Podcasts about Infrastructure

3. Stevenson-Yang 3: Ghost Cities and the Infrastructure Credit Boom. Massive cash injections fueled construction of empty "ghost cities" and emulated Western landmarks, leading to a monumental credit expansion that eventually dwarfed the American system. Guest: Anne Stevenson-Yang.

AI agents differ from chatbots by pursuing autonomous goals through the ReACT loop rather than responding to turn-based prompts. While coding agents are currently the most reliable due to verifiable feedback loops, the market is expanding into desktop and browser automation via tools like Claude co-work and open claw. Links Notes and resources at ocdevel.com/mlg/mla-28 Try a walking desk - stay healthy & sharp while you learn & code Generate a podcast - use my voice to listen to any AI generated content you want Fundamental Definitions Agent vs. Chatbot: Chatbots are turn-based and human-driven. Agents receive objectives and dynamically direct their own processes. The ReACT Loop: Every modern agent uses the cycle: Thought -> Action -> Observation. This interleaved reasoning and tool usage allows agents to update plans and handle exceptions. Performance: Models using agentic loops with self-correction outperform stronger zero-shot models. GPT-3.5 with an agent loop scored 95.1% on HumanEval, while zero-shot GPT-4 scored 67.0%. The Agentic Spectrum Chat: No tools or autonomy. Chat + Tools: Human-driven web search or code execution. Workflows: LLMs used in predefined code paths. The human designs the flow, the AI adds intelligence at specific nodes. Agents: LLMs dynamically choose their own path and tools based on observations. Tool Categories and Market Players Developer Frameworks: Use LangGraph for complex, stateful graphs or CrewAI for role-based multi-agent delegation. OpenAI Agents SDK provides minimalist primitives (Handoffs, Sessions), while the Claude Agent SDK focuses on local computer interaction. Workflow Automation: n8n and Zapier provide low-code interfaces. These are stable for repeatable business tasks but limited by fixed paths and a lack of persistent memory between runs. Coding Agents: Claude Code, Cursor, and GitHub Copilot are the most advanced agents. They succeed because code provides an unambiguous feedback loop (pass/fail) for the ReACT cycle. Desktop and Browser Agents: Claude Cowork( (released Jan 2026) operates in isolated VMs to produce documents. ChatGPT Atlas is a Chromium-based browser with integrated agent capabilities for web tasks. Autonomous Agents: open claw is an open-source, local system with broad permissions across messaging, file systems, and hardware. While powerful, it carries high security risks, including 512 identified vulnerabilities and potential data exfiltration. Infrastructure and Standards MCP (Model Context Protocol): A universal standard for connecting agents to tools. It has 10,000+ servers and is used by Anthropic, OpenAI, and Google. Future Outlook: By 2028, multi-agent coordination will be the default architecture. Gartner predicts 38% of organizations will utilize AI agents as formal team members, and the developer role will transition primarily to objective specification and output evaluation.

Atomic Eagle offers a compelling entry into the uranium bull market, backed by a proven team from Matador Capital—the original architects behind Boss Energy's success and Lotus Resources' recent mine restart. Through a strategic RTO of GovEx Uranium, they've acquired the advanced Muntanga project in mining-friendly Zambia: a 47.4M lb resource at 344 ppm U3O8, with a feasibility study showing robust economics at $90/lb uranium. But the current investment thesis is not that of a mine build story. Atomic Eagle's focus is on aggressive exploration to double resources via a current 50,000m drill program, targeting a 40-100M lb upside which conceptually could see a mega-mine producing 4-5M lbs/year through low-cost heap leaching (90%+ recovery with low acid consumption). Well-funded with ~A$20M cash, Atomic is undervalued when compared, on an enterprise value to pounds-in-the-ground basis, to ASX peers like Deep Yellow and Bannerman. Near-term catalysts: Resource upgrade (early March), feasibility re-release, and exploration drill results. Bonus optionality: Potential recovery of the world-class Madaouela asset in Niger (120M lbs at >1,300 ppm), if current talks with the Niger government are fruitful. In this MSE episode, listen to Atomic Eagle CEO Phil Hoskins explain the company's full investment thesis. https://atomiceagle.com.au/ ASX: AEU - OTCQB: AEUXF 00:00 Intro 00:34 Meet Atomic Eagle: ASX RTO of GoviEx & Who's Behind It 01:28 Matador's Uranium Track Record: Boss Energy to Lotus Restart Success 03:12 Why the GoviEx Deal Happened: ASX Valuation Comps & Timing 04:31 US OTCQB Listing: Tapping North American Uranium Investors 06:05 Friedland Connections & Geopolitics: US/China/Russia in Africa 08:26 The Muntanga Project Breakdown: Resource, Tenure & 2025 FS Context 10:08 Growth Strategy: New Drilling, Resource Upgrade & 4–5M lb/yr Heap Leach Concept 12:32 Funding & 2025 Drill Plan: 50,000m Program and Priority Targets 14:15 Zambia Advantage: Mining-Friendly Jurisdiction, Infrastructure & Export Route 17:12 The Niger Asset: Expropriation, Arbitration & Potential Upside 19:27 Near-Term Catalysts + Technical Upsides: Recovery, Acid Use, Permitting 21:42 Wrap-Up, Tickers, and Sponsor Coverage Ahead Sponsor Atomic Eagle pays MSE a United States dollar ten thousand per month coverage fee. The forward-looking statement disclaimer found in Atomic Eagle's most-recent company slide deck found at www.AtomicEagle.com.au applies to everything discussed in this interview. Mining Stock Education (MSE) offers informational content based on available data but it does not constitute investment, tax, or legal advice. It may not be appropriate for all situations or objectives. Readers and listeners should seek professional advice, make independent investigations and assessments before investing. MSE does not guarantee the accuracy or completeness of its content and should not be solely relied upon for investment decisions. MSE and its owner may hold financial interests in the companies discussed and can trade such securities without notice. MSE is biased towards its advertising sponsors which make this platform possible. MSE is not liable for representations, warranties, or omissions in its content. By accessing MSE content, users agree that MSE and its affiliates bear no liability related to the information provided or the investment decisions you make. Full disclaimer: https://www.miningstockeducation.com/disclaimer/

How do you design financial infrastructure that keeps running when the unexpected hits, whether that is a regional outage, a regulatory shift, or a sudden spike in digital demand? In this episode of Tech Talks Daily, I'm joined by Katsutoshi Itoh from Sony and Masahisa Kawashima from NTT, both representing the IOWN Global Forum, to unpack how photonics-based networks could change the foundations of digital finance. Speaking with me from Kyoto, they share how the Innovative Optical and Wireless Network vision is moving beyond theory and into practical, finance-specific use cases. Financial institutions are under constant pressure to deliver uninterrupted services while meeting ever tighter compliance standards. Yet as we discuss, many existing architectures still rely on asynchronous data replication and layered resilience added after the fact. On paper, it works. In a real disruption, gaps quickly appear. Itoh and Kawashima explain how synchronous replication over ultra-low latency optical networks can reduce the risk of data loss while simplifying disaster recovery and lowering operational complexity. We also explore the role of Open All-Photonic Networks and why reducing packet forwarding layers can dramatically cut latency and infrastructure costs. Instead of concentrating compute and storage in dense urban data centers, photonics enables distributed computing across regions while maintaining deterministic performance. That shift opens the door to improved resilience, better infrastructure utilization, and new approaches to scaling without constant over-provisioning. Sustainability sits alongside resilience in this conversation. Rather than treating energy efficiency as a compromise, the IOWN vision distributes power demand geographically, making better use of locally available renewable energy and reducing concentrated load pressures. It is a subtle but important rethink of how infrastructure supports broader societal goals. Looking ahead, we consider what this could mean for digital banking platforms, AI-driven risk management, and cross-border financial services. If infrastructure limitations fall away, institutions can design services around business needs rather than technical constraints. If you are curious about how photonics could underpin the next generation of financial services, this episode offers a grounded and thoughtful perspective. As always, I would love to hear your thoughts after listening.

Jelena Djuric, Co-Founder & CEO at Noble, sat down with me for an interview at the Halborn Access 2026 Summit at the NYSE. We discussed how Noble's infrastructure is helping companies to get access to stablecoin liquidity around the world. Recorded January 23rd.Brought to you by

On our radar this week… “The Party told you to reject the evidence of your eyes and ears. It was their final, most essential command.” George Orwell wrote those words 76 years ago in “1984” – seemingly the operating manual for Donald Trump's administration. In fact, Trump used those exact words in a campaign speech and has lived by them ever since.  George Orwell also wrote: “Every record has been destroyed or falsified, every book rewritten, every picture has been repainted, every statue and street building has been renamed, every date has been altered. And the process is continuing day by day and minute by minute. History has stopped. Nothing exists except an endless present in which the Party is always right.” Every day is an exercise in taking attention away from the growing coverup of the Epstein files and financial corruption, with Trump apparently terrified that his sordid decades-long history as a sexual predator will finally catch up with him.  It's a stark contrast with England, where the Andrew formerly known as “Prince” is celebrated his 66th birthday in police custody as England actually holds the powerful accountable for the Epstein-led sexual abuse of children, while in Epstein's home country the White House continues to coverup the crimes of the rich and powerful … very possibly a group that includes Trump. Case in point: the Department of Justice spoke four separate times to a woman who credibly accused Donald Trump of having sex with a 13-year-old he met through Jeffrey Epstein—but most accusations against the president appear to have been removed from the government's documents on the alleged sex trafficker.  A part of Trump's defense is also right out of “1984”: “Who controls the past controls the future. Who controls the present controls the past.” That means silencing his critics including the late night comedians who, in the tradition of Will Rogers, lampoon him non-stop. But the tactic is backfiring: Kimmel's banishment lasted a few days, and Stephen Colbert has become even more focused in the last weeks of his days on CBS. The made-for-YouTube video of Colbert with Texas Senate candidate James Talarico has racked up more than 7.5 million views which is triple the Colbert TV show ratings. And Talarico raised a staggering $2.5-million in the day following the incident. Trump wants everything possible named after him. Now, he apparently wants to profit from those efforts: his company has filed papers to trademark use of his name at airports even as his Florida fans in the state's legislature pass a bill to rename Palm Beach International Airport in his honor and he pressures Congress to rename Dulles Airport. If signed into law, the Palm Beach International change would cost the airport $5.5 million to remake signs, uniforms, promotional products, equipment, and more, according to Palm Beach County's department of airports. Also on our radar The Supreme Court kicked off another Trump tantrum by axing his tariffs. The war between Dozing Donald and the court he thought he controlled is now started. Trump got a little nap time during the initial meeting of his made-up Institute for Peace, nodding off repeatedly in front of the world leaders who had ponied up the $1-billion membership fee. Before nap time, Trump pledged a $10-billion U.S. contribution to what amounts to his personal slush fund – ignoring the constitutional requirement that spending needed to be authorized by Congress. Governor Whitmer attended the Munich International Security Conference. At the conference, she joined AOC, and Trump's NATO ambassador on a panel discussion where she was highly critical of Trump's economic war with Canada has driven our neighbors to the north to get cozy with China. Independent gubernatorial candidate Mike Duggan has a new problem. His campaign claimed union endorsements he hasn't received. It's a near certainty that the Service Employees International Union and the United Auto Workers will ultimately endorse Jocelyn Benson. Benson, meanwhile, picked up the endorsement of the Michigan Nurses Association. Mark has a new neighbor. ICE has opened a regional headquarters next door to my office … and also is opening a detention center in Romulus. Nobody's happy about this except Stephen Miller. Is this a staging area for masked ICE agents outside Democratic-leaning voting sites in southeast Michigan this November? And we can't unwatch the incredibly insane 90-second, taxpayer-funded video of RFK Jr. and Kid Rock flexing and sweating, apparently to promote physical fitness. RFK thankfully did not include snorting cocaine from toilet seats as part of his workout regimen. On a far more serious note, we recognize the unique contributions of two men we lost this week: the internationally known Rev. Jesse Jackson, and one of the “good guys” who made Michigan State government work better over his decades of service, our friend Bill Gnodtke. On Tuesday, west Michigan Congresswoman Hilary Scholten went inside an ICE concentration camp. Scholten, who was an immigration attorney before being elected to Congress, joins this week’s conversation. Congresswoman Scholten is a fourth-generation West Michigander. Prior to her election in 2022 she was an immigration attorney who served in the U.S. Department of Justice.  Scholten began her own career as a social worker, working with people affected by issues of housing and homelessness. During this time, she worked with individuals in the LGBTQ community who were facing homelessness and housing insecurity—often because of their sexual orientation or gender identity.  Congresswoman Scholten obtained her law degree from the University of Maryland Thurgood Marshall School of Law, and then went on to clerk for the U.S. Court of Appeals for the Second Circuit in their special unit focused on immigration issues. Following her clerkship, she joined the Justice Department through the Attorney General Honors Program, where she continued to work on matters of immigration and civil rights. In Congress she serves on the House Committee on Transportation and Infrastructure and the House Committee on Small Business. We’re now on YouTube every week! Click here to subscribe. A Republic, If You Can Keep It is sponsored by ©Clay Jones/claytooz.com  

Traditional routing protocols like OSPF simply choose the “shortest” path. If the shortest path is full of traffic and there are alternate paths carrying nothing, OSPF can't help you. Path Computation Element (PCE) along with Path Computation Element Protocol (PCEP) is a way to construct forwarding paths through the network based on factors that distributed... Read more »

Traditional routing protocols like OSPF simply choose the “shortest” path. If the shortest path is full of traffic and there are alternate paths carrying nothing, OSPF can't help you. Path Computation Element (PCE) along with Path Computation Element Protocol (PCEP) is a way to construct forwarding paths through the network based on factors that distributed... Read more »

Powering AI 2.0 is no longer just a technology story — it's an energy and infrastructure story reshaping capital markets and the global economy. As artificial intelligence scales from training to real-world inference, electricity demand is accelerating at a pace few anticipated.In this episode of The Bid, host Oscar Pulido is joined by Will Su from BlackRock's Fundamental Equities Group to examine how Powering AI 2.0 is transforming utilities, natural gas markets, renewables, and nuclear power. With data centers expanding rapidly and gigawatt-scale facilities coming online, the AI build-out is driving a structural shift in U.S. electricity demand after more than a decade of stagnation.Will explains why the energy sector sits at the center of AI investing. From the rise of “bring your own power” models to the growing role of natural gas as a dispatchable, scalable fuel source, the infrastructure required to support AI represents one of the largest capital investment cycles in modern history. The conversation also explores renewables, battery storage, and nuclear power — including the limits of restarts and the long timeline for new reactor construction.Key moments:00:00 Introduction Power Is Knowledge: AI's Exponential Energy Appetite02:31 From Tokens to ‘Yottaflops': Why Smarter Models Need More Electricity05:04 Training LLMs vs. Inference: The Next Wave of AI Power Demand06:45 Data Centers at City Scale: How Big Is the Load?11:15 Bring Your Own Power (BYOP): Why Natural Gas Is Back in Focus16:04 Renewables Reality Check: Solar Momentum, Wind Headwinds, and Batteries19:14 Nuclear's Comeback - Restarts Now, New Builds Later21:26 Can AI Beat Humans at Investing? Man + Machine as the Edge23:33 Wrap-Up, What's NextKey insights from this episode:· Why natural gas has emerged as a key “here and now” fuel for AI infrastructure· How renewables and battery storage fit into the AI electricity mix· The long-term outlook for nuclear power and reactor construction· What “bring your own power” means for hyperscalers and utilities· How electrification and reshoring intersect with AI investing· Why the relationship between compute and energy is reshaping stock market trendsPowering AI 2.0, AI investing, infrastructure, capital markets, energy transition, utilities, stock market trends, megaforcesSources: “From CES 2026 to Yottaflops: Why the AMD Keynote Highlights a Turning Point for AI Compute”, AMD 2026; “The Industrial Revolution, coal mining, and the Felling Colliery Disaster”, Lancaster University, 2026; Bureau of Economic Analysis data 2026; “Stargate's First Data Center Site is Size of Central Park, With At Least 57 Jobs”, Bloomberg 2026; “Energy Demand from AI”, IEA 2026; “Scaling bigger, faster, cheaper data centers with smarter designs”, McKinsey 2025; EEI 2024 Review; “Data Centers Ditching the Power Grid, Mark Carney's Viral Speech, and Some Joy”, Clearview Energy; “2024 North American Energy Inventory”, IER;This content is for informational purposes only and is not an offer or a solicitation. Reliance upon information in this material is at the sole discretion of the listener. Reference to any company or investment strategy mentioned is for illustrative purposes only and not investment advice. In the UK and non-European Economic Area countries, this is authorized and regulated by the Financial Conduct Authority. In the European Economic Area, this is authorized and regulated by the Netherlands Authority for the Financial Markets. For full disclosures, visit blackrock.com/corporate/compliance/bid-disclosures.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Is AI security just "Cloud Security 2.0"? Toni De La Fuente, creator of the open-source tool Prowler, joins Ashish to explain why securing AI workloads requires a fundamentally different approach than traditional cloud infrastructure.We dive deep into the "Shared Responsibility Gap" emerging with managed AI services like AWS Bedrock and OpenAI. Toni spoke about the hidden dangers of default AI architectures, why you should never connect an MCP (Model Context Protocol) directly to a database.We discuss the new AI-driven SDLC, where tools like Claude Code can generate infrastructure but also create massive security blind spots if not monitored.Guest Socials -⁠ ⁠⁠⁠⁠⁠Toni's LinkedinPodcast Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CloudSecPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Podcast- Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you are interested in AI Security, you can check out our sister podcast -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ AI Security Podcast⁠Questions asked:(00:00) Introduction(02:50) Who is Toni De La Fuente? (Creator of Prowler)(03:50) AI Security vs. Cloud Security: What's the Difference? (07:20) The Shared Responsibility Gap in AI Services (Bedrock, OpenAI) (11:30) The "Fifth Party" Risk: Managed AI Access (13:40) AI Architecture Best Practices: Never Connect MCP to DB Directly (16:40) Prowler's AI Pillars: Generating Dashboards & Detections (22:30) The New SDLC: Securing Code from Claude Code & Lovable (25:30) The "Magic" Trap: Why AI Doesn't Know Your Security Context (28:30) Top 3 Priorities for Security Leaders (Infra, LLM, Shadow AI) (30:40) Future Predictions: Why Predicting 12 Months Out is Impossible

Traditional routing protocols like OSPF simply choose the “shortest” path. If the shortest path is full of traffic and there are alternate paths carrying nothing, OSPF can't help you. Path Computation Element (PCE) along with Path Computation Element Protocol (PCEP) is a way to construct forwarding paths through the network based on factors that distributed... Read more »

Host Brian Walsh takes up ImpactAlpha's top stories with editor David Bank. Up this week: Highlights from this week's Agents of Impact Call on Shaping the Algorithm for good AI; how regional housing finance agencies in California are leveraging public funding to crowd private capital into affordable housing (12:20); and the emergence of local guarantee facilities for local investors in infrastructure in Africa and Asia (17:20).Story links:Call roundup“Building regional engines for affordable housing in California,” by Andrew Fremier, Ryan Johnson and Cody Petterson“Local guarantees for local investors in infrastructure projects in Africa and Asia,” by Lucy Ngige

Dr. Lee Warren joins us each month to talk about self-brain surgery. Today they talk about how to make space in your life for hope.  Find Dr. Lee Warren's podcast here. Dr. Warren's book is "Hope Is the First Dose: A Treatment Plan for Recovering from Trauma, Tragedy, and Other Massive Things." Originally aired September 12, 2025 Check out Susie's new podcast God Impressions on Apple, Spotify, or wherever you listen to podcasts! Faith Radio podcasts are made possible by your support. Give now: click here

Most companies don't realize it yet, but the way they built their technology foundations is quietly becoming a liability.Cloud costs are rising. Platforms change underneath you. AI is reshaping infrastructure from hardware to data to governance. And the strategies that once felt “safe” are now the ones creating the most risk.In this episode of IT Visionaries, host Chris Brandt sits down with Mano Bhattacharya, CTO of Nutanix, to unpack what's really happening inside enterprise technology right now. This isn't a conversation about chasing the newest tools or betting on a single future. It's about why adaptability has become the most important design principle in modern tech.Mano explains why many organizations are rethinking long-held assumptions about virtualization, cloud, and containers, and why the smartest teams are building infrastructure that gives them options over the next three to five years. They explore how AI changes the entire stack, not just applications, why data has become the real bottleneck, and why moving fast without a coherent plan can be more dangerous than moving slowly. Chapters:00:00 - The VMware Exodus Wave is Coming03:34 - VMware Broadcom Acquisition: What Changed and Why It Matters05:56 - Three Migration Paths: Stay, Move to Cloud, or Modernize09:59 - Why Containers on VMs Make Sense for Most Enterprises15:40 - The Five Stages of VMware Migration Grief21:20 - VMware Admin to Nutanix Admin: Closing the Skills Gap24:14 - The Cloud-in-a-Box Philosophy: From Boxes to Software32:30 - Opening Up the Platform: Pure Storage and Third-Party Integrations40:54 - AI Infrastructure: The End-to-End Challenge48:01 - Enterprise AI Strategy: Use Cases, Economics, and Governance56:44 - What's Next: Building the Invisible Platform for AI  -- This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.---IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Today, Ethan and Holly provide an overview of firewalls. While cybersecurity is a separate discipline from network engineering, much of what happens in cybersecurity is interesting at the packet level, so there’s a good deal of overlap. It's likely that as a network engineer, you'll be managing, or at least dealing with, firewalls in your... Read more »

Tickets for AIEi Miami and AIE Europe are live, with first wave speakers announced!From pioneering software-defined networking to backing many of the most aggressive AI model companies of this cycle, Martin Casado and Sarah Wang sit at the center of the capital, compute, and talent arms race reshaping the tech industry. As partners at a16z investing across infrastructure and growth, they've watched venture and growth blur, model labs turn dollars into capability at unprecedented speed, and startups raise nine-figure rounds before monetization.Martin and Sarah join us to unpack the new financing playbook for AI: why today's rounds are really compute contracts in disguise, how the “raise → train → ship → raise bigger” flywheel works, and whether foundation model companies can outspend the entire app ecosystem built on top of them. They also share what's underhyped (boring enterprise software), what's overheated (talent wars and compensation spirals), and the two radically different futures they see for AI's market structure.We discuss:* Martin's “two futures” fork: infinite fragmentation and new software categories vs. a small oligopoly of general models that consume everything above them* The capital flywheel: how model labs translate funding directly into capability gains, then into revenue growth measured in weeks, not years* Why venture and growth have merged: $100M–$1B hybrid rounds, strategic investors, compute negotiations, and complex deal structures* The AGI vs. product tension: allocating scarce GPUs between long-term research and near-term revenue flywheels* Whether frontier labs can out-raise and outspend the entire app ecosystem built on top of their APIs* Why today's talent wars ($10M+ comp packages, $B acqui-hires) are breaking early-stage founder math* Cursor as a case study: building up from the app layer while training down into your own models* Why “boring” enterprise software may be the most underinvested opportunity in the AI mania* Hardware and robotics: why the ChatGPT moment hasn't yet arrived for robots and what would need to change* World Labs and generative 3D: bringing the marginal cost of 3D scene creation down by orders of magnitude* Why public AI discourse is often wildly disconnected from boardroom reality and how founders should navigate the noiseShow Notes:* “Where Value Will Accrue in AI: Martin Casado & Sarah Wang” - a16z show* “Jack Altman & Martin Casado on the Future of Venture Capital”* World Labs—Martin Casado• LinkedIn: https://www.linkedin.com/in/martincasado/• X: https://x.com/martin_casadoSarah Wang• LinkedIn: https://www.linkedin.com/in/sarah-wang-59b96a7• X: https://x.com/sarahdingwanga16z• https://a16z.com/Timestamps00:00:00 – Intro: Live from a16z00:01:20 – The New AI Funding Model: Venture + Growth Collide00:03:19 – Circular Funding, Demand & “No Dark GPUs”00:05:24 – Infrastructure vs Apps: The Lines Blur00:06:24 – The Capital Flywheel: Raise → Train → Ship → Raise Bigger00:09:39 – Can Frontier Labs Outspend the Entire App Ecosystem?00:11:24 – Character AI & The AGI vs Product Dilemma00:14:39 – Talent Wars, $10M Engineers & Founder Anxiety00:17:33 – What's Underinvested? The Case for “Boring” Software00:19:29 – Robotics, Hardware & Why It's Hard to Win00:22:42 – Custom ASICs & The $1B Training Run Economics00:24:23 – American Dynamism, Geography & AI Power Centers00:26:48 – How AI Is Changing the Investor Workflow (Claude Cowork)00:29:12 – Two Futures of AI: Infinite Expansion or Oligopoly?00:32:48 – If You Can Raise More Than Your Ecosystem, You Win00:34:27 – Are All Tasks AGI-Complete? Coding as the Test Case00:38:55 – Cursor & The Power of the App Layer00:44:05 – World Labs, Spatial Intelligence & 3D Foundation Models00:47:20 – Thinking Machines, Founder Drama & Media Narratives00:52:30 – Where Long-Term Power Accrues in the AI StackTranscriptLatent.Space - Inside AI's $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z[00:00:00] Welcome to Latent Space (Live from a16z) + Meet the Guests[00:00:00] Alessio: Hey everyone. Welcome to the Latent Space podcast, live from a 16 z. Uh, this is Alessio founder Kernel Lance, and I'm joined by Twix, editor of Latent Space.[00:00:08] swyx: Hey, hey, hey. Uh, and we're so glad to be on with you guys. Also a top AI podcast, uh, Martin Cado and Sarah Wang. Welcome, very[00:00:16] Martin Casado: happy to be here and welcome.[00:00:17] swyx: Yes, uh, we love this office. We love what you've done with the place. Uh, the new logo is everywhere now. It's, it's still getting, takes a while to get used to, but it reminds me of like sort of a callback to a more ambitious age, which I think is kind of[00:00:31] Martin Casado: definitely makes a statement.[00:00:33] swyx: Yeah.[00:00:34] Martin Casado: Not quite sure what that statement is, but it makes a statement.[00:00:37] swyx: Uh, Martin, I go back with you to Netlify.[00:00:40] Martin Casado: Yep.[00:00:40] swyx: Uh, and, uh, you know, you create a software defined networking and all, all that stuff people can read up on your background. Yep. Sarah, I'm newer to you. Uh, you, you sort of started working together on AI infrastructure stuff.[00:00:51] Sarah Wang: That's right. Yeah. Seven, seven years ago now.[00:00:53] Martin Casado: Best growth investor in the entire industry.[00:00:55] swyx: Oh, say[00:00:56] Martin Casado: more hands down there is, there is. [00:01:00] I mean, when it comes to AI companies, Sarah, I think has done the most kind of aggressive, um, investment thesis around AI models, right? So, worked for Nom Ja, Mira Ia, FEI Fey, and so just these frontier, kind of like large AI models.[00:01:15] I think, you know, Sarah's been the, the broadest investor. Is that fair?[00:01:20] Venture vs. Growth in the Frontier Model Era[00:01:20] Sarah Wang: No, I, well, I was gonna say, I think it's been a really interesting tag, tag team actually just ‘cause the, a lot of these big C deals, not only are they raising a lot of money, um, it's still a tech founder bet, which obviously is inherently early stage.[00:01:33] But the resources,[00:01:36] Martin Casado: so many, I[00:01:36] Sarah Wang: was gonna say the resources one, they just grow really quickly. But then two, the resources that they need day one are kind of growth scale. So I, the hybrid tag team that we have is. Quite effective, I think,[00:01:46] Martin Casado: what is growth these days? You know, you don't wake up if it's less than a billion or like, it's, it's actually, it's actually very like, like no, it's a very interesting time in investing because like, you know, take like the character around, right?[00:01:59] These tend to [00:02:00] be like pre monetization, but the dollars are large enough that you need to have a larger fund and the analysis. You know, because you've got lots of users. ‘cause this stuff has such high demand requires, you know, more of a number sophistication. And so most of these deals, whether it's US or other firms on these large model companies, are like this hybrid between venture growth.[00:02:18] Sarah Wang: Yeah. Total. And I think, you know, stuff like BD for example, you wouldn't usually need BD when you were seed stage trying to get market biz Devrel. Biz Devrel, exactly. Okay. But like now, sorry, I'm,[00:02:27] swyx: I'm not familiar. What, what, what does biz Devrel mean for a venture fund? Because I know what biz Devrel means for a company.[00:02:31] Sarah Wang: Yeah.[00:02:32] Compute Deals, Strategics, and the ‘Circular Funding' Question[00:02:32] Sarah Wang: You know, so a, a good example is, I mean, we talk about buying compute, but there's a huge negotiation involved there in terms of, okay, do you get equity for the compute? What, what sort of partner are you looking at? Is there a go-to market arm to that? Um, and these are just things on this scale, hundreds of millions, you know, maybe.[00:02:50] Six months into the inception of a company, you just wouldn't have to negotiate these deals before.[00:02:54] Martin Casado: Yeah. These large rounds are very complex now. Like in the past, if you did a series A [00:03:00] or a series B, like whatever, you're writing a 20 to a $60 million check and you call it a day. Now you normally have financial investors and strategic investors, and then the strategic portion always still goes with like these kind of large compute contracts, which can take months to do.[00:03:13] And so it's, it's very different ties. I've been doing this for 10 years. It's the, I've never seen anything like this.[00:03:19] swyx: Yeah. Do you have worries about the circular funding from so disease strategics?[00:03:24] Martin Casado: I mean, listen, as long as the demand is there, like the demand is there. Like the problem with the internet is the demand wasn't there.[00:03:29] swyx: Exactly. All right. This, this is like the, the whole pyramid scheme bubble thing, where like, as long as you mark to market on like the notional value of like, these deals, fine, but like once it starts to chip away, it really Well[00:03:41] Martin Casado: no, like as, as, as, as long as there's demand. I mean, you know, this, this is like a lot of these sound bites have already become kind of cliches, but they're worth saying it.[00:03:47] Right? Like during the internet days, like we were. Um, raising money to put fiber in the ground that wasn't used. And that's a problem, right? Because now you actually have a supply overhang.[00:03:58] swyx: Mm-hmm.[00:03:59] Martin Casado: And even in the, [00:04:00] the time of the, the internet, like the supply and, and bandwidth overhang, even as massive as it was in, as massive as the crash was only lasted about four years.[00:04:09] But we don't have a supply overhang. Like there's no dark GPUs, right? I mean, and so, you know, circular or not, I mean, you know, if, if someone invests in a company that, um. You know, they'll actually use the GPUs. And on the other side of it is the, is the ask for customer. So I I, I think it's a different time.[00:04:25] Sarah Wang: I think the other piece, maybe just to add onto this, and I'm gonna quote Martine in front of him, but this is probably also a unique time in that. For the first time, you can actually trace dollars to outcomes. Yeah, right. Provided that scaling laws are, are holding, um, and capabilities are actually moving forward.[00:04:40] Because if you can put translate dollars into capabilities, uh, a capability improvement, there's demand there to martine's point. But if that somehow breaks, you know, obviously that's an important assumption in this whole thing to make it work. But you know, instead of investing dollars into sales and marketing, you're, you're investing into r and d to get to the capability, um, you know, increase.[00:04:59] And [00:05:00] that's sort of been the demand driver because. Once there's an unlock there, people are willing to pay for it.[00:05:05] Alessio: Yeah.[00:05:06] Blurring Lines: Models as Infra + Apps, and the New Fundraising Flywheel[00:05:06] Alessio: Is there any difference in how you built the portfolio now that some of your growth companies are, like the infrastructure of the early stage companies, like, you know, OpenAI is now the same size as some of the cloud providers were early on.[00:05:16] Like what does that look like? Like how much information can you feed off each other between the, the two?[00:05:24] Martin Casado: There's so many lines that are being crossed right now, or blurred. Right. So we already talked about venture and growth. Another one that's being blurred is between infrastructure and apps, right? So like what is a model company?[00:05:35] Mm-hmm. Like, it's clearly infrastructure, right? Because it's like, you know, it's doing kind of core r and d. It's a horizontal platform, but it's also an app because it's um, uh, touches the users directly. And then of course. You know, the, the, the growth of these is just so high. And so I actually think you're just starting to see a, a, a new financing strategy emerge and, you know, we've had to adapt as a result of that.[00:05:59] And [00:06:00] so there's been a lot of changes. Um, you're right that these companies become platform companies very quickly. You've got ecosystem build out. So none of this is necessarily new, but the timescales of which it's happened is pretty phenomenal. And the way we'd normally cut lines before is blurred a little bit, but.[00:06:16] But that, that, that said, I mean, a lot of it also just does feel like things that we've seen in the past, like cloud build out the internet build out as well.[00:06:24] Sarah Wang: Yeah. Um, yeah, I think it's interesting, uh, I don't know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, um.[00:06:33] You raise money for compute, you pour that or you, you pour the money into compute, you get some sort of breakthrough. You funnel the breakthrough into your vertically integrated application. That could be chat GBT, that could be cloud code, you know, whatever it is. You massively gain share and get users.[00:06:49] Maybe you're even subsidizing at that point. Um, depending on your strategy. You raise money at the peak momentum and then you repeat, rinse and repeat. Um, and so. And that wasn't [00:07:00] true even two years ago, I think. Mm-hmm. And so it's sort of to your, just tying it to fundraising strategy, right? There's a, and hiring strategy.[00:07:07] All of these are tied, I think the lines are blurring even more today where everyone is, and they, but of course these companies all have API businesses and so they're these, these frenemy lines that are getting blurred in that a lot of, I mean, they have billions of dollars of API revenue, right? And so there are customers there.[00:07:23] But they're competing on the app layer.[00:07:24] Martin Casado: Yeah. So this is a really, really important point. So I, I would say for sure, venture and growth, that line is blurry app and infrastructure. That line is blurry. Um, but I don't think that that changes our practice so much. But like where the very open questions are like, does this layer in the same way.[00:07:43] Compute traditionally has like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer. But that might not, might not be the case here. It may be the case that you actually can't verticalize on the token string. Like you can't build an app like it, it necessarily goes down just because there are no [00:08:00] abstractions.[00:08:00] So those are kinda the bigger existential questions we ask. Another thing that is very different this time than in the history of computer sciences is. In the past, if you raised money, then you basically had to wait for engineering to catch up. Which famously doesn't scale like the mythical mammoth. It take a very long time.[00:08:18] But like that's not the case here. Like a model company can raise money and drop a model in a, in a year, and it's better, right? And, and it does it with a team of 20 people or 10 people. So this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before.[00:08:39] And I think everybody's trying to understand what the consequences are. So I think it's less about like. Big companies and growth and this, and more about these more systemic questions that we actually don't have answers to.[00:08:49] Alessio: Yeah, like at Kernel Labs, one of our ideas is like if you had unlimited money to spend productively to turn tokens into products, like the whole early stage [00:09:00] market is very different because today you're investing X amount of capital to win a deal because of price structure and whatnot, and you're kind of pot committing.[00:09:07] Yeah. To a certain strategy for a certain amount of time. Yeah. But if you could like iteratively spin out companies and products and just throw, I, I wanna spend a million dollar of inference today and get a product out tomorrow.[00:09:18] swyx: Yeah.[00:09:19] Alessio: Like, we should get to the point where like the friction of like token to product is so low that you can do this and then you can change the Right, the early stage venture model to be much more iterative.[00:09:30] And then every round is like either 100 k of inference or like a hundred million from a 16 Z. There's no, there's no like $8 million C round anymore. Right.[00:09:38] When Frontier Labs Outspend the Entire App Ecosystem[00:09:38] Martin Casado: But, but, but, but there's a, there's a, the, an industry structural question that we don't know the answer to, which involves the frontier models, which is, let's take.[00:09:48] Anthropic it. Let's say Anthropic has a state-of-the-art model that has some large percentage of market share. And let's say that, uh, uh, uh, you know, uh, a company's building smaller models [00:10:00] that, you know, use the bigger model in the background, open 4.5, but they add value on top of that. Now, if Anthropic can raise three times more.[00:10:10] Every subsequent round, they probably can raise more money than the entire app ecosystem that's built on top of it. And if that's the case, they can expand beyond everything built on top of it. It's like imagine like a star that's just kind of expanding, so there could be a systemic. There could be a, a systemic situation where the soda models can raise so much money that they can out pay anybody that bills on top of ‘em, which would be something I don't think we've ever seen before just because we were so bottlenecked in engineering, and this is a very open question.[00:10:41] swyx: Yeah. It's, it is almost like bitter lesson applied to the startup industry.[00:10:45] Martin Casado: Yeah, a hundred percent. It literally becomes an issue of like raise capital, turn that directly into growth. Use that to raise three times more. Exactly. And if you can keep doing that, you literally can outspend any company that's built the, not any company.[00:10:57] You can outspend the aggregate of companies on top of [00:11:00] you and therefore you'll necessarily take their share, which is crazy.[00:11:02] swyx: Would you say that kind of happens in character? Is that the, the sort of postmortem on. What happened?[00:11:10] Sarah Wang: Um,[00:11:10] Martin Casado: no.[00:11:12] Sarah Wang: Yeah, because I think so,[00:11:13] swyx: I mean the actual postmortem is, he wanted to go back to Google.[00:11:15] Exactly. But like[00:11:18] Martin Casado: that's another difference that[00:11:19] Sarah Wang: you said[00:11:21] Martin Casado: it. We should talk, we should actually talk about that.[00:11:22] swyx: Yeah,[00:11:22] Sarah Wang: that's[00:11:23] swyx: Go for it. Take it. Take,[00:11:23] Sarah Wang: yeah.[00:11:24] Character.AI, Founder Goals (AGI vs Product), and GPU Allocation Tradeoffs[00:11:24] Sarah Wang: I was gonna say, I think, um. The, the, the character thing raises actually a different issue, which actually the Frontier Labs will face as well. So we'll see how they handle it.[00:11:34] But, um, so we invest in character in January, 2023, which feels like eons ago, I mean, three years ago. Feels like lifetimes ago. But, um, and then they, uh, did the IP licensing deal with Google in August, 2020. Uh, four. And so, um, you know, at the time, no, you know, he's talked publicly about this, right? He wanted to Google wouldn't let him put out products in the world.[00:11:56] That's obviously changed drastically. But, um, he went to go do [00:12:00] that. Um, but he had a product attached. The goal was, I mean, it's Nome Shair, he wanted to get to a GI. That was always his personal goal. But, you know, I think through collecting data, right, and this sort of very human use case, that the character product.[00:12:13] Originally was and still is, um, was one of the vehicles to do that. Um, I think the real reason that, you know. I if you think about the, the stress that any company feels before, um, you ultimately going one way or the other is sort of this a GI versus product. Um, and I think a lot of the big, I think, you know, opening eyes, feeling that, um, anthropic if they haven't started, you know, felt it, certainly given the success of their products, they may start to feel that soon.[00:12:39] And the real. I think there's real trade-offs, right? It's like how many, when you think about GPUs, that's a limited resource. Where do you allocate the GPUs? Is it toward the product? Is it toward new re research? Right? Is it, or long-term research, is it toward, um, n you know, near to midterm research? And so, um, in a case where you're resource constrained, um, [00:13:00] of course there's this fundraising game you can play, right?[00:13:01] But the fund, the market was very different back in 2023 too. Um. I think the best researchers in the world have this dilemma of, okay, I wanna go all in on a GI, but it's the product usage revenue flywheel that keeps the revenue in the house to power all the GPUs to get to a GI. And so it does make, um, you know, I think it sets up an interesting dilemma for any startup that has trouble raising up until that level, right?[00:13:27] And certainly if you don't have that progress, you can't continue this fly, you know, fundraising flywheel.[00:13:32] Martin Casado: I would say that because, ‘cause we're keeping track of all of the things that are different, right? Like, you know, venture growth and uh, app infra and one of the ones is definitely the personalities of the founders.[00:13:45] It's just very different this time I've been. Been doing this for a decade and I've been doing startups for 20 years. And so, um, I mean a lot of people start this to do a GI and we've never had like a unified North star that I recall in the same [00:14:00] way. Like people built companies to start companies in the past.[00:14:02] Like that was what it was. Like I would create an internet company, I would create infrastructure company, like it's kind of more engineering builders and this is kind of a different. You know, mentality. And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider a GI, but others have not.[00:14:20] And so like there is always this tension with personnel. And so I think we're seeing more kind of founder movement.[00:14:27] Sarah Wang: Yeah.[00:14:27] Martin Casado: You know, as a fraction of founders than we've ever seen. I mean, maybe since like, I don't know the time of like Shockly and the trade DUR aid or something like that. Way back in the beginning of the industry, I, it's a very, very.[00:14:38] Unusual time of personnel.[00:14:39] Sarah Wang: Totally.[00:14:40] Talent Wars, Mega-Comp, and the Rise of Acquihire M&A[00:14:40] Sarah Wang: And it, I think it's exacerbated by the fact that talent wars, I mean, every industry has talent wars, but not at this magnitude, right? No. Yeah. Very rarely can you see someone get poached for $5 billion. That's hard to compete with. And then secondly, if you're a founder in ai, you could fart and it would be on the front page of, you know, the information these days.[00:14:59] And so there's [00:15:00] sort of this fishbowl effect that I think adds to the deep anxiety that, that these AI founders are feeling.[00:15:06] Martin Casado: Hmm.[00:15:06] swyx: Uh, yes. I mean, just on, uh, briefly comment on the founder, uh, the sort of. Talent wars thing. I feel like 2025 was just like a blip. Like I, I don't know if we'll see that again.[00:15:17] ‘cause meta built the team. Like, I don't know if, I think, I think they're kind of done and like, who's gonna pay more than meta? I, I don't know.[00:15:23] Martin Casado: I, I agree. So it feels so, it feel, it feels this way to me too. It's like, it is like, basically Zuckerberg kind of came out swinging and then now he's kind of back to building.[00:15:30] Yeah,[00:15:31] swyx: yeah. You know, you gotta like pay up to like assemble team to rush the job, whatever. But then now, now you like you, you made your choices and now they got a ship.[00:15:38] Martin Casado: I mean, the, the o other side of that is like, you know, like we're, we're actually in the job hiring market. We've got 600 people here. I hire all the time.[00:15:44] I've got three open recs if anybody's interested, that's listening to this for investor. Yeah, on, on the team, like on the investing side of the team, like, and, um, a lot of the people we talk to have acting, you know, active, um, offers for 10 million a year or something like that. And like, you know, and we pay really, [00:16:00] really well.[00:16:00] And just to see what's out on the market is really, is really remarkable. And so I would just say it's actually, so you're right, like the really flashy one, like I will get someone for, you know, a billion dollars, but like the inflated, um, uh, trickles down. Yeah, it is still very active today. I mean,[00:16:18] Sarah Wang: yeah, you could be an L five and get an offer in the tens of millions.[00:16:22] Okay. Yeah. Easily. Yeah. It's so I think you're right that it felt like a blip. I hope you're right. Um, but I think it's been, the steady state is now, I think got pulled up. Yeah. Yeah. I'll pull up for[00:16:31] Martin Casado: sure. Yeah.[00:16:32] Alessio: Yeah. And I think that's breaking the early stage founder math too. I think before a lot of people would be like, well, maybe I should just go be a founder instead of like getting paid.[00:16:39] Yeah. 800 KA million at Google. But if I'm getting paid. Five, 6 million. That's different but[00:16:45] Martin Casado: on. But on the other hand, there's more strategic money than we've ever seen historically, right? Mm-hmm. And so, yep. The economics, the, the, the, the calculus on the economics is very different in a number of ways. And, uh, it's crazy.[00:16:58] It's cra it's causing like a, [00:17:00] a, a, a ton of change in confusion in the market. Some very positive, sub negative, like, so for example, the other side of the, um. The co-founder, like, um, acquisition, you know, mark Zuckerberg poaching someone for a lot of money is like, we were actually seeing historic amount of m and a for basically acquihires, right?[00:17:20] That you like, you know, really good outcomes from a venture perspective that are effective acquihires, right? So I would say it's probably net positive from the investment standpoint, even though it seems from the headlines to be very disruptive in a negative way.[00:17:33] Alessio: Yeah.[00:17:33] What's Underfunded: Boring Software, Robotics Skepticism, and Custom Silicon Economics[00:17:33] Alessio: Um, let's talk maybe about what's not being invested in, like maybe some interesting ideas that you would see more people build or it, it seems in a way, you know, as ycs getting more popular, it's like access getting more popular.[00:17:47] There's a startup school path that a lot of founders take and they know what's hot in the VC circles and they know what gets funded. Uh, and there's maybe not as much risk appetite for. Things outside of that. Um, I'm curious if you feel [00:18:00] like that's true and what are maybe, uh, some of the areas, uh, that you think are under discussed?[00:18:06] Martin Casado: I mean, I actually think that we've taken our eye off the ball in a lot of like, just traditional, you know, software companies. Um, so like, I mean. You know, I think right now there's almost a barbell, like you're like the hot thing on X, you're deep tech.[00:18:21] swyx: Mm-hmm.[00:18:22] Martin Casado: Right. But I, you know, I feel like there's just kind of a long, you know, list of like good.[00:18:28] Good companies that will be around for a long time in very large markets. Say you're building a database, you know, say you're building, um, you know, kind of monitoring or logging or tooling or whatever. There's some good companies out there right now, but like, they have a really hard time getting, um, the attention of investors.[00:18:43] And it's almost become a meme, right? Which is like, if you're not basically growing from zero to a hundred in a year, you're not interesting, which is just, is the silliest thing to say. I mean, think of yourself as like an introvert person, like, like your personal money, right? Mm-hmm. So. Your personal money, will you put it in the stock market at 7% or you put it in this company growing five x in a very large [00:19:00] market?[00:19:00] Of course you can put it in the company five x. So it's just like we say these stupid things, like if you're not going from zero to a hundred, but like those, like who knows what the margins of those are mean. Clearly these are good investments. True for anybody, right? True. Like our LPs want whatever.[00:19:12] Three x net over, you know, the life cycle of a fund, right? So a, a company in a big market growing five X is a great investment. We'd, everybody would be happy with these returns, but we've got this kind of mania on these, these strong growths. And so I would say that that's probably the most underinvested sector.[00:19:28] Right now.[00:19:29] swyx: Boring software, boring enterprise software.[00:19:31] Martin Casado: Traditional. Really good company.[00:19:33] swyx: No, no AI here.[00:19:34] Martin Casado: No. Like boring. Well, well, the AI of course is pulling them into use cases. Yeah, but that's not what they're, they're not on the token path, right? Yeah. Let's just say that like they're software, but they're not on the token path.[00:19:41] Like these are like they're great investments from any definition except for like random VC on Twitter saying VC on x, saying like, it's not growing fast enough. What do you[00:19:52] Sarah Wang: think? Yeah, maybe I'll answer a slightly different. Question, but adjacent to what you asked, um, which is maybe an area that we're not, uh, investing [00:20:00] right now that I think is a question and we're spending a lot of time in regardless of whether we pull the trigger or not.[00:20:05] Um, and it would probably be on the hardware side, actually. Robotics, right? And the robotics side. Robotics. Right. Which is, it's, I don't wanna say that it's not getting funding ‘cause it's clearly, uh, it's, it's sort of non-consensus to almost not invest in robotics at this point. But, um, we spent a lot of time in that space and I think for us, we just haven't seen the chat GPT moment.[00:20:22] Happen on the hardware side. Um, and the funding going into it feels like it's already. Taking that for granted.[00:20:30] Martin Casado: Yeah. Yeah. But we also went through the drone, you know, um, there's a zip line right, right out there. What's that? Oh yeah, there's a zip line. Yeah. What the drone, what the av And like one of the takeaways is when it comes to hardware, um, most companies will end up verticalizing.[00:20:46] Like if you're. If you're investing in a robot company for an A for agriculture, you're investing in an ag company. ‘cause that's the competition and that's surprising. And that's supply chain. And if you're doing it for mining, that's mining. And so the ad team does a lot of that type of stuff ‘cause they actually set up to [00:21:00] diligence that type of work.[00:21:01] But for like horizontal technology investing, there's very little when it comes to robots just because it's so fit for, for purpose. And so we kinda like to look at software. Solutions or horizontal solutions like applied intuition. Clearly from the AV wave deep map, clearly from the AV wave, I would say scale AI was actually a horizontal one for That's fair, you know, for robotics early on.[00:21:23] And so that sort of thing we're very, very interested. But the actual like robot interacting with the world is probably better for different team. Agree.[00:21:30] Alessio: Yeah, I'm curious who these teams are supposed to be that invest in them. I feel like everybody's like, yeah, robotics, it's important and like people should invest in it.[00:21:38] But then when you look at like the numbers, like the capital requirements early on versus like the moment of, okay, this is actually gonna work. Let's keep investing. That seems really hard to predict in a way that is not,[00:21:49] Martin Casado: I think co, CO two, kla, gc, I mean these are all invested in in Harvard companies. He just, you know, and [00:22:00] listen, I mean, it could work this time for sure.[00:22:01] Right? I mean if Elon's doing it, he's like, right. Just, just the fact that Elon's doing it means that there's gonna be a lot of capital and a lot of attempts for a long period of time. So that alone maybe suggests that we should just be investing in robotics just ‘cause you have this North star who's Elon with a humanoid and that's gonna like basically willing into being an industry.[00:22:17] Um, but we've just historically found like. We're a huge believer that this is gonna happen. We just don't feel like we're in a good position to diligence these things. ‘cause again, robotics companies tend to be vertical. You really have to understand the market they're being sold into. Like that's like that competitive equilibrium with a human being is what's important.[00:22:34] It's not like the core tech and like we're kind of more horizontal core tech type investors. And this is Sarah and I. Yeah, the ad team is different. They can actually do these types of things.[00:22:42] swyx: Uh, just to clarify, AD stands for[00:22:44] Martin Casado: American Dynamism.[00:22:45] swyx: Alright. Okay. Yeah, yeah, yeah. Uh, I actually, I do have a related question that, first of all, I wanna acknowledge also just on the, on the chip side.[00:22:51] Yeah. I, I recall a podcast that where you were on, i, I, I think it was the a CC podcast, uh, about two or three years ago where you, where you suddenly said [00:23:00] something, which really stuck in my head about how at some point, at some point kind of scale it makes sense to. Build a custom aic Yes. For per run.[00:23:07] Martin Casado: Yes.[00:23:07] It's crazy. Yeah.[00:23:09] swyx: We're here and I think you, you estimated 500 billion, uh, something.[00:23:12] Martin Casado: No, no, no. A billion, a billion dollar training run of $1 billion training run. It makes sense to actually do a custom meic if you can do it in time. The question now is timelines. Yeah, but not money because just, just, just rough math.[00:23:22] If it's a billion dollar training. Then the inference for that model has to be over a billion, otherwise it won't be solvent. So let's assume it's, if you could save 20%, which you could save much more than that with an ASIC 20%, that's $200 million. You can tape out a chip for $200 million. Right? So now you can literally like justify economically, not timeline wise.[00:23:41] That's a different issue. An ASIC per model, which[00:23:44] swyx: is because that, that's how much we leave on the table every single time. We, we, we do like generic Nvidia.[00:23:48] Martin Casado: Exactly. Exactly. No, it, it is actually much more than that. You could probably get, you know, a factor of two, which would be 500 million.[00:23:54] swyx: Typical MFU would be like 50.[00:23:55] Yeah, yeah. And that's good.[00:23:57] Martin Casado: Exactly. Yeah. Hundred[00:23:57] swyx: percent. Um, so, so, yeah, and I mean, and I [00:24:00] just wanna acknowledge like, here we are in, in, in 2025 and opening eyes confirming like Broadcom and all the other like custom silicon deals, which is incredible. I, I think that, uh, you know, speaking about ad there's, there's a really like interesting tie in that obviously you guys are hit on, which is like these sort, this sort of like America first movement or like sort of re industrialized here.[00:24:17] Yeah. Uh, move TSMC here, if that's possible. Um, how much overlap is there from ad[00:24:23] Martin Casado: Yeah.[00:24:23] swyx: To, I guess, growth and, uh, investing in particularly like, you know, US AI companies that are strongly bounded by their compute.[00:24:32] Martin Casado: Yeah. Yeah. So I mean, I, I would view, I would view AD as more as a market segmentation than like a mission, right?[00:24:37] So the market segmentation is, it has kind of regulatory compliance issues or government, you know, sale or it deals with like hardware. I mean, they're just set up to, to, to, to, to. To diligence those types of companies. So it's a more of a market segmentation thing. I would say the entire firm. You know, which has been since it is been intercepted, you know, has geographical biases, right?[00:24:58] I mean, for the longest time we're like, you [00:25:00] know, bay Area is gonna be like, great, where the majority of the dollars go. Yeah. And, and listen, there, there's actually a lot of compounding effects for having a geographic bias. Right. You know, everybody's in the same place. You've got an ecosystem, you're there, you've got presence, you've got a network.[00:25:12] Um, and, uh, I mean, I would say the Bay area's very much back. You know, like I, I remember during pre COVID, like it was like almost Crypto had kind of. Pulled startups away. Miami from the Bay Area. Miami, yeah. Yeah. New York was, you know, because it's so close to finance, came up like Los Angeles had a moment ‘cause it was so close to consumer, but now it's kind of come back here.[00:25:29] And so I would say, you know, we tend to be very Bay area focused historically, even though of course we've asked all over the world. And then I would say like, if you take the ring out, you know, one more, it's gonna be the US of course, because we know it very well. And then one more is gonna be getting us and its allies and Yeah.[00:25:44] And it goes from there.[00:25:45] Sarah Wang: Yeah,[00:25:45] Martin Casado: sorry.[00:25:46] Sarah Wang: No, no. I agree. I think from a, but I think from the intern that that's sort of like where the companies are headquartered. Maybe your questions on supply chain and customer base. Uh, I, I would say our customers are, are, our companies are fairly international from that perspective.[00:25:59] Like they're selling [00:26:00] globally, right? They have global supply chains in some cases.[00:26:03] Martin Casado: I would say also the stickiness is very different.[00:26:05] Sarah Wang: Yeah.[00:26:05] Martin Casado: Historically between venture and growth, like there's so much company building in venture, so much so like hiring the next PM. Introducing the customer, like all of that stuff.[00:26:15] Like of course we're just gonna be stronger where we have our network and we've been doing business for 20 years. I've been in the Bay Area for 25 years, so clearly I'm just more effective here than I would be somewhere else. Um, where I think, I think for some of the later stage rounds, the companies don't need that much help.[00:26:30] They're already kind of pretty mature historically, so like they can kind of be everywhere. So there's kind of less of that stickiness. This is different in the AI time. I mean, Sarah is now the, uh, chief of staff of like half the AI companies in, uh, in the Bay Area right now. She's like, ops Ninja Biz, Devrel, BizOps.[00:26:48] swyx: Are, are you, are you finding much AI automation in your work? Like what, what is your stack.[00:26:53] Sarah Wang: Oh my, in my personal stack.[00:26:54] swyx: I mean, because like, uh, by the way, it's the, the, the reason for this is it is triggering, uh, yeah. We, like, I'm hiring [00:27:00] ops, ops people. Um, a lot of ponders I know are also hiring ops people and I'm just, you know, it's opportunity Since you're, you're also like basically helping out with ops with a lot of companies.[00:27:09] What are people doing these days? Because it's still very manual as far as I can tell.[00:27:13] Sarah Wang: Hmm. Yeah. I think the things that we help with are pretty network based, um, in that. It's sort of like, Hey, how do do I shortcut this process? Well, let's connect you to the right person. So there's not quite an AI workflow for that.[00:27:26] I will say as a growth investor, Claude Cowork is pretty interesting. Yeah. Like for the first time, you can actually get one shot data analysis. Right. Which, you know, if you're gonna do a customer database, analyze a cohort retention, right? That's just stuff that you had to do by hand before. And our team, the other, it was like midnight and the three of us were playing with Claude Cowork.[00:27:47] We gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like, aha moment. That sounds so boring. But you know, that's, that's the kind of thing that a growth investor is like, [00:28:00] you know, slaving away on late at night. Um, done in a few seconds.[00:28:03] swyx: Yeah. You gotta wonder what the whole, like, philanthropic labs, which is like their new sort of products studio.[00:28:10] Yeah. What would that be worth as an independent, uh, startup? You know, like a[00:28:14] Martin Casado: lot.[00:28:14] Sarah Wang: Yeah, true.[00:28:16] swyx: Yeah. You[00:28:16] Martin Casado: gotta hand it to them. They've been executing incredibly well.[00:28:19] swyx: Yeah. I, I mean, to me, like, you know, philanthropic, like building on cloud code, I think, uh, it makes sense to me the, the real. Um, pedal to the metal, whatever the, the, the phrase is, is when they start coming after consumer with, uh, against OpenAI and like that is like red alert at Open ai.[00:28:35] Oh, I[00:28:35] Martin Casado: think they've been pretty clear. They're enterprise focused.[00:28:37] swyx: They have been, but like they've been free. Here's[00:28:40] Martin Casado: care publicly,[00:28:40] swyx: it's enterprise focused. It's coding. Right. Yeah.[00:28:43] AI Labs vs Startups: Disruption, Undercutting & the Innovator's Dilemma[00:28:43] swyx: And then, and, but here's cloud, cloud, cowork, and, and here's like, well, we, uh, they, apparently they're running Instagram ads for Claudia.[00:28:50] I, on, you know, for, for people on, I get them all the time. Right. And so, like,[00:28:54] Martin Casado: uh,[00:28:54] swyx: it, it's kind of like this, the disruption thing of, uh, you know. Mo Open has been doing, [00:29:00] consumer been doing the, just pursuing general intelligence in every mo modality, and here's a topic that only focus on this thing, but now they're sort of undercutting and doing the whole innovator's dilemma thing on like everything else.[00:29:11] Martin Casado: It's very[00:29:11] swyx: interesting.[00:29:12] Martin Casado: Yeah, I mean there's, there's a very open que so for me there's like, do you know that meme where there's like the guy in the path and there's like a path this way? There's a path this way. Like one which way Western man. Yeah. Yeah.[00:29:23] Two Futures for AI: Infinite Market vs AGI Oligopoly[00:29:23] Martin Casado: And for me, like, like all the entire industry kind of like hinges on like two potential futures.[00:29:29] So in, in one potential future, um, the market is infinitely large. There's perverse economies of scale. ‘cause as soon as you put a model out there, like it kind of sublimates and all the other models catch up and like, it's just like software's being rewritten and fractured all over the place and there's tons of upside and it just grows.[00:29:48] And then there's another path which is like, well. Maybe these models actually generalize really well, and all you have to do is train them with three times more money. That's all you have to [00:30:00] do, and it'll just consume everything beyond it. And if that's the case, like you end up with basically an oligopoly for everything, like, you know mm-hmm.[00:30:06] Because they're perfectly general and like, so this would be like the, the a GI path would be like, these are perfectly general. They can do everything. And this one is like, this is actually normal software. The universe is complicated. You've got, and nobody knows the answer.[00:30:18] The Economics Reality Check: Gross Margins, Training Costs & Borrowing Against the Future[00:30:18] Martin Casado: My belief is if you actually look at the numbers of these companies, so generally if you look at the numbers of these companies, if you look at like the amount they're making and how much they, they spent training the last model, they're gross margin positive.[00:30:30] You're like, oh, that's really working. But if you look at like. The current training that they're doing for the next model, their gross margin negative. So part of me thinks that a lot of ‘em are kind of borrowing against the future and that's gonna have to slow down. It's gonna catch up to them at some point in time, but we don't really know.[00:30:47] Sarah Wang: Yeah.[00:30:47] Martin Casado: Does that make sense? Like, I mean, it could be, it could be the case that the only reason this is working is ‘cause they can raise that next round and they can train that next model. ‘cause these models have such a short. Life. And so at some point in time, like, you know, they won't be able to [00:31:00] raise that next round for the next model and then things will kind of converge and fragment again.[00:31:03] But right now it's not.[00:31:04] Sarah Wang: Totally. I think the other, by the way, just, um, a meta point. I think the other lesson from the last three years is, and we talk about this all the time ‘cause we're on this. Twitter X bubble. Um, cool. But, you know, if you go back to, let's say March, 2024, that period, it felt like a, I think an open source model with an, like a, you know, benchmark leading capability was sort of launching on a daily basis at that point.[00:31:27] And, um, and so that, you know, that's one period. Suddenly it's sort of like open source takes over the world. There's gonna be a plethora. It's not an oligopoly, you know, if you fast, you know, if you, if you rewind time even before that GPT-4 was number one for. Nine months, 10 months. It's a long time. Right.[00:31:44] Um, and of course now we're in this era where it feels like an oligopoly, um, maybe some very steady state shifts and, and you know, it could look like this in the future too, but it just, it's so hard to call. And I think the thing that keeps, you know, us up at [00:32:00] night in, in a good way and bad way, is that the capability progress is actually not slowing down.[00:32:06] And so until that happens, right, like you don't know what's gonna look like.[00:32:09] Martin Casado: But I, I would, I would say for sure it's not converged, like for sure, like the systemic capital flows have not converged, meaning right now it's still borrowing against the future to subsidize growth currently, which you can do that for a period of time.[00:32:23] But, but you know, at the end, at some point the market will rationalize that and just nobody knows what that will look like.[00:32:29] Alessio: Yeah.[00:32:29] Martin Casado: Or, or like the drop in price of compute will, will, will save them. Who knows?[00:32:34] Alessio: Yeah. Yeah. I think the models need to ask them to, to specific tasks. You know? It's like, okay, now Opus 4.5 might be a GI at some specific task, and now you can like depreciate the model over a longer time.[00:32:45] I think now, now, right now there's like no old model.[00:32:47] Martin Casado: No, but let, but lemme just change that mental, that's, that used to be my mental model. Lemme just change it a little bit.[00:32:53] Capital as a Weapon vs Task Saturation: Where Real Enterprise Value Gets Built[00:32:53] Martin Casado: If you can raise three times, if you can raise more than the aggregate of anybody that uses your models, that doesn't even matter.[00:32:59] It doesn't [00:33:00] even matter. See what I'm saying? Like, yeah. Yeah. So, so I have an API Business. My API business is 60% margin, or 70% margin, or 80% margin is a high margin business. So I know what everybody is using. If I can raise more money than the aggregate of everybody that's using it, I will consume them whether I'm a GI or not.[00:33:14] And I will know if they're using it ‘cause they're using it. And like, unlike in the past where engineering stops me from doing that.[00:33:21] Alessio: Mm-hmm.[00:33:21] Martin Casado: It is very straightforward. You just train. So I also thought it was kind of like, you must ask the code a GI, general, general, general. But I think there's also just a possibility that the, that the capital markets will just give them the, the, the ammunition to just go after everybody on top of ‘em.[00:33:36] Sarah Wang: I, I do wonder though, to your point, um, if there's a certain task that. Getting marginally better isn't actually that much better. Like we've asked them to it, to, you know, we can call it a GI or whatever, you know, actually, Ali Goi talks about this, like we're already at a GI for a lot of functions in the enterprise.[00:33:50] Um. That's probably those for those tasks, you probably could build very specific companies that focus on just getting as much value out of that task that isn't [00:34:00] coming from the model itself. There's probably a rich enterprise business to be built there. I mean, could be wrong on that, but there's a lot of interesting examples.[00:34:08] So, right, if you're looking the legal profession or, or whatnot, and maybe that's not a great one ‘cause the models are getting better on that front too, but just something where it's a bit saturated, then the value comes from. Services. It comes from implementation, right? It comes from all these things that actually make it useful to the end customer.[00:34:24] Martin Casado: Sorry, what am I, one more thing I think is, is underused in all of this is like, to what extent every task is a GI complete.[00:34:31] Sarah Wang: Mm-hmm.[00:34:32] Martin Casado: Yeah. I code every day. It's so fun.[00:34:35] Sarah Wang: That's a core question. Yeah.[00:34:36] Martin Casado: And like. When I'm talking to these models, it's not just code. I mean, it's everything, right? Like I, you know, like it's,[00:34:43] swyx: it's healthcare.[00:34:44] It's,[00:34:44] Martin Casado: I mean, it's[00:34:44] swyx: Mele,[00:34:45] Martin Casado: but it's every, it is exactly that. Like, yeah, that's[00:34:47] Sarah Wang: great support. Yeah.[00:34:48] Martin Casado: It's everything. Like I'm asking these models to, yeah, to understand compliance. I'm asking these models to go search the web. I'm asking these models to talk about things I know in the history, like it's having a full conversation with me while I, I engineer, and so it could be [00:35:00] the case that like, mm-hmm.[00:35:01] The most a, you know, a GI complete, like I'm not an a GI guy. Like I think that's, you know, but like the most a GI complete model will is win independent of the task. And we don't know the answer to that one either.[00:35:11] swyx: Yeah.[00:35:12] Martin Casado: But it seems to me that like, listen, codex in my experience is for sure better than Opus 4.5 for coding.[00:35:18] Like it finds the hardest bugs that I work in with. Like, it is, you know. The smartest developers. I don't work on it. It's great. Um, but I think Opus 4.5 is actually very, it's got a great bedside manner and it really, and it, it really matters if you're building something very complex because like, it really, you know, like you're, you're, you're a partner and a brainstorming partner for somebody.[00:35:38] And I think we don't discuss enough how every task kind of has that quality.[00:35:42] swyx: Mm-hmm.[00:35:43] Martin Casado: And what does that mean to like capital investment and like frontier models and Submodels? Yeah.[00:35:47] Why “Coding Models” Keep Collapsing into Generalists (Reasoning vs Taste)[00:35:47] Martin Casado: Like what happened to all the special coding models? Like, none of ‘em worked right. So[00:35:51] Alessio: some of them, they didn't even get released.[00:35:53] Magical[00:35:54] Martin Casado: Devrel. There's a whole, there's a whole host. We saw a bunch of them and like there's this whole theory that like, there could be, and [00:36:00] I think one of the conclusions is, is like there's no such thing as a coding model,[00:36:04] Alessio: you know?[00:36:04] Martin Casado: Like, that's not a thing. Like you're talking to another human being and it's, it's good at coding, but like it's gotta be good at everything.[00:36:10] swyx: Uh, minor disagree only because I, I'm pretty like, have pretty high confidence that basically open eye will always release a GPT five and a GT five codex. Like that's the code's. Yeah. The way I call it is one for raisin, one for Tiz. Um, and, and then like someone internal open, it was like, yeah, that's a good way to frame it.[00:36:32] Martin Casado: That's so funny.[00:36:33] swyx: Uh, but maybe it, maybe it collapses down to reason and that's it. It's not like a hundred dimensions doesn't life. Yeah. It's two dimensions. Yeah, yeah, yeah, yeah. Like and exactly. Beside manner versus coding. Yeah.[00:36:43] Martin Casado: Yeah.[00:36:44] swyx: It's, yeah.[00:36:46] Martin Casado: I, I think for, for any, it's hilarious. For any, for anybody listening to this for, for, for, I mean, for you, like when, when you're like coding or using these models for something like that.[00:36:52] Like actually just like be aware of how much of the interaction has nothing to do with coding and it just turns out to be a large portion of it. And so like, you're, I [00:37:00] think like, like the best Soto ish model. You know, it is going to remain very important no matter what the task is.[00:37:06] swyx: Yeah.[00:37:07] What He's Actually Coding: Gaussian Splats, Spark.js & 3D Scene Rendering Demos[00:37:07] swyx: Uh, speaking of coding, uh, I, I'm gonna be cheeky and ask like, what actually are you coding?[00:37:11] Because obviously you, you could code anything and you are obviously a busy investor and a manager of the good. Giant team. Um, what are you calling?[00:37:18] Martin Casado: I help, um, uh, FEFA at World Labs. Uh, it's one of the investments and um, and they're building a foundation model that creates 3D scenes.[00:37:27] swyx: Yeah, we had it on the pod.[00:37:28] Yeah. Yeah,[00:37:28] Martin Casado: yeah. And so these 3D scenes are Gaussian splats, just by the way that kind of AI works. And so like, you can reconstruct a scene better with, with, with radiance feels than with meshes. ‘cause like they don't really have topology. So, so they, they, they produce each. Beautiful, you know, 3D rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn't great.[00:37:50] It's just never, you know, it's always been meshes and like, things like unreal use meshes. And so I work on a open source library called Spark js, which is a. Uh, [00:38:00] a JavaScript rendering layer ready for Gaussian splats. And it's just because, you know, um, you, you, you need that support and, and right now there's kind of a three js moment that's all meshes and so like, it's become kind of the default in three Js ecosystem.[00:38:13] As part of that to kind of exercise the library, I just build a whole bunch of cool demos. So if you see me on X, you see like all my demos and all the world building, but all of that is just to exercise this, this library that I work on. ‘cause it's actually a very tough algorithmics problem to actually scale a library that much.[00:38:29] And just so you know, this is ancient history now, but 30 years ago I paid for undergrad, you know, working on game engines in college in the late nineties. So I've got actually a back and it's very old background, but I actually have a background in this and so a lot of it's fun. You know, but, but the, the, the, the whole goal is just for this rendering library to, to,[00:38:47] Sarah Wang: are you one of the most active contributors?[00:38:49] The, their GitHub[00:38:50] Martin Casado: spark? Yes.[00:38:51] Sarah Wang: Yeah, yeah.[00:38:51] Martin Casado: There's only two of us there, so, yes. No, so by the way, so the, the pri The pri, yeah. Yeah. So the primary developer is a [00:39:00] guy named Andres Quist, who's an absolute genius. He and I did our, our PhDs together. And so like, um, we studied for constant Quas together. It was almost like hanging out with an old friend, you know?[00:39:09] And so like. So he, he's the core, core guy. I did mostly kind of, you know, the side I run venture fund.[00:39:14] swyx: It's amazing. Like five years ago you would not have done any of this. And it brought you back[00:39:19] Martin Casado: the act, the Activ energy, you're still back. Energy was so high because you had to learn all the framework b******t.[00:39:23] Man, I f*****g used to hate that. And so like, now I don't have to deal with that. I can like focus on the algorithmics so I can focus on the scaling and I,[00:39:29] swyx: yeah. Yeah.[00:39:29] LLMs vs Spatial Intelligence + How to Value World Labs' 3D Foundation Model[00:39:29] swyx: And then, uh, I'll observe one irony and then I'll ask a serious investor question, uh, which is like, the irony is FFE actually doesn't believe that LMS can lead us to spatial intelligence.[00:39:37] And here you are using LMS to like help like achieve spatial intelligence. I just see, I see some like disconnect in there.[00:39:45] Martin Casado: Yeah. Yeah. So I think, I think, you know, I think, I think what she would say is LLMs are great to help with coding.[00:39:51] swyx: Yes.[00:39:51] Martin Casado: But like, that's very different than a model that actually like provides, they, they'll never have the[00:39:56] swyx: spatial inte[00:39:56] Martin Casado: issues.[00:39:56] And listen, our brains clearly listen, our brains, brains clearly have [00:40:00] both our, our brains clearly have a language reasoning section and they clearly have a spatial reasoning section. I mean, it's just, you know, these are two pretty independent problems.[00:40:07] swyx: Okay. And you, you, like, I, I would say that the, the one data point I recently had, uh, against it is the DeepMind, uh, IMO Gold, where, so, uh, typically the, the typical answer is that this is where you start going down the neuros symbolic path, right?[00:40:21] Like one, uh, sort of very sort of abstract reasoning thing and one form, formal thing. Um, and that's what. DeepMind had in 2024 with alpha proof, alpha geometry, and now they just use deep think and just extended thinking tokens. And it's one model and it's, and it's in LM.[00:40:36] Martin Casado: Yeah, yeah, yeah, yeah, yeah.[00:40:37] swyx: And so that, that was my indication of like, maybe you don't need a separate system.[00:40:42] Martin Casado: Yeah. So, so let me step back. I mean, at the end of the day, at the end of the day, these things are like nodes in a graph with weights on them. Right. You know, like it can be modeled like if you, if you distill it down. But let me just talk about the two different substrates. Let's, let me put you in a dark room.[00:40:56] Like totally black room. And then let me just [00:41:00] describe how you exit it. Like to your left, there's a table like duck below this thing, right? I mean like the chances that you're gonna like not run into something are very low. Now let me like turn on the light and you actually see, and you can do distance and you know how far something away is and like where it is or whatever.[00:41:17] Then you can do it, right? Like language is not the right primitives to describe. The universe because it's not exact enough. So that's all Faye, Faye is talking about. When it comes to like spatial reasoning, it's like you actually have to know that this is three feet far, like that far away. It is curved.[00:41:37] You have to understand, you know, the, like the actual movement through space.[00:41:40] swyx: Yeah.[00:41:40] Martin Casado: So I do, I listen, I do think at the end of these models are definitely converging as far as models, but there's, there's, there's different representations of problems you're solving. One is language. Which, you know, that would be like describing to somebody like what to do.[00:41:51] And the other one is actually just showing them and the space reasoning is just showing them.[00:41:55] swyx: Yeah, yeah, yeah. Right. Got it, got it. Uh, the, in the investor question was on, on, well labs [00:42:00] is, well, like, how do I value something like this? What, what, what work does the, do you do? I'm just like, Fefe is awesome.[00:42:07] Justin's awesome. And you know, the other two co-founder, co-founders, but like the, the, the tech, everyone's building cool tech. But like, what's the value of the tech? And this is the fundamental question[00:42:16] Martin Casado: of, well, let, let, just like these, let me just maybe give you a rough sketch on the diffusion models. I actually love to hear Sarah because I'm a venture for, you know, so like, ventures always, always like kind of wild west type[00:42:24] swyx: stuff.[00:42:24] You, you, you, you paid a dream and she has to like, actually[00:42:28] Martin Casado: I'm gonna say I'm gonna mar to reality, so I'm gonna say the venture for you. And she can be like, okay, you a little kid. Yeah. So like, so, so these diffusion models literally. Create something for, for almost nothing. And something that the, the world has found to be very valuable in the past, in our real markets, right?[00:42:45] Like, like a 2D image. I mean, that's been an entire market. People value them. It takes a human being a long time to create it, right? I mean, to create a, you know, a, to turn me into a whatever, like an image would cost a hundred bucks in an hour. The inference cost [00:43:00] us a hundredth of a penny, right? So we've seen this with speech in very successful companies.[00:43:03] We've seen this with 2D image. We've seen this with movies. Right? Now, think about 3D scene. I mean, I mean, when's Grand Theft Auto coming out? It's been six, what? It's been 10 years. I mean, how, how like, but hasn't been 10 years.[00:43:14] Alessio: Yeah.[00:43:15] Martin Casado: How much would it cost to like, to reproduce this room in 3D? Right. If you, if you, if you hired somebody on fiber, like in, in any sort of quality, probably 4,000 to $10,000.[00:43:24] And then if you had a professional, probably $30,000. So if you could generate the exact same thing from a 2D image, and we know that these are used and they're using Unreal and they're using Blend, or they're using movies and they're using video games and they're using all. So if you could do that for.[00:43:36] You know, less than a dollar, that's four or five orders of magnitude cheaper. So you're bringing the marginal cost of something that's useful down by three orders of magnitude, which historically have created very large companies. So that would be like the venture kind of strategic dreaming map.[00:43:49] swyx: Yeah.[00:43:50] And, and for listeners, uh, you can do this yourself on your, on your own phone with like. Uh, the marble.[00:43:55] Martin Casado: Yeah. Marble.[00:43:55] swyx: Uh, or but also there's many Nerf apps where you just go on your iPhone and, and do this.[00:43:59] Martin Casado: Yeah. Yeah. [00:44:00] Yeah. And, and in the case of marble though, it would, what you do is you literally give it in.[00:44:03] So most Nerf apps you like kind of run around and take a whole bunch of pictures and then you kind of reconstruct it.[00:44:08] swyx: Yeah.[00:44:08] Martin Casado: Um, things like marble, just that the whole generative 3D space will just take a 2D image and it'll reconstruct all the like, like[00:44:16] swyx: meaning it has to fill in. Uh,[00:44:18] Martin Casado: stuff at the back of the table, under the table, the back, like, like the images, it doesn't see.[00:44:22] So the generator stuff is very different than reconstruction that it fills in the things that you can't see.[00:44:26] swyx: Yeah. Okay.[00:44:26] Sarah Wang: So,[00:44:27] Martin Casado: all right. So now the,[00:44:28] Sarah Wang: no, no. I mean I love that[00:44:29] Martin Casado: the adult[00:44:29] Sarah Wang: perspective. Um, well, no, I was gonna say these are very much a tag team. So we, we started this pod with that, um, premise. And I think this is a perfect question to even build on that further.[00:44:36] ‘cause it truly is, I mean, we're tag teaming all of these together.[00:44:39] Investing in Model Labs, Media Rumors, and the Cursor Playbook (Margins & Going Down-Stack)[00:44:39] Sarah Wang: Um, but I think every investment fundamentally starts with the same. Maybe the same two premises. One is, at this point in time, we actually believe that there are. And of one founders for their particular craft, and they have to be demonstrated in their prior careers, right?[00:44:56] So, uh, we're not investing in every, you know, now the term is NEO [00:45:00] lab, but every foundation model, uh, any, any company, any founder trying to build a foundation model, we're not, um, contrary to popular opinion, we're

Episode: #444 Hosts: Andy Shiles & Lalo Solorzano Guest: Kristi App, Chief Commercial Officer, Port of New Orleans Published: February 2026 Length: ~35 minutes Presented by: Global Training Center

If you dread opening Slack on Monday mornings, if you can't take time off without chaos, if you're working all day only to finish admin tasks at night, your business doesn't fit your life anymore. It's not that you fell out of love with your business. The structure of how it runs doesn't support the life you actually want.I'm breaking down the signs your business has become a prison (dread, schedule dictated by business, rest that doesn't restore you), the pivot points where you chose the wrong path (saying "it's faster if I do it," staying in execution when you should delegate, normalizing exhaustion), and the complete LIFE FIT Framework to audit your business: Lifestyle alignment, Income sustainability, Function and flow, Energy and role alignment, Flexibility and seasonality, Infrastructure and support, Trajectory and longevity.This isn't about blowing up your entire business. It's about identifying the biggest constraint and making strategic refinements. If you're making great revenue but feel trapped, this framework shows you exactly what needs to change.Timestamps:02:26 Your Life Changed: Did Your Business Evolve Too?04:23 Red Flags: Dread, Anxiety, and a Business That Feels Heavy09:21 The ‘Brick Wall' Effect: Micro-Decisions That Trap You11:31 From Operator to CEO: Raising the Lid of Leadership14:23 Future-Proofing: ‘This Won't Work in 5–10 Years'20:46 Boundary Breakers: ‘It's Faster If I Do It' (Delegation Traps)23:38 Staying in Delivery: Structuring Offers Around Your Life28:48 Stop forcing a strategy: choose marketing that matches your lifestyle29:58 Personal brand vs. founder-led: are you willing to be the magnet long-term?37:21 Rerouting to alignment: introduce the Life Fit Framework + define your destination42:00 Life Fit audit: L-I-F-E-F-I-T questions to score your business47:37 What to do with your scores: fix the biggest constraint (don't rebuild everything)To join the Ambitious Network for free, click HERE. To connect with Kate on Instagram, click ⁠HERE⁠. To apply for ITI, click ⁠HERE⁠.To submit a question to be answered on the podcast, click HERE.

Today's episode breaks down Christian Briggs' Part Two of his policy paper, "China's Strategic Assault on Dollar Hegemony Through Banking Infrastructure, Critical Mineral Dominance, and the Architecture of De-Dollarization". What we're witnessing isn't just economic competition—it's a coordinated financial war against the United States. According to the breakdown, China, Russia, and the expanding BRICS alliance are executing a decades-long strategy to dismantle dollar dominance and build a parallel global financial system that cuts America out entirely.The podcast argues that the weaponization of sanctions—especially after the Russia-Ukraine conflict—was the turning point. When the U.S. froze foreign reserves, it sent a signal to the world: your money isn't safe in dollars. Since then, nations have been racing to protect themselves by abandoning U.S.-controlled systems like SWIFT and moving toward alternative settlement rails.At the center of this shift? China's Cross-Border Interbank Payment System (CIPS) and the rapid growth of BRICS as a financial counterweight to the West. Countries that once depended on dollar settlements are now trading in yuan, rubles, and rupees. The episode warns that this isn't symbolic diplomacy—it's structural separation.Then comes the gold bombshell.Central banks around the world are hoarding gold at record levels. Why? Because gold doesn't freeze. It doesn't get sanctioned. It doesn't require U.S. permission. The host frames this as the clearest signal yet that global leaders are hedging against a weakening dollar.But it gets even bigger.The BRICS bloc is reportedly developing a gold-backed settlement mechanism—sometimes referred to as the “Unit”—designed to operate completely outside the dollar system. Combine that with multilateral digital currency platforms like mBridge, and you have the skeleton of an entirely new monetary architecture forming in real time.Meanwhile, the episode raises alarming questions about U.S. regulatory policy. Why are Chinese banks allegedly linked to financial misconduct still operating under U.S. licenses? Why is Basel III reshaping Western banking rules while Eastern nations aggressively accumulate hard assets?The conclusion is stark: this isn't just about trade. It's about power.If the dollar loses its reserve dominance, America's geopolitical leverage shrinks overnight. The podcast leaves listeners with a sobering message—the global financial order is shifting, and whether by strategy or complacency, the United States may already be late to the fight.

Description: In this episode, we dive deep into Tesla's long-promised Cybercab as production reportedly begins and Elon Musk claims a sub-$30,000 price tag could arrive this year. We examine conflicting narratives around Tesla's robotaxi progress, including sharp criticism of the program eight months in and fresh timeline promises. Tesla's software push continues with Grok-enabled navigation in Europe and a renewed FSD launch in China backed by a local data center. Meanwhile, Tesla faces mounting pressure overseas, with UK sales plunging as BYD surges ahead and broader EV market share slipping despite overall auto growth. Legacy automakers aren't sitting still either—Ford is lobbying for access to Chinese EV tech, while BYD and Geely eye major North American production capacity. We also cover Polestar's aggressive expansion plans, Cruise's admission over a false pedestrian report, and the evolving EV policy and infrastructure conversation in Manitoba. Support the Show Support Kilowatt Other Podcasts: Beyond the Post YouTube Beyond the Post Podcast Shuffle Playlist 918Digital Website News Links: Manitoba EV Policy & Infrastructure Future of Electric Vehicle Sales Targets and Infrastructure in Manitoba (CBC Audio) Manitobans Welcome EV Rebates, but Fear Infrastructure Still Lacking Manitoba EV Fans Charged Up by Federal Strategy Tesla Cybercab & Robotaxi Developments Elon Musk Says Tesla Will Sell Cybercab to Customers for $30,000 or Less This Year Tesla's $30,000 Cybercab Begins Production With No Steering Wheel Tesla Begins Cybercab Production. Now Comes The Hard Part Elon Musk Doubles Down on Tesla Cybercab Timeline Once Again Tesla ‘Robotaxi' Status Check 8 Months In: A Complete Joke Tesla Software & Global Strategy Tesla to Re-Launch FSD in China With Local Data Center Tesla Launches Grok With Nav Commands in Europe Tesla UK Sales Plunge 57% in January as BYD Races Ahead New Car Market Starts Year With Growth but EV Share Falls – SMMT Competition & Industry Shifts BYD, Geely Bid for 230,000-Unit Nissan-Mercedes Mexico Plant in North American Push Ford Asks Trump Administration to Allow Chinese EV Tech in the US Polestar Goes Offensive With Four New Models in Three Years Autonomous & Policy News Cruise Admits to False Report in 2023 Dragging of San Francisco Pedestrian Show Art Created By Dall-E Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of Founded & Funded, Ben Gilbert, co-host of the Acquired podcast, sits down with Chase Lochmiller, co-founder and CEO of Crusoe-AI, the company building what it calls AI factories, including its massive campus in Abilene, Texas, which are designed to power this new era of intelligence. In this conversation, Ben and Chase explore the physical reality behind today's AI revolution. Why modern AI workloads demand entirely new infrastructure. How energy has become the primary bottleneck to scaling intelligence. What it takes to compress multi-year building timelines into months. And how Crusoe's energy-first philosophy, from capturing flared methane to siting facilities near abundant wind power, shaped its path to building one of the world's largest AI computing campuses. This is a must-watch for anyone building in AI or rethinking infrastructure for the next era of intelligence. Full Transcript: https://www.madrona.com/the-infrastructure-of-intelligence-inside-crusoes-ai-factory-in-texas Chapters: (00:00) - Introduction (02:46) - Scale and Power Requirements (03:55) - Job Creation and Construction Progress (05:11) - Creative Solutions and Manufacturing Capabilities (06:25) - Modular Design and Infrastructure Optimization (07:42) - Data Center Construction and Assembly Process (09:07) - Technical Infrastructure and Cooling System (10:33) - Power Sourcing and Renewable Energy (11:56) - Wind Energy Utilization and AI Infrastructure (13:16) - AI Workload Flexibility and Energy Considerations (14:38) - Entrepreneurial Journey and Company Evolution (16:11) - Background in AI and Transition to Data Centers (17:40) - Early Business Model and Bitcoin Mining (19:14) - Infrastructure Evolution and Future Outlook (21:17) - Cloud Platform Services

Kat Traxler, Principal Security Researcher at Vectra AI, returns to the podcast to discuss her AI-powered vulnerability research workflow. She explains how she uses two different AI models to act as the “blackboard” while she applies her expertise to triage AI-generated ideas to increase her productivity. She also asks a concerning question: As AI automates... Read more »

Supply chains are recalibrating, and the Middle East and Africa are investing aggressively to meet the moment.In this episode of Supply Chain Now, Scott W. Luton and special guest co-host Yaseen Ahmid welcome Toby Maier, CEO for Middle East and Africa at DHL Global Forwarding, for a wide-ranging conversation on what is changing trade and logistics across the region. Toby breaks down how recent tariffs are redirecting export flows into the Middle East and Africa, why GCC countries are racing to build world-class logistics hubs, and how production is shifting from Turkey toward markets like Egypt and Morocco.They also explore what it will take to build stronger, more reliable supply chains across Africa, from investment in life sciences and healthcare infrastructure to modernized regulation that reduces delays at customs. Toby shares how DHL's publicly announced $300 million investment through 2030 prioritizes end-to-end capability that helps medicines, vaccines, and other critical products reach communities across a fast-growing population. The conversation also tackles the practical realities of energy access, data centers, and the cost to deliver goods, plus how sustainability efforts like electrified fleets and sustainable aviation fuel can support performance and emissions goals at the same time.Jump into the conversation:(00:00) Intro(03:13) Getting to know guest Toby Maier and co-host Yaseen Ahmid(06:05) Toby's journey in global logistics leadership(11:17) Trade shifts and what they mean for Africa(15:24) DHL's investment focus across Africa(18:18) Infrastructure and power realities on the ground(22:50) Building efficiency and sustainability into the network(24:22) Renewable energy progress and practical pathways(26:37) What commitment to sustainability looks like at DHL(30:26) Developing talent and leadership across the continent(40:09) Why emerging markets belong on your career mapAdditional Links & Resources:Connect with Toby Maier: https://www.linkedin.com/in/toby-maier/Connect with Yaseen Ahmid: https://www.linkedin.com/in/yaseen-ahmid/Learn more about DHL Global Forwarding: https://www.dhl.com/Learn more about Luna: https://luna-resume.com/Learn more about our hosts: https://supplychainnow.com/aboutLearn more about Supply Chain Now: https://supplychainnow.comWatch and listen to more Supply Chain Now episodes here:

In this episode of Climate Positive, Gil Jenkins speaks with Malcolm Woolf, President and CEO of the National Hydropower Association (NHA). They discuss the current state of the U.S. hydropower industry, its role in providing carbon-free electricity, and the challenges and opportunities facing the sector. A central focus of the conversation is the hydropower relicensing process -- how it works, where projects can stall, and how lengthy reviews can delay investment, upgrades, and in some cases lead facilities to shut down.Malcolm shares real-world examples to illustrate what's at stake, while also exploring the potential to add generation to non-powered dams, the role of pumped storage in supporting grid reliability, and emerging marine energy technologies.Links:NHA WebsiteMalcom Woolf LinkedInNHA on LinkedInPress Release: The Hydropower Foundation and NHA Align to Strengthen Workforce Development EffortsArticle: US hydropower is at a make-or-break momentArticle: Google to buy up to 3 GW of hydro power from BrookfieldVideo: Whooshh Innovations' "Salmon Cannon" Gives Fish A Boost Over Dams Email your feedback to Chad, Gil, Hilary, and Guy at climatepositive@hasi.com.

Kat Traxler, Principal Security Researcher at Vectra AI, returns to the podcast to discuss her AI-powered vulnerability research workflow. She explains how she uses two different AI models to act as the “blackboard” while she applies her expertise to triage AI-generated ideas to increase her productivity. She also asks a concerning question: As AI automates... Read more »

Tech commentator Tony Grasso looks at new warnings from security agencies here and in Australia about 'Volt Typhoon', a Chinese-sponsored hacking group that looks to disrupt critical infrastructure. 

Market update for Wednesday February 18, 2026 Check out the Public app for incredible investing tools and to support the show (LINK)Follow us on Instagram (@TheRundownDaily) for bonus content and instant reactions.In today's episode, Zaid covers:Berkshire Hathaway slashes Amazon stake, trims AppleGold and silver pull back after recent runMeta strikes multiyear deal to buy millions of Nvidia AI chipsUber invests $100M in autonomous vehicle charging hubsCaesars jumps on Vegas reboundSandisk falls after Western Digital announces $3.1B share saleRecord CEO turnover hits Corporate America, and leaders are getting younger

Today's episode breaks down Christian Briggs' Part One of his policy paper, arguing that China is running a two-front campaign aimed at weakening U.S. power: a global banking machine and a chokehold on critical minerals.lays out a blunt warning: China is executing a coordinated, two-pronged operation to collapse American leverage—without firing a shot. The first weapon is finance. The second is resources. And both are aimed straight at dollar dominance, U.S. sovereignty, and national security.Part One of the policy paper argues that Chinese state-controlled mega-banks—sitting on $23+ trillion in assets—aren't “banks” in the Western sense. They're arms of the CCP, deployed across 40+ countries to bankroll Belt & Road expansion, lock nations into Beijing-controlled debt relationships, and build the plumbing for a post-dollar world through alternative settlement systems. The podcast stresses that China's banking reach in Latin America and the Caribbean, plus infrastructure positioning near the Panama Canal, isn't business—it's strategic encirclement of the Western Hemisphere.Then comes the chokehold: critical minerals. The episode claims China has monopolized the materials that power everything America needs to function—defense systems, AI hardware, clean energy, advanced manufacturing—with dominance that reaches near-total control in rare-earth processing and permanent magnets. Export controls aren't “trade policy.” They're resource warfare, a warning shot that says: We control the inputs. You don't.The podcast doesn't mince words about how we got here: while China declared minerals strategic, restricted foreign involvement, and built industrial capacity, the U.S. allegedly regulated itself into dependence—outsourcing the supply chain to an adversary.Now Washington is scrambling. The paper frames late-2025/early-2026 moves as a reboot of the 1974 petrodollar playbook—but updated into a “mineral dollar” strategy: build a minerals security bloc (a “minerals NATO”), force alignment, and use commodity control to prop up the dollar as the old system weakens. Even gold's absence from the critical list is portrayed as intentional sequencing, not an oversight.Bottom line: China's checkmate is already on the board. The only question is whether America wakes up before the embargoes—and the dollar shock—hit.

In this segment, Mark is joined by Jeff Rainford, with Rainford & Associates and a Former Chief of Staff to Mayor Slay. He shares his take on the St Louis Business Journal's piece that outlines all of the infrastructure problems facing the city of St. Louis.

The Government's Employment Relations Amendment Bill has passed its third and final reading. The Government has revealed the first-ever National Infrastructure Plan. And the Salvation Army has released its annual State of the Nation report for 2026. For our weekly catch-up with the Labour Party, Wire Host Caeden spoke to MP Shanan Halbert about all of these topics.

In this episode, we sit down with Anish Agarwal, CEO and Co-founder of Traversal, for a deep dive into the substance behind the AI noise. Anish, a former Columbia professor and researcher in causal AI, shares his unique journey from academia to founding an organization disrupting the site reliability engineering (SRE) and observability space. We explore the critical difference between "AI wrappers" and companies building genuine infrastructure, the emergence of the "Forward Deployed Engineer" in the sales pod, and how to identify technical moats in a world where models are rapidly evolving.

How Utilities Are Changing While Keeping The Lights On – Nicole Pearson & Brad Johnson of Bentley Systems   "The really great thing about electric utilities is that they maintain a singular focus almost regardless of what's going on around them. It's safety, reliability, resilience. So they'll adapt and flex…They're not going to take a risk that sacrifices safety, that sacrifices your light coming on….The difference now that I've noticed is their willingness to move forward with technology and change decades, long processes and workflows and legacy built systems……. because they see how they can still maintain and maintain more efficiently, safety, reliability, resiliency." Nicole Pearson on Electric Ladies Podcast   Utilities are being transformed even as they keep the lights on. They have to become more resilient to the effects of climate changes and be responsive to new energy sources and technologies and even invent new business models while also regulated – all without dropping a moment of power.  How? Listen to Nicole Pearson, Director of Marketing for Energy and Brad Johnson, Industry head of Energy Systems at Bentley Systems in this fascinating conversation with Electric Ladies Podcast host Joan Michelson. It was recorded live at the Bentley Systems "Year in Infrastructure" 2025 conference. You'll hear about: ●        How utilities have evolved and are evolving without risking service, safety, reliability and under the regulatory microscope. ●        How Bentley Systems' software is reducing risk and increasing resilience of utilities. ●        How utilities are leveraging A.I. while monitoring for cyberattacks and hallucinations to keep systems, workers and consumers safe. ●        Utilities have to plan decades ahead, even 30+ years ahead, yet technologies evolve so fast. How can they plan that far ahead? ●        Plus, career advice, such as:   "The first one is own it. Own it. What is it? Your career. And when I say own it, I'm constantly, even today having meetings with people that I don't know, requesting mentorship, reaching out through LinkedIn, going to events. I try to stay very connected and not just people in my close industry or even work type …Every single person you meet is a connection and could have an impact on your life and vice versa….When you meet somebody, keep the conversation going. …So one day if they need something or you need something, you have a connection….My second piece of advice is… you should always have a list of things that you want to accomplish, that you want to put on your résumé. And regardless of what's going on around you in your job, focus on those things." Nicole Pearson on Electric Ladies Podcast     Read Joan's Forbes article on whether A.I. makes our infrastructure safer or not here, and her Joan's other Forbes articles here.   You'll also like: ·       Using Software & AI to Reduce CO2 & Increase Resilience – Lydia Walpole & Chris Bradshaw of Bentley Systems ·       Leveraging AI for Sustainability – Mandi McReynolds, VP of External Affairs & Chief Sustainability Office at Workiva ·       Artificial Intelligence and the Climate: Stephanie Hare, Ph.D, author of "Technology is Not Neutral" and BBC Broadcaster ·       How Design & Technology Are Redesigning Cities: Nikki Greenberg, Real Estate of the Future, live at the Smart City Expo World Congress 2025 ·       88% of Companies Say Sustainability Increases Long-Term Value: Maura Hodge, Chief Sustainability Officer, KPMG ·       The Politics of Climate & Energy – with Congresswoman Chrissy Houlahan, Co-Chair, Bipartisan Climate Solutions Caucus   Subscribe to our newsletter to receive our podcasts, blog, events and special coaching offers.   Thanks for subscribing on Apple Podcasts or iHeartRadio and leaving us a review! Follow us on Twitter @joanmichelson

Mike Kelly joins Brandon Sedloff to share the journey that took him from a first-generation college student in Queens to co-President and Chief Investment Officer of Future Standard, an $88 billion alternative investment manager. He reflects on the formative experiences that shaped his investing philosophy, from cold-calling Lee Cooperman for an internship to working at Tiger Management and helping build FrontPoint. The conversation traces the evolution of alternatives from family offices and endowments to the private wealth channel, and how Future Standard has positioned itself at the center of that shift by building both distribution infrastructure and in-house investment capabilities. They discuss: How Mike broke into hedge funds by cold-calling Lee Cooperman and what he learned at Omega and Tiger Management The historical arc of alternatives from the endowment model to today's private wealth opportunity The evolution of Franklin Square into Future Standard and the shift from packaging and distribution to internally managed strategies Why the middle market offers structural advantages across private credit, private equity, and real assets The case for rethinking 60 40 portfolios in a more inflationary, deglobalized macro regime Links: Future Standard - https://www.futurestandard.com/ Mike on LinkedIn - https://www.linkedin.com/in/mike-kelly-9b32166/ Brandon on LinkedIn - ⁠https://www.linkedin.com/in/bsedloff/⁠ Juniper Square - ⁠https://www.junipersquare.com/⁠ Topics: (00:00:00) - Intro (00:03:16) - Mike Kelly's career journey (00:04:21) - Early influences and education (00:06:43) - Breaking into the investment world (00:19:47) - Joining Future Standard (00:20:12) - Evolution of Future Standard (00:22:45) - Distribution and internal management (00:25:17) - Infrastructure and operations (00:27:57) - The commitment of investment management (00:28:52) - Future Standard's focus areas (00:32:06) - Evergreen structures in investment (00:39:13) - The new economic regime (00:50:10) - The future of asset management (00:54:35) - Conclusion and final thoughts

Christian Magoon, CEO of Amplify ETFs, introduces some of the firm's products to viewers. The largest inflows have been into international ETFs and their U.S.-based Enhanced Dividend ETF (DIVO). They also offer IDVO, which is an international version of DIVO. Junior Silver Miners (SILJ) had a “great year last year” and continues to see strength. Another option is their SLJY, a covered call strategy on silver miners. Natural gas is a “heck of an investment,” and he highlights Amplify's U.S. Nat Gas Infrastructure ETF (USNG). Lastly, he shares their blockchain ETF, BLOK. ======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

Series 5 – Episode 5 Hosts: Renee Chiuchiarelli & Julie Parks (Hammer & Heels) Length: ~12 minutes Format: Simply Trade Tips Episode Summary In this episode of Simply Trade Tips, Renee and Julie tackle a topic that doesn't always get the spotlight it deserves: learning. With trade rules shifting constantly — tariff changes, executive orders, enforcement priorities, and regulatory updates — relying on static training or tribal knowledge simply isn't enough. Traditional “calendar training” (scheduled webinars, annual sessions, policy rollouts) still has value, but it's disconnected from daily operations. The big idea? Learning must move from a side activity to core infrastructure. Renee and Julie introduce the concept of “inflow learning” — contextual, real-time training embedded directly into workflow systems. Instead of pausing work to learn, professionals access guidance at the exact moment they need it. This shift makes compliance more resilient, scalable, and defensible in today's enforcement environment. Key Topics Covered Why static training creates operational gaps The difference between: Calendar training (scheduled sessions) Inflow training (embedded, contextual learning) How automation can trigger learning during: Origin determinations Classification decisions Filing processes Audit reviews Role-based learning paths for importers, exporters, and compliance teams Micro-learning triggered by real-time errors Using AI to generate contextual training from existing materials Why regulators expect documented training as part of “reasonable care” How embedded learning reduces repeat errors and improves confidence Why knowledge in trade is no longer theoretical — it impacts entries, exports, and balance sheets immediately Key Takeaways Learning must be integrated into daily workflow, not isolated from it Automation supports better decision-making without replacing expertise Real-time learning reduces repeat errors and strengthens defensibility Training documentation can become powerful evidence during audits Trade compliance in today's environment requires resilience, not just proactivity This Episode's FIO (Figure It Out) Take a moment to evaluate your current training model: Is your team relying mostly on scheduled sessions? Do your systems provide contextual, real-time learning prompts? Have you asked your software provider about embedded guidance tools? Have you explored advanced or hybrid learning models that combine live instruction with digital access? Dip your toe in. Explore what's available. Demand better integration between learning and operations. Join the Conversation How is your organization approaching trade training in 2026? Are you relying on memory — or building infrastructure? Join us inside the Trade Geeks Community and share what you're doing to make learning more resilient. Credits Hosts: Renee Chiuchiarelli Julie Parks Producer: Lalo Solorzano

This episode is brought to you by B2B Better. Stop sending "just checking in" emails. We build owned media systems that give your sales team actual reasons to reach out, turning podcasts into sales enablement assets that move deals forward. If your sales reps send "just checking in" emails to prospects who've gone quiet, this episode explains why those fail and what to do instead. Host Jason Bradwell breaks down how to create milestone moments, legitimate, value-led reasons to reach out through long sales cycles without sounding desperate. Jason's core point is clear: most B2B sales cycles are brutally long, but sales teams don't know how to stay present without being annoying. The traditional approach fails. Discovery call, proposal sent, then "just checking in" emails with no response. Twelve months later, the prospect went with a competitor because "you didn't understand our business." After month two, you had nothing valuable to say. 95% of B2B Better's clients have sales cycles over six months. And 95% of your customers are out of market at any given time. Your job is to stay present so when they flip to being in market, they think of you first. Most owned media falls apart here. Marketing creates beautiful content. Sales sees it but has no idea how to use it in actual sales motion. Marketing measures downloads. Sales measures meetings. Different languages, different outcomes. Milestone moments fix this. Month one: send proposal plus a podcast clip addressing their exact challenge. Month three: benchmark report. Month four: webinar invite. Month five: customer story. Month six: check in with context. Months seven to twelve: new episodes create new reasons to reach out. Month eighteen: deal closes because you felt like a partner. B2B Better's sales enablement kits deliver three clips with email copy, key graphics, follow-up sequences, and tags showing which funnel stage each piece serves. One innovation: sales stitch videos where reps record 30-second reactions to clips. Personal brands beat company brands these generate thousands of views when originals get hundreds. This works with three things: marketing-sales alignment through quarterly planning, infrastructure for sales to contribute to production, and attribution through CRM tracking plus conversations about where content surfaces in deals. Chapter Markers 00:00 - The "just checking in" problem that kills deals 01:00 - Context on long sales cycles and 95% out-of-market buyers 02:00 - Why owned media strategies fall apart at sales activation 03:00 - Traditional approach: proposal to dead deal in 12 months 04:00 - Milestone moments approach: 18-month cycle done right 06:00 - What qualifies as a milestone moment 07:00 - Timing and sequencing content to the buyer journey 08:00 - Sales enablement kit components 09:00 - Tags and metadata for searchable, attributable content 10:00 - Sales stitch videos: personalising content at scale 11:00 - Why personal brands beat company brands in B2B 12:00 - Three requirements: alignment between teams 13:00 - Infrastructure for sales to contribute to production 14:00 - Attribution: quantitative and qualitative tracking 15:00 - The challenge: audit your last five gone-quiet emails Useful Links Connect with Jason Bradwell on LinkedIn Read the Ehrenberg-Bass 95-5 rule research Explore HubSpot CRM for tracking content touches Check out Salesforce CRM Explore B2B Better website and the Pipe Dream podcast 

Link to episode page This week's Department of Know is hosted by Sarah Lane with guests Jon Collins, Field CTO, GigaOm, and Adam Palmer, CISO, First Hawaiian Bank Thanks to our show sponsor, Conveyor Ever dream of giving customers instant answers to their security questions without ever filling out another questionnaire? Meet Conveyor's new Trust Center Agent. The Agent lives in your Conveyor Trust Center and answers every customer question, surfaces documents and even completes full questionnaires instantly so customers can finish their review and be on their way. Top tech companies like Atlassian, Zapier, and more are using Conveyor to automate away tedious work. Learn more at www. conveyor.com. All links and the video of this episode can be found on CISO Series.com    

The country's first National Infrastructure Plan has been revealed, detailing an 'affordable' plan to tackle the country's infrastructure woes. It said building and maintaining infrastructure was becoming more expensive as climate change was making the natural hazard risks more severe. Minister for Infrastructure Chris Bishop spoke to Lisa Owen.

Nicola Willis and Carmel Sepuloni joined Morning Report this morning for the Weekly Political Panel.

A new report that sets out an "affordable" plan to tackle New Zealand's infrastructure has just been released. It was written by the Infrastructure Commission, and it covers all aspects of infrastructure from water pipes, roads, power lines, hospitals and schools to courts. In the 200 plus page report, there are 16 recommendations and 10 priorities for the next ten years. RNZ Political reporter Anneke Smith joins Jesse to discuss it.

Can your business make a million in one year?Most people will say no. Not because it's impossible, but because they're thinking about it the wrong way. Making your first $1 Million is not about hustle. It's not about stacking side projects. It's not about 14 income streams and burnout disguised as ambition.It's about leverage.Leverage over effort.Outcomes over deliverables.Focus over distraction.If your income is tied directly to your time, you're capped. If you're solving small problems, you're paid small money. If you're scattered across too many offers, too many audiences, too many channels, you're diluted.The path to $1 Million requires three uncomfortable shifts:Obsess over leverage, not effort.Solve a $10 Million problem to earn $1 Million.Go narrower to go bigger with one flagship offer, one defined buyer, and one primary distribution engine.This episode also confronts the uncomfortable truth about wealth: if it costs you your family, your health, or your identity, that's not success. That's ego dressed up as ambition. The real question becomes this: " If you had to build a $1 Million business with only one offer, one audience, and one channel… what would you choose?"Your answer will reveal everything...What You'll Learn:Why leverage beats effort if you want real scaleHow to reverse-engineer $1 Million without the hustle trapThe “solve a $10 Million problem” mindset shiftWhy outcomes sell and deliverables get negotiated downHow focus becomes your unfair advantage when discomfort hitsThe one-offer, one-audience, one-channel test that clarifies everythingHow to build recurring revenue while protecting your energyBeyond The Episode Gems:Buy My Book, Strategize Up: The Blueprint To Scale Your Business: StrategizeUpBook.comDiscover All Podcasts On The HubSpot Podcast NetworkGet Free HubSpot Marketing Tools To Help You Grow Your BusinessGrow Your Business Faster Using HubSpot's CRM PlatformListen to My First Million on the HubSpot Podcast NetworkSupport The Podcast & Connect With Troy: Rate & Review iDigress: iDigress.fm/ReviewsFollow Troy's Socials @FindTroy: LinkedIn, Instagram, Threads, TikTokSubscribe to Troy's YouTube Channel For Strategy Videos & See Masterclass EpisodesNeed Growth Strategy, A Keynote Speaker, Or Want To Sponsor The Podcast? Go To FindTroy.com

In this episode of the Everything Electric podcast, Imogen Bhogal sits down with Tom Hurst, UK Country Director for Fastned, to pull back the curtain on the UK's rapid charging revolution. Tom explains why Fastned is moving beyond simple "parking bays with plugs" to build high-visibility, amenity-first charging hubs that keep you sheltered from the rain and your battery topped up at speeds of up to 400kW. They also dive into the "mammoth" joint venture with Places for London (TfL's property arm) and why legal contracts, not just grid power, are often the biggest hurdle to a seamless charging experience.   00:00 Welcome to Everything Electric 01:40 Who is Fastned? Petrol Stations for the Electric Age 03:50 Hunting "White Whales": Top Priorities for 2026 05:40 Infrastructure Reality: Is Charging Actually Improving? 07:25 Consistency is Key: Beyond Basic Reliability 09:00 The Magic Number: How Many Chargers per Site? 11:55 Newcastle Airport: The Future of Drive-Through Hubs 14:15 The Northeast Advantage: Why Fastned Started in Sunderland 18:40 The Developer's Headache: Landlords, Power, and Law 21:45 Zombie Projects: Clearing the Grid Connection Backlog 23:33 The Places for London Partnership (Joint Venture) 26:33 The Cost of Charging: Breaking Down Energy & Grid Fees 32:53 Tom Hurst: Transitioning from Consultancy to Infrastructure 36:43 The Industry Wishlist: Simplifying Legal Landscapes 41:13 Conclusion: Designing for the Next 30 Years   Why not come and join us at our next Everything Electric expo: www.everythingelectric.show    Check out our sister channel: https://www.youtube.com/c/EverythingElectricShow   Support our StopBurningStuff campaign: https://www.patreon.com/STOPBurningStuff Become an Everything Electric Patreon: https://www.patreon.com/fullychargedshow Become a YouTube member: use JOIN button above Buy the Fully Charged Guide to Electric Vehicles & Clean Energy : https://buff.ly/2GybGt0 Subscribe for episode alerts and the Everything Electric newsletter: https://fullycharged.show/zap-sign-up/ Visit: https://FullyCharged.Show Find us on X: https://x.com/Everyth1ngElec Follow us on Instagram: https://instagram.com/officialeverythingelectric To partner, exhibit or sponsor at our award-winning expos email: commercial@fullycharged.show   EE NORTH (Harrogate) - 8th & 9th May 2026  EE WEST (Cheltenham) - 12th & 13th June 2026  EE GREATER LONDON (Twickenham) - 11th & 12th Sept 2026 EE SYDNEY - Sydney Olympic Park - 18th - 20th Sept 2026

The DOJ's so-called “list” is being framed as transparency, but it reads like controlled optics rather than a serious accounting of Jeffrey Epstein's network. A genuine disclosure would distinguish between casual mentions and operational roles, provide context, explain methodology, and prioritize the people who facilitated recruitment, logistics, finances, and legal shielding. Instead, the document appears to emphasize ambiguity and volume over clarity, which fuels politicization and confusion. When key operational figures are absent and no structured explanation is offered, it raises legitimate questions about whether the release was designed to inform the public or to exhaust and divide it. Transparency without context isn't transparency—it's misdirection.At its core, the issue is institutional credibility. A trafficking enterprise of this scale required coordination, staffing, money flows, and protection, and any meaningful disclosure should illuminate that infrastructure rather than obscure it. If leadership presents a curated list without methodology, document categories, or clear definitions, the public is left to speculate while officials claim compliance. That dynamic erodes trust and shifts attention away from survivors and toward political infighting. The demand is straightforward: show the work, clarify omissions, and provide structured, auditable disclosure. Anything less invites suspicion that the priority is reputational protection, not accountability.to contact me:bobbycapucci@protonmail.com

Agentic AI is being deployed as production infrastructure in enterprise settings, but prevailing frameworks remain unreliable for mission-critical operations. Dave Sobel and Ron Aroussi from Muxie underscored that while AI agents are functional—especially in non-deterministic contexts like customer support—expectations of deterministic, workflow-based reliability are not met. The move from demonstration agents to production-scale tools brings heightened attention to issues of reliability, observability, and especially risk of vendor lock-in for Managed Service Providers (MSPs) and their clients.Operational deployment of AI agents currently gravitates toward roles with minimal operational risk, such as customer-facing chatbots or internal chief-of-staff assistants. Aroussi explained that while such agents can automate initial support tiers and internal daily briefings, their unpredictability and potential for error limit their use in processes demanding strict oversight and accountability. He identified two core use cases—external (customer support) and internal (personalized information management)—explicitly noting that agents are best positioned to augment rather than fully automate complex workflows at this stage.A critical risk for MSPs lies in attempting to retrofit existing software frameworks to support agents, which introduces integration complexity and increases the likelihood of operational failures. Purpose-built infrastructure for agentic AI offers better alignment between AI capabilities and production requirements, with Aroussi citing drastically reduced hallucination rates and improved oversight when using native tools. Open source is identified as a foundational element for AI development, but it incurs its own risks, particularly around third-party code quality and the long-term sustainability of community-driven projects.The practical implication for MSPs and IT service providers is clear: a cautious, incremental adoption approach focused on low-risk use cases, coupled with rigorous controls on agent permissions and robust audit trails, is essential. Decision-makers should avoid assuming agents operate with the reliability or accountability of traditional software, prioritize operational transparency, and ensure that responsibilities for agent actions are clearly defined and enforced at the implementation level. Vendor lock-in and software provenance remain significant governance concerns as agentic AI moves from experiment to infrastructure.

Join us as Marian explains what AI governance means for vSphere administrators and why it matters now. Marian walks through practical governance frameworks that vSphere admins need to understand, from IEEE 7000 series standards to mapping governance controls onto infrastructure you already manage. You'll learn what your CISO will ask for, how to respond using your existing VMware stack, and why governance isn't about slowing innovation� it's about enabling it safely. This episode covers real-world scenarios from data lineage and model transparency to integrating governance tools with existing infrastructure, and addresses the gap between compliance requirements and practical implementation for virtualized environments. Timestamps 0:00 Welcome & Introduction 5:16 Marian's Background in Tech & Governance 6:37 What is Governance? 12:45 IEEE 7000 Series Standards Overview 18:22 AI Governance for vSphere Admins 24:16 Data Lineage & Model Transparency 30:41 Risk Assessment Frameworks 36:52 Practical Implementation Strategies 42:18 Integration with Existing Tools 47:35 Common Governance Challenges 51:12 Vendor Landscape Discussion 54:27 Missing Innovation in the Space 58:09 Wrap-up & Resources How to find Marian: https://www.linkedin.com/in/mariannewsome/ Links from the show: https://ethicaltechmatters.com/

What if concrete could store energy that turned buildings, roads, and infrastructure into massive power banks? In this episode, we're joined by Damian Stefaniuk, Research Scientist at MIT's Department of Civil and Environmental Engineering, the Concrete Sustainability Hub (CSHub), and the Electron-Conductive Cement-based Materials Hub (EC³ Hub). Damian's research explores how concrete can be engineered to conduct electricity and store energy at up to 10x the capacity of traditional materials — while simultaneously reducing the carbon footprint of cement production… Damian is a structural and materials engineering scientist who specializes in the development of sustainable construction materials and structures. His research focuses on science-enabled engineering of cement-based materials, with applications ranging from corrosion-resistant prestressed bridges and carbon-storing pre-cure carbonation to electron-conductive carbon concrete for renewable energy storage.   Dive in now to discover: How concrete can be made into a conductive material. Carbon-based conductive cement and nanomaterials. Infrastructure's role in clean energy and emissions reduction. You can follow along with Damian and his work here!

1. Minnesota AG Keith Ellison’s Senate Testimony Minnesota Attorney General Keith Ellison was questioned in the Senate regarding alleged failures to prevent and address large‑scale fraud in the state’s Feeding Our Future program. Senators—primarily Josh Hawley—accused Ellison of: Ignoring whistleblower warnings as early as 2018–2019. Meeting with individuals later indicted for fraud and allegedly offering to “look into” investigators who were scrutinizing them. Accepting approximately $10,000 in campaign donations from individuals tied to the fraud shortly after their meeting. Ellison strongly denied wrongdoing, describing the claims as false and politically motivated. The session was tense, marked by interruptions, raised voices, and confrontational exchanges. 2. Refusal to Condemn Louis Farrakhan During questioning, Ellison declined to explicitly condemn antisemitic statements attributed to Louis Farrakhan. He attempted to redirect discussion to immigration topics, expressing discomfort with the line of questioning. 3. Democrats Linked to Record Sewage Spill Democratic officials in Washington, D.C., Maryland, and Virginia oversaw infrastructure failures leading to the largest sewage spill in U.S. history. A burst 72‑inch sewer pipe released nearly one billion gallons of raw sewage into the Potomac River. Criticism is directed at: Lack of media coverage. Slow response times. Infrastructure mismanagement. Emergency pumps had to be transported from Texas and Florida to address the crisis. 4. Discussion of Government Competence Democratic‑run cities and states mismanage public systems (snow removal, wildfire mitigation, infrastructure maintenance, etc.). They argue such patterns reflect systemic governmental incompetence. 5. Save America Act & Voter ID Debate The Save America Act, passed in the House with near‑unanimous Republican support, requires: Proof of U.S. citizenship to register to vote. Photo ID to vote. Senator Ted Cruz advocates for aggressive procedural tactics in the Senate, including: Forcing a “talking filibuster.” Using the two‑speech rule to pressure Democratic senators. The argument made: voter ID laws are widely supported across political and demographic groups. Please Hit Subscribe to this podcast Right Now. Also Please Subscribe to the 47 Morning Update with Ben Ferguson and The Ben Ferguson Show Podcast Wherever You get You're Podcasts. And don't forget to follow the show on Social Media so you never miss a moment! Thanks for Listening YouTube: https://www.youtube.com/@VerdictwithTedCruz/ Facebook: https://www.facebook.com/verdictwithtedcruz X: https://x.com/tedcruz X: https://x.com/benfergusonshow YouTube: https://www.youtube.com/@VerdictwithTedCruzSee omnystudio.com/listener for privacy information.

At the end of January, Trump's Justice Department released what it said was the last tranche of the Epstein files: millions of pages of emails and texts, F.B.I. documents and court records. Much was redacted and millions more pages have been withheld. There is a lot we want to know that remains unclear.But what has come into clear view is the role Epstein played as a broker of information, connections, wealth and women and girls for a slice of the global elite. This was the infrastructure of Epstein's power — and it reveals much about the infrastructure of elite networks more generally.Anand Giridharadas is something of a sociologist of American elites. He's the author of, among other books, “Winners Take All: The Elite Charade of Changing the World” and the forthcoming “Man in the Mirror: Hope, Struggle and Belonging in an American City.” He also publishes the great newsletter The.Ink.Back in November, after the release of an earlier batch of Epstein files, Giridharadas wrote a great Times Opinion guest essay, taking a sociologist's lens to the messages Epstein exchanged with his elite friends. So after the government released this latest, enormous tranche of materials, I wanted to talk to Giridharadas to help make sense of it. What do they reveal — about how Epstein operated in the world, the vulnerabilities he exploited and what that says about how power works in America today?Note: This conversation was recorded on Tuesday, Feb. 10. On Thursday, Feb. 12, Kathryn Ruemmler announced she would be resigning from her role as chief legal officer and general counsel at Goldman Sachs.This episode contains strong language.Mentioned:“How the Elite Behave When No One Is Watching: Inside the Epstein Emails” by Anand Giridharadas“How JPMorgan Enabled the Crimes of Jeffrey Epstein” by David Enrich, Matthew Goldstein and Jessica Silver-Greenberg“Scams, Schemes, Ruthless Cons: The Untold Story of How Jeffrey Epstein Got Rich” by David Enrich, Steve Eder, Jessica Silver-Greenberg and Matthew GoldsteinBook Recommendations:Random Family by Adrian Nicole LeBlancBehind the Beautiful Forevers by Katherine BooUnpublished Work by Conchita SarnoffThoughts? Guest suggestions? Email us at ezrakleinshow@nytimes.com.You can find transcripts (posted midday) and more episodes of “The Ezra Klein Show” at nytimes.com/ezra-klein-podcast, and you can find Ezra on Twitter @ezraklein. Book recommendations from all our guests are listed at https://www.nytimes.com/article/ezra-klein-show-book-recs.This episode of “The Ezra Klein Show” was produced by Jack McCordick. Fact-checking by Michelle Harris, with Kate Sinclair and Mary Marge Locker. Our senior engineer is Jeff Geld, mixing by Aman Sahota and Isaac Jones. Our executive producer is Claire Gordon. The show's production team also includes Marie Cascione, Annie Galvin, Rollin Hu, Kristin Lin, Emma Kehlbeck, Marina King and Jan Kobal. Original music by Pat McCusker and Aman Sahota. Audience strategy by Kristina Samulewski and Shannon Busta. The director of New York Times Opinion Audio is Annie-Rose Strasser. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Guest: Cleo Paskal. Paskal contrasts U.S. actions in Palau with worsening corruption in the Northern Marianasand new Chinese infrastructure in Yap, highlighting vulnerabilities in Pacific defense.1939 BRITISH SOLOMONS