing abou them. 32 As of March 2026, the US are looking at reviving part of the Savannah River plant to produce limited amounts of fuel for testing of advanced reactors. The issue driving this is the shortage of uranium enriched to just below 20%. This fuel is used in certain types of small SMR. 33 The main commercial supplier of this material was a plant in Russia, but "certain events in Europe in recent years" shall we say, have resulted in that supply no longer being available to commercial operations in the US. MOX fuel based on surplus weapons grade plutonium is intended as a short term quick fix for that problem. 34 Another driving force is legal requirements following from domestic commitments for the US government to dispose of certain stockpiles of weapons grade plutonium from certain sites in the US where it is "temporarily" stored, and the solution to that is seen as burning it up in power reactors. 35 So the history is the US banned fuel reprocessing. Then a few years later they un-banned it. Then the US government started building a MOX plant which was intended to get rid of surplus weapons grade material by burning it up in power reactors. Then they decided they didn't want to do that. Then they decided they may want to make MOX fuel after all to replace supplies of special grades of fuel for experimental or prototype reactors. 36 What is missing from the above history is any actual interest from the US commercial nuclear industry in MOX fuel. The reason for this is, as mentioned in the previous episodes, uranium is so cheap and abundant that fuel made from fresh uranium is cheaper than MOX fuel. 37 Some countries such as France wish to recycle spent fuel to reduce their dependence upon imports. Recall that France's drive to build nuclear power plants was in response to the 1970s era energy crisis when oil imports from the Middle East were suddenly cut off. However, the US are not concerned about this issue and so do not make it national security policy as France did. 38 As a result, US commercial demand is for cheaper fuel made from fresh uranium rather than for MOX fuel. Until such time as fresh uranium greatly increases in price there is little economic incentive for the use of MOX fuel in the US. 39 However, there is another aspect to this. If you recall in previous episodes I described molten salt reactors which used dissolved uranium fuel. These reactors inherently reprocess fuel as part of their normal operation. They just do it as part of maintaining the molten salt chemistry at the correct values rather than doing it as a separate process. 40 If these types of reactors become widely used then they would be achieving the same thing as creating MOX fuel, but without an explicit separate step. 41 As a final footnote to the above, the US has almost exclusively use enriched uranium light water reactors. As mentioned in previous episodes, there are ways of recycling spent fuel from light water reactors which do not involve chemically reprocessing it to make MOX fuel. 42 Experiments have been done involving South Korea, China, and Canada which take spent fuel from light water reactors and repackage it to fit it into natural uranium heavy water reactors. What is used up or "spent" fuel for a light water reactor is high grade fuel to a natural uranium reactor. However, the US has, for whatever reason, never built commercial natural uranium reactors such as are used in a number of other countries around the world. 43 If they were to do so, then nuclear fuel could be used twice, once in a light water reactor, and again in a natural uranium reactor, all without having to turn it into MOX fuel in a separate reprocessing step. However, this particular alternative would likely face the same issue in the sense that fresh fuel would still be cheaper than reusing spent fuel. -------------------- A Variety of Questions from Clinton 44 Next we have a variety of questions from Clinton. Clinton asked I would like some commentary in the current situation, why has hinkley gone off the rails, the new american approach, the odd things done after fukushima, the new radiation rules in the states. 45 Question 1 why has hinkley gone off the rails, 46 Answer The question refers to cost overruns at the Hinkley Point nuclear power project in the UK. The UK government looked into this issue in a more general sense in 2025. They published a report on it titled Nuclear Regulatory Review 2025 Enabling nuclear delivery through regulatory reform John Fingleton There is a link to the report in the show notes. https://assets.publishing.service.gov.uk/media/692080f75c394e481336ab89/nuclear-regulatory-review-2025.pdf 47 As the report is 162 pages long, I won't try to cover it all in this answer. I will however give a few simple examples. The report focuses on civilian nuclear power and the defence nuclear industry as well. However it also draws examples from outside the nuclear industry to show that the problem is not limited to nuclear. It shows that the same problems exist in the offshore wind industry, and in the HS2 High Speed Rail project. 48 In the view of the authors of the report, the essence of the problem seems to be a lack of any degree of proportionality in terms of mitigating negative effects from any project. Big nuclear projects make the headlines because they are inherently big projects, but as I have just mentioned, they affect things like wind power development and rail transport as well. 49 I will pick one example from Hinkley Point specifically. This is "Case Study: Hinkley Point C Fish Protection" A summary of this is that they spent £700 million of additional money on the cooling water intakes to protect an estimated 0.083 salmon per year, along with 0.028 sea trout, 6 river lamprey, 18 Allis shad, and somewhere between 100 and 528 twaite shad. The report points out that there are ways to protect far more fish for far less money by spending it in other areas, and gives some examples. Again, this problem is not limited to nuclear power, and they give similar examples connected with offshore wind development and HS2 High Speed Rail. 50 I would like to emphasize that I am not expressing an opinion on whether or not any of these decisions were good or bad ones or whether the money was well spent. I am just summarizing the report's explanation of why large projects of all sorts initiated and approved by the UK parliament were not turning out as initially expected. I will leave it up to people in the UK to decide whether or not they are satisfied with the current situation. 51 Question 2 the new american approach, 52 Answer The US have apparently announced changes to their regulatory system. I don't know enough about the subject to really judge the practical effects of regulation within the US. However, I have read and listened to many interviews of people from both the industry and the regulatory side of things who are from outside the US but are familiar with it. They generally contrast two different approaches to regulation. On the one hand there is the US approach, which they see as being more of a box ticking exercise than an in depth safety review. This makes it very hard to get a design other than a traditional PWR or BWR approved in the US. 53 It has the advantage from the regulator side of things though in that it reduces the amount of work required as it primarily requires just following a set of defined procedures. These people then contrast that approach with the one used in the UK and in Canada, both of which they see as being very similar to one another. In those two countries, regulators work with industry to review designs from basic principles rather than just seeing if it meets a pre-defined list of criteria. This is a results oriented system rather than a process oriented system as used in the US. 54 As a result of this, designers of new nuclear reactors are going to the UK and Canada first to go through preliminary review there, and only going to the US later. What designers are looking for is feedback on their design as they go along in order to align the design with what safety regulators see as being required from their standpoint. They want to go into a review process before the design is finalized so they can get guidance on how they should approach things rather than trying to add safety as additional features on top of a finished design. 55 It would take someone with deep familiarity with nuclear regulation systems to understand the practical effects of recent changes in US regulatory systems, but it is quite possible that people within the regulatory structure in the US have been taking the above on board and trying to adapt to current circumstances. However, I can only speculate on that. This is about the best answer that I can give. 56 Question 3 the odd things done after fukushima, 57 Answer This covers a lot of topics, some of which are probably political and so are not suited to HPR. I will try to list a few events however. As a brief summary if the Fukushima events go however, a historic scale earthquake and tsunami in Japan in 2011 caused huge loss of life and widespread damage. About 20,000 people were killed by the earthquake and tsunami. Three nuclear reactors based on 1960s era GE BWR designs were seriously damaged by hydrogen explosions caused by loss of power to backup generators when they were flooded by the tsunami. However, there were no radiation related deaths or cases of radiation sickness. 58 Following events in Japan was a general review of designs around the world, with various improvements made in some areas, particularly backup generators and hydrogen management. It seems to be conventional wisdom that the Fukushima event caused a number of countries to decide to phase out nuclear power. 59 However, when I tried to make a list of such countries for this episode I found things were not as is often heard. The countries which decided to get rid of nuclear power had largely started down that road at least a decade before then and generally for reasons unrelated to any specific events outside of their own country. In other cases they reversed that decision or are in the process of doing so. Japan itself has restarted many of their nuclear power plants and plant to replace decommissioned nuclear power plants with new ones, although many of the older and smaller ones were considered not economically worth upgrading at this point in their life to restart them. 60 The one possible exception to this may be Taiwan which decided to phase out nuclear power in 2016. However, I don't know enough about Taiwanese politics to state with any confidence that their decision in 2016 was based on anything related to events in Japan, or whether in fact they were a byproduct of other political changes within Taiwan and the shut down of nuclear plants happened to be carried along with those. Currently Taiwan get their electricity primarily from natural gas and coal. 61 Meanwhile across mainland Asia from Turkey to China, large numbers of nuclear power plants were built or are under construction. Taken together on a global scale, did anything really change after Fukushima, or did the countries which had already decided to close down their nuclear power plants simply continue to do so, and those countries who decided they wanted more of them continue to build them? That's a good question for which I don't think anyone has the perspective to answer at this point. 62 Another side of this which is hard to disentangle from it though is the increased use of natural gas for electric power generation which was happening at around the same time. Increased use of fracking in a number of countries, plus increased supplies from Russia and LNG from the Middle East and other places resulted in falls in natural gas prices in many places. Since combined cycle natural gas turbines form the main competitor to nuclear power, anything which improves the economics of natural gas will act to reduce demand for nuclear power. This makes it hard to decide to what degree the reduction in the number of reactors being built was due to the political effects of the earthquake and tsunami and to what degree it was due to cheaper natural gas through fracking and other means. I'll leave that question at that. 63 Question 4 the new radiation rules in the states. 64 Answer I'm not deeply familiar with US radiation rules, but I will attempt to answer the question. Apparently there are wide variety of different things being addressed, only some of which have any relevance to the nuclear power industry. One of these is an epidemiological study on the current exposure limits for workers in the nuclear industry. This study will take place over about 5 years. In the end it may not result in any changes. This is for a number of reasons. 65 One is that US exposure thresholds for workers are currently aligned with international standards. It would be difficult for the US industry to operate on a different basis than the rest of the world when supply chains are global and kit is designed to meet currently recognized standards. Another is that apparently the nuclear industry are not, so far as I can discern, asking for any changes to limits. They instead are looking for changes to how some of the details are being applied, such as for example the criteria for deciding when respirators are required in low risk environments. 66 Some point to recent changes in UK regulations as an example of what they are looking for. I will post a link to the new (November of 2025) UK regulations in the show notes. https://www.gov.uk/government/publications/nuclear-industry-principles-to-guide-the-application-of-as-low-as-reasonably-practicable-alarp-and-best-available-techniques-bat/ways-of-working-principles-to-guide-the-application-of-alarp-and-bat-in-the-nuclear-industry-accessible-webpage This is about as much detail as I think I can comment on when it comes to this question, as I think it is a subject that requires a fair bit more practical knowledge of than I have in order to give a thorough and balanced answer. -------------------- 67 Question from Antoine Were/are the designs patented? Hi, Whiskeyjack. Nice ep. You said AGR, based on Magnox, was a nuclear reactor type that did not sell well outside the UK. I then started thinking if it were (is) possible to another countries to develop by themselves based on that project, or if it had (has) a commercial restriction for exploration of the technology. I have yet to listen to the following episodes (doing little by little) and may learn better on the choices, but I felt free to present the question by now... Thanks! 68 Answer This is a very good question because it offers the opportunity to talk about a number of interesting things that haven't been touched on yet. Let's cover a bit of background first. 69 A patent is a time limited right to exploit a defined bit of valuable technical knowledge. Patents were involved from the very earliest days of commercial nuclear power, and I will give an example of this later. A key point to keep in mind though is that the nuclear power field moves very slowly and it takes a long time for new knowledge to make it from the lab to commercial application. Patents will often expire before they reach the point where they can be used. 70 Contracts on the other hand are legally enforceable agreements between two parties. A contract may have a time limited life, but that is an arrangement between the parties. A commercial nuclear power plant is a very large and complex bit of kit and not easily copied in detail. It can be far more effective to cover designs under contracts and licenses than to rely on patents. If a country wished to build their own nuclear power plants rather than buying them from someone else, there are a large number of companies who have commercial designs they are willing to license to third parties for them to build themselves. Indeed a number of these companies base their business around licensing of designs or have other reasons for wishing to do so. 71 From a licensee perspective, it could take decades of work and hundreds of millions or even billions of dollars to take a design from first principle to the ready to build state, wheras licensing a design give you a proven design right away. As mentioned in previous episodes, there many types of reactor in the world. The selection of what sort of reactor a country decides to buy often depends more on commercial considerations revolving around licensing terms and conditions than it does with respect to any technical considerations. Here's an example which shows how South Korea decided to license a design, build it for themselves, and then export it to other countries. 72 KunMo Chung - Professor at the Korea Advanced Institute of Science and Technology, stated in an interview in 2019 that South Korea wanted to standardize on a single reactor technology in the early 1980s. They had reactors from multiple different vendors, but wanted to license an existing successful design to produce for themselves and for the export market. One of the major factors in deciding to standardize was to allow them to improve operator training by focusing on one design. Professor Chung stated that one of the key factors in selecting a design from ABB-Combustion Engineering was that he personally knew and had a good relationship with the Chief Technical Officer of ABB-Combustion Engineering going back to a time when Professor Chung had been studying and working in the USA. 73 On their side, ABB-Combustion Engineering were having financial problems and they needed a partner to help further develop their new PWR design. Also they stood to gain revenue from this partnership as well. Based on this relationship, the two sides came to a business agreement and South Korea began producing reactors based on this design, while also continuing to develop and improve it further. 74 Here's an example of a case where the developers of a promising technology decided that they had more to gain by not patenting their technology. Instead they decided to freely share their information in order to get other researchers elsewhere to help to advance the technology so that all could benefit from it. 75 In an interview Wacław Gudowski - Prof. Emeritus, Royal Institute of Technology KTH Stockholm stated that the Soviets and later the Russian were the leaders in lead-bismuth cooled reactors. These reactors use lead-bismuth liquid metal alloy as a coolant. In the 1990s the Russian institute working on commercializing this technology were working with Western partners on nuclear technology in general. They considered patenting this technology, but in the end decided to simply publish it openly. 76 Professor Gudowski had even smuggled $60,000 in cash into Russia to finance the patent application in order to get the Russian institute to publish their technology, but the money was not needed. They based this decision on the judgment that it would take 20 years of R&D before the technology was ready for the commercial market, so they wouldn't see a penny on any patents anyway. They were right on this, as it was another 20 years of R&D in Europe, Russia, China, and Korea before lead-bismuth technology was ready for commercial use. 77 It had already seen use in submarine reactors, but the commercial market demanded a more thoroughly developed technology to satisfy commercial needs. By deciding to not patent the technology, the original developers gained from shared R&D rather than chasing the illusary gains from patent licenses on technology that was not ready for the commercial market anyway. 78 I said that patents were involved in nuclear technology from the very earliest days, and I will now turn to that story. When I say the earliest days, I mean probably earlier than you are imaging. I am talking about before WWII. 79 First though I need to give some background information. France and Britain were working on nuclear weapons from the very earliest days of WWII. In Britain's case this was called Tube Alloys. Canada also was conducting nuclear experiments, including building an "atomic pile", but it's not clear if this had any clear practical goals or was done to understand the physics better. 80 If you read the Wikipedia version of history, it states that Tube Alloys was merged into the Manhattan Project. However, participants have stated in interviews that this was not the case, and the Quebec Agreement which supposedly merged them makes no such mention of any merger of the projects, just the setting up of a board to coordinate efforts between the three countries, that is the US, UK, and Canada. In fact the two projects didn't get along that well, and as we shall see below, a big part of that was disputes over patents. ### 81 The following is based on a paper written by Bertrand Goldschmidt, a French nuclear scientist. Two of his colleagues, Hans Halban and Lew Kowarski played a critical role in early nuclear research. Halban in particular was one of the greatest scientific names in nuclear fission. In March of 1939 Halban conducted an experiment showing that neutrons were emitted by the fissioning of uranium. 82 In April Joliot, Halban, Kowarski and Perrin had a pretty good idea of how to use nuclear fission to produce energy and to make an explosive device and decided to file patents on their invention. Each of the four would receive a 5% share of any benefits and the other 80% would go to the research instittute they worked at in Paris. I will now quote from Goldschmidt's paper. 83 The first two patents concerned energy production and were entitled "Device for energy production" and "Method for stabilizing a device for energy production." They roughly defined the principles of the main components of our present power reactors: moderator in heterogeneous or homogeneous arrangements, cooling fluid, control rods, protection shield. The third patent called "Method for perfecting explosive charges" was less brilliant from a foresight point of view though it proposed valid solutions for the trigger, the tamper, and the rapid obtainment of the critical assembly of a possible explosive device. Finally, nearly a year later, after Alfred Nier's experimental confirmation in March 1940 of Niels Bohr's theoretical prediction that uranium 235, the rare isotope of the mixture in natural uranium, was responsible for fission by slow neutrons, the French took out an additional patent on the advantage of using enriched uranium for the chain reaction. End of quote. 84 In May of 1940, the CNRS, the French research institute in Paris, negotiated an agreement with Belgian mining company Union Miniere, who were the world's biggest producer of uranium, at the time a byproduct of radium mining, about a partnership for the world wide exploitation of these patents. However the agreement was not finalized due to the ongoing events in the war. At the beginning of the war, the French government had approved the development of an energy generator - or a nuclear reactor as we would say today, with the intention of creating an engine for submarines. 85 With the fall of France, Halban and Kowarski travelled to the UK with their supply of heavy water where they were received by their UK counterparts, James Chadwick and John Cockroft. The British were already working on an atomic bomb. In the UK the two conducted an experiment showing that it was possible to create nuclear energy using natural uranium and heavy water. In 1941 the British nuclear project was reorganized and given the name Tube Alloys. In 1942 it was decided to move the work on a plutonium bomb to Canada, and Canada would pay for the project. A lab was set up in Montreal and Halban was put in charge of the project. 86 Halban had negotiated this arrangement by offering to arrange to have the French patents for world wide rights outside of France and the French empire transferred to the UK. In return the French team were to be given a key role in the British nuclear project. The author of the paper I am referencing, Bertrand Goldschmidt, was a section leader in Montreal and a colleague of Halban from France. The Montreal group cooperated with the American Manhattan Project and the two shared information and exchanged visits. 87 However, relations between the two began to break down, with a major cause of this being the Americans being unhappy about the French patents and Halban's arrangement to give the British world wide rights to them. The postwar commercial potential for nuclear power was seen to be huge, and this was a major bone of contention. The extensive participation of ICI (Imperial Chemical Industries) engineers in the Tube Alloys project was also objectionable to the Americans. Presumably this had something to do with potential for ICI being involved in future commercialization of the technology. The American Dupont company, a commercial rival of ICI, was also heavily involved in the American atomic bomb project. The eventual result of this was that the US cut off cooperation with the UK-Canada nuclear project. 88 Finally Halban was forced out of the project at the insistence of the Americans, and he was replaced by John Cockroft who moved to Montreal to take charge of the project. The Americans now restore limited cooperation. Kowarski was put in charge of building a heavy water moderated natural uranium reactor at a new site north of Ottawa at Chalk River. This reactor was turned on on the 5th of September, 1945, three days after Japan's surrender. So in what was supposedly a titanic war for survival, key allies were falling out with respect to their ultimate weapon over issues of patents covering post war commercialization. 89 With the end of the war, the nuclear weapons project in Montreal and Chalk River was wound up. Halban, Kowarski, and Goldschmidt returned to France and Cockroft to the UK where they all played senior roles in the nuclear programs of their respective countries. John Cockroft played an important role in the development of the Magnox reactors which Antoine asked about. The Chalk River Site remains as Canada's main nuclear research centre to this day, and Canada was to continue development of heavy water moderated natural uranium reactors. 90 The first commercial nuclear power plant was commissioned in the UK in 1956, roughly 17 years after the original French nuclear patents. At that time, UK patents had a term of 16 years. While I am not a patent lawyer, it would appear that these patents would likely have expired before nuclear power was ever commercialized. So to answer the question about patents, the first patents on nuclear energy date to before WWII started, and the very first two were about nuclear power plants and it was only the third one which covered nuclear weapons. -------------------- 91 Thanks to other listeners. A number of other listeners made comments saying they were really enjoying the series. I would like to thank the following for their kind words of encouragement. They helped make the work required to do this worthwhile. They are brian-in-ohio mnw Clinton Antoine bjb Kevin O'Brien Trey L'andrew Archer72 Jim DeVore If you have commented but I have forgotten your name, or if the show was recorded before I got a chance to read your comment, I would still like to thank you. 92 Conclusion I would like to thank all the listeners for their kind comments and insightful questions. I hope that I have answered these questions to the satisfaction of everyone. I look forward to hearing from all of you in future podcast episodes including those on other topics. -------------------- Proceedings of the 29th annual conference of the Canadian Nuclear Association and 10th annual conference of the Canadian Nuclear Society. V. 1-3 https://inis.iaea.org/records/m2s41-40917 This has a paper by Bertrand Goldschmidt about the work of the French scientists in Canada. -------------------- Provide feedback on this episode.

Who was Edgar Queeny, the cold-eyed social Darwinist who inherited a chemical company from his father and turned it into one of the most powerful industrial forces in American history? Why did the man who helped trigger the atomic bomb, kept the Arsenal of Democracy running, and photographed brown bears in Alaska with Walt Disney also preside over a factory in West Virginia where workers were quietly being poisoned — and then denied compensation? And how does the story of a chemical explosion in Coal Country, a misfiled letter about dioxin, and thirteen men who couldn't afford to say no open a window onto the darkest chapter in the history of American agriculture; from the Manhattan Project to the jungles of Vietnam?Join John and Patrick for the second episode of their Monsanto series — the PCBs, the explosion at Nitro, the workers whose wives caught chloracne from their husbands' clothes, and the herbicide that was about to go to war — in an age when the company that made the bomb also made the weedkiller on your lawn...----------In Sponsorship with J&K Fresh.The customs broker who is your fruit and veggies' personal bodyguard. Learn more here!-----------Join the History of Fresh Produce Club for ad-free listening, bonus episodes, book discounts and access to an exclusive chatroom community.Support us!Share this episode with your friendsGive a 5-star ratingWrite a review-----------Subscribe to our biweekly newsletter here for extra stories related to recent episodes, book recommendations, a sneak peek of upcoming episodes and more.-----------Instagram, TikTok, Threads:@historyoffreshproduceEmail: historyoffreshproduce@gmail.com

Hard News on Friday with Tara Green and Rama Arjuna and guests Celestial Convergence, Navigating the Shift to Soul Identity This podcast episode explores the profound spiritual and astrological shifts occurring in late April 2026, centered on a rare celestial alignment and the transition from "wounded human" consciousness to "soul identity." The discussion blends Mayan calendar wisdom, galactic weather reports, and channeled guidance to support listeners through a period of intense energetic transformation. The Mayan Record and Galactic Weather The discourse begins with a ceremonial invocation of the sacred galactic directions, setting a tone of harmony and planetary healing. The current period is identified as a "double warrior" phase in the Mayan tradition, emphasizing equality, organization, and fearlessness in questioning existing structures. This spiritual framework coincides with significant "galactic weather," specifically the perihelion of Comet R3 Pan-STARRS—a celestial body with a 170,000-year orbit—and recent X-class solar flares that are intensifying the Earth's atmospheric and Schumann resonance. The Great Conjunction: A 12,000-Year Cycle A central theme of the discussion is the extraordinary astrological alignment of Venus, Uranus, the Pleiades, and the trans-Neptunian object Sedna. This specific configuration is described as a "once-every-12,000-year" event that signals a radical shift in human evolution. While Uranus acts as a "Promethean" agent of liberation and disruption, Sedna provides a counterbalancing frequency of deep peace and crystalline light. Historical Context and Global Shifts The speakers analyze previous occurrences of similar conjunctions (dating back to 1606, 1773, and 1942), noting a recurring pattern of exploration, rebellion, and shifts in global commerce. Historical touchpoints include the founding of Jamestown, the Boston Tea Party, and the Manhattan Project. These cycles suggest that the current alignment will likely disrupt international trade, financial systems, and energy technologies, moving humanity toward a more equitable "new earth" model. Channeled Guidance: From Wounded to Whole Channeled messages from the Pleiadian consciousness emphasize a 48-hour window of intense "liver cleansing" and the dissolution of the "witch wound" and other ancient traumas. The goal of this energetic influx is to shift human focus away from "trying to fix" the wounded self and toward surrendering to the soul's identity. This transition is described as the "Phoenix rising," where the mind becomes the servant of the heart, and the soul is fully anchored within the physical body rather than hovering in the higher energy centers. The episode highlights a pivotal moment of cosmic alignment that invites humanity to move beyond historical cycles of trauma. By embracing the incoming "divine feminine" frequencies and anchoring the soul within the physical body, individuals can participate in the co-creation of a balanced and harmonious "New Earth."

This is one of my favorite books over recent years. Sebastian Mallaby is the Paul A. Cocker Senior Fellow for International Economics at the Council of Foreign Relations and author of 6 bestselling books. THE INFINITY MACHINE tells the story of AI's progress over the past 15 years largely, but not exclusively, from Demis Hassabis as the protagonist and leader of DeepMind', with its 2010 mission statement to achieve superintelligence by 2030. It's a rich, informative, page turner.What We Discussed:—What is an Infinity Machine?—Influence of Claude Shannon's Information Theory and Douglas Hofstadter's Pulitzer Prize winning book Gödel, Escher, Bach—Origin of DeepMind in 2010. Prescient. Charter, business plan, included use of agents. How Demis Hassabis was made for the mission!—Contrasts with Sam Altman and the other AI leaders, the Oligopoly (cover of The Economist this week). For example, Nature papers vs white papers on company websites. —In March 2016, the same day when DeepMind's AlphaGo beat Lee Sedol, Hassabis says it's time to do protein folding (later known as AlphaFold).—Symbolic AI (historic, deductive, rule-based) vs Deep Learning (Toronto tribe) and Reinforcement Learning (Alberta tribe).—The Big Miss: DeepMind's lack of early recognition of the importance of transformer models (leading to ChatGPT), creating a big opening for OpenAI. And why was this missed? The Comeback Story. Is this happening again with coding (not in the book)?—The AI Arms Race and Hyperscaling—How the complex relationship between Google and DeepMind evolved —The Double Cross —With the dangers anticipated (parallels to Oppenheimer, Manhattan Project, and the atomic bomb), how to promote AI safety?—Is the major build up of data centers justified?Thank you Bob Fleischman, Jeanie, Ruben Max, FelonBroke America, Seitzinator ❌

The White House quietly opened decades of federal data to AI, and the headlines barely flinched. We did. The move is being framed through the Genesis Mission, and it signals something bigger than “cool new tech”: agentic AI plus massive datasets is about to reset what customers expect from every company, including the smallest local business trying to stay ahead of the next curve. We connect the dots using a Manhattan Project scale analogy, then bring it down to street level with practical AI for small business workflows. If the government can combine data and experts to solve national problems like health, energy, manufacturing, biotech, and safety, we can do our own version with the data already sitting in our tools. We talk through concrete examples: exporting Google Calendar history to find patterns, using customer questions as early market signals, drafting proactive service emails, analyzing invoices to identify your best customers, and creating “big wins for little input” that improve revenue without chasing new leads. We also dig into measurable pattern spotting with QuickBooks and Stripe so we can move from gut feelings to early warning indicators, plus smart hiring tactics that use your top performers as a model for better job descriptions. If you want to future proof your business, get comfortable using LLMs like ChatGPT or Gemini as a thinking partner and start stacking small experiments today. Subscribe, share this with a business owner who needs a head start, and leave a review with the one dataset you want to analyze first.Support the show

Cloak and Dagger (1946) is one of the most overlooked spy films ever made — and this episode proves it deserves a second look. Dan and Tom decode this gritty WWII thriller starring Gary Cooper and directed by Fritz Lang. It's raw, tense, and surprisingly realistic. Before James Bond made espionage glamorous, this film showed what it really looked like. No gadgets. No tuxedos. Just survival. A physics professor is yanked from the Manhattan Project and sent to Europe — with no training and no safety net. His mission: find out how close Nazi Germany is to building an atomic bomb. That fear was real. The stakes were enormous. We dig deep into what makes this film stand out: the brutal, visceral staircase fight scene, the film noir cinematography by Sol Polito, and Max Steiner's understated score. We also explore the Hitchcock parallels, the Bond connections, and the censored anti-nuclear ending Fritz Lang never got to film. Episode highlights: ·      

“Doing science is like reading the mind of God.” — Demis Hassabis, quoted in The Infinity MachineThis week's New Yorker uncomplimentary profile of OpenAI's CEO is entitled “The Many Faces of Sam Altman.” But not all AI leaders are quite as many faced as slippery Sam. Take, for example, Demis Hassabis, the North London based co-founder and CEO of Google's DeepMind. In his new biography, The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence, the British journalist Sebastian Mallaby argues that Hassabis is, in contrast, one faced. And that face is not only decent, but informed by the enlightened ethics of Baruch Spinoza and Immanuel Kant.Mallaby presents Hassabis as the anti-Altman. He's stayed at DeepMind for sixteen years, lived in the same London house, drives a decade-old car. Rather than power, Google's AI supremo seeks scientific enlightenment. Like Spinoza, his God is the master watchmaker of the universe. And so doing science, Hassabis explained to Mallaby in one of their many conversations in the backroom of a North London pub, is like reading the mind of God. Decent Demis. Honest Hassabis. Let's just hope this modest and thoughtful tech leviathan can bring Kantian ethics to Silicon Valley's sprint for artificial general intelligence. Five Takeaways•       Hassabis Is the Anti-Altman: Sam Altman has managed to annoy almost everyone he's worked with by saying one thing and doing the opposite. Hassabis has run DeepMind continuously for sixteen years, lives in the same house in Highgate, drives a decade-old car, and spends his discretionary money on Liverpool season tickets. He doesn't want power. He wants scientific enlightenment. Mallaby uses the word advisedly.•       Doing Science Is Like Reading the Mind of God: Hassabis is a Spinozan. The god he believes in is the god Einstein talked about — the fabric of reality understood through scientific inquiry. He reads Kant, he reads Spinoza, he reads widely enough to be a proper polymath. Mallaby sat with him in a Highgate pub for more than thirty hours. What he found was not a Silicon Valley sociopath but an enlightenment figure who thinks AI is the modern version of the telescope.•       The Szilard Pedestrian Crossing: Mallaby asked Hassabis what it felt like to set up DeepMind in 2010. Instead of the usual vague answer, Hassabis painted the scene: the attic office on Russell Square, the heat, the stairs, the greenery outside, the London Mathematical Society three doors down where Turing lectured, and the zebra crossing where the Hungarian physicist Leo Szilard conceived of the nuclear chain reaction in the 1930s. The perfect metaphor: DeepMind as the modern Manhattan Project.•       The Two Categories of Things That Go Wrong: There's the idiot-in-charge category — an evil or stupid person making bad decisions, and you could swap them out. Then there's the structural category: a good person trying their best, defeated by larger forces they cannot control. Hassabis is category two. He wants to make AI safe, but race dynamics between US and China labs make safety nearly impossible to deliver. The failure of governments to intervene is the real story. Not individuals.•       The Go Players Who Quit: When AlphaGo beat the best players in the world, some professional Go players retired — centuries of accumulated human understanding devalued overnight. Others kept playing, using the machine as a tutor to discover patterns they'd never seen. Two responses to superintelligence in one domain. One is mourning. The other is curiosity. Mallaby thinks the second response is the only one worth having. Hassabis agrees. About the GuestSebastian Mallaby is the Paul A. Volcker senior fellow for international economics at the Council on Foreign Relations. A former Washington Post columnist and Economist contributing editor, he is the author of More Money Than God, The Man Who Knew (winner of the FT and McKinsey Business Book of the Year), The Power Law, and now The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence.References:•       The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence by Sebastian Mallaby.•       Episode 2862: Truth Is Dead — Steven Rosenbaum on AI as a spectacularly good liar. Mallaby's quiet counter-argument.•       Episode 2860: We Shape Our AI, Thereafter It Shapes Us — Keith Teare on agency in our agentic age. Hassabis thinks he can still steer.About Keen On AmericaNobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 2,800 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting.WebsiteSubstackYouTubeApple PodcastsSpotify Chapters:(00:31) - Introduction: the many faces of Sam Altman (02:00) - Altman's duplicity versus Hassabis's consistency (02:56) - The moral wrestling: is this the Manhattan Project? (04:45) - The ordinary genius in Highgate (06:29) - The Szilard pedestrian crossing and a storyteller off the charts (09:10) - Responding to The Guardian: why Hassabis isn't Altman (12:58) - The two categories of things that go wrong (14:48) - Mustafa Suleiman's remarkable backstory (17:01) - Did Demis fire Mustafa? (19:46) - Class, Eton, and the North London grammar school (22:27) - Spinoza, Kant, and the god of science (25:27) - Doing science is like reading the mind of God (29:57) - Why not Princeton? The money problem (34:12) - The secret DeepMind vs Google negotiation (43:11) - Is Hassabis the next CEO of Google? (48:05) - The Go players who quit

In this Legacy Series episode of NucleCast, Manolis Priniotakis explores the history and evolution of nuclear intelligence—from its origins during the Manhattan Project to the sophisticated capabilities shaping nuclear security today. Manolis brings deep insight into the missions, institutions, and technologies that have defined this critical field.The conversation traces early efforts such as the Alsos Mission in World War II, the transition from military-led intelligence to civilian agencies, and the expanding role of organizations like the Department of Energy and the Office of the Director of National Intelligence. We also examine how advances in nuclear forensics have strengthened attribution and nonproliferation efforts, while highlighting the challenges posed by emerging technologies and evolving threat environments.Socials:Follow on Twitter at @NucleCastFollow on LinkedIn: https://linkedin.com/company/nuclecastpodcastSubscribe RSS Feed: https://rss.com/podcasts/nuclecast-podcast/Rate: https://podcasts.apple.com/us/podcast/nuclecast/id1644921278Email comments and topic/guest suggestions to NucleCast@anwadeter.org

We've got some hoppy Texas beers from Austin's Meanwhile and Dallas's Manhattan Project for this one! Pairs with mammograms, hamograms, lamograms, all the Clue weapons, and HEBuddy. Meanwhile Lil' Joy Meanwhile Tender Robot Manhattan Project Half Life Manhattan Project Double Half Life Theme Music by Adrian Quesada of Black Pumas End Credits Music: High by Loving Caliber Additional music licensed through Epidemic Sound And we have shirts! Get them at the Hello Crawlers store! The Beerists are John Rubio, Grant Davis, Pam Catoe, and Mark Raup. Subscribe on Apple Podcasts, Google Podcasts, or point your podcatcher to our RSS feed. You should also subscribe to our YouTube Channel. Support us by making a per-episode pledge at patreon.com/thebeerists and get some sweet rewards! Follow us on twitter, facebook, and instagram. Want to send us beer? Check our beer donation guidelines, and then shoot us and email at info@thebeerists.com

Are you someone who prefer the old episodes over the later seasons?  If you're that person, The One with the Interview is a clip show, littered with clips from the old episodes, featuring mostly Joey highlights.  We are also talking about the One with the Baby Shower, which has a baby shower in it, and a wonderful game show called Bamboozled.  And no, we are not talking about the Spike Lee movie.   We would love to know some of your favorite games. Reach us at april5k@gmail.com https://seinfeldpodcast.libsyn.com/website www.patreon.com/wrightonnetwork  

Watch the best scene reading: https://www.youtube.com/watch?v=sYUWG_PJSLY A grieving tourism commissioner and a physicist on probation rescue a relic of the Manhattan Project from the world's largest landfill. Get to know the writer: What is your screenplay about? The Demon Core of Fresh Kills is the story of a grieving tourism commissioner and a physicist on probation, who discover the reason for their failures and frustrations in life are tied to a supernatural relic of the Manhattan Project, which they have to rescue from Fresh Kills Landfill in Staten Island. What genres does your screenplay fall under? It's science fiction, in the form of an alternate history of the borough of Staten Island in New York City. It's told as a mockumentary. —— Subscribe to the podcast: https://twitter.com/wildsoundpod https://www.instagram.com/wildsoundpod/ https://www.facebook.com/wildsoundpod

Author Michelle Shocklee discusses her novel The Women of Oak Ridge and the personal, historical, and emotional paths that led her to the story of the Secret City during the Manhattan Project. Growing up near Los Alamos and coming from a World War II family shaped her long-standing connection to the era, but a chance encounter with a reader ultimately sparked her discovery of Oak Ridge. Shocklee describes her deep research process, drawing on oral histories, archival photographs, museum resources, and firsthand accounts to authentically portray life inside a city built on secrecy. She explains how she weaves history into fiction by grounding the narrative in the lived experiences of her characters, particularly women whose wartime work reshaped their futures. The conversation highlights the challenges of secrecy, segregation, and stress faced by Oak Ridge residents, as well as the lasting impact of women entering the workforce during the war. Ultimately, Shocklee reflects on Oak Ridge as both a hidden chapter of history and a powerful source of human resilience and transformation.

The administration's Genesis Mission aims to build an integrated ecosystem that connects supercomputers, AI systems and emerging quantum capabilities, calling on the Energy Department and its 17 national laboratories. Argonne National Laboratory's broad multidisciplinary research portfolio positions it to harness AI to accelerate scientific discovery, Associate Laboratory Director for the Computing, Environment and Life Sciences (CELS) Directorate Rick Stevens told GovCIO Media & Research. Stevens compared the urgency of the Genesis Mission to the Manhattan Project, citing intensifying global competition and rapid advances in AI. He also highlighted Argonne initiatives such as Synapse, which uses AI to accelerate imaging analysis, and Quarks to Cosmos, an effort to integrate large physics datasets to enable new scientific insights.

I'm back this week with the promised second part to my Women in STEM special. This week, we'll explore the stories of 6 more women who changed the world, beginning with Genevieve Grotjan Feinstein who cracked the elusive Japanese Purple code during World War II. Chien-Shiung Wu made breakthrough discoveries in physics and helped develop the first atomic bomb with her critical involvement in the Manhattan Project. Katherine Johnson helped put the first man in orbit and send men to the moon. Sally Ride became the first American woman in space followed shortly after by Mae Jemison, the first African American woman in space. And, a cameo you may not be expecting, Judith Love Cohen, mother of actor and musician Jack Black, helped bring the astronauts home during the failed Apollo 13 mission to the moon. Support the show! Join the Patreon (patreon.com/historyfixpodcast)Buy some merchBuy Me a CoffeeVenmo @Shea-LaFountaineSources: Wikipedia "Genevieve Grotjan Feinstein"National Security Agency "Genevieve Grotjan Feinstein"National Women's History Museum "Chien-Shiung Wu"NASA "Katherine Johnson Biography"National Women's History Museum "Sally Ride"NASA "Sally Ride"National Women's History Museum "Mae Jemison"Wikipedia "Judith Love Cohen"Shoot me a message! Support the show

The Manhattan Project is back to talk about the gang, including Alec Baldwin's character, Parker, who doesn't believe in curbing his enthusiasm, taking a trek to Massapequa, where the Gellers, not Ross and Rachel, are celebrating their 35th wedding anniversary.  Write in to april5k@gmail.com if you would like to share any cool wedding anniversary celebrations, or if you would like to share your favorite F*R*I*E*N*D*S guest cast member.  We would love to hear from you!  And what is a topic that you can't curb your enthusiam about lately??? https://seinfeldpodcast.libsyn.com/website www.patreon.com/wrightonnetwork @april5k on bluesky  

The Radiation Exposure Compensation Act was expanded in July to include 21 ZIP codes impacted by atomic waste leftover from the Manhattan Project dumped in different parts of the St. Louis region. The move followed years of advocacy and alarm from residents like Dawn Chapman, co-founder of Just Moms STL, who acted after seeing her neighbors and relatives become sickened with cancers. Since RECA's expansion, the Department of Justice has received more than 11,000 claims and paid out more than $63 million. More people are still applying, but their cases and medical conditions can be complex. Many have found aid from Brent Trout, manager of the St. Louis County Library's history and genealogy department. Trout and Chapman discuss the challenges of applying to RECA, why records like yearbooks can be critical evidence, and why advocates are encouraging people to apply before the program's deadline in 2027.

The most important political question in the age of advanced AI might not be who wins elections. It might be whether elections continue to matter at all.That's the view of Rose Hadshar, researcher at Forethought, who believes we could see extreme, AI-enabled power concentration without a coup or dramatic ‘end of democracy' moment.She foresees something more insidious: an elite group with access to such powerful AI capabilities that the normal mechanisms for checking elite power — law, elections, public pressure, the threat of strikes — cease to have much effect. Those mechanisms could continue to exist on paper, but become ineffectual in a world where humans are no longer needed to execute even the largest-scale projects.Almost nobody wants this to happen — but we may find ourselves unable to prevent it.If AI disrupts our ability to make sense of things, will we even notice power getting severely concentrated, or be able to resist it? Once AI can substitute for human labour across the economy, what leverage will citizens have over those in power? And what does all of this imply for the institutions we're relying on to prevent the worst outcomes?Rose has answers, and they're not all reassuring.But she's also hopeful we can make society more robust against these dynamics. We've got literally centuries of thinking about checks and balances to draw on. And there are some interventions she's excited about — like building sophisticated AI tools for making sense of the world, or ensuring multiple branches of government have access to the best AI systems.Rose discusses all of this, and more, with host Zershaaneh Qureshi in today's episode.Links to learn more, video, and full transcript: https://80k.info/rhThis episode was recorded on December 18, 2025.Chapters:Cold open (00:00:00)Who's Rose Hadshar? (00:01:05)Three dynamics that could reshape political power in the AI era (00:02:37)AI gives small groups the productive power of millions (00:12:49)Dynamic 1: When a software update becomes a power grab (00:20:41)Dynamic 2: When AI labour means governments no longer need their citizens (00:31:20)How democracy could persist in name but not substance (00:45:15)Dynamic 3: When AI filters our reality (00:54:54)Good intentions won't stop power concentration (01:08:27)Slower-moving worlds could still get scary (01:23:57)Why AI-powered tyranny will be tough to topple (01:31:53)How power concentration compares to "gradual disempowerment" (01:38:18)Some interventions are cross-cutting — and others could backfire (01:43:54)What fighting back actually looks like (01:55:15)Why power concentration researchers should avoid getting too "spicy" (02:04:10)Why the "Manhattan Project" approach should worry you — but truly international projects might not be safe either (02:09:18)Rose wants to keep humans around! (02:12:06)Video and audio editing: Dominic Armstrong, Milo McGuire, Luke Monsour, and Simon MonsourMusic: CORBITCoordination, transcripts, and web: Nick Stockton and Katy Moore

Are employers hurting themselves by only asking for 'soft skills' and ignoring their real technical needs? How can homeschool students get the same access to STEM labs as students in traditional schools? And what can education leaders learn from the way the Manhattan Project mobilized talent and innovation to solve an enormous problem?These questions (and more!) came directly from the you, and we're answering them on this episode of Ask Us Anything. Entrepreneurship, career strategy, workforce skills, and the rapidly evolving role of AI in modern organizations - we cover them all!In this episode:The best times to take entrepreneurial risksWhy your professional network is probably bigger than you thinkThe "soft skills" issue and why employers are actually hurting themselves by asking educators to teach themRapid-fire real examples of AI being used in business3 Big Takeaways from this Episode:1.Early-career risk can be an advantage for entrepreneurs. Matt explains that early in your career the consequences of failure are often much smaller, which makes it an ideal time to experiment with starting a business or pursuing a bold opportunity. With fewer financial obligations and more flexibility, young professionals often have the greatest ability to take meaningful risks.2. Industry is hurting itself by only asking schools for soft skills instead of technical ones. Businesses frequently tell educators they want graduates who communicate well, collaborate, and show initiative. So why is industry so shocked that there aren't enough students with any technical background or interest? Employers: take a look at your job postings and start asking education to teach all the skills for those jobs: soft and technical (hard) skills alike.3. Private schools have a unique opportunity to innovate in STEM education. Because they often have more flexibility than traditional public systems, private schools can move quickly to adopt emerging technologies, modern equipment, and new instructional models. That freedom creates an opportunity to design programs that expose students to advanced STEM fields earlier and more creatively.Resources in this Episode:Jack Dorsey's Block to Lay Off 40% of Its Workforce in AI RemakeOppenheimer movieDeveloping an AI Strategy: Best Practices for Business Leaders - Todd Wanek, CEO of Ashley Furniture IndustriesUsing AI to Build Better Relationships with Your Network - Canay Deniz, CEO of RenMore notes & resources on the episode page!We want to hear from you! Send us a text.Instagram - Facebook - YouTube - TikTok - Twitter - LinkedIn

The U.S. Department of Energy spends roughly $8 billion annually cleaning up nuclear waste from Manhattan Project-era sites like Hanford, Washington and Oak Ridge, Tennessee. On this episode of the Environmental Transformation Podcast, host Sean Grady sits down with Steve Moore, president and CEO of Veolia Nuclear Solutions Federal Services Group, to discuss the agency's most pressing environmental liabilities and innovative remediation technologies.Moore describes the scale of the challenge: 56 million gallons of high-level radioactive waste currently stored in above-ground tanks at Hanford alone. He explains how Veolia's patented GeoMelt vitrification technology transforms reactive metals and radioactive waste into stable glass, and discusses the company's operations of two of the largest radioactive waste landfills in the nation.The conversation covers emerging opportunities to revitalize federal sites as data centers and advanced reactor facilities, the "competetition" model where contractors collaborate on complex projects, and why the nuclear remediation field offers meaningful careers for young professionals seeking challenging environmental work.

AI enthusiasts love to say that the technology is as revolutionary and important as nuclear weapons. Even the Trump administration has adopted the metaphor. The President and the Department of Energy have repeatedly referred to the development of AI in the US as “Manhattan Project 2.0.”But is the buildout of LLMs and machine learning systems really as important as the development of the atom bomb? And what are the lessons from the atomic age that AI scientists should then learn? Do we need an AI Non Proliferation Treaty? An AI International Atomic Energy Agency?On this episode of Angry Planet, Ankit Panda comes on to talk about the uses and limitations of the “AI as nuclear weapons” metaphor. Panda is an expert in nukes and a Senior Fellow at the Carnegie Endowment for International Peace. He's been sharing his extended thoughts on the AI-nuclear connection at his Nukesletter Substack.Stanislav PetrovAI as nuclear weaponsWhy nuclear weapons resonate with people in the AI fieldThe Strategic Air Command storyThat time we spilled nuclear material all over Greenland and SpainNNSA and AnthropicAI as the next Manhattan ProjectA massive infrastructure projectFissile material as siliconWhat's the AI version of an NPT and IAEA?AI and nuclear are both dual useOn AI wintersWhat AI is actually being used for, what it might be used forThe socialization around AI will change.AI Arms and Influence: Frontier Models Exhibit Sophisticated Reasoning in Simulated Nuclear CrisisSupport this show http://supporter.acast.com/warcollege. Hosted on Acast. See acast.com/privacy for more information.

We're back to talk about is it a Faded Salmon shirt or a Pink Shirt?  Write into april5k@gmail.com to let us know. Also, do you still have your Compact Discs?  Spin Magazine has an article about how CDs are still huge in the United Kingdom.  And were the Tea Leaves correct?  Will Phoebe find love via the Universe?  And please, let the healing begin between Joey and Rachel.   https://seinfeldpodcast.libsyn.com/website www.patreon.com/wrightonnetwork  

90: Epstein's Nephilim Children: Zorro Ranch, Eugenics & Occult Rituals - The Jeffrey Epstein case shocked the world with revelations of elite sex trafficking, blackmail, and connections to some of the most powerful figures on earth. But beyond the headlines lies a deeper story filled with strange symbols, secretive belief systems, and locations tied to occult traditions that few investigators have explored.Joel returns with a new episode examining the hidden layers of the Epstein network, beginning on Little St. James Island in the Caribbean. He breaks down the strange architecture of Epstein's infamous temple — its painted door, golden dome, and alignment with geomagnetic north — and explores ancient traditions of apotropaic magic, spirit-warding symbols, and occult architecture. Could the island's structures have served a symbolic or ritual purpose far beyond what mainstream media reported? Next, the investigation moves to New Mexico and Epstein's sprawling Zorro Ranch, located in a region long associated with UFO sightings, underground base rumors, and the birthplace of the atomic bomb. Joel examines the connections between Epstein's ranch and Los Alamos, the Manhattan Project, and the controversial rocket scientist and occultist Jack Parsons, whose infamous Babalon Working rituals sought to summon supernatural forces into the physical world. Finally, Joel explores Epstein's reported obsession with genetics and eugenics, including claims that he planned to seed humanity with his own DNA. From occult symbolism embedded in Epstein's properties to ancient legends about the artificial creation of life — known as the Homunculus — this episode asks a disturbing question: was Jeffrey Epstein simply a criminal financier… or part of a much older tradition seeking power through ritual, science, and hidden knowledge?Merchandise: https://freetherabbits.myshopify.comBuy Me A Coffee: DonateFollow: Website | Instagram | X | FacebookWatch: YouTube | RumbleMusic: YouTube | Spotify | Apple Music Films: https://merkelfilms.com Email: freetherabbitspodcast@gmail.comDistributed by: merkel.mediaIntro Music:Joel Thomas – Free The RabbitsYouTube | Spotify | Apple MusicOutro Music:Joel Thomas – Imago DeiYouTube | Spotify | Apple MusicTopics discussed in this episode: Jeffrey Epstein, Little St. James Island, Epstein Temple symbolism, occult architecture, apotropaic magic, Zorro Ranch New Mexico, Manhattan Project history, Los Alamos secrets, Jack Parsons occult rituals, Babalon Working, Aleister Crowley, homunculus legends, occult symbolism in elite power networks, underground bases, UFO history in New Mexico, esoteric science and secret societies.

We talk pain meds, the Manhattan Project, and rodents!See omnystudio.com/listener for privacy information.

What are large language models, really? A genuinely different frame for what AI is, where it came from, and what it means to live alongside it. This episode of Permanently Moved is an hour-long audio essay on artificial intelligence, agency, and the history of computing that made LLMs possible. The essay moves from the invention of the mirror to double-entry bookkeeping, the printing press, the Manhattan Project, the transistor, and the particular civilisational strangeness of ChatGPT and its successors. It argues that the question everyone is asking about AI "is it intelligent?" is a trap, and it tries to ask a better one.  START SELECT RESET Issue 15 of SSRZ, the print companion to this episode, is available to pre-order now until Monday 16th March. 36 pages, A5, containing the full transcript plus an introduction and afterword not in the audio. Membership (£5/month) shipping included: https://thejaymo.net/support/ Pre-order the zine (£18 + shipping): https://thejaymo.etsy.com/uk/listing/4465040020/monsters-in-the-mirror-start-select CHAPTERS (00:00:02) Introduction (00:00:34) Monsters In The Mirror (00:01:00) Part 1. The Mirror of Modernity (00:05:33) Part 2. The State's Gaze (00:11:28) Part 3. Paper Truths (00:17:27) Part 4. Crystal Fire (00:26:13) Part 5. The Silicon Mirror (00:31:54) Part 6. World(view) in the West (00:35:28) Part 7. Haunted Habitats (00:40:45) Part 8. Thinking Machines (00:49:28) Part 9. Abracadabra (00:54:26) Part 10. Monsters in the Mirror (00:59:03) Afterward (01:00:05) Support the Show (01:00:48) Credits Mentioned in this episode Tyson Yunkaporta — Sand Talk James C. Scott — Seeing Like a State Marion Fourcade & Kieran Healy — The Ordinal Society Marek Poliks & Roberto Alonso Trillo — Exocapitalism Marshall McLuhan — The Gutenberg Galaxy Walter Ong — Orality and Literacy Jean Baudrillard — Simulacra and Simulation Nick Land — Fanged Noumena Monica Gagliano — Thus Spoke the Plant Benedict Anderson — Imagined Communities Bayo Akomolafe Gordon White — Rune Soup Permanently Moved is a quarterly podcast written, recorded, and edited by Jay Springett.  --- SHOW NOTES https://thejaymo.net/2026/03/02/302-monsters-in-the-mirror-permanently-moved/ FIND EVERYTHING ELSE thejaymo.net: https://thejaymo.net/ Experience.Computer: https://experience.computer/ Worldrunning.guide: https://worldrunning.guide/ Subscriber Zine support the show! https://startselectreset.com/

In this episode of The Paragould Podcast, we sit down with John Bland to talk about the history and legacy of his father, Francis Bland, a man whose influence is still woven into the fabric of our city. From the widening of 8 Mile Creek to help prevent flooding, to Bland Baseball Park, to the Dr. Pepper plant that employed hundreds of local families, the Bland name has played a meaningful role in shaping Paragould. We also explore the lesser-known story of Francis Bland as a chemist who was interviewed for the Manhattan Project during World War II and how life unfolded from there. More than a history lesson, this is a conversation about family, leadership, work, and the quiet ways one life can leave a lasting impact on a community. If you love Paragould and the stories behind the people who helped build it, this episode is for you.

The American Physical Society presents itself as a nonprofit dedicated to advancing scientific knowledge. Its financial disclosures tell a larger story. With over $106 million in annual revenue and more than $313 million in accumulated assets, APS operates at the center of a permanent scientific infrastructure funded largely by government-backed research institutions.At the helm is Jonathan Bagger, a theoretical physicist whose career has spanned Johns Hopkins University, the SLAC National Accelerator Laboratory, and the international accelerator network, including TRIUMF. These laboratories were not temporary research projects. They were built as permanent federal infrastructure, tracing their lineage to the Manhattan Project and later expanding during the Cold War under programs like the Strategic Defense Initiative.SLAC itself operates under the authority of the U.S. Department of Energy, part of a national laboratory system created to sustain high-energy physics capability across generations. Scientists move through these laboratories, into universities, and eventually into leadership roles within scientific governing bodies. APS sits above that system, publishing the journals, hosting the conferences, and formalizing the research produced by government-funded institutions.The story extends further back, to defense research networks that predate the Cold War, including scientists connected to early Marconi research efforts. These networks evolved over decades, forming a continuous institutional framework linking laboratories, universities, nonprofit scientific societies, and government agencies. This episode examines that framework. Not the discoveries themselves, but the infrastructure behind them. The laboratories that remained. The organizations that accumulated influence. And the nonprofit society at the center of the scientific establishment.Email: thefacthunter@mail.com

The North American Waterfowl Management Plan--a continent-wide, multidisciplinary, science-based partnership--is the envy of the world. But are key elements silently eroding? Are there future assurances? Jim Sedinger, Rick Kaminski, and Dan Connelly introduce concept for the North American Waterfowl Science Center and explain why it exists: to rebuild expertise, coordinate continental research, mentor future generations, and keep science-based decisions grounded in hunters and habitat. We discuss northern pintails--and why they may expose cracks in the existing system-- harvest controversy, disappearing waterfowl experts, hollowed-out research universities, glory-day research programs,mentoring future generations, fragile federal funding, the private sector's growing role in protecting independent science, and more--to include why we duck hunters should care. ********** For more info: coverton@calwaterfowl.org ********** Visit the Legendary Brands That Make MOJO's Duck Season Somewhere Podcast Possible: MOJO Outdoors  Alberta Professional Outfitters Society Benelli Shotguns Bow and Arrow Outdoors Ducks Unlimited  Flash Back Decoys GetDucks.com Migra Ammunitions onX Maps  Use code GetDucks25 to save 25% Sitka Gear SoundGear Use code GetDucks20 to save 25% Tom Beckbe USHuntList.com   Like what you heard? Let us know! • Tap Subscribe so you never miss an episode. • Drop a rating—it's like a high-five in the duck blind. • Leave a quick comment: What hit home? What made you laugh? What hunt did it remind you of? • Share this episode with a buddy who lives for duck season.   Want to partner? Have or know a story to share? Contact: Ramsey Russell ramsey@getducks.com  

Allen and RD try Plutonium 239 by Manhattan Project Beer out of Dallas Texas. 6.2% ABVThanks for watching!Cheers!#beer #craftbeer #strikeoutbeerBecome a supporter of this podcast: https://www.spreaker.com/podcast/strikeout-beer--2992189/support.

The Boys continue the story of the Du Pont dynasty as they evolve from World War I profiteers into architects of the modern age, embedding themselves in everything from General Motors to the chemicals in your very own bloodstream. From leaded gasoline and the coup to overthrow Franklin D. Roosevelt to their role in the Manhattan Project and napalm, the 20th century becomes a Du Pont production. War, coups, forever chemicals - profit at every step, with no accountability. For Live Shows, Merch, and More Visit: www.LastPodcastOnTheLeft.comKevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 4.0 Licensehttp://creativecommons.org/licenses/by/4.0/Subscribe to SiriusXM Podcasts+ to listen to new episodes of Last Podcast on the Left ad-free, plus get Friday episodes a whole week early. Start a free trial now on Apple Podcasts or by visiting siriusxm.com/podcastsplus. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This week, we discuss the Demon Core - a plutonium-gallium alloy sphere that was part of the Manhattan Project and involved in two fatal accidents.

BASED ON A TRUE STORY (BOATS EP. 383) — Did the Oppenheimer movie get the Manhattan Project right? Today, we'll dig into the film's portrayal of the project. From the Los Alamos we see in the movie to what we don't see in the movie such as Oak Ridge's massive factories, Hanford's plutonium production, and more that you don't ever see in the movie.Get Alice's bookAlice Lovejoy is the author of Tales of Militant Chemistry, film scholar, and a professor at the University of Minnesota. She'll join us today to unravel the true story behind the Manhattan Project in the movie.Find more of Alice's workArmy Film and the Avant GardeRemapping Cold War MediaVisit Alice's websiteFollow Alice on BlueskyFollow Alice on InstagramListen to the other BOATS episode on OppenheimerChapters00:00 Manhattan Project Overview04:50 Uranium vs. Plutonium Bombs09:45 Scale of the Project15:28 Einstein's Role20:42 Trinity Test Depiction25:04 Radiation Concerns30:51 Project's End & Legacy33:52 Post-War ImpactsSupport my workSupport my sponsorsBecome a BOATS ProducerEmail me: dan@basedonatruestorypodcast.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Today I'm joined by Leslie Schover, clinical psychologist turned novelist and author of Fission: A Novel of Atomic Heartbreak.Set during the Manhattan Project in Oak Ridge, Tennessee, Fission explores love, ambition, secrecy, identity, and moral conflict at a moment when the world was being reshaped—both scientifically and emotionally.Drawing on her parents' lived experiences and her own deep understanding of relationships, Leslie brings a uniquely human lens to one of history's most consequential chapters.From Family Stories to Historical FictionFission is rooted in the stories your parents told about life in Oak Ridge during the Manhattan Project. When did you first realize these personal memories could become a novel—and what shifted for you in turning family history into fiction?Atomic Power and Emotional FissionThe title Fission works on so many levels—scientific, emotional, relational. How did you think about the parallel between splitting the atom and the fractures within marriage, identity, and moral responsibility?Doris Friedman: Ambition, Identity, and ConstraintDoris is such a complex character—a young mother, a frustrated artist, a woman navigating marriage, ambition, antisemitism, and gender expectations in the 1940s. What drew you to tell the story through her eyes, and what does she represent to you?Psychology, Secrecy, and Relationships Under PressureAs a clinical psychologist, you've spent decades studying relationships, sexuality, and identity under stress. How did that background shape the way you portrayed marriage, desire, betrayal, and resilience in a world defined by secrecy and existential fear?Moral Ambivalence and LegacyBy the end of the novel, Doris and Rob are left with pride, guilt, love, and doubt—having helped save the world and also put it at risk. What questions do you hope readers sit with after finishing Fission, especially as we think about scientific progress and ethical responsibility today?As someone who returned to fiction after a long and impactful career in psychology and healthcare, what would you say to creatives who feel it may be ‘too late' to return to an earlier calling?

In this episode of Gangland Wire, host Gary Jenkins, retired Kansas City Police Intelligence Unit detective, steps outside traditional Mafia territory and into a shadowy world just as dangerous—and just as fascinating: the international theft of ultra-rare automobiles.  Gary is joined by author Stayton Bonner, former senior editor at Rolling Stone, and legendary car-recovery specialist Joe Ford, the real-life figure behind Bonner's book The Million Dollar Car Detective. At the center of the story is a breathtaking pre-World War II automobile—the Talbot-Lago Teardrop Coupé—once described as the most beautiful car in the world. Stolen from a Milwaukee industrialist's garage in 2001, the car vanished into the international underground of elite collectors, forged paperwork, and high-stakes deception. Joe Ford explains how he became the go-to investigator when rare cars worth millions disappear—and why stolen vehicles are far harder to recover than stolen art. What follows is a years-long global hunt involving disgruntled mechanics, fabricated titles, shell corporations, Swiss intermediaries, and a billionaire buyer now locked in civil litigation. Bonner adds rich historical context, tracing the car's glamorous past—from European aristocracy to Hollywood royalty—and exposing how loneliness, obsession, and greed often surround these legendary machines. The conversation expands into other notorious cases, including the disappearance of the original James Bond Aston Martin from Goldfinger, and how wealthy collectors sometimes knowingly harbor stolen artifacts. This episode is a true-crime story without guns or gangs—but filled with deception, betrayal, and the relentless pursuit of justice across borders. If you love investigative work, high-end crime, and stories that feel like James Bond meets Gone in 60 Seconds, this one's for you.

A bill to designate domestic terrorist organizations advances in the Florida House, raising civil liberties concerns as lawmakers push expanded state powers. New reporting claims Jeffrey Epstein recruited NSA codebreakers for a genome “Manhattan Project,” while Epstein survivors blast Attorney General Pam Bondi following a combative House hearing over the files. Thanks to Shopify and Zip Recruiter for today's episode: Sign up for your one-dollar-per-month trial today at shopify.com/tyt Just go to this exclusive web address right now to try ZipRecruiter FOR FREE: ziprecruiter.com/tyt Hosts: Ana Kasparian & Cenk Uygur SUBSCRIBE on YOUTUBE ☞  https://www.youtube.com/@TheYoungTurks FOLLOW US ON: FACEBOOK  ☞   https://www.facebook.com/theyoungturks TWITTER  ☞       https://twitter.com/TheYoungTurks INSTAGRAM  ☞  https://www.instagram.com/theyoungturks TIKTOK  ☞          https://www.tiktok.com/@theyoungturks

Nathan Lambert and Sebastian Raschka are machine learning researchers, engineers, and educators. Nathan is the post-training lead at the Allen Institute for AI (Ai2) and the author of The RLHF Book. Sebastian Raschka is the author of Build a Large Language Model (From Scratch) and Build a Reasoning Model (From Scratch). Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep490-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/ai-sota-2026-transcript CONTACT LEX: Feedback – give feedback to Lex: https://lexfridman.com/survey AMA – submit questions, videos or call-in: https://lexfridman.com/ama Hiring – join our team: https://lexfridman.com/hiring Other – other ways to get in touch: https://lexfridman.com/contact SPONSORS: To support this podcast, check out our sponsors & get discounts: Box: Intelligent content management platform. Go to https://box.com/ai Quo: Phone system (calls, texts, contacts) for businesses. Go to https://quo.com/lex UPLIFT Desk: Standing desks and office ergonomics. Go to https://upliftdesk.com/lex Fin: AI agent for customer service. Go to https://fin.ai/lex Shopify: Sell stuff online. Go to https://shopify.com/lex CodeRabbit: AI-powered code reviews. Go to https://coderabbit.ai/lex LMNT: Zero-sugar electrolyte drink mix. Go to https://drinkLMNT.com/lex Perplexity: AI-powered answer engine. Go to https://perplexity.ai/ OUTLINE: (00:00) – Introduction (01:39) – Sponsors, Comments, and Reflections (16:29) – China vs US: Who wins the AI race? (25:11) – ChatGPT vs Claude vs Gemini vs Grok: Who is winning? (36:11) – Best AI for coding (43:02) – Open Source vs Closed Source LLMs (54:41) – Transformers: Evolution of LLMs since 2019 (1:02:38) – AI Scaling Laws: Are they dead or still holding? (1:18:45) – How AI is trained: Pre-training, Mid-training, and Post-training (1:51:51) – Post-training explained: Exciting new research directions in LLMs (2:12:43) – Advice for beginners on how to get into AI development & research (2:35:36) – Work culture in AI (72+ hour weeks) (2:39:22) – Silicon Valley bubble (2:43:19) – Text diffusion models and other new research directions (2:49:01) – Tool use (2:53:17) – Continual learning (2:58:39) – Long context (3:04:54) – Robotics (3:14:04) – Timeline to AGI (3:21:20) – Will AI replace programmers? (3:39:51) – Is the dream of AGI dying? (3:46:40) – How AI will make money? (3:51:02) – Big acquisitions in 2026 (3:55:34) – Future of OpenAI, Anthropic, Google DeepMind, xAI, Meta (4:08:08) – Manhattan Project for AI (4:14:42) – Future of NVIDIA, GPUs, and AI compute clusters (4:22:48) – Future of human civilization

BASED ON A TRUE STORY (BOATS EP. 382) — Join me in this throwback style BOATS episode without any guests. It'll just be you and I learning about the true story behind 2023's Oppenheimer. Was the poisoned apple scene real? How well was the Trinity Test portrayed on screen? What does the movie leave out from Oppenheimer's life? If you've seen the movie and wondered what really happened, this episode is your guided tour through the history behind the film.Chapters0:00 Intro, synopsis & Two Truths and a Lie3:30 Prometheus myth and poisoned apple story10:15 Oppenheimer's education in Europe and early career18:40 Relationships with Jean Tatlock and Kitty Oppenheimer28:20 FBI surveillance, communism fears, and the Manhattan Project scale39:10 Trinity Test, “I am become Death,” and bombing Japan51:00 Meeting President Truman and the Strauss feud1:01:30 Oppenheimer's fall, later life, and modern reevaluation1:08:00 Two Truths and a Lie answers & closingResourcesAmerican PrometheusTales of Militant Chemistry109 East PalaceRobert Oppenheimer: A Life Inside the CenterJ. Robert Oppenheimer: A LifeAn Atomic Love Story: The Extraordinary Women in Robert Oppenheimer's LifeThe Ruin of J. Robert OppenheimerIf your podcast app doesn't support clickable links, you can also find all the links at https://links.boatspodcast.com/382Support My WorkSupport my sponsorsBecome a BOATS Producer (name in credits + ad-free episodes)Join the BOATS DiscordGet the BOATS email newsletterEmail me: dan@basedonatruestorypodcast.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Dr. Michael Levin (@drmichaellevin) is the Vannevar Bush Distinguished Professor of Biology at Tufts University and director of the Allen Discovery Center. He is primarily interested in how intelligence self-organizes in a diverse range of natural, engineered, and hybrid embodiments. Applied to the collective intelligence of cell groups undergoing morphogenesis, these ideas have allowed the Levin Lab to develop new applications in birth defects, organ regeneration, and cancer suppression.This episode is brought to you by:ShipStation shipping software: ShipStation.com/TimAG1 all-in-one nutritional supplement: DrinkAG1.com/TimOur Place's Titanium Always Pan® Pro using nonstick technology that's coating-free and made without PFAS, otherwise known as “forever chemicals”: FromOurPlace.com/TimTIMESTAMPS:[00:00:00] Start[00:03:18] The Body Electric: A Vancouver bookstore discovery that launched a career.[00:04:19] Bioelectricity 101: Your brain uses it to think; your body used it before you had a brain.[00:06:05] The lesson learned by scrambled tadpole faces that rearrange themselves.[00:08:51] Software vs. hardware: The genome is your factory settings, not your destiny.[00:11:43] Two-headed flatworms: Rewriting biological memory without touching DNA.[00:16:20] Seeing memories: Voltage-sensitive dyes reveal the body's hidden blueprints.[00:20:12] Three killer apps for humans: Birth defects, regeneration, and cancer.[00:24:27] Cancer as identity crisis: Cells forgetting they're part of a team.[00:25:40] The boredom theory of aging: Goal-seeking systems with nothing left to do.[00:30:09] Planaria's immortality hack: Rip yourself in half every two weeks.[00:31:27] Manhattan Project for aging: Crack cellular cognition, everything else falls into place.[00:33:47] Giving cells new goals: Convince a gut to become an eye.[00:37:42] Must mammalian mortality be mandatory?[00:40:25] Cross-pollination: Why biologists would benefit from programming courses.[00:47:15] Does acupuncture actually do anything?[00:50:57] Placebo as feature, not bug: Words and drugs share the same mechanism.[00:55:06] The frame problem: Why robots explode and rats intuit what matters.[00:59:41] Binary thinking is a trap: “Is it intelligent?” is the wrong question.[01:07:46] Minimal brain, normal IQ: Clinical cases that break neuroscience.[01:08:45] Super panpsychism: Your liver might have opinions.[01:13:48] The Platonic space: Bodies as thin clients for patterns from elsewhere.[01:15:24] Keep asking “why” and you end up in the math department.[01:23:07] Polycomputing: Sorting algorithms secretly doing side quests.[01:28:24] Power scaling for the future and avoiding red herrings for understanding machine minds.[01:34:06] Sci-fi recommendations.[01:37:24] Cliff Tabin's toast and Dan Dennett's steel manning.[01:41:21] Parting thoughts.*For show notes and past guests on The Tim Ferriss Show, please visit tim.blog/podcast.For deals from sponsors of The Tim Ferriss Show, please visit tim.blog/podcast-sponsorsSign up for Tim's email newsletter (5-Bullet Friday) at tim.blog/friday.For transcripts of episodes, go to tim.blog/transcripts.Discover Tim's books: tim.blog/books.Follow Tim:Twitter: twitter.com/tferriss Instagram: instagram.com/timferrissYouTube: youtube.com/timferrissFacebook: facebook.com/timferriss LinkedIn: linkedin.com/in/timferrissSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Although the worldwide Netflix phenomenon Stranger Things was released in 2016, the Internet is finally catching up a decade later in realizing that the show is both loosely and firmly based on both real historical programs and popular conspiracy theories. But Stranger Things is more than that; it incorporates cultural nostalgia, even of the negative kind, like the Satanic Panic, and puts historical Easter eggs in every season. From Project Rainbow to Astral Projection, and from MK Ultra to child abuse and Project Montauk, many of the themes present in the show are also anything but unique. They pull from myth, legend, and folklore, and in some ways touch on ritual magic and the dark arts. The synchronicities of the show are as powerful today as the archetypal characters of Star Wars were to the 1970s. In the upside down of all this, however, is a world of real science, including real DOE National Laboratories like the Idaho National Lab alongside the labs involved in the Manhattan Project. Tonight on Ground Zero, Ryan Gable fills in for Clyde Lewis and talks with synchrocity researcher, Derek Murphy about STRANGER THINGAMAJIGS. Listen at 7pm, pacific time on groundzeroplus.com.

Behind the heavily guarded gates of Oak Ridge National Laboratory in Tennessee lie some of the government's latest efforts in the war for AI dominance. In a new Daily Wire documentary, Senior Editor Cabot Phillips takes us inside the facility as the U.S. races to finish what it's calling the second Manhattan Project. Get the facts first with Morning Wire. - - - Ep. 2559 - - - Wake up with new Morning Wire merch: https://bit.ly/4lIubt3 - - - Today's Sponsor: Daily Wire Shop - Visit https://dailywire.com/shop today! - - - Privacy Policy: https://www.dailywire.com/privacy morning wire,morning wire podcast,the morning wire podcast,Georgia Howe,John Bickley,daily wire podcast,podcast,news podcast Learn more about your ad choices. Visit podcastchoices.com/adchoices

Cabot Phillips enters the headquarters of the next arms race. Inside Oak Ridge, the former secret city of the original Manhattan Project, he uncovers how nuclear energy and artificial intelligence are being fused into a growing national effort to achieve American dominance over China. - - - Today's Sponsors: Balance of Nature - New and existing customers can go to https://balanceofnature.com and get 50% off the Whole Health System FOR LIFE. - - - DailyWire+: