Podcasts about Infrastructure

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.comBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-epstein-chronicles--5003294/support.

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

Welcome to Exponential View, the show where I explore how exponential technologies such as AI are reshaping our future. I've been studying AI and exponential technologies at the frontier for over ten years. Each week, I share some of my analysis or speak with an expert guest to make light of a particular topic. To keep up with the Exponential transition, subscribe to this channel or to my newsletter: https://www.exponentialview.co/ ----In this episode, I'm joined by Jaime Sevilla, founder of Epoch AI; Hannah Petrovic from my team at Exponential View; and financial journalist Matt Robinson from AI Street. Together we investigate a fundamental question: do the economics of AI companies actually work? We analysed OpenAI's financials from public data to examine whether their revenues can sustain the staggering R&D costs of frontier models. The findings reveal a picture far more precarious than many assume; we also explore where the real infrastructure bottlenecks lie, why compute demand will dwarf energy constraints, and what the rise of long-running agentic workloads means for the entire industry. Read the study here: https://www.exponentialview.co/p/inside-openais-unit-economics-epoch-exponentialviewWe covered: (00:00) Do the economics of frontier AI actually work? (02:48) Piecing together OpenAI's finances from public data (05:24) GPT-5's "rapidly depreciating asset" problem (13:25) Why OpenAI is flirting with ads (17:31) If you were Sam Altman, what would you do differently? (22:54) Energy vs. GPUs; where the real infrastructure bottleneck lies (29:15) What surging compute demand actually looks like (33:12) The most surprising finding from the research (38:02) The race to avoid commoditization (43:35) Agents that outlive their models  Where to find me: Exponential View newsletter: https://www.exponentialview.co/ Website: https://www.azeemazhar.com/ LinkedIn: https://www.linkedin.com/in/azhar/ Twitter/X: https://x.com/azeem  Where to find Jamie: https://epoch.ai or https://epochai.substack.com Where to find Matt: https://www.ai-street.co  Production by supermix.io and EPIIPLUS1 Production and research: Chantal Smith and Marija Gavrilov. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Our topic today is building and running network workflows. If your network workflows live in a spreadsheet, a SharePoint document, or in your head, you really need a workflow manager. A workflow manager brings scalability, repeatability, and consistency to your network operations team. In this sponsored episode, we discuss Cisco Crosswork Workflow Manager. Our guests... Read more »

Our topic today is building and running network workflows. If your network workflows live in a spreadsheet, a SharePoint document, or in your head, you really need a workflow manager. A workflow manager brings scalability, repeatability, and consistency to your network operations team. In this sponsored episode, we discuss Cisco Crosswork Workflow Manager. Our guests... Read more »

Christopher des Fontaines, Co-CEO & Co-founder of Dfns, sat down with me for an interview at the Halborn Access 2026 Summit at the NYSE. We discussed how Dfns is helping institutions such as IBM to build digital asset and crypto infrastructure.Brought to you by

Thematic investing is increasingly shaping how investors interpret markets heading into 2026, as artificial intelligence, geopolitical fragmentation, and infrastructure constraints intersect across the global economy.Jay Jacobs, Head of U.S. Equity ETFs at BlackRock, joins Oscar to discuss why mega forces are becoming harder to ignore—and harder to diversify away from—than in past market cycles. Their conversation explores how AI investing is evolving from a growth narrative into one focused on usage intensity, how national security considerations are reshaping the definition of defense, and why physical infrastructure is emerging as a critical market constraint.Key insights include:· Why thematic investing is gaining relevance alongside sector and style frameworks· How AI usage intensity reframes the AI investment conversation· Where infrastructure and energy constraints may influence adoption timelines· How geopolitical fragmentation is expanding the definition of defense· Why overlapping mega forces may shape market outcomes into 2026Key moments in this episode:00:00 Introduction to Thematic Investing in 2026: AI and Market Forces00:40 The Rise of Thematic Investing01:43 Deep Dive into AI's Market Impact05:22 Understanding Token Consumption07:55 Evaluating AI Investments11:12 Geopolitical Fragmentation and Defense13:51 Infrastructure's Evolving Role16:42 Future of AI and Broader Implications18:38 Conclusion and Final Thoughts Thematic investing, AI investing, Capital markets, Infrastructure, Megaforces, Stock market trends, Geopolitical fragmentation, Defense spendingSources: iShares Thematic Outlook, 2026This 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.

Saying yes to God is often presented as inspiring and exciting. What's talked about less is the weight, responsibility, resistance, and formation required to actually carry what God entrusts.   In this teaching, we walk through the realities of stewarding a God dream:   – Why following the Lord can feel lonely in transition seasons – The difference between a personal ambition and a God assignment – What changes relationally when your capacity increases – Why early seasons require more structure, not less – How pressure exposes where formation is still needed – Staying aligned when opposition or misunderstanding surfaces – Guarding your heart and your assignment with maturity   This message is for those in a formation season—when something is being built, stretched, or restructured internally.     If this message resonates with your current season:   Subscribe for weekly teachings on formation, capacity, and Spirit-led leadership. Share this with someone who is stewarding a calling right now. Leave a comment: What has God asked you to carry in this season?     Join the Full Capacity Live Journey: https://julianapage.info/fullcapacity   Register for the Full Capacity Book Launch Event: https://julianapage.info/fullcapacitylaunch

Our topic today is building and running network workflows. If your network workflows live in a spreadsheet, a SharePoint document, or in your head, you really need a workflow manager. A workflow manager brings scalability, repeatability, and consistency to your network operations team. In this sponsored episode, we discuss Cisco Crosswork Workflow Manager. Our guests... Read more »

In this episode of Technovation, Peter High speaks with Saam Motamedi, General Partner at Greylock Partners, about the evolving role of artificial intelligence within the enterprise technology stack. They discuss how venture capital approaches enterprise AI companies at an early stage, how large enterprises are evaluating changes to their technology stacks, and what implications AI may have for workforce dynamics. Saam shares perspectives on how AI may influence infrastructure decisions, application development, and software business models over time. Key insights include: Shifts in enterprise infrastructure strategy Usage- and outcome-based software economics The future of AI agents What large enterprises should understand about emerging AI startups

Arista Networks (ANET) rallied after its earnings by showing strength Cisco (CSCO) did not the day before. Marley Kayden takes a look at the company tied to the AI infrastructure trade to see what's drawing investors to the stock. Dan Deming offers an example options trade for Arista Networks. ======== 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

Karl and Erum break down how biology is transforming the production of everything from cosmetics to construction materials. They explore why the petrochemical era is giving way to biological manufacturing, examining both the spectacular failures of early biofuels and the emerging success stories of companies like K18 and Mango Materials. Karl and Erum explain the fundamentals of fermentation, precision fermentation, and cell-free manufacturing, while introducing concepts like distributed biomanufacturing and "dirty biology." Drawing on insights from previous guests including Doug Friedman, Michelle Stansfield, Veronica Breckenridge, and Phil Morle, they reveal why 95% of executives are now pursuing bio-solutions and how three converging forces—falling technology costs, rising consumer expectations, and new infrastructure—are making this the moment for biomanufacturing to finally deliver on its promise.Grow Everything brings the bioeconomy to life. Hosts Karl Schmieder and Erum Azeez Khan share stories and interview the leaders and influencers changing the world by growing everything. Biology is the oldest technology. And it can be engineered. What are we growing?Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.messaginglab.com/groweverything⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Chapters:(00:00:00) - Why AI might just become our CEO (plus haircuts, Pilates, and gene therapy for hearing loss)(00:02:05) - Eli Lilly's $1B gene therapy deal for hearing loss(00:05:00) - Long Now podcast recommendation and NASA astrobiologist Lynn Rothschild(00:07:00) - Discussion of Apple TV's Scion and Drops of God(00:11:00) - What is biomanufacturing and why does it matter?(00:13:00) - The history of petrochemicals as "green technology"(00:16:00) - The opportunity: removing gigatons of carbon and unlocking trillion-dollar markets(00:19:00) - Types of biomanufacturing: fermentation, precision fermentation, and continuous fermentation(00:22:00) - Cell-free manufacturing and plant cell bioreactors(00:26:00) - Growing products with mycelium and dirty biology approaches(00:29:00) - Why biomanufacturing has been hard: the valley of death(00:30:00) - The biofuels bust and lessons from 60 failed companies(00:34:00) - Infrastructure challenges and the capacity gap(00:36:00) - New solutions: performance over sustainability and the K18 example(00:40:00) - Orchestration beats invention: connecting the entire value chain(00:43:00) - Distributed biomanufacturing and making products from waste(00:48:00) - The bio-better reality: what consumers and CPG companies need(00:51:00) - Three forces converging to make biomanufacturing work now(00:53:00) - Quickfire questions: luxury vs. commodities, funding, and AI's roleLinks and Resources:Links and Resources DOCTopics Covered: biomanufacturing 101, industrial biotechnology, precision fermentation, continuous fermentation, cell-free biomanufacturing, distributed biomanufacturing, dirty biology, bio-based materials, performance vs sustainability, CPG reformulationHave a question or comment? Message us here:Text or Call (804) 505-5553⁠Instagram⁠  / ⁠Twitter⁠ / ⁠LinkedIn⁠ / ⁠Youtube⁠ / ⁠Grow Everything⁠Music by: Nihilore Production by:  Amplafy Media

At a time when America's meat industry faces increasing consolidation, fragile supply chains, and the closure of rural processing facilities, Better For Butchery's acquisition of the Princeton Kentucky plant represents a rare, forward-looking investment in independent meat infrastructure. Backed by USDA Rural Development financing, the facility will serve as a scalable, high-integrity co-packing and processing hub designed to help farmers, ranchers, and emerging meat brands reach market without sacrificing quality, transparency, or control.  USDA Rural Development played ia critical role n enabling the acquisition. The facility was financed through an MPILP loan backed by the USDA  aimed at strengthening rural economies, expanding domestic meat processing capacity, and supporting independent producers seeking alternatives to large-scale industrial packers. the facility now serves as Better For Butchery's centralized processing, packaging, cold storage, and fulfillment hub. Purpose-built to support third-party brands, the operation enables consistent quality, reliable scheduling, and national distribution for farmers and food businesses that have historically struggled to access scalable processing. Better For Butchery's acquisition marks a turning point for the company—from turnaround operator to platform-scale processor—and formally launches its co-packing and third-party processing services for emerging and established food brands committed to ethical sourcing and operational transparency. Chris Roach, CEO of Better Butchery joins Farm To Table TAlk to share what's possible when public investment and private execution align. “With USDA Rural Development's support, we're rebuilding meat infrastructure in a way that works for farmers, workers, and brands alike—right here in rural Kentucky. Our approach is proving that modern, compliant, and values-driven meat processing can be decentralized to establish a new meat economy that is better for farmers, better for animals and better for all of us.” www.BetterForButhery.com  www.porterroad.com  

 Ryan Strachan is back!

Alberta sees itself not just as a contributor to Canadian agriculture, but as a leader—and it wants the flexibility and infrastructure to match that role. During the RDAR Showcase held in January, Shaun Haney was joined by Alberta Deputy Minister of Agriculture and Irrigation, Jason Hale, to talk about the province’s strengths, trade priorities, and... Read More

Pour débuter l'émission de ce vendredi 13 février 2026, les GG : Charles Consigny, avocat, Jérôme Marty, médecin urgentiste, et Sandrine Pégand, avocate, débattent du sujet du jour : Tempêtes, infrastructures inadaptées, l'État en cause ?

Overgrown roadside hedges can seriously put lives at risk by blocking road signs and forcing vehicles into oncoming traffic. Anton discusses further with Barry Kehoe, Chair of the County and City Management Association on Transport, Infrastructure and Networks Committee and Chief Executive of Westmeath County Council.

In this dynamic conversation, Dennis Thompson and Corrado reflect on how their creative partnership began—sparked by a mutual connection and built through shared vision and collaboration. They unpack the realities of working together in the content space, touching on brand partnerships, creative alignment, and the constant challenge of staying authentic in an ever-evolving social media landscape. Beyond content creation, the discussion expands into balancing family life with entrepreneurship, the power of meaningful audience engagement, and their anticipation for the upcoming World Cup. They explore what the tournament means culturally for Toronto and how soccer continues to shape identity and community in the city. The conversation also dives into broader themes of sports, cultural pride, and Canada's economic environment. From infrastructure readiness for global events to the impact of taxes and financial pressures on athletes, they offer candid perspectives on opportunity and responsibility. Ultimately, the dialogue highlights the importance of mindset, accountability, and community-driven values in defining success both in business and in life. TakeawaysCollaboration can lead to creative synergy and expanded reach.Understanding your audience is key to successful content creation.Maintaining authenticity is crucial when working with brands.Engaging with your audience fosters a loyal community.Balancing family life and content creation requires effective time management.Social media platforms encourage collaboration for greater visibility.Cultural representation is vital in sports events like the World Cup.Navigating brand partnerships requires clear communication and alignment of values.Content creation can be a business that supports family needs.The dynamics of soccer and its cultural impact are significant in community engagement. Cultural identity plays a significant role in sports fandom.Infrastructure readiness is crucial for hosting major events.Economic factors heavily influence the sports landscape.Taxes and financial decisions impact athletes' choices.A mindset shift is necessary for business success in Canada.Community engagement is vital for fostering local talent.Accountability partners can help navigate business challenges.Understanding one's value is key in negotiations.Sports and entertainment require a long-term perspective.Cultural values shape our approach to success and opportunity.CONTACT CORRADO BELOW INSTAGRAM: https://www.instagram.com/corrado/?hl=enTIKTOK: https://www.tiktok.com/@corradoarangio?is_from_webapp=1&sender_device=pc YOUTUBE: https://www.youtube.com/corradoarangioFACEBOOK: https://www.facebook.com/CorradoArangio/?utm_source=hoobe&utm_medium=socialCONTACT DENNIS BELOW INSTAGRAM: https://www.instagram.com/iam_trixafa.ent/?hl=enTHREADS: https://www.threads.com/@iam_trixafa.entBOOKING INQUIRY: https://docs.google.com/forms/d/e/1FAIpQLSfrfy-BJYi-KoxzsOr0_ReEsVBy905ZJwzYT0W7pvUU46B7Mw/viewform

Patrick McKenzie (patio11) reads an essay about "industrial-scale" fraud and why it should be treated as a professional business process rather than a series of isolated accidents. He explains how fraudsters leverage specialized supply chains—shared CPAs, incorporation agents, and "least attentive" banks—to loot public funds. Patrick argues that the government's "pay-and-chase" model is fundamentally broken and suggests that simple "proof of work" functions, like a 30-second cell phone video of a workspace, could provide the visceral signal that paperwork lacks, and examines the state's lack of "object permanence" regarding serial fraudsters and how scaled data provides the defense-side advantage needed to catch modern frauds.–Full transcript available here: www.complexsystemspodcast.com/fraud-as-infrastructure/–Presenting Sponsor: Mercury Complex Systems is presented by Mercury—radically better banking for founders. Mercury offers the best wire experience anywhere: fast, reliable, and free for domestic U.S. wires, so you can stay focused on growing your business. Apply online in minutes at mercury.com.Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.–Links:Bits about Money: https://www.bitsaboutmoney.com/archive/fraud-investigation/ Dan Davies on Complex Systems: https://open.spotify.com/episode/5QKxzgumJXSQuaWCmYAoM9 Jetson Leder-Luis on Complex Systems podcast: https://open.spotify.com/episode/3NiC7x9edoxJXkNW9vRfAT Stripe's Emily Sands on Complex Systems: https://open.spotify.com/episode/64Dyh6Gbg1lg4qUFwId0hc –Timestamps:(00:00) Intro(05:23) In which we briefly return to Minnesota(09:26) Common signals, methods, and epiphenomena of fraud(09:30) Fraudsters are playing an iterated game(11:29) The fraud supply chain is detectable(14:27) Investigators should expect to find ethnically clustered fraud(20:11) Sponsor: Mercury(21:47) High growth rate opportunities attract frauds(26:04) Fraudsters find the weakest links in the financial system(32:35) Frauds openly suborn identities(35:57) Asymmetry in attacker and defender burdens of proof(40:13) Fraudsters under-paperwork their epiphenomena(44:22) Machine learning can adaptively identify fraud(48:14) Frauds have a lifecycle(50:34) Should we care about fraud investigation, anyway

Neoborn Caveman delivers a pro-humanity critique of compliance experiments reshaping choices into cages, exposing how banks, parking, and services add friction to analog options through app mandates while presenting digital paths as convenient, warns of inertia leading to total tracking where refusal becomes suspicious, highlights how each reasonable rung builds inescapable infrastructure linking to digital IDs and programmable currency, and urges embracing inconvenience now through cash use and analog insistence to preserve autonomy before alternatives vanish.Key TakeawaysCompliance relies on voluntary inertia.Friction disguises digital mandates.Analog alternatives become burdensome.Normalization expands control scope.Refusal signals wrongdoing in systems.Infrastructure locks in surveillance.Inconvenience preserves future options.Cash maintains independent choices.Awareness breaks gradual entrapment.Humanity requires deliberate resistance.Sound Bites"Have you noticed how we're living through the largest compliance experiment in human history, and most people think they're just getting better customer service?""The world is being reshaped so that certain choices become nearly impossible to make.""Many banks now require app-based authentication for anything beyond basic logins.""Don't have a smartphone? Well, you can visit a branch during business hours—assuming there's still one near you, and assuming you can get there when it's actually open.""It's friction disguised as security. Inconvenience packaged as protection.""Have you tried to park somewhere recently without an app? Tried to access certain government services without downloading something?""Each system, taken individually, seems reasonable. Each one offers an analog alternative. Technically.""But have you noticed how those alternatives work? They're slower. They require extra steps. They make you feel like you're being difficult.""What we're watching is a carefully constructed ladder where each rung seems reasonable in isolation.""Once the infrastructure is fully digital, fully tracked, fully programmable—asking nicely for your freedom back isn't going to cut it."Join the tea house at patreon.com/theneoborncavemanshow —free to enter, real talk, lives, no ads, no algorithms.keywords: compliance experiments, app mandates, analog friction, digital cage, voluntary control, surveillance normalization, digital ids, programmable currency, autonomy loss, resistance inconvenienceHumanity centered satirical takes on the world & news + music - with a marble mouthed host.Free speech marinated in comedy.Supporting Purple Rabbits.Viva los Conejos Morados. Hosted on Acast. See acast.com/privacy for more information.

This podcast features Gabriele Corso and Jeremy Wohlwend, co-founders of Boltz and authors of the Boltz Manifesto, discussing the rapid evolution of structural biology models from AlphaFold to their own open-source suite, Boltz-1 and Boltz-2. The central thesis is that while single-chain protein structure prediction is largely “solved” through evolutionary hints, the next frontier lies in modeling complex interactions (protein-ligand, protein-protein) and generative protein design, which Boltz aims to democratize via open-source foundations and scalable infrastructure.Full Video PodOn YouTube!Timestamps* 00:00 Introduction to Benchmarking and the “Solved” Protein Problem* 06:48 Evolutionary Hints and Co-evolution in Structure Prediction* 10:00 The Importance of Protein Function and Disease States* 15:31 Transitioning from AlphaFold 2 to AlphaFold 3 Capabilities* 19:48 Generative Modeling vs. Regression in Structural Biology* 25:00 The “Bitter Lesson” and Specialized AI Architectures* 29:14 Development Anecdotes: Training Boltz-1 on a Budget* 32:00 Validation Strategies and the Protein Data Bank (PDB)* 37:26 The Mission of Boltz: Democratizing Access and Open Source* 41:43 Building a Self-Sustaining Research Community* 44:40 Boltz-2 Advancements: Affinity Prediction and Design* 51:03 BoltzGen: Merging Structure and Sequence Prediction* 55:18 Large-Scale Wet Lab Validation Results* 01:02:44 Boltz Lab Product Launch: Agents and Infrastructure* 01:13:06 Future Directions: Developpability and the “Virtual Cell”* 01:17:35 Interacting with Skeptical Medicinal ChemistsKey SummaryEvolution of Structure Prediction & Evolutionary Hints* Co-evolutionary Landscapes: The speakers explain that breakthrough progress in single-chain protein prediction relied on decoding evolutionary correlations where mutations in one position necessitate mutations in another to conserve 3D structure.* Structure vs. Folding: They differentiate between structure prediction (getting the final answer) and folding (the kinetic process of reaching that state), noting that the field is still quite poor at modeling the latter.* Physics vs. Statistics: RJ posits that while models use evolutionary statistics to find the right “valley” in the energy landscape, they likely possess a “light understanding” of physics to refine the local minimum.The Shift to Generative Architectures* Generative Modeling: A key leap in AlphaFold 3 and Boltz-1 was moving from regression (predicting one static coordinate) to a generative diffusion approach that samples from a posterior distribution.* Handling Uncertainty: This shift allows models to represent multiple conformational states and avoid the “averaging” effect seen in regression models when the ground truth is ambiguous.* Specialized Architectures: Despite the “bitter lesson” of general-purpose transformers, the speakers argue that equivariant architectures remain vastly superior for biological data due to the inherent 3D geometric constraints of molecules.Boltz-2 and Generative Protein Design* Unified Encoding: Boltz-2 (and BoltzGen) treats structure and sequence prediction as a single task by encoding amino acid identities into the atomic composition of the predicted structure.* Design Specifics: Instead of a sequence, users feed the model blank tokens and a high-level “spec” (e.g., an antibody framework), and the model decodes both the 3D structure and the corresponding amino acids.* Affinity Prediction: While model confidence is a common metric, Boltz-2 focuses on affinity prediction—quantifying exactly how tightly a designed binder will stick to its target.Real-World Validation and Productization* Generalized Validation: To prove the model isn't just “regurgitating” known data, Boltz tested its designs on 9 targets with zero known interactions in the PDB, achieving nanomolar binders for two-thirds of them.* Boltz Lab Infrastructure: The newly launched Boltz Lab platform provides “agents” for protein and small molecule design, optimized to run 10x faster than open-source versions through proprietary GPU kernels.* Human-in-the-Loop: The platform is designed to convert skeptical medicinal chemists by allowing them to run parallel screens and use their intuition to filter model outputs.TranscriptRJ [00:05:35]: But the goal remains to, like, you know, really challenge the models, like, how well do these models generalize? And, you know, we've seen in some of the latest CASP competitions, like, while we've become really, really good at proteins, especially monomeric proteins, you know, other modalities still remain pretty difficult. So it's really essential, you know, in the field that there are, like, these efforts to gather, you know, benchmarks that are challenging. So it keeps us in line, you know, about what the models can do or not.Gabriel [00:06:26]: Yeah, it's interesting you say that, like, in some sense, CASP, you know, at CASP 14, a problem was solved and, like, pretty comprehensively, right? But at the same time, it was really only the beginning. So you can say, like, what was the specific problem you would argue was solved? And then, like, you know, what is remaining, which is probably quite open.RJ [00:06:48]: I think we'll steer away from the term solved, because we have many friends in the community who get pretty upset at that word. And I think, you know, fairly so. But the problem that was, you know, that a lot of progress was made on was the ability to predict the structure of single chain proteins. So proteins can, like, be composed of many chains. And single chain proteins are, you know, just a single sequence of amino acids. And one of the reasons that we've been able to make such progress is also because we take a lot of hints from evolution. So the way the models work is that, you know, they sort of decode a lot of hints. That comes from evolutionary landscapes. So if you have, like, you know, some protein in an animal, and you go find the similar protein across, like, you know, different organisms, you might find different mutations in them. And as it turns out, if you take a lot of the sequences together, and you analyze them, you see that some positions in the sequence tend to evolve at the same time as other positions in the sequence, sort of this, like, correlation between different positions. And it turns out that that is typically a hint that these two positions are close in three dimension. So part of the, you know, part of the breakthrough has been, like, our ability to also decode that very, very effectively. But what it implies also is that in absence of that co-evolutionary landscape, the models don't quite perform as well. And so, you know, I think when that information is available, maybe one could say, you know, the problem is, like, somewhat solved. From the perspective of structure prediction, when it isn't, it's much more challenging. And I think it's also worth also differentiating the, sometimes we confound a little bit, structure prediction and folding. Folding is the more complex process of actually understanding, like, how it goes from, like, this disordered state into, like, a structured, like, state. And that I don't think we've made that much progress on. But the idea of, like, yeah, going straight to the answer, we've become pretty good at.Brandon [00:08:49]: So there's this protein that is, like, just a long chain and it folds up. Yeah. And so we're good at getting from that long chain in whatever form it was originally to the thing. But we don't know how it necessarily gets to that state. And there might be intermediate states that it's in sometimes that we're not aware of.RJ [00:09:10]: That's right. And that relates also to, like, you know, our general ability to model, like, the different, you know, proteins are not static. They move, they take different shapes based on their energy states. And I think we are, also not that good at understanding the different states that the protein can be in and at what frequency, what probability. So I think the two problems are quite related in some ways. Still a lot to solve. But I think it was very surprising at the time, you know, that even with these evolutionary hints that we were able to, you know, to make such dramatic progress.Brandon [00:09:45]: So I want to ask, why does the intermediate states matter? But first, I kind of want to understand, why do we care? What proteins are shaped like?Gabriel [00:09:54]: Yeah, I mean, the proteins are kind of the machines of our body. You know, the way that all the processes that we have in our cells, you know, work is typically through proteins, sometimes other molecules, sort of intermediate interactions. And through that interactions, we have all sorts of cell functions. And so when we try to understand, you know, a lot of biology, how our body works, how disease work. So we often try to boil it down to, okay, what is going right in case of, you know, our normal biological function and what is going wrong in case of the disease state. And we boil it down to kind of, you know, proteins and kind of other molecules and their interaction. And so when we try predicting the structure of proteins, it's critical to, you know, have an understanding of kind of those interactions. It's a bit like seeing the difference between... Having kind of a list of parts that you would put it in a car and seeing kind of the car in its final form, you know, seeing the car really helps you understand what it does. On the other hand, kind of going to your question of, you know, why do we care about, you know, how the protein falls or, you know, how the car is made to some extent is that, you know, sometimes when something goes wrong, you know, there are, you know, cases of, you know, proteins misfolding. In some diseases and so on, if we don't understand this folding process, we don't really know how to intervene.RJ [00:11:30]: There's this nice line in the, I think it's in the Alpha Fold 2 manuscript, where they sort of discuss also like why we even hopeful that we can target the problem in the first place. And then there's this notion that like, well, four proteins that fold. The folding process is almost instantaneous, which is a strong, like, you know, signal that like, yeah, like we should, we might be... able to predict that this very like constrained thing that, that the protein does so quickly. And of course that's not the case for, you know, for, for all proteins. And there's a lot of like really interesting mechanisms in the cells, but yeah, I remember reading that and thought, yeah, that's somewhat of an insightful point.Gabriel [00:12:10]: I think one of the interesting things about the protein folding problem is that it used to be actually studied. And part of the reason why people thought it was impossible, it used to be studied as kind of like a classical example. Of like an MP problem. Uh, like there are so many different, you know, type of, you know, shapes that, you know, this amino acid could take. And so, this grows combinatorially with the size of the sequence. And so there used to be kind of a lot of actually kind of more theoretical computer science thinking about and studying protein folding as an MP problem. And so it was very surprising also from that perspective, kind of seeing. Machine learning so clear, there is some, you know, signal in those sequences, through evolution, but also through kind of other things that, you know, us as humans, we're probably not really able to, uh, to understand, but that is, models I've, I've learned.Brandon [00:13:07]: And so Andrew White, we were talking to him a few weeks ago and he said that he was following the development of this and that there were actually ASICs that were developed just to solve this problem. So, again, that there were. There were many, many, many millions of computational hours spent trying to solve this problem before AlphaFold. And just to be clear, one thing that you mentioned was that there's this kind of co-evolution of mutations and that you see this again and again in different species. So explain why does that give us a good hint that they're close by to each other? Yeah.RJ [00:13:41]: Um, like think of it this way that, you know, if I have, you know, some amino acid that mutates, it's going to impact everything around it. Right. In three dimensions. And so it's almost like the protein through several, probably random mutations and evolution, like, you know, ends up sort of figuring out that this other amino acid needs to change as well for the structure to be conserved. Uh, so this whole principle is that the structure is probably largely conserved, you know, because there's this function associated with it. And so it's really sort of like different positions compensating for, for each other. I see.Brandon [00:14:17]: Those hints in aggregate give us a lot. Yeah. So you can start to look at what kinds of information about what is close to each other, and then you can start to look at what kinds of folds are possible given the structure and then what is the end state.RJ [00:14:30]: And therefore you can make a lot of inferences about what the actual total shape is. Yeah, that's right. It's almost like, you know, you have this big, like three dimensional Valley, you know, where you're sort of trying to find like these like low energy states and there's so much to search through. That's almost overwhelming. But these hints, they sort of maybe put you in. An area of the space that's already like, kind of close to the solution, maybe not quite there yet. And, and there's always this question of like, how much physics are these models learning, you know, versus like, just pure like statistics. And like, I think one of the thing, at least I believe is that once you're in that sort of approximate area of the solution space, then the models have like some understanding, you know, of how to get you to like, you know, the lower energy, uh, low energy state. And so maybe you have some, some light understanding. Of physics, but maybe not quite enough, you know, to know how to like navigate the whole space. Right. Okay.Brandon [00:15:25]: So we need to give it these hints to kind of get into the right Valley and then it finds the, the minimum or something. Yeah.Gabriel [00:15:31]: One interesting explanation about our awful free works that I think it's quite insightful, of course, doesn't cover kind of the entirety of, of what awful does that is, um, they're going to borrow from, uh, Sergio Chinico for MIT. So he sees kind of awful. Then the interesting thing about awful is God. This very peculiar architecture that we have seen, you know, used, and this architecture operates on this, you know, pairwise context between amino acids. And so the idea is that probably the MSA gives you this first hint about what potential amino acids are close to each other. MSA is most multiple sequence alignment. Exactly. Yeah. Exactly. This evolutionary information. Yeah. And, you know, from this evolutionary information about potential contacts, then is almost as if the model is. of running some kind of, you know, diastro algorithm where it's sort of decoding, okay, these have to be closed. Okay. Then if these are closed and this is connected to this, then this has to be somewhat closed. And so you decode this, that becomes basically a pairwise kind of distance matrix. And then from this rough pairwise distance matrix, you decode kind of theBrandon [00:16:42]: actual potential structure. Interesting. So there's kind of two different things going on in the kind of coarse grain and then the fine grain optimizations. Interesting. Yeah. Very cool.Gabriel [00:16:53]: Yeah. You mentioned AlphaFold3. So maybe we have a good time to move on to that. So yeah, AlphaFold2 came out and it was like, I think fairly groundbreaking for this field. Everyone got very excited. A few years later, AlphaFold3 came out and maybe for some more history, like what were the advancements in AlphaFold3? And then I think maybe we'll, after that, we'll talk a bit about the sort of how it connects to Bolt. But anyway. Yeah. So after AlphaFold2 came out, you know, Jeremy and I got into the field and with many others, you know, the clear problem that, you know, was, you know, obvious after that was, okay, now we can do individual chains. Can we do interactions, interaction, different proteins, proteins with small molecules, proteins with other molecules. And so. So why are interactions important? Interactions are important because to some extent that's kind of the way that, you know, these machines, you know, these proteins have a function, you know, the function comes by the way that they interact with other proteins and other molecules. Actually, in the first place, you know, the individual machines are often, as Jeremy was mentioning, not made of a single chain, but they're made of the multiple chains. And then these multiple chains interact with other molecules to give the function to those. And on the other hand, you know, when we try to intervene of these interactions, think about like a disease, think about like a, a biosensor or many other ways we are trying to design the molecules or proteins that interact in a particular way with what we would call a target protein or target. You know, this problem after AlphaVol2, you know, became clear, kind of one of the biggest problems in the field to, to solve many groups, including kind of ours and others, you know, started making some kind of contributions to this problem of trying to model these interactions. And AlphaVol3 was, you know, was a significant advancement on the problem of modeling interactions. And one of the interesting thing that they were able to do while, you know, some of the rest of the field that really tried to try to model different interactions separately, you know, how protein interacts with small molecules, how protein interacts with other proteins, how RNA or DNA have their structure, they put everything together and, you know, train very large models with a lot of advances, including kind of changing kind of systems. Some of the key architectural choices and managed to get a single model that was able to set this new state-of-the-art performance across all of these different kind of modalities, whether that was protein, small molecules is critical to developing kind of new drugs, protein, protein, understanding, you know, interactions of, you know, proteins with RNA and DNAs and so on.Brandon [00:19:39]: Just to satisfy the AI engineers in the audience, what were some of the key architectural and data, data changes that made that possible?Gabriel [00:19:48]: Yeah, so one critical one that was not necessarily just unique to AlphaFold3, but there were actually a few other teams, including ours in the field that proposed this, was moving from, you know, modeling structure prediction as a regression problem. So where there is a single answer and you're trying to shoot for that answer to a generative modeling problem where you have a posterior distribution of possible structures and you're trying to sample this distribution. And this achieves two things. One is it starts to allow us to try to model more dynamic systems. As we said, you know, some of these structures can actually take multiple structures. And so, you know, you can now model that, you know, through kind of modeling the entire distribution. But on the second hand, from more kind of core modeling questions, when you move from a regression problem to a generative modeling problem, you are really tackling the way that you think about uncertainty in the model in a different way. So if you think about, you know, I'm undecided between different answers, what's going to happen in a regression model is that, you know, I'm going to try to make an average of those different kind of answers that I had in mind. When you have a generative model, what you're going to do is, you know, sample all these different answers and then maybe use separate models to analyze those different answers and pick out the best. So that was kind of one of the critical improvement. The other improvement is that they significantly simplified, to some extent, the architecture, especially of the final model that takes kind of those pairwise representations and turns them into an actual structure. And that now looks a lot more like a more traditional transformer than, you know, like a very specialized equivariant architecture that it was in AlphaFold3.Brandon [00:21:41]: So this is a bitter lesson, a little bit.Gabriel [00:21:45]: There is some aspect of a bitter lesson, but the interesting thing is that it's very far from, you know, being like a simple transformer. This field is one of the, I argue, very few fields in applied machine learning where we still have kind of architecture that are very specialized. And, you know, there are many people that have tried to replace these architectures with, you know, simple transformers. And, you know, there is a lot of debate in the field, but I think kind of that most of the consensus is that, you know, the performance... that we get from the specialized architecture is vastly superior than what we get through a single transformer. Another interesting thing that I think on the staying on the modeling machine learning side, which I think it's somewhat counterintuitive seeing some of the other kind of fields and applications is that scaling hasn't really worked kind of the same in this field. Now, you know, models like AlphaFold2 and AlphaFold3 are, you know, still very large models.RJ [00:29:14]: in a place, I think, where we had, you know, some experience working in, you know, with the data and working with this type of models. And I think that put us already in like a good place to, you know, to produce it quickly. And, you know, and I would even say, like, I think we could have done it quicker. The problem was like, for a while, we didn't really have the compute. And so we couldn't really train the model. And actually, we only trained the big model once. That's how much compute we had. We could only train it once. And so like, while the model was training, we were like, finding bugs left and right. A lot of them that I wrote. And like, I remember like, I was like, sort of like, you know, doing like, surgery in the middle, like stopping the run, making the fix, like relaunching. And yeah, we never actually went back to the start. We just like kept training it with like the bug fixes along the way, which was impossible to reproduce now. Yeah, yeah, no, that model is like, has gone through such a curriculum that, you know, learned some weird stuff. But yeah, somehow by miracle, it worked out.Gabriel [00:30:13]: The other funny thing is that the way that we were training, most of that model was through a cluster from the Department of Energy. But that's sort of like a shared cluster that many groups use. And so we were basically training the model for two days, and then it would go back to the queue and stay a week in the queue. Oh, yeah. And so it was pretty painful. And so we actually kind of towards the end with Evan, the CEO of Genesis, and basically, you know, I was telling him a bit about the project and, you know, kind of telling him about this frustration with the compute. And so luckily, you know, he offered to kind of help. And so we, we got the help from Genesis to, you know, finish up the model. Otherwise, it probably would have taken a couple of extra weeks.Brandon [00:30:57]: Yeah, yeah.Brandon [00:31:02]: And then, and then there's some progression from there.Gabriel [00:31:06]: Yeah, so I would say kind of that, both one, but also kind of these other kind of set of models that came around the same time, were kind of approaching were a big leap from, you know, kind of the previous kind of open source models, and, you know, kind of really kind of approaching the level of AlphaVault 3. But I would still say that, you know, even to this day, there are, you know, some... specific instances where AlphaVault 3 works better. I think one common example is antibody antigen prediction, where, you know, AlphaVault 3 still seems to have an edge in many situations. Obviously, these are somewhat different models. They are, you know, you run them, you obtain different results. So it's, it's not always the case that one model is better than the other, but kind of in aggregate, we still, especially at the time.Brandon [00:32:00]: So AlphaVault 3 is, you know, still having a bit of an edge. We should talk about this more when we talk about Boltzgen, but like, how do you know one is, one model is better than the other? Like you, so you, I make a prediction, you make a prediction, like, how do you know?Gabriel [00:32:11]: Yeah, so easily, you know, the, the great thing about kind of structural prediction and, you know, once we're going to go into the design space of designing new small molecule, new proteins, this becomes a lot more complex. But a great thing about structural prediction is that a bit like, you know, CASP was doing, basically the way that you can evaluate them is that, you know, you train... You know, you train a model on a structure that was, you know, released across the field up until a certain time. And, you know, one of the things that we didn't talk about that was really critical in all this development is the PDB, which is the Protein Data Bank. It's this common resources, basically common database where every biologist publishes their structures. And so we can, you know, train on, you know, all the structures that were put in the PDB until a certain date. And then... And then we basically look for recent structures, okay, which structures look pretty different from anything that was published before, because we really want to try to understand generalization.Brandon [00:33:13]: And then on this new structure, we evaluate all these different models. And so you just know when AlphaFold3 was trained, you know, when you're, you intentionally trained to the same date or something like that. Exactly. Right. Yeah.Gabriel [00:33:24]: And so this is kind of the way that you can somewhat easily kind of compare these models, obviously, that assumes that, you know, the training. You've always been very passionate about validation. I remember like DiffDoc, and then there was like DiffDocL and DocGen. You've thought very carefully about this in the past. Like, actually, I think DocGen is like a really funny story that I think, I don't know if you want to talk about that. It's an interesting like... Yeah, I think one of the amazing things about putting things open source is that we get a ton of feedback from the field. And, you know, sometimes we get kind of great feedback of people. Really like... But honestly, most of the times, you know, to be honest, that's also maybe the most useful feedback is, you know, people sharing about where it doesn't work. And so, you know, at the end of the day, it's critical. And this is also something, you know, across other fields of machine learning. It's always critical to set, to do progress in machine learning, set clear benchmarks. And as, you know, you start doing progress of certain benchmarks, then, you know, you need to improve the benchmarks and make them harder and harder. And this is kind of the progression of, you know, how the field operates. And so, you know, the example of DocGen was, you know, we published this initial model called DiffDoc in my first year of PhD, which was sort of like, you know, one of the early models to try to predict kind of interactions between proteins, small molecules, that we bought a year after AlphaFold2 was published. And now, on the one hand, you know, on these benchmarks that we were using at the time, DiffDoc was doing really well, kind of, you know, outperforming kind of some of the traditional physics-based methods. But on the other hand, you know, when we started, you know, kind of giving these tools to kind of many biologists, and one example was that we collaborated with was the group of Nick Polizzi at Harvard. We noticed, started noticing that there was this clear, pattern where four proteins that were very different from the ones that we're trained on, the models was, was struggling. And so, you know, that seemed clear that, you know, this is probably kind of where we should, you know, put our focus on. And so we first developed, you know, with Nick and his group, a new benchmark, and then, you know, went after and said, okay, what can we change? And kind of about the current architecture to improve this pattern and generalization. And this is the same that, you know, we're still doing today, you know, kind of, where does the model not work, you know, and then, you know, once we have that benchmark, you know, let's try to, through everything we, any ideas that we have of the problem.RJ [00:36:15]: And there's a lot of like healthy skepticism in the field, which I think, you know, is, is, is great. And I think, you know, it's very clear that there's a ton of things, the models don't really work well on, but I think one thing that's probably, you know, undeniable is just like the pace of, pace of progress, you know, and how, how much better we're getting, you know, every year. And so I think if you, you know, if you assume, you know, any constant, you know, rate of progress moving forward, I think things are going to look pretty cool at some point in the future.Gabriel [00:36:42]: ChatGPT was only three years ago. Yeah, I mean, it's wild, right?RJ [00:36:45]: Like, yeah, yeah, yeah, it's one of those things. Like, you've been doing this. Being in the field, you don't see it coming, you know? And like, I think, yeah, hopefully we'll, you know, we'll, we'll continue to have as much progress we've had the past few years.Brandon [00:36:55]: So this is maybe an aside, but I'm really curious, you get this great feedback from the, from the community, right? By being open source. My question is partly like, okay, yeah, if you open source and everyone can copy what you did, but it's also maybe balancing priorities, right? Where you, like all my customers are saying. I want this, there's all these problems with the model. Yeah, yeah. But my customers don't care, right? So like, how do you, how do you think about that? Yeah.Gabriel [00:37:26]: So I would say a couple of things. One is, you know, part of our goal with Bolts and, you know, this is also kind of established as kind of the mission of the public benefit company that we started is to democratize the access to these tools. But one of the reasons why we realized that Bolts needed to be a company, it couldn't just be an academic project is that putting a model on GitHub is definitely not enough to get, you know, chemists and biologists, you know, across, you know, both academia, biotech and pharma to use your model to, in their therapeutic programs. And so a lot of what we think about, you know, at Bolts beyond kind of the, just the models is thinking about all the layers. The layers that come on top of the models to get, you know, from, you know, those models to something that can really enable scientists in the industry. And so that goes, you know, into building kind of the right kind of workflows that take in kind of, for example, the data and try to answer kind of directly that those problems that, you know, the chemists and the biologists are asking, and then also kind of building the infrastructure. And so this to say that, you know, even with models fully open. You know, we see a ton of potential for, you know, products in the space and the critical part about a product is that even, you know, for example, with an open source model, you know, running the model is not free, you know, as we were saying, these are pretty expensive model and especially, and maybe we'll get into this, you know, these days we're seeing kind of pretty dramatic inference time scaling of these models where, you know, the more you run them, the better the results are. But there, you know, you see. You start getting into a point that compute and compute costs becomes a critical factor. And so putting a lot of work into building the right kind of infrastructure, building the optimizations and so on really allows us to provide, you know, a much better service potentially to the open source models. That to say, you know, even though, you know, with a product, we can provide a much better service. I do still think, and we will continue to put a lot of our models open source because the critical kind of role. I think of open source. Models is, you know, helping kind of the community progress on the research and, you know, from which we, we all benefit. And so, you know, we'll continue to on the one hand, you know, put some of our kind of base models open source so that the field can, can be on top of it. And, you know, as we discussed earlier, we learn a ton from, you know, the way that the field uses and builds on top of our models, but then, you know, try to build a product that gives the best experience possible to scientists. So that, you know, like a chemist or a biologist doesn't need to, you know, spin off a GPU and, you know, set up, you know, our open source model in a particular way, but can just, you know, a bit like, you know, I, even though I am a computer scientist, machine learning scientist, I don't necessarily, you know, take a open source LLM and try to kind of spin it off. But, you know, I just maybe open a GPT app or a cloud code and just use it as an amazing product. We kind of want to give the same experience. So this front world.Brandon [00:40:40]: I heard a good analogy yesterday that a surgeon doesn't want the hospital to design a scalpel, right?Brandon [00:40:48]: So just buy the scalpel.RJ [00:40:50]: You wouldn't believe like the number of people, even like in my short time, you know, between AlphaFold3 coming out and the end of the PhD, like the number of people that would like reach out just for like us to like run AlphaFold3 for them, you know, or things like that. Just because like, you know, bolts in our case, you know, just because it's like. It's like not that easy, you know, to do that, you know, if you're not a computational person. And I think like part of the goal here is also that, you know, we continue to obviously build the interface with computational folks, but that, you know, the models are also accessible to like a larger, broader audience. And then that comes from like, you know, good interfaces and stuff like that.Gabriel [00:41:27]: I think one like really interesting thing about bolts is that with the release of it, you didn't just release a model, but you created a community. Yeah. Did that community, it grew very quickly. Did that surprise you? And like, what is the evolution of that community and how is that fed into bolts?RJ [00:41:43]: If you look at its growth, it's like very much like when we release a new model, it's like, there's a big, big jump, but yeah, it's, I mean, it's been great. You know, we have a Slack community that has like thousands of people on it. And it's actually like self-sustaining now, which is like the really nice part because, you know, it's, it's almost overwhelming, I think, you know, to be able to like answer everyone's questions and help. It's really difficult, you know. The, the few people that we were, but it ended up that like, you know, people would answer each other's questions and like, sort of like, you know, help one another. And so the Slack, you know, has been like kind of, yeah, self, self-sustaining and that's been, it's been really cool to see.RJ [00:42:21]: And, you know, that's, that's for like the Slack part, but then also obviously on GitHub as well. We've had like a nice, nice community. You know, I think we also aspire to be even more active on it, you know, than we've been in the past six months, which has been like a bit challenging, you know, for us. But. Yeah, the community has been, has been really great and, you know, there's a lot of papers also that have come out with like new evolutions on top of bolts and it's surprised us to some degree because like there's a lot of models out there. And I think like, you know, sort of people converging on that was, was really cool. And, you know, I think it speaks also, I think, to the importance of like, you know, when, when you put code out, like to try to put a lot of emphasis and like making it like as easy to use as possible and something we thought a lot about when we released the code base. You know, it's far from perfect, but, you know.Brandon [00:43:07]: Do you think that that was one of the factors that caused your community to grow is just the focus on easy to use, make it accessible? I think so.RJ [00:43:14]: Yeah. And we've, we've heard it from a few people over the, over the, over the years now. And, you know, and some people still think it should be a lot nicer and they're, and they're right. And they're right. But yeah, I think it was, you know, at the time, maybe a little bit easier than, than other things.Gabriel [00:43:29]: The other thing part, I think led to, to the community and to some extent, I think, you know, like the somewhat the trust in the community. Kind of what we, what we put out is the fact that, you know, it's not really been kind of, you know, one model, but, and maybe we'll talk about it, you know, after Boltz 1, you know, there were maybe another couple of models kind of released, you know, or open source kind of soon after. We kind of continued kind of that open source journey or at least Boltz 2, where we are not only improving kind of structure prediction, but also starting to do affinity predictions, understanding kind of the strength of the interactions between these different models, which is this critical component. critical property that you often want to optimize in discovery programs. And then, you know, more recently also kind of protein design model. And so we've sort of been building this suite of, of models that come together, interact with one another, where, you know, kind of, there is almost an expectation that, you know, we, we take very at heart of, you know, always having kind of, you know, across kind of the entire suite of different tasks, the best or across the best. model out there so that it's sort of like our open source tool can be kind of the go-to model for everybody in the, in the industry. I really want to talk about Boltz 2, but before that, one last question in this direction, was there anything about the community which surprised you? Were there any, like, someone was doing something and you're like, why would you do that? That's crazy. Or that's actually genius. And I never would have thought about that.RJ [00:45:01]: I mean, we've had many contributions. I think like some of the. Interesting ones, like, I mean, we had, you know, this one individual who like wrote like a complex GPU kernel, you know, for part of the architecture on a piece of, the funny thing is like that piece of the architecture had been there since AlphaFold 2, and I don't know why it took Boltz for this, you know, for this person to, you know, to decide to do it, but that was like a really great contribution. We've had a bunch of others, like, you know, people figuring out like ways to, you know, hack the model to do something. They click peptides, like, you know, there's, I don't know if there's any other interesting ones come to mind.Gabriel [00:45:41]: One cool one, and this was, you know, something that initially was proposed as, you know, as a message in the Slack channel by Tim O'Donnell was basically, he was, you know, there are some cases, especially, for example, we discussed, you know, antibody-antigen interactions where the models don't necessarily kind of get the right answer. What he noticed is that, you know, the models were somewhat stuck into predicting kind of the antibodies. And so he basically ran the experiments in this model, you can condition, basically, you can give hints. And so he basically gave, you know, random hints to the model, basically, okay, you should bind to this residue, you should bind to the first residue, or you should bind to the 11th residue, or you should bind to the 21st residue, you know, basically every 10 residues scanning the entire antigen.Brandon [00:46:33]: Residues are the...Gabriel [00:46:34]: The amino acids. The amino acids, yeah. So the first amino acids. The 11 amino acids, and so on. So it's sort of like doing a scan, and then, you know, conditioning the model to predict all of them, and then looking at the confidence of the model in each of those cases and taking the top. And so it's sort of like a very somewhat crude way of doing kind of inference time search. But surprisingly, you know, for antibody-antigen prediction, it actually kind of helped quite a bit. And so there's some, you know, interesting ideas that, you know, obviously, as kind of developing the model, you say kind of, you know, wow. This is why would the model, you know, be so dumb. But, you know, it's very interesting. And that, you know, leads you to also kind of, you know, start thinking about, okay, how do I, can I do this, you know, not with this brute force, but, you know, in a smarter way.RJ [00:47:22]: And so we've also done a lot of work on that direction. And that speaks to, like, the, you know, the power of scoring. We're seeing that a lot. I'm sure we'll talk about it more when we talk about BullsGen. But, you know, our ability to, like, take a structure and determine that that structure is, like... Good. You know, like, somewhat accurate. Whether that's a single chain or, like, an interaction is a really powerful way of improving, you know, the models. Like, sort of like, you know, if you can sample a ton and you assume that, like, you know, if you sample enough, you're likely to have, like, you know, the good structure. Then it really just becomes a ranking problem. And, you know, now we're, you know, part of the inference time scaling that Gabby was talking about is very much that. It's like, you know, the more we sample, the more we, like, you know, the ranking model. The ranking model ends up finding something it really likes. And so I think our ability to get better at ranking, I think, is also what's going to enable sort of the next, you know, next big, big breakthroughs. Interesting.Brandon [00:48:17]: But I guess there's a, my understanding, there's a diffusion model and you generate some stuff and then you, I guess, it's just what you said, right? Then you rank it using a score and then you finally... And so, like, can you talk about those different parts? Yeah.Gabriel [00:48:34]: So, first of all, like, the... One of the critical kind of, you know, beliefs that we had, you know, also when we started working on Boltz 1 was sort of like the structure prediction models are somewhat, you know, our field version of some foundation models, you know, learning about kind of how proteins and other molecules interact. And then we can leverage that learning to do all sorts of other things. And so with Boltz 2, we leverage that learning to do affinity predictions. So understanding kind of, you know, if I give you this protein, this molecule. How tightly is that interaction? For Boltz 1, what we did was taking kind of that kind of foundation models and then fine tune it to predict kind of entire new proteins. And so the way basically that that works is sort of like instead of for the protein that you're designing, instead of fitting in an actual sequence, you fit in a set of blank tokens. And you train the models to, you know, predict both the structure of kind of that protein. The structure also, what the different amino acids of that proteins are. And so basically the way that Boltz 1 operates is that you feed a target protein that you may want to kind of bind to or, you know, another DNA, RNA. And then you feed the high level kind of design specification of, you know, what you want your new protein to be. For example, it could be like an antibody with a particular framework. It could be a peptide. It could be many other things. And that's with natural language or? And that's, you know, basically, you know, prompting. And we have kind of this sort of like spec that you specify. And, you know, you feed kind of this spec to the model. And then the model translates this into, you know, a set of, you know, tokens, a set of conditioning to the model, a set of, you know, blank tokens. And then, you know, basically the codes as part of the diffusion models, the codes. It's a new structure and a new sequence for your protein. And, you know, basically, then we take that. And as Jeremy was saying, we are trying to score it and, you know, how good of a binder it is to that original target.Brandon [00:50:51]: You're using basically Boltz to predict the folding and the affinity to that molecule. So and then that kind of gives you a score? Exactly.Gabriel [00:51:03]: So you use this model to predict the folding. And then you do two things. One is that you predict the structure and with something like Boltz2, and then you basically compare that structure with what the model predicted, what Boltz2 predicted. And this is sort of like in the field called consistency. It's basically you want to make sure that, you know, the structure that you're predicting is actually what you're trying to design. And that gives you a much better confidence that, you know, that's a good design. And so that's the first filtering. And the second filtering that we did as part of kind of the Boltz2 pipeline that was released is that we look at the confidence that the model has in the structure. Now, unfortunately, kind of going to your question of, you know, predicting affinity, unfortunately, confidence is not a very good predictor of affinity. And so one of the things that we've actually done a ton of progress, you know, since we released Boltz2.Brandon [00:52:03]: And kind of we have some new results that we are going to kind of announce soon is kind of, you know, the ability to get much better hit rates when instead of, you know, trying to rely on confidence of the model, we are actually directly trying to predict the affinity of that interaction. Okay. Just backing up a minute. So your diffusion model actually predicts not only the protein sequence, but also the folding of it. Exactly.Gabriel [00:52:32]: And actually, you can... One of the big different things that we did compared to other models in the space, and, you know, there were some papers that had already kind of done this before, but we really scaled it up was, you know, basically somewhat merging kind of the structure prediction and the sequence prediction into almost the same task. And so the way that Boltz2 works is that you are basically the only thing that you're doing is predicting the structure. So the only sort of... Supervision is we give you a supervision on the structure, but because the structure is atomic and, you know, the different amino acids have a different atomic composition, basically from the way that you place the atoms, we also understand not only kind of the structure that you wanted, but also the identity of the amino acid that, you know, the models believed was there. And so we've basically, instead of, you know, having these two supervision signals, you know, one discrete, one continuous. That somewhat, you know, don't interact well together. We sort of like build kind of like an encoding of, you know, sequences in structures that allows us to basically use exactly the same supervision signal that we were using to Boltz2 that, you know, you know, largely similar to what AlphaVol3 proposed, which is very scalable. And we can use that to design new proteins. Oh, interesting.RJ [00:53:58]: Maybe a quick shout out to Hannes Stark on our team who like did all this work. Yeah.Gabriel [00:54:04]: Yeah, that was a really cool idea. I mean, like looking at the paper and there's this is like encoding or you just add a bunch of, I guess, kind of atoms, which can be anything, and then they get sort of rearranged and then basically plopped on top of each other so that and then that encodes what the amino acid is. And there's sort of like a unique way of doing this. It was that was like such a really such a cool, fun idea.RJ [00:54:29]: I think that idea was had existed before. Yeah, there were a couple of papers.Gabriel [00:54:33]: Yeah, I had proposed this and and Hannes really took it to the large scale.Brandon [00:54:39]: In the paper, a lot of the paper for Boltz2Gen is dedicated to actually the validation of the model. In my opinion, all the people we basically talk about feel that this sort of like in the wet lab or whatever the appropriate, you know, sort of like in real world validation is the whole problem or not the whole problem, but a big giant part of the problem. So can you talk a little bit about the highlights? From there, that really because to me, the results are impressive, both from the perspective of the, you know, the model and also just the effort that went into the validation by a large team.Gabriel [00:55:18]: First of all, I think I should start saying is that both when we were at MIT and Thomas Yacolas and Regina Barzillai's lab, as well as at Boltz, you know, we are not a we're not a biolab and, you know, we are not a therapeutic company. And so to some extent, you know, we were first forced to, you know, look outside of, you know, our group, our team to do the experimental validation. One of the things that really, Hannes, in the team pioneer was the idea, OK, can we go not only to, you know, maybe a specific group and, you know, trying to find a specific system and, you know, maybe overfit a bit to that system and trying to validate. But how can we test this model? So. Across a very wide variety of different settings so that, you know, anyone in the field and, you know, printing design is, you know, such a kind of wide task with all sorts of different applications from therapeutic to, you know, biosensors and many others that, you know, so can we get a validation that is kind of goes across many different tasks? And so he basically put together, you know, I think it was something like, you know, 25 different. You know, academic and industry labs that committed to, you know, testing some of the designs from the model and some of this testing is still ongoing and, you know, giving results kind of back to us in exchange for, you know, hopefully getting some, you know, new great sequences for their task. And he was able to, you know, coordinate this, you know, very wide set of, you know, scientists and already in the paper, I think we. Shared results from, I think, eight to 10 different labs kind of showing results from, you know, designing peptides, designing to target, you know, ordered proteins, peptides targeting disordered proteins, which are results, you know, of designing proteins that bind to small molecules, which are results of, you know, designing nanobodies and across a wide variety of different targets. And so that's sort of like. That gave to the paper a lot of, you know, validation to the model, a lot of validation that was kind of wide.Brandon [00:57:39]: And so those would be therapeutics for those animals or are they relevant to humans as well? They're relevant to humans as well.Gabriel [00:57:45]: Obviously, you need to do some work into, quote unquote, humanizing them, making sure that, you know, they have the right characteristics to so they're not toxic to humans and so on.RJ [00:57:57]: There are some approved medicine in the market that are nanobodies. There's a general. General pattern, I think, in like in trying to design things that are smaller, you know, like it's easier to manufacture at the same time, like that comes with like potentially other challenges, like maybe a little bit less selectivity than like if you have something that has like more hands, you know, but the yeah, there's this big desire to, you know, try to design many proteins, nanobodies, small peptides, you know, that just are just great drug modalities.Brandon [00:58:27]: Okay. I think we were left off. We were talking about validation. Validation in the lab. And I was very excited about seeing like all the diverse validations that you've done. Can you go into some more detail about them? Yeah. Specific ones. Yeah.RJ [00:58:43]: The nanobody one. I think we did. What was it? 15 targets. Is that correct? 14. 14 targets. Testing. So we typically the way this works is like we make a lot of designs. All right. On the order of like tens of thousands. And then we like rank them and we pick like the top. And in this case, and was 15 right for each target and then we like measure sort of like the success rates, both like how many targets we were able to get a binder for and then also like more generally, like out of all of the binders that we designed, how many actually proved to be good binders. Some of the other ones I think involved like, yeah, like we had a cool one where there was a small molecule or design a protein that binds to it. That has a lot of like interesting applications, you know, for example. Like Gabri mentioned, like biosensing and things like that, which is pretty cool. We had a disordered protein, I think you mentioned also. And yeah, I think some of those were some of the highlights. Yeah.Gabriel [00:59:44]: So I would say that the way that we structure kind of some of those validations was on the one end, we have validations across a whole set of different problems that, you know, the biologists that we were working with came to us with. So we were trying to. For example, in some of the experiments, design peptides that would target the RACC, which is a target that is involved in metabolism. And we had, you know, a number of other applications where we were trying to design, you know, peptides or other modalities against some other therapeutic relevant targets. We designed some proteins to bind small molecules. And then some of the other testing that we did was really trying to get like a more broader sense. So how does the model work, especially when tested, you know, on somewhat generalization? So one of the things that, you know, we found with the field was that a lot of the validation, especially outside of the validation that was on specific problems, was done on targets that have a lot of, you know, known interactions in the training data. And so it's always a bit hard to understand, you know, how much are these models really just regurgitating kind of what they've seen or trying to imitate. What they've seen in the training data versus, you know, really be able to design new proteins. And so one of the experiments that we did was to take nine targets from the PDB, filtering to things where there is no known interaction in the PDB. So basically the model has never seen kind of this particular protein bound or a similar protein bound to another protein. So there is no way that. The model from its training set can sort of like say, okay, I'm just going to kind of tweak something and just imitate this particular kind of interaction. And so we took those nine proteins. We worked with adaptive CRO and basically tested, you know, 15 mini proteins and 15 nanobodies against each one of them. And the very cool thing that we saw was that on two thirds of those targets, we were able to, from this 15 design, get nanomolar binders, nanomolar, roughly speaking, just a measure of, you know, how strongly kind of the interaction is, roughly speaking, kind of like a nanomolar binder is approximately the kind of binding strength or binding that you need for a therapeutic. Yeah. So maybe switching directions a bit. Bolt's lab was just announced this week or was it last week? Yeah. This is like your. First, I guess, product, if that's if you want to call it that. Can you talk about what Bolt's lab is and yeah, you know, what you hope that people take away from this? Yeah.RJ [01:02:44]: You know, as we mentioned, like I think at the very beginning is the goal with the product has been to, you know, address what the models don't on their own. And there's largely sort of two categories there. I'll split it in three. The first one. It's one thing to predict, you know, a single interaction, for example, like a single structure. It's another to like, you know, very effectively search a space, a design space to produce something of value. What we found, like sort of building on this product is that there's a lot of steps involved, you know, in that there's certainly need to like, you know, accompany the user through, you know, one of those steps, for example, is like, you know, the creation of the target itself. You know, how do we make sure that the model has like a good enough understanding of the target? So we can like design something and there's all sorts of tricks, you know, that you can do to improve like a particular, you know, structure prediction. And so that's sort of like, you know, the first stage. And then there's like this stage of like, you know, designing and searching the space efficiently. You know, for something like BullsGen, for example, like you, you know, you design many things and then you rank them, for example, for small molecule process, a little bit more complicated. We actually need to also make sure that the molecules are synthesizable. And so the way we do that is that, you know, we have a generative model that learns. To use like appropriate building blocks such that, you know, it can design within a space that we know is like synthesizable. And so there's like, you know, this whole pipeline really of different models involved in being able to design a molecule. And so that's been sort of like the first thing we call them agents. We have a protein agent and we have a small molecule design agents. And that's really like at the core of like what powers, you know, the BullsLab platform.Brandon [01:04:22]: So these agents, are they like a language model wrapper or they're just like your models and you're just calling them agents? A lot. Yeah. Because they, they, they sort of perform a function on behalf of.RJ [01:04:33]: They're more of like a, you know, a recipe, if you wish. And I think we use that term sort of because of, you know, sort of the complex pipelining and automation, you know, that goes into like all this plumbing. So that's the first part of the product. The second part is the infrastructure. You know, we need to be able to do this at very large scale for any one, you know, group that's doing a design campaign. Let's say you're designing, you know, I'd say a hundred thousand possible candidates. Right. To find the good one that is, you know, a very large amount of compute, you know, for small molecules, it's on the order of like a few seconds per designs for proteins can be a bit longer. And so, you know, ideally you want to do that in parallel, otherwise it's going to take you weeks. And so, you know, we've put a lot of effort into like, you know, our ability to have a GPU fleet that allows any one user, you know, to be able to do this kind of like large parallel search.Brandon [01:05:23]: So you're amortizing the cost over your users. Exactly. Exactly.RJ [01:05:27]: And, you know, to some degree, like it's whether you. Use 10,000 GPUs for like, you know, a minute is the same cost as using, you know, one GPUs for God knows how long. Right. So you might as well try to parallelize if you can. So, you know, a lot of work has gone, has gone into that, making it very robust, you know, so that we can have like a lot of people on the platform doing that at the same time. And the third one is, is the interface and the interface comes in, in two shapes. One is in form of an API and that's, you know, really suited for companies that want to integrate, you know, these pipelines, these agents.RJ [01:06:01]: So we're already partnering with, you know, a few distributors, you know, that are gonna integrate our API. And then the second part is the user interface. And, you know, we, we've put a lot of thoughts also into that. And this is when I, I mentioned earlier, you know, this idea of like broadening the audience. That's kind of what the, the user interface is about. And we've built a lot of interesting features in it, you know, for example, for collaboration, you know, when you have like potentially multiple medicinal chemists or. We're going through the results and trying to pick out, okay, like what are the molecules that we're going to go and test in the lab? It's powerful for them to be able to, you know, for example, each provide their own ranking and then do consensus building. And so there's a lot of features around launching these large jobs, but also around like collaborating on analyzing the results that we try to solve, you know, with that part of the platform. So Bolt's lab is sort of a combination of these three objectives into like one, you know, sort of cohesive platform. Who is this accessible to? Everyone. You do need to request access today. We're still like, you know, sort of ramping up the usage, but anyone can request access. If you are an academic in particular, we, you know, we provide a fair amount of free credit so you can play with the platform. If you are a startup or biotech, you may also, you know, reach out and we'll typically like actually hop on a call just to like understand what you're trying to do and also provide a lot of free credit to get started. And of course, also with larger companies, we can deploy this platform in a more like secure environment. And so that's like more like customizing. You know, deals that we make, you know, with the partners, you know, and that's sort of the ethos of Bolt. I think this idea of like servicing everyone and not necessarily like going after just, you know, the really large enterprises. And that starts from the open source, but it's also, you know, a key design principle of the product itself.Gabriel [01:07:48]: One thing I was thinking about with regards to infrastructure, like in the LLM space, you know, the cost of a token has gone down by I think a factor of a thousand or so over the last three years, right? Yeah. And is it possible that like essentially you can exploit economies of scale and infrastructure that you can make it cheaper to run these things yourself than for any person to roll their own system? A hundred percent. Yeah.RJ [01:08:08]: I mean, we're already there, you know, like running Bolts on our platform, especially on a large screen is like considerably cheaper than it would probably take anyone to put the open source model out there and run it. And on top of the infrastructure, like one of the things that we've been working on is accelerating the models. So, you know. Our small molecule screening pipeline is 10x faster on Bolts Lab than it is in the open source, you know, and that's also part of like, you know, building a product, you know, of something that scales really well. And we really wanted to get to a point where like, you know, we could keep prices very low in a way that it would be a no-brainer, you know, to use Bolts through our platform.Gabriel [01:08:52]: How do you think about validation of your like agentic systems? Because, you know, as you were saying earlier. Like we're AlphaFold style models are really good at, let's say, monomeric, you know, proteins where you have, you know, co-evolution data. But now suddenly the whole point of this is to design something which doesn't have, you know, co-evolution data, something which is really novel. So now you're basically leaving the domain that you thought was, you know, that you know you are good at. So like, how do you validate that?RJ [01:09:22]: Yeah, I like every complete, but there's obviously, you know, a ton of computational metrics. That we rely on, but those are only take you so far. You really got to go to the lab, you know, and test, you know, okay, with this method A and this method B, how much better are we? You know, how much better is my, my hit rate? How stronger are my binders? Also, it's not just about hit rate. It's also about how good the binders are. And there's really like no way, nowhere around that. I think we're, you know, we've really ramped up the amount of experimental validation that we do so that we like really track progress, you know, as scientifically sound, you know. Yeah. As, as possible out of this, I think.Gabriel [01:10:00]: Yeah, no, I think, you know, one thing that is unique about us and maybe companies like us is that because we're not working on like maybe a couple of therapeutic pipelines where, you know, our validation would be focused on those. We, when we do an experimental validation, we try to test it across tens of targets. And so that on the one end, we can get a much more statistically significant result and, and really allows us to make progress. From the methodological side without being, you know, steered by, you know, overfitting on any one particular system. And of course we choose, you know, w

GuestDarren Wang, Founder & Chairman at OwlTing GroupCompany: OwlTing GroupTicker: OWLSWebsite:https://www.owlting.com/portal/?lang=enBioWith a background in cryptography with prior experience at tech companies in Silicon Valley, Darren founded OwlTing in 2010 to leverage blockchain technology to connect the world and drive industry transformation; today, OwlTing's core focus is building compliant stablecoin infrastructure for the future through OwlPay, advancing global payment and settlement capabilities for enterprises and platforms. The group also operates broader enterprise and consumer businesses, including blockchain services, hospitality and e-commerce platform.Darren holds a master's degree in Electrical Engineering from Boston University, completed the Owner/President Management Program (OPM 63) at Harvard Business School.Company Bio OBOOK Holdings Inc. is a global fintech company operating as the OwlTing Group (NASDAQ: OWLS). The Company was founded and is headquartered in Taiwan, with subsidiaries in the United States, Japan, Poland, Singapore, Hong Kong, Thailand, and Malaysia. The Company operates a diversified ecosystem across payments, hospitality, and e-commerce. In 2025, according to CB Insights' Stablecoin Market Map, OwlTing was ranked among the top 2 global players in the “Enterprise & B2B” category. The Company's mission is to use blockchain technology to provide businesses with more reliable and transparent data management, to reinvent global flow of funds for businesses and consumers and to lead the digital transformation of business operations. To this end, the Company introduced OwlPay, a Web2 and Web3 hybrid payment solution, to empower global businesses to operate confidently in the expanding stablecoin economy. For more information, visit https://www.owlting.com/portal/?lang=en.

Investors are poking at a potential AI bubble but haven't been able to burst it yet, says Kevin Mahn. He thinks traders should be looking at the companies that are receiving AI capex rather than the companies spending it, arguing the growth opportunities are there this year. However, he wouldn't count Mag 7 names like Microsoft (MSFT) out yet. He believes the AI revolution is a “long and winding road” with plenty of “potholes” ahead. He highlights Comfort Systems (FIX) and Duke Energy (DUK), Emcor (EME), Taiwan Semi (TSM) and NuScale Power (SMR).======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe 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 – / schwabnetwork Follow us on Facebook – / schwabnetwork Follow us on LinkedIn - / schwab-network About Schwab Network - https://schwabnetwork.com/about

Clement Manyathela speaks to Dean Macpherson, the Minister of Public Works and Infrastructure, and ANC NEC member and Minister of Higher Education, Buti Manamela on what we can expect ahead of the State of the Nation Address. The Clement Manyathela Show is broadcast on 702, a Johannesburg based talk radio station, weekdays from 09:00 to 12:00 (SA Time). Clement Manyathela starts his show each weekday on 702 at 9 am taking your calls and voice notes on his Open Line. In the second hour of his show, he unpacks, explains, and makes sense of the news of the day. Clement has several features in his third hour from 11 am that provide you with information to help and guide you through your daily life. As your morning friend, he tackles the serious as well as the light-hearted, on your behalf. Thank you for listening to a podcast from The Clement Manyathela Show. Listen live on Primedia+ weekdays from 09:00 and 12:00 (SA Time) to The Clement Manyathela Show broadcast on 702 https://buff.ly/gk3y0Kj For more from the show go to https://buff.ly/XijPLtJ or find all the catch-up podcasts here https://buff.ly/p0gWuPE Subscribe to the 702 Daily and Weekly Newsletters https://buff.ly/v5mfetc Follow us on social media: 702 on Facebook https://www.facebook.com/TalkRadio702 702 on TikTok https://www.tiktok.com/@talkradio702 702 on Instagram: https://www.instagram.com/talkradio702/ 702 on X: https://x.com/Radio702 702 on YouTube: https://www.youtube.com/@radio702 See omnystudio.com/listener for privacy information.

Aligning application and API security with the demands of the modern AI eraEnabling secure, high-performance infrastructure for AI and LLM environmentsSecuring APIs and your network without overspending on securityThom Langford, Host, teissTalkhttps://www.linkedin.com/in/thomlangford/Tiago Rosado, Chief Information Security Office, Asitehttps://www.linkedin.com/in/tiagorosado/Jamison Utter, Field CISO, A10 Networkslinkedin.com/in/jamisonutter/

In this episode of Texas Talks, Brad Swail interviews Margaret Byfield, Executive Director of American Stewards of Liberty, to break down the growing controversy surrounding proposed transmission lines across Texas. The discussion explores property rights, eminent domain, data-center energy demand, grid reliability after Winter Storm Uri, and the debate between local dispatchable power and large-scale transmission infrastructure.Byfield shares firsthand insight into how landowners could be affected by thousands of miles of new transmission corridors, the rising cost of electricity tied to infrastructure expansion, and the broader policy questions shaping Texas' energy future.Whether you're interested in energy policy, land use, rural property rights, or the economics behind grid expansion, this conversation offers a detailed look at one of the most consequential infrastructure debates unfolding in Texas today. Watch Full-Length Interviews: https://www.youtube.com/@TexasTalks

A shared ambition for the Canterbury region - is expected to secure the region's long term success. Business Canterbury, The Canterbury Mayoral Forum and the private sector have come together to form shared goals for the region. Business Canterbury Chief Executive Leeann Watson told Mike Hosking that everyone agrees they need to invest in what makes the region unique - which is better infrastructure, housing, affordability and the environment. LISTEN ABOVESee omnystudio.com/listener for privacy information.

Jack Chambers, Minister for Public Expenditure, Infrastructure, Public Service Reform and Digitalisation

Industrial Talk is onsite at PowerGen and talking to Bert Warner, Director of Commercial Business Development at Propane Education & Research Council. The Industrial Talk podcast, sponsored by the Propane Education and Research Council, discusses the growing role of propane in power generation. At Power Gen in San Antonio, industry professionals highlight the advantages of propane, including its speed of deployment, cost-effectiveness, and environmental benefits. Propane is a byproduct of natural gas extraction, with the U.S. using only about 10 billion gallons annually, while the rest is exported. The conversation emphasizes propane's potential to replace diesel and natural gas in certain applications, offering flexibility and economic benefits. The discussion also touches on the need for infrastructure and equipment to meet growing demand. Outline Introduction and Overview of Industrial Talk Podcast Scott introduces the episode of Industrial Talk, sponsored by the Propane Education and Research Council, highlighting their commitment to safety, training, and innovative propane power technology.Scott Mackenzie, welcomes listeners to the Industrial Talk podcast, emphasizing the focus on industry innovations and trends.Scott thanks listeners for joining the top industrial podcast, celebrating industry professionals who solve daily problems in power generation.The podcast is broadcasting from PowerGen in San Antonio, with Scott encouraging listeners to attend future events. Discussion on Power Generation and Propane Scott introduces Bert Warner from the Propane Education and Research Council, focusing on the role of propane in power generation.Bert Warner, comments on the increased size and interest at PowerGen, emphasizing the need for diverse equipment solutions.Scott discuss the challenges of meeting immediate power demands in a fast-paced world, highlighting the importance of pragmatic solutions.Bert Warner explains the advantages of propane, particularly its speed of deployment compared to traditional infrastructure build-outs. Propane as a Prime Power Source Bert Warner discusses the potential for propane to be a prime power source, not just a backup or emergency fuel.He explains the flexibility of using propane for base loading, peak shaving, and other applications, providing economic and environmental benefits.Scott and Bert Warner delve into the historical use of propane in remote and emergency situations, and its growing potential in urban areas.Bert Warner highlights the abundant supply of propane and its clean, safe, and affordable nature, making it a viable first choice for energy needs. Propane Extraction and Market Potential Bert Warner explains that propane is a byproduct of natural gas extraction, with a significant amount exported globally.He discusses the increasing demand for natural gas and propane due to electrification efforts, emphasizing the abundant supply available.Scott and Bert Warner explore the shift from natural gas to propane in urban areas, highlighting the advantages of propane over diesel and other fuels.Bert Warner emphasizes the resiliency and cost-effectiveness of propane, making it an attractive option for commercial and industrial applications. Infrastructure and Distribution of Propane Bert Warner discusses the extensive infrastructure of propane companies in the US, ensuring reliable delivery and support.He highlights the advancements in tank monitoring and automatic deliveries, enhancing the convenience and efficiency of propane use.Scott and Bert Warner discuss the

Bill Thompson is a retired Chief Warrant Officer 4. He is also a former Cyber Network Operations advisor and program evaluator at DARPA with experience in the fields of AI, Signals, and Human Intelligence. He is also the founder of the Spartan Forge hunting app: https://spartanforge.ai/ Change Agents is an IRONCLAD Original Chapters: (01:03) What Is DARPA? (05:58) Drone Warfare Ukraine (13:20) The Dangers of Terrorists Using Drones (16:50) Smartphone Surveillance in the Maduro Raid (26:00) Using the Internet in Authoritarian Countries (31:50) Spying on Cell Phones and Stealing Data (36:50) Choosing Targeted People to Spy On (39:35) The Vulnerability of Infrastructure to Cyberattacks (46:35) How Can You Protect Your Data? Subscribe: Subscribe on Apple Podcasts: https://podcasts.apple.com/us/podcast/change-agents-with-andy-stumpf/id1677415740 Subscribe on Spotify: https://open.spotify.com/show/3SKmtN55V2AGbzHDo34DHI?si=5aefbba9abc844ed Sponsors: Firecracker Farm Use code IRONCLAD to get 15% off your first order at https://firecracker.farm/ GHOSTBED: Go to https://www.GhostBed.com/CHANGEAGENTS and use code CHANGEAGENTS for an extra 15% off sitewide. Norwood Sawmills: Learn more about Norwood Sawmills and how you can start milling your own lumber at https://norwoodsawmills.com/ Learn more about your ad choices. Visit megaphone.fm/adchoices

Today we are joined by Matt Remke, who has spent years in the trenches of network automation projects as a consultant. Matt offers a unique, non-engineer perspective on scaling network automation in real-world, complex environments for some of the world’s largest companies. Matt shares what worked, what backfired, and the hard-earned lessons he has gained... Read more »

Today we are joined by Matt Remke, who has spent years in the trenches of network automation projects as a consultant. Matt offers a unique, non-engineer perspective on scaling network automation in real-world, complex environments for some of the world’s largest companies. Matt shares what worked, what backfired, and the hard-earned lessons he has gained... Read more »

Infrastructure was passé…uncool. Difficult to get dollars from Private Equity and Growth funds, and almost impossible to get a VC fund interested. Now?! Now, it's cool. Infrastructure seems to be having a Renaissance, a full on Rebirth, not just fueled by commercial interests (e.g. advent of AI), but also by industrial policy and geopolitical considerations. In this episode of Tech Deciphered, we explore what's cool in the infrastructure spaces, including mega trends in semiconductors, energy, networking & connectivity, manufacturing Navigation: Intro We're back to building things Why now: the 5 forces behind the renaissance Semiconductors: compute is the new oil Networking & connectivity: digital highways get rebuilt Energy: rebuilding the power stack (not just renewables) Manufacturing: the return of “atoms + bits” Wrap: what it means for startups, incumbents, and investors Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show: Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Nuno Gonçalves Pedro Introduction Welcome to episode 73 of Tech Deciphered, Infrastructure, the Rebirth or Renaissance. Infrastructure was passé, it wasn’t cool, but all of a sudden now everyone’s talking about network, talking about compute and semiconductors, talking about logistics, talking about energy. What gives? What’s happened? It was impossible in the past to get any funds, venture capital, even, to be honest, some private equity funds or growth funds interested in some of these areas, but now all of a sudden everyone thinks it’s cool. The infrastructure seems to be having a renaissance, a full-on rebirth. In this episode, we will explore in which cool ways the infrastructure spaces are moving and what’s leading to it. We will deep dive into the forces that are leading us to this. We will deep dive into semiconductors, networking and connectivity, energy, manufacturing, and then we’ll wrap up. Bertrand, so infrastructure is cool now. Bertrand Schmitt We're back to building things Yes. I thought software was going to eat the world. I cannot believe it was then, maybe even 15 years ago, from Andreessen, that quote about software eating the world. I guess it’s an eternal balance. Sometimes you go ahead of yourself, you build a lot of software stack, and at some point, you need the hardware to run this software stack, and there is only so much the bits can do in a world of atoms. Nuno Gonçalves Pedro Obviously, we’ve gone through some of this before. I think what we’re going through right now is AI is eating the world, and because AI is eating the world, it’s driving a lot of this infrastructure building that we need. We don’t have enough energy to be consumed by all these big data centers and hyperscalers. We need to be innovative around network as well because of the consumption in terms of network bandwidth that is linked to that consumption as well. In some ways, it’s not software eating the world, AI is eating the world. Because AI is eating the world, we need to rethink everything around infrastructure and infrastructure becoming cool again. Bertrand Schmitt There is something deeper in this. It’s that the past 10, even 15 years were all about SaaS before AI. SaaS, interestingly enough, was very energy-efficient. When I say SaaS, I mean cloud computing at large. What I mean by energy-efficient is that actually cloud computing help make energy use more efficient because instead of companies having their own separate data centers in many locations, sometimes poorly run from an industrial perspective, replace their own privately run data center with data center run by the super scalers, the hyperscalers of the world. These data centers were run much better in terms of how you manage the coolings, the energy efficiency, the rack density, all of this stuff. Actually, the cloud revolution didn’t increase the use of electricity. The cloud revolution was actually a replacement from your private data center to the hyperscaler data center, which was energy efficient. That’s why we didn’t, even if we are always talking about that growth of cloud computing, we were never feeling the pinch in term of electricity. As you say, we say it all changed because with AI, it was not a simple “Replacement” of locally run infrastructure to a hyperscaler run infrastructure. It was truly adding on top of an existing infrastructure, a new computing infrastructure in a way out of nowhere. Not just any computing infrastructure, an energy infrastructure that was really, really voracious in term of energy use. Nuno Gonçalves Pedro There was one other effect. Obviously, we’ve discussed before, we are in a bubble. We won’t go too much into that today. But the previous big bubble in tech, which is in the late ’90s, there was a lot of infrastructure built. We thought the internet was going to take over back then. It didn’t take over immediately, but there was a lot of network connectivity, bandwidth built back in the day. Companies imploded because of that as well, or had to restructure and go in their chapter 11. A lot of the big telco companies had their own issues back then, etc., but a lot of infrastructure was built back then for this advent of the internet, which would then take a long time to come. In some ways, to your point, there was a lot of latent supply that was built that was around that for a while wasn’t used, but then it was. Now it’s been used, and now we need new stuff. That’s why I feel now we’re having the new moment of infrastructure, new moment of moving forward, aligned a little bit with what you just said around cloud computing and the advent of SaaS, but also around the fact that we had a lot of buildup back in the late ’90s, early ’90s, which we’re now still reaping the benefits on in today’s world. Bertrand Schmitt Yeah, that’s actually a great point because what was built in the late ’90s, there was a lot of fibre that was built. Laying out the fibre either across countries, inside countries. This fibre, interestingly enough, you could just change the computing on both sides of the fibre, the routing, the modems, and upgrade the capacity of the fibre. But the fibre was the same in between. The big investment, CapEx investment, was really lying down that fibre, but then you could really upgrade easily. Even if both ends of the fibre were either using very old infrastructure from the ’90s or were actually dark and not being put to use, step by step, it was being put to use, equipment was replaced, and step by step, you could keep using more and more of this fibre. It was a very interesting development, as you say, because it could be expanded over the years, where if we talk about GPUs, use for AI, GPUs, the interesting part is actually it’s totally the opposite. After a few years, it’s useless. Some like Google, will argue that they can depreciate over 5, 6 years, even some GPUs. But at the end of the day, the difference in perf and energy efficiency of the GPUs means that if you are energy constrained, you just want to replace the old one even as young as three-year-old. You have to look at Nvidia increasing spec, generation after generation. It’s pretty insane. It’s usually at least 3X year over year in term of performance. Nuno Gonçalves Pedro At this moment in time, it’s very clear that it’s happening. Why now: the 5 forces behind the renaissance Maybe let’s deep dive into why it’s happening now. What are the key forces around this? We’ve identified, I think, five forces that are particularly vital that lead to the world we’re in right now. One we’ve already talked about, which is AI, the demand shock and everything that’s happened because of AI. Data centers drive power demand, drive grid upgrades, drive innovative ways of getting energy, drive chips, drive networking, drive cooling, drive manufacturing, drive all the things that we’re going to talk in just a bit. One second element that we could probably highlight in terms of the forces that are behind this is obviously where we are in terms of cost curves around technology. Obviously, a lot of things are becoming much cheaper. The simulation of physical behaviours has become a lot more cheap, which in itself, this becomes almost a vicious cycle in of itself, then drives the adoption of more and more AI and stuff. But anyway, the simulation is becoming more and more accessible, so you can do a lot of simulation with digital twins and other things off the real world before you go into the real world. Robotics itself is becoming, obviously, cheaper. Hardware, a lot of the hardware is becoming cheaper. Computer has become cheaper as well. Obviously, there’s a lot of cost curves that have aligned that, and that’s maybe the second force that I would highlight. Obviously, funds are catching up. We’ll leave that a little bit to the end. We’ll do a wrap-up and talk a little bit about the implications to investors. But there’s a lot of capital out there, some capital related to industrial policy, other capital related to private initiative, private equity, growth funds, even venture capital, to be honest, and a few other elements on that. That would be a third force that I would highlight. Bertrand Schmitt Yes. Interestingly enough, in terms of capital use, and we’ll talk more about this, but some firms, if we are talking about energy investment, it was very difficult to invest if you are not investing in green energy. Now I think more and more firms and banks are willing to invest or support different type of energy infrastructure, not just, “Green energy.” That’s an interesting development because at some point it became near impossible to invest more in gas development, in oil development in the US or in most Western countries. At least in the US, this is dramatically changing the framework. Nuno Gonçalves Pedro Maybe to add the two last forces that I think we see behind the renaissance of what’s happening in infrastructure. They go hand in hand. One is the geopolitics of the world right now. Obviously, the world was global flat, and now it’s becoming increasingly siloed, so people are playing it to their own interests. There’s a lot of replication of infrastructure as well because people want to be autonomous, and they want to drive their own ability to serve end consumers, businesses, etc., in terms of data centers and everything else. That ability has led to things like, for example, chips shortage. The fact that there are semiconductors, there are shortages across the board, like memory shortages, where everything is packed up until 2027 of 2028. A lot of the memory that was being produced is already spoken for, which is shocking. There’s obviously generation of supply chain fragilities, obviously, some of it because of policies, for example, in the US with tariffs, etc, security of energy, etc. Then the last force directly linked to the geopolitics is the opposite of it, which is the policy as an accelerant, so to speak, as something that is accelerating development, where because of those silos, individual countries, as part their industrial policy, then want to put capital behind their local ecosystems, their local companies, so that their local companies and their local systems are for sure the winners, or at least, at the very least, serve their own local markets. I think that’s true of a lot of the things we’re seeing, for example, in the US with the Chips Act, for semiconductors, with IGA, IRA, and other elements of what we’ve seen in terms of practices, policies that have been implemented even in Europe, China, and other parts of the world. Bertrand Schmitt Talking about chips shortages, it’s pretty insane what has been happening with memory. Just the past few weeks, I have seen a close to 3X increase in price in memory prices in a matter of weeks. Apparently, it started with a huge order from OpenAI. Apparently, they have tried to corner the memory market. Interestingly enough, it has flat-footed the entire industry, and that includes Google, that includes Microsoft. There are rumours of their teams now having moved to South Korea, so they are closer to the action in terms of memory factories and memory decision-making. There are rumours of execs who got fired because they didn’t prepare for this type of eventuality or didn’t lock in some of the supply chain because that memory was initially for AI, but obviously, it impacts everything because factories making memories, you have to plan years in advance to build memories. You cannot open new lines of manufacturing like this. All factories that are going to open, we know when they are going to open because they’ve been built up for years. There is no extra capacity suddenly. At the very best, you can change a bit your line of production from one type of memory to another type. But that’s probably about it. Nuno Gonçalves Pedro Just to be clear, all these transformations we’re seeing isn’t to say just hardware is back, right? It’s not just hardware. There’s physicality. The buildings are coming back, right? It’s full stack. Software is here. That’s why everything is happening. Policy is here. Finance is here. It’s a little bit like the name of the movie, right? Everything everywhere all at once. Everything’s happening. It was in some ways driven by the upper stacks, by the app layers, by the platform layers. But now we need new infrastructure. We need more infrastructure. We need it very, very quickly. We need it today. We’re already lacking in it. Semiconductors: compute is the new oil Maybe that’s a good segue into the first piece of the whole infrastructure thing that’s driving now the most valuable company in the world, NVIDIA, which is semiconductors. Semiconductors are driving compute. Semis are the foundation of infrastructure as a compute. Everyone needs it for every thing, for every activity, not just for compute, but even for sensors, for actuators, everything else. That’s the beginning of it all. Semiconductor is one of the key pieces around the infrastructure stack that’s being built at scale at this moment in time. Bertrand Schmitt Yes. What’s interesting is that if we look at the market gap of Semis versus software as a service, cloud companies, there has been a widening gap the past year. I forgot the exact numbers, but we were talking about plus 20, 25% for Semis in term of market gap and minus 5, minus 10 for SaaS companies. That’s another trend that’s happening. Why is this happening? One, because semiconductors are core to the AI build-up, you cannot go around without them. But two, it’s also raising a lot of questions about the durability of the SaaS, a software-as-a-service business model. Because if suddenly we have better AI, and that’s all everyone is talking about to justify the investment in AI, that it keeps getting better, and it keeps improving, and it’s going to replace your engineers, your software engineers. Then maybe all of this moat that software companies built up over the years or decades, sometimes, might unravel under the pressure of newly coded, newly built, cheaper alternatives built from the ground up with AI support. It’s not just that, yes, semiconductors are doing great. It’s also as a result of that AI underlying trend that software is doing worse right now. Nuno Gonçalves Pedro At the end of the day, this foundational piece of infrastructure, semiconductor, is obviously getting manifest to many things, fabrication, manufacturing, packaging, materials, equipment. Everything’s being driven, ASML, etc. There are all these different players around the world that are having skyrocket valuations now, it’s because they’re all part of the value chain. Just to be very, very clear, there’s two elements of this that I think are very important for us to remember at this point in time. One, it’s the entire value chains are being shifted. It’s not just the chips that basically lead to computing in the strict sense of it. It’s like chips, for example, that drive, for example, network switching. We’re going to talk about networking a bit, but you need chips to drive better network switching. That’s getting revolutionised as well. For example, we have an investment in that space, a company called the eridu.ai, and they’re revolutionising one of the pieces around that stack. Second part of the puzzle, so obviously, besides the holistic view of the world that’s changing in terms of value change, the second piece of the puzzle is, as we discussed before, there’s industrial policy. We already mentioned the CHIPS Act, which is something, for example, that has been done in the US, which I think is 52 billion in incentives across a variety of things, grants, loans, and other mechanisms to incentivise players to scale capacity quick and to scale capacity locally in the US. One of the effects of that now is obviously we had the TSMC, US expansion with a factory here in the US. We have other levels of expansion going on with Intel, Samsung, and others that are happening as we speak. Again, it’s this two by two. It’s market forces that drive the need for fundamental shifts in the value chain. On the other industrial policy and actual money put forward by states, by governments, by entities that want to revolutionise their own local markets. Bertrand Schmitt Yes. When you talk about networking, it makes me think about what NVIDIA did more than six years ago when they acquired Mellanox. At the time, it was largest acquisition for NVIDIA in 2019, and it was networking for the data center. Not networking across data center, but inside the data center, and basically making sure that your GPUs, the different computers, can talk as fast as possible between each of them. I think that’s one piece of the puzzle that a lot of companies are missing, by the way, about NVIDIA is that they are truly providing full systems. They are not just providing a GPU. Some of their competitors are just providing GPUs. But NVIDIA can provide you the full rack. Now, they move to liquid-cool computing as well. They design their systems with liquid cooling in mind. They have a very different approach in the industry. It’s a systematic system-level approach to how do you optimize your data center. Quite frankly, that’s a bit hard to beat. Nuno Gonçalves Pedro For those listening, you’d be like, this is all very different. Semiconductors, networking, energy, manufacturing, this is all different. Then all of a sudden, as Bertrand is saying, well, there are some players that are acting across the stack. Then you see in the same sentence, you’re talking about nuclear power in Microsoft or nuclear power in Google, and you’re like, what happened? Why are these guys in the same sentence? It’s like they’re tech companies. Why are they talking about energy? It’s the nature of that. These ecosystems need to go hand in hand. The value chains are very deep. For you to actually reap the benefits of more and more, for example, semiconductor availability, you have to have better and better networking connectivity, and you have to have more and more energy at lower and lower costs, and all of that. All these things are intrinsically linked. That’s why you see all these big tech companies working across stack, NVIDIA being a great example of that in trying to create truly a systems approach to the world, as Bertrand was mentioning. Networking & connectivity: digital highways get rebuilt On the networking and connectivity side, as we said, we had a lot of fibre that was put down, etc, but there’s still more build-out needs to be done. 5G in terms of its densification is still happening. We’re now starting to talk, obviously, about 6G. I’m not sure most telcos are very happy about that because they just have been doing all this CapEx and all this deployment into 5G, and now people already started talking about 6G and what’s next. Obviously, data center interconnect is quite important, and all the hubbing that needs to happen around data centers is very, very important. We are seeing a lot movements around connectivity that are particularly important. Network gear and the emergence of players like Broadcom in terms of the semiconductor side of the fence, obviously, Cisco, Juniper, Arista, and others that are very much present in this space. As I said, we made an investment on the semiconductor side of networking as well, realizing that there’s still a lot of bottlenecks happening there. But obviously, the networking and connectivity stack still needs to be built at all levels within the data centers, outside of the data centers in terms of last mile, across the board in terms of fibre. We’re seeing a lot of movements still around the space. It’s what connects everything. At the end of the day, if there’s too much latency in these systems, if the bandwidths are not high enough, then we’re going to have huge bottlenecks that are going to be put at the table by a networking providers. Obviously, that doesn’t help anyone. If there’s a button like anywhere, it doesn’t work. All of this doesn’t work. Bertrand Schmitt Yes. Interestingly enough, I know we said for this episode, we not talk too much about space, but when you talk about 6G, it make me think about, of course, Starlink. That’s really your last mile delivery that’s being built as well. It’s a massive investment. We’re talking about thousands of satellites that are interconnected between each other through laser system. This is changing dramatically how companies can operate, how individuals can operate. For companies, you can have great connectivity from anywhere in the world. For military, it’s the same. For individuals, suddenly, you won’t have dead space, wide zones. This is also a part of changing how we could do things. It’s quite important even in the development of AI because, yes, you can have AI at the edge, but that interconnect to the rest of the system is quite critical. Having that availability of a network link, high-quality network link from anywhere is a great combo. Nuno Gonçalves Pedro Then you start seeing regions of the world that want to differentiate to attract digital nomads by saying, “We have submarine cables that come and hub through us, and therefore, our connectivity is amazing.” I was just in Madeira, and they were talking about that in Portugal. One of the islands of Portugal. We have some Marine cables. You have great connectivity. We’re getting into that discussion where people are like, I don’t care. I mean, I don’t know. I assume I have decent connectivity. People actually care about decent connectivity. This discussion is not just happening at corporate level, at enterprise level? Etc. Even consumers, even people that want to work remotely or be based somewhere else in the world. It’s like, This is important Where is there a great connectivity for me so that I can have access to the services I need? Etc. Everyone becomes aware of everything. We had a cloud flare mishap more recently that the CEO had to jump online and explain deeply, technically and deeply, what happened. Because we’re in their heads. If Cloudflare goes down, there’s a lot of websites that don’t work. All of this, I think, is now becoming du jour rather than just an afterthought. Maybe we’ll think about that in the future. Bertrand Schmitt Totally. I think your life is being changed for network connectivity, so life of individuals, companies. I mean, everything. Look at airlines and ships and cruise ships. Now is the advent of satellite connectivity. It’s dramatically changing our experience. Nuno Gonçalves Pedro Indeed. Energy: rebuilding the power stack (not just renewables) Moving maybe to energy. We’ve talked about energy quite a bit in the past. Maybe we start with the one that we didn’t talk as much, although we did mention it, which was, let’s call it the fossil infrastructure, what’s happening around there. Everyone was saying, it’s all going to be renewables and green. We’ve had a shift of power, geopolitics. Honestly, I the writing was on the wall that we needed a lot more energy creation. It wasn’t either or. We needed other sources to be as efficient as possible. Obviously, we see a lot of work happening around there that many would have thought, Well, all this infrastructure doesn’t matter anymore. Now we’re seeing LNG terminals, pipelines, petrochemical capacity being pushed up, a lot of stuff happening around markets in terms of export, and not only around export, but also around overall distribution and increases and improvements so that there’s less leakage, distribution of energy, etc. In some ways, people say, it’s controversial, but it’s like we don’t have enough energy to spare. We’re already behind, so we need as much as we can. We need to figure out the way to really extract as much as we can from even natural resources, which In many people’s mind, it’s almost like blasphemous to talk about, but it is where we are. Obviously, there’s a lot of renaissance also happening on the fossil infrastructure basis, so to speak. Bertrand Schmitt Personally, I’m ecstatic that there is a renaissance going regarding what is called fossil infrastructure. Oil and gas, it’s critical to humanity well-being. You never had growth of countries without energy growth and nothing else can come close. Nuclear could come close, but it takes decades to deploy. I think it’s great. It’s great for developed economies so that they do better, they can expand faster. It’s great for third-world countries who have no realistic other choice. I really don’t know what happened the past 10, 15 years and why this was suddenly blasphemous. But I’m glad that, strangely, thanks to AI, we are back to a more rational mindset about energy and making sure we get efficient energy where we can. Obviously, nuclear is getting a second act. Nuno Gonçalves Pedro I know you would be. We’ve been talking about for a long time, and you’ve been talking about it in particular for a very long time. Bertrand Schmitt Yes, definitely. It’s been one area of interest of mine for 25 years. I don’t know. I’ve been shocked about what happened in Europe, that willingness destruction of energy infrastructure, especially in Germany. Just a few months ago, they keep destroying on live TV some nuclear station in perfect working condition and replacing them with coal. I’m not sure there is a better definition of insanity at this stage. It looks like it’s only the Germans going that hardcore for some reason, but at least the French have stopped their program of decommissioning. America, it seems to be doing the same, so it’s great. On top of it, there are new generations that could be put to use. The Chinese are building up a very large nuclear reactor program, more than 100 reactors in construction for the next 10 years. I think everybody has to catch up because at some point, this is the most efficient energy solution. Especially if you don’t build crazy constraints around the construction of these nuclear reactors. If we are rational about permits, about energy, about safety, there are great things we could be doing with nuclear. That might be one of the only solution if we want to be competitive, because when energy prices go down like crazy, like in China, they will do once they have reach delivery of their significant build-up of nuclear reactors, we better be ready to have similar options from a cost perspective. Nuno Gonçalves Pedro From the outside, at the very least, nuclear seems to be probably in the energy one of the areas that’s more being innovated at this moment in time. You have startups in the space, you have a lot really money going into it, not just your classic industrial development. That’s very exciting. Moving maybe to the carbonization and what’s happening. The CCUS, and for those who don’t know what it is, carbon capture, utilization, and storage. There’s a lot of stuff happening around that space. That’s the area that deals with the ability to capture CO₂ emissions from industrial sources and/or the atmosphere and preventing their release. There’s a lot of things happening in that space. There’s also a lot of things happening around hydrogen and geothermal and really creating the ability to storage or to store, rather, energy that then can be put back into the grids at the right time. There’s a lot of interesting pieces happening around this. There’s some startup movement in the space. It’s been a long time coming, the reuse of a lot of these industrial sources. Not sure it’s as much on the news as nuclear, and oil and gas, but certainly there’s a lot of exciting things happening there. Bertrand Schmitt I’m a bit more dubious here, but I think geothermal makes sense if it’s available at reasonable price. I don’t think hydrogen technology has proven its value. Concerning carbon capture, I’m not sure how much it’s really going to provide in terms of energy needs, but why not? Nuno Gonçalves Pedro Fuels niche, again, from the outside, we’re not energy experts, but certainly, there are movements in the space. We’ll see what’s happening. One area where there’s definitely a lot of movement is this notion of grid and storage. On the one hand, that transmission needs to be built out. It needs to be better. We’ve had issues of blackouts in the US. We’ve had issues of blackouts all around the world, almost. Portugal as well, for a significant part of the time. The ability to work around transmission lines, transformers, substations, the modernization of some of this infrastructure, and the move forward of it is pretty critical. But at the other end, there’s the edge. Then, on the edge, you have the ability to store. We should have, better mechanisms to store energy that are less leaky in terms of energy storage. Obviously, there’s a lot of movement around that. Some of it driven just by commercial stuff, like Tesla a lot with their storage stuff, etc. Some of it really driven at scale by energy players that have the interest that, for example, some of the storage starts happening closer to the consumption as well. But there’s a lot of exciting things happening in that space, and that is a transformative space. In some ways, the bottleneck of energy is also around transmission and then ultimately the access to energy by homes, by businesses, by industries, etc. Bertrand Schmitt I would say some of the blackout are truly man-made. If I pick on California, for instance. That’s the logical conclusion of the regulatory system in place in California. On one side, you limit price that energy supplier can sell. The utility company can sell, too. On the other side, you force them to decommission the most energy-efficient and least expensive energy source. That means you cap the revenues, you make the cost increase. What is the result? The result is you cannot invest anymore to support a grid and to support transmission. That’s 100% obvious. That’s what happened, at least in many places. The solution is stop crazy regulations that makes no economic sense whatsoever. Then, strangely enough, you can invest again in transmission, in maintenance, and all I love this stuff. Maybe another piece, if we pick in California, if you authorize building construction in areas where fires are easy, that’s also a very costly to support from utility perspective, because then you are creating more risk. You are forced buy the state to connect these new constructions to the grid. You have more maintenance. If it fails, you can create fire. If you create fire, you have to pay billions of fees. I just want to highlight that some of this is not a technological issue, is not per se an investment issue, but it’s simply the result of very bad regulations. I hope that some will learn, and some change will be made so that utilities can do their job better. Nuno Gonçalves Pedro Then last, but not the least, on the energy side, energy is becoming more and more digitally defined in some ways. It’s like the analogy to networks that they’ve become more, and more software defined, where you have, at the edge is things like smart meters. There’s a lot of things you can do around the key elements of the business model, like dynamic pricing and other elements. Demand response, one of the areas that I invested in, I invest in a company called Omconnect that’s now merged with what used to be Google Nest. Where to deploy that ability to do demand response and also pass it to consumers so that consumers can reduce their consumption at times where is the least price effective or the less green or the less good for the energy companies to produce energy. We have other things that are happening, which are interesting. Obviously, we have a lot more electric vehicles in cars, etc. These are also elements of storage. They don’t look like elements of storage, but the car has electricity in it once you charge it. Once it’s charged, what do you do with it? Could you do something else? Like the whole reverse charging piece that we also see now today in mobile devices and other edge devices, so to speak. That also changes the architecture of what we’re seeing around the space. With AI, there’s a lot of elements that change around the value chain. The ability to do forecasting, the ability to have, for example, virtual power plans because of just designated storage out there, etc. Interesting times happening. Not sure all utilities around the world, all energy providers around the world are innovating at the same pace and in the same way. But certainly just looking at the industry and talking to a lot of players that are CEOs of some of these companies. That are leading innovation for some of these companies, there’s definitely a lot more happening now in the last few years than maybe over the last few decades. Very exciting times. Bertrand Schmitt I think there are two interesting points in what you say. Talking about EVs, for instance, a Cybertruck is able to send electricity back to your home if your home is able to receive electricity from that source. Usually, you have some changes to make to the meter system, to your panel. That’s one great way to potentially use your car battery. Another piece of the puzzle is that, strangely enough, most strangely enough, there has been a big push to EV, but at the same time, there has not been a push to provide more electricity. But if you replace cars that use gasoline by electric vehicles that use electricity, you need to deliver more electricity. It doesn’t require a PhD to get that. But, strangely enough, nothing was done. Nuno Gonçalves Pedro Apparently, it does. Bertrand Schmitt I remember that study in France where they say that, if people were all to switch to EV, we will need 10 more nuclear reactors just on the way from Paris to Nice to the Côte d’Azur, the French Rivière, in order to provide electricity to the cars going there during the summer vacation. But I mean, guess what? No nuclear plant is being built along the way. Good luck charging your vehicles. I think that’s another limit that has been happening to the grid is more electric vehicles that require charging when the related infrastructure has not been upgraded to support more. Actually, it has quite the opposite. In many cases, we had situation of nuclear reactors closing down, so other facilities closing down. Obviously, the end result is an increase in price of electricity, at least in some states and countries that have not sold that fully out. Nuno Gonçalves Pedro Manufacturing: the return of “atoms + bits” Moving to manufacturing and what’s happening around manufacturing, manufacturing technology. There’s maybe the case to be made that manufacturing is getting replatformed, right? It’s getting redefined. Some of it is very obvious, and it’s already been ongoing for a couple of decades, which is the advent of and more and more either robotic augmented factories or just fully roboticized factories, where there’s very little presence of human beings. There’s elements of that. There’s the element of software definition on top of it, like simulation. A lot of automation is going on. A lot of AI has been applied to some lines in terms of vision, safety. We have an investment in a company called Sauter Analytics that is very focused on that from the perspective of employees and when they’re still humans in the loop, so to speak, and the ability to really figure out when people are at risk and other elements of what’s happening occurring from that. But there’s more than that. There’s a little bit of a renaissance in and of itself. Factories are, initially, if we go back a couple of decades ago, factories were, and manufacturing was very much defined from the setup. Now it’s difficult to innovate, it’s difficult to shift the line, it’s difficult to change how things are done in the line. With the advent of new factories that have less legacy, that have more flexible systems, not only in terms of software, but also in terms of hardware and robotics, it allows us to, for example, change and shift lines much more easily to different functions, which will hopefully, over time, not only reduce dramatically the cost of production. But also increase dramatically the yield, it increases dramatically the production itself. A lot of cool stuff happening in that space. Bertrand Schmitt It’s exciting to see that. One thing this current administration in the US has been betting on is not just hoping for construction renaissance. Especially on the factory side, up of factories, but their mindset was two things. One, should I force more companies to build locally because it would be cheaper? Two, increase output and supply of energy so that running factories here in the US would be cheaper than anywhere else. Maybe not cheaper than China, but certainly we get is cheaper than Europe. But three, it’s also the belief that thanks to AI, we will be able to have more efficient factories. There is always that question, do Americans to still keep making clothes, for instance, in factories. That used to be the case maybe 50 years ago, but this move to China, this move to Bangladesh, this move to different places. That’s not the goal. But it can make sense that indeed there is ability, thanks to robots and AI, to have more automated factories, and these factories could be run more efficiently, and as a result, it would be priced-competitive, even if run in the US. When you want to think about it, that has been, for instance, the South Korean playbook. More automated factories, robotics, all of this, because that was the only way to compete against China, which has a near infinite or used to have a near infinite supply of cheaper labour. I think that all of this combined can make a lot of sense. In a way, it’s probably creating a perfect storm. Maybe another piece of the puzzle this administration has been working on pretty hard is simplifying all the permitting process. Because a big chunk of the problem is that if your permitting is very complex, very expensive, what take two years to build become four years, five years, 10 years. The investment mass is not the same in that situation. I think that’s a very important part of the puzzle. It’s use this opportunity to reduce regulatory state, make sure that things are more efficient. Also, things are less at risk of bribery and fraud because all these regulations, there might be ways around. I think it’s quite critical to really be careful about this. Maybe last piece of the puzzle is the way accounting works. There are new rules now in 2026 in the US where you can fully depreciate your CapEx much faster than before. That’s a big win for manufacturing in the US. Suddenly, you can depreciate much faster some of your CapEx investment in manufacturing. Nuno Gonçalves Pedro Just going back to a point you made and then moving it forward, even China, with being now probably the country in the world with the highest rate of innovation and take up of industrial robots. Because of demographic issues a little bit what led Japan the first place to be one of the real big innovators around robots in general. The fact that demographics, you’re having an aging population, less and less children. How are you going to replace all these people? Moving that into big winners, who becomes a big winner in a space where manufacturing is fundamentally changing? Obviously, there’s the big four of robots, which is ABB, FANUC, KUKA, and Yaskawa. Epson, I think, is now in there, although it’s not considered one of the big four. Kawasaki, Denso, Universal Robots. There’s a really big robotics, industrial robotic companies in the space from different origins, FANUC and Yaskawa, and Epson from Japan, KUKA from Germany, ABB from Switzerland, Sweden. A lot of now emerging companies from China, and what’s happening in that space is quite interesting. On the other hand, also, other winners will include players that will be integrators that will build some of the rest of the infrastructure that goes into manufacturing, the Siemens of the world, the Schneider’s, the Rockwell’s that will lead to fundamental industrial automation. Some big winners in there that whose names are well known, so probably not a huge amount of surprises there. There’s movements. As I said, we’re still going to see the big Chinese players emerging in the world. There are startups that are innovating around a lot of the edges that are significant in this space. We’ll see if this is a space that will just be continued to be dominated by the big foreign robotics and by a couple of others and by the big integrators or not. Bertrand Schmitt I think you are right to remind about China because China has been moving very fast in robotics. Some Chinese companies are world-class in their use of robotics. You have this strange mix of some older industries where robotics might not be so much put to use and typically state-owned, versus some private companies, typically some tech companies that are reconverting into hardware in some situation. That went all in terms of robotics use and their demonstrations, an example of what’s happening in China. Definitely, the Chinese are not resting. Everyone smart enough is playing that game from the Americans, the Chinese, Japanese, the South Koreans. Nuno Gonçalves Pedro Exciting things are manufacturing, and maybe to bring it all together, what does it mean for all the big players out there? If we talk with startups and talk about startups, we didn’t mention a ton of startups today, right? Maybe incumbent wind across the board. But on a more serious note, we did mention a few. For example, in nuclear energy, there’s a lot of startups that have been, some of them, incredibly well-funded at this moment in time. Wrap: what it means for startups, incumbents, and investors There might be some big disruptions that will come out of startups, for example, in that space. On the chipset side, we talked about the big gorillas, the NVIDIAs, AMDs, Intel, etc., of the world. But we didn’t quite talk about the fact that there’s a lot of innovation, again, happening on the edges with new players going after very large niches, be it in networking and switching. Be it in compute and other areas that will need different, more specialized solutions. Potentially in terms of compute or in terms of semiconductor deployments. I think there’s still some opportunities there, maybe not to be the winner takes all thing, but certainly around a lot of very significant niches that might grow very fast. Manufacturing, we mentioned the same. Some of the incumbents seem to be in the driving seat. We’ll see what happens if some startups will come in and take some of the momentum there, probably less likely. There are spaces where the value chains are very tightly built around the OEMs and then the suppliers overall, classically the tier one suppliers across value chains. Maybe there is some startup investment play. We certainly have played in the couple of the spaces. I mentioned already some of them today, but this is maybe where the incumbents have it all to lose. It’s more for them to lose rather than for the startups to win just because of the scale of what needs to be done and what needs to be deployed. Bertrand Schmitt I know. That’s interesting point. I think some players in energy production, for instance, are moving very fast and behaving not only like startups. Usually, it’s independent energy suppliers who are not kept by too much regulations that get moved faster. Utility companies, as we just discussed, have more constraints. I would like to say that if you take semiconductor space, there has been quite a lot of startup activities way more than usual, and there have been some incredible success. Just a few weeks ago, Rock got more or less acquired. Now, you have to play games. It’s not an outright acquisition, but $20 billion for an IP licensing agreement that’s close to an acquisition. That’s an incredible success for a company. Started maybe 10 years ago. You have another Cerebras, one of the competitor valued, I believe, quite a lot in similar range. I think there is definitely some activity. It’s definitely a different game compared to your software startup in terms of investment. But as we have seen with AI in general, the need for investment might be larger these days. Yes, it might be either traditional players if they can move fast enough, to be frank, because some of them, when you have decades of being run as a slow-moving company, it’s hard to change things. At the same time, it looks like VCs are getting bigger. Wall Street is getting more ready to finance some of these companies. I think there will be opportunities for startups, but definitely different types of startups in terms of profile. Nuno Gonçalves Pedro Exactly. From an investor standpoint, I think on the VC side, at least our core belief is that it’s more niche. It’s more around big niches that need to be fundamentally disrupted or solutions that require fundamental interoperability and integration where the incumbents have no motivation to do it. Things that are a little bit more either packaging on the semiconductor side or other elements of actual interoperability. Even at the software layer side that feeds into infrastructure. If you’re a growth investor, a private equity investor, there’s other plays that are available to you. A lot of these projects need to be funded and need to be scaled. Now we’re seeing projects being funded even for a very large, we mentioned it in one of the previous episodes, for a very large tech companies. When Meta, for example, is going to the market to get funding for data centers, etc. There’s projects to be funded there because just the quantum and scale of some of these projects, either because of financial interest for specifically the tech companies or for other reasons, but they need to be funded by the market. There’s other place right now, certainly if you’re a larger private equity growth investor, and you want to come into the market and do projects. Even public-private financing is now available for a lot of things. Definitely, there’s a lot of things emanating that require a lot of funding, even for large-scale projects. Which means the advent of some of these projects and where realization is hopefully more of a given than in other circumstances, because there’s actual commercial capital behind it and private capital behind it to fuel it as well, not just industrial policy and money from governments. Bertrand Schmitt There was this quite incredible stat. I guess everyone heard about that incredible growth in GDP in Q3 in the US at 4.4%. Apparently, half of that growth, so around 2.2% point, has been coming from AI and related infrastructure investment. That’s pretty massive. Half of your GDP growth coming from something that was not there three years ago or there, but not at this intensity of investment. That’s the numbers we are talking about. I’m hearing that there is a good chance that in 2026, we’re talking about five, even potentially 6% GDP growth. Again, half of it potentially coming from AI and all the related infrastructure growth that’s coming with AI. As a conclusion for this episode on infrastructure, as we just said, it’s not just AI, it’s a whole stack, and it’s manufacturing in general as well. Definitely in the US, in China, there is a lot going on. As we have seen, computing needs connectivity, networks, need power, energy and grid, and all of this needs production capacity and manufacturing. Manufacturing can benefit from AI as well. That way the loop is fully going back on itself. Infrastructure is the next big thing. It’s an opportunity, probably more for incumbents, but certainly, as usual, with such big growth opportunities for startups as well. Thank you, Nuno. Nuno Gonçalves Pedro Thank you, Bertrand.

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Washougal has secured federal funding for two major infrastructure projects, including PFAS remediation design at city drinking water wells and improvements to the 32nd Street Rail Crossing, with support from Washington's congressional delegation. https://www.clarkcountytoday.com/news/washougal-secures-federal-support-for-infrastructure-projects/ #Washougal #ClarkCounty #Infrastructure #PublicHealth #Transportation #FederalFunding

Guests: Bill Roggio and John Hardie. Trilateral peace talks regarding Ukraine show limited progress on core issues, while Russia faces communication disruptions from Starlink denials and continues striking Ukrainian energy infrastructure.1917 odessa

What does it really take to move AI from experimentation into something enterprises can trust, scale, and rely on every day? In this episode of Tech Talks Daily, I'm joined by Rob Lay, CTO and Solutions Engineering Director for Cisco UK and Ireland, recorded in the run-up to Cisco Live EMEA in Amsterdam. As agentic AI dominates conference agendas on both sides of the Atlantic, this conversation steps away from model hype. It focuses on the less glamorous, but far more decisive layer underneath it all: infrastructure. Rob explains why the biggest constraint on scaling AI agents in production is no longer imagination or ambition, but the readiness of the environments those agents run on. We talk about how legacy technical debt, latency, fragmented networks, and disconnected security tools can quietly undermine AI investments long before leaders see any return. As organizations move out of pilot mode and into real execution, those cracks become impossible to ignore. A big part of the discussion centers on why AI changes the relationship between network, compute, and security teams. Traditional silos struggle to keep up as autonomous systems make decisions at machine speed. Rob shares how Cisco is approaching this shift through tighter integration across the stack, with security designed directly into the network rather than bolted on later. When AI agents act independently, routing everything through centralized chokepoints does not hold up. We also explore how operational complexity is evolving. Tool sprawl is already overwhelming many IT leaders, and agent sprawl is clearly coming next. Rob outlines Cisco's platform strategy, including how agent-driven operations, human oversight, and context-aware automation are shaping a new approach to day-to-day resilience. This leads into a wider conversation about digital resilience as a business issue, where visibility, assurance, and learning from incidents matter more than static continuity plans that only get tested once a year. For European leaders in particular, data sovereignty and control remain at the forefront. Rob explains how Cisco is responding with flexible deployment models, local data residency options, and air-gapped environments that support AI innovation without forcing customers into a single rigid operating model. We close by looking at where enterprises are actually seeing value today, where expectations are still running ahead of reality, and what leaders attending Cisco Live should really be listening to as announcements roll in. If you are responsible for infrastructure, security, or technology strategy in an AI-driven organization, this conversation offers a grounded view of what needs to be ready before agents can truly deliver on their promise. As AI-powered systems start to move faster than most roadmaps anticipated, are you confident the foundations underneath them are ready to keep up, and what would you change if you were starting that journey today? Useful Links Connect with Rob Lay Cisco Live Follow Cisco on LinkedIn

Using Software, AI To Reduce CO2 & Increase Resilience – Lydia Walpole & Chris Bradshaw of Bentley Systems   "For example, if we have a concrete pile, we can change the parameters and use AI to suggest actually a more optimal design with regards to how much concrete is going to be used. So we quite often, as engineers, we are risk averse. So sometimes you can over design to make sure consequences in construction and infrastructure are real. We do need to be precise, but we can use AI to ensure that we have a reduced amount of carbon and concrete in that pile, but still meeting the outcomes that we set out to achieve." Lydia Walpole on Electric Ladies Podcast Infrastructure like roads and bridges, as well as buildings today need to be built with strong climate resilience, as well as reduced carbon footprint. Innovative software systems are leveraging AI to increase performance. How? Listen to Lydia Walpole, Senior Director of Global Performance and Chris Bradshaw, Chief Sustainability Officer both of 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 Bentley Systems' digital twin technology is reducing risk, waste and CO2 and improving performance. ●        How sustainability and climate resilience shift the approach to infrastructure builds from "reactive to predictive," as Chris said, as extreme weather increases. ●        How their technologies are transforming infrastructure builds across the globe. ●        Plus, career advice, such as:   "I often hear about imposter syndrome, and I know it's easy to say, but I've worked in a male dominated environment my whole career, and I've never felt like I shouldn't be here. And it is easy to say, don't feel that, but you deserve to be where you are. You've worked hard and recognize that. …Secondly, be curious and remain curious, and make sure you are continuing to learn and educate yourself every day….Keep abreast of new technologies." Lydia Walpole on Electric Ladies Podcast  And Chris Bradshaw added: "My biggest piece of advice would be to be bold. Don't be shy. Diverse groups make better decisions, always…. You are bringing a different point of view." 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: ·       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

America's rail network is at a pivotal moment, and the future is moving fast.    In this episode of The Optimistic Outlook, Siemens USA Interim CEO Ann Fairchild sits down with Tobias “Tobi” Bauer, CEO of Siemens Mobility North America, to explore what's driving renewed momentum across passenger and freight rail in the United States. From modernizing legacy infrastructure to building state-of-the-art trains right here at home, Tobi shares why the outlook for rail has never been more positive.    The conversation dives into Siemens' growing manufacturing footprint on both coasts, the role of industrial AI in improving safety, efficiency, and the passenger experience, and how long-term partnerships are shaping the transportation systems our cities will rely on for decades to come. Tobi also reflects on workforce development, career pathways in manufacturing and engineering, and why reindustrialization is critical to America's future.    Looking ahead, Ann and Tobi discuss urbanization, high-speed rail, and what it will take to deliver reliable, comfortable, and sustainable mobility solutions that truly improve quality of life. The episode wraps with an optimistic vision for passenger rail in America, one built on trust, innovation, and delivering on promises.    The future of rail is being built now, and it's bringing people, cities, and opportunity closer together.  Show notes More about Siemens Mobility USA

Send a textMiguel Armaza welcomes Edward Woodford, founder and CEO of ZeroHash, to Fintech Leaders for a candid, insightful conversation on the future of blockchain infrastructure and the lessons learned from nearly a decade of building in crypto.Edward offers a unique perspective as a London-born, MIT-educated entrepreneur who's seen ZeroHash grow into one of the most critical and least visible companies powering the global blockchain ecosystem. He shares his journey— from getting rejected by Oxford and taking the leap to the US, to building his first fintech business and discovering Bitcoin at the MIT bookstore.Join Miguel and Edward for a conversation packed with actionable insights for fintech founders, builders, and investors aiming to navigate the fast-evolving landscape at the intersection of crypto, infrastructure, and financial innovation.Timestamped Overview00:00 Intro & Edward's Background05:49 Balancing Business and Personal Life08:58 MIT Bitcoin and Career Exploration13:14 Tech-Driven Market Shift Insights16:07 Tokenization and Global Interoperability20:08 Impact of the founding team21:54 Founding team dynamics explored26:32 Power versus influence in investing29:25 Effective Infrastructure Pricing Strategy33:44 Weighted performance-based churn metric35:00 Founder Mode Intensity DefinedWant more podcast episodes? Join me and follow Fintech Leaders today on Apple, Spotify, or your favorite podcast app for weekly conversations with today's global leaders that will dominate the 21st century in fintech, business, and beyond.Do you prefer a written summary? Check out the Fintech Leaders newsletter and join ~85,000+ readers and listeners worldwide!Miguel Armaza is Co-Founder and General Partner of Gilgamesh Ventures, a seed-stage investment fund focused on fintech in the Americas. He also hosts and writes the Fintech Leaders podcast and newsletter.Miguel on LinkedIn: https://bit.ly/3nKha4ZMiguel on Twitter: https://bit.ly/2Jb5oBcFintech Leaders Newsletter: https://bit.ly/3jWIpqp

Lauren shares how clear boundaries serve as vital leadership infrastructure, helping regulate teams, reduce anxiety, and prevent burnout. She explains how predictable limits build psychological safety and support creativity and sustainable performance, while unclear boundaries lead to over-availability, resentment, and exhaustion.She also offers practical guidance on system-level boundaries like response times, recovery periods, escalation protocols, and shared agreements, encouraging leaders to start by tightening just one boundary to support long-term sustainability for both themselves and their organizations.Sign up for the University of Pennsylvania Behavior Breakthrough Accredited CourseLearn about the Staff Sustainability System a proven system to reduce burnout at the rootResources: Clockwork by Mike MichalowiczGino WickmanOther related resources from Five Ives: Blog Post: Why Traditional Employee Wellness Programs Fail (And What Works Instead)Survive Mode: Recognizing When Your Organization is in CrisisWhat are the Five Ives?Podcast:Clarity as a Safety CueWhen Leaders Become the StressorEpisode 2: Authority Without FearEpisode 1: What Stress Does to Decision MakingThe Pause Between Now and NextLeading From a Regulated CoreDesigning Rhythms that RegulateWhen Culture DysregulatesGrowth & Feedback Without FearOnboarding as Co-RegulationPolicy as a Nervous SystemWhy Women in Leadership MicromanageThe Regulated Organization: What it Means to be a Regulated OrganizationOur Online Programs: Behavior BreakthroughPolicing Under PressureBoard Governance TrainingUniversity of Pennsylvania Behavior Breakthrough Accredited CourseSubscribe to our mailing list and find out more about Stress, Trauma, Behavior and the Brain!Check out our Facebook Group – Five Ives!Five Ives WebsiteThe Behavior Hub blogIf you're looking for support as you grow your organization's capacity for caring for staff and the community, we would love to be part of that journey. Schedule a free discovery call and let us be your guideAs an Amazon Associate, I earn from qualifying purchases.

This week's podcast delivers a hard-hitting reality check for RVers.:- We break down a viral insider video from a top RV dealer CEO who openly calls out price gouging, overproduction, copycat designs, and why so many buyers end up upside down faster than they expect. If you are shopping, or even thinking about it, this one matters.- We also cover an RV recall blitz affecting more than 18,000 RVs across multiple brands, including fire risks, fuel leaks, and labeling errors that should have never made it past quality control.- Plus, a growing warning for RV travelers as aging water systems cripple access at Big Bend National Park and other popular National Park destinations.- Our take on Love's expanding RV hookups, convenient but noisy and pricey- How AI is being used for RV Travel PlanningNo spin, no fluff, just straight talk about the RV lifestyle. Listen to the Monday News Edition wherever you get your podcasts or at RVPodcast.com.

Today's episode continues our 12-part series: 12 Shifts in 2026 for Social Impact. Over twelve episodes, we're unpacking the mindset + strategy shifts shaping the future of fundraising, leadership, and doing good in 2026. Explore the series at weareforgood.com/12shiftsShift 11 / Story as InfrastructureIn today's episode, Jon and Becky welcome Carolina Garcia Jayaram, CEO of the Elevate Prize Foundation, for a reflective and forward-looking conversation on why story is no longer a communications tool — it's essential infrastructure for mission and culture.As attention fragments, trust erodes, and technology reshapes how people connect, Carolina invites nonprofit leaders to rethink storytelling as a relational practice rooted in humanity, proximity, and long-term investment. Together, they explore how centering people over issues, building trust-based relationships, and intentionally distributing stories can expand influence without sacrificing integrity.Carolina shares insights from Elevate's work at the intersection of philanthropy, media, and culture — from scaling visibility for proximate leaders to embracing AI in ways that deepen creativity rather than replace it. This episode is both a mindset shift and a practical invitation for leaders ready to treat story as something to protect, resource, and evolve from the inside out.Episode Highlights: People Over Issues: What Actually Moves Audiences to Action (03:45)Trust → Relationship-Based Philanthropy (05:10)Distribution as Strategy: Reaching Beyond the Choir (07:20)Owning Platforms & Visibility (YouTube, Creators, Times Square) (08:45)Case Study: Scaling Impact Through Story — Hannah Freed & Democracy Defenders (11:00)Scaffolding Stories: Why Nothing Should Be One-and-Done (14:50)Building Story Systems: Briefs, Libraries, and Iteration (16:30)Low-Fi Tools That Make High-Impact Stories Possible (18:40)Visibility = Fundraising: What the Data Shows (20:30)AI, Creativity & Neurodiversity: Scaling Without Losing Humanity (23:35)Carolina's One Good Thing (25:50)Episode Shownotes: www.weareforgood.com/episode/681Save your free seat at the We Are For Good Summit

Welcome back to The New Warehouse Podcast. In this episode, Kevin chats with Blake Chroman, Principal at Sitex Group. They discuss how warehouse power infrastructure is reshaping industrial real estate decisions. Drawing from Sitex Group's portfolio across New Jersey, New York, and South Florida, Chroman explains how electrical capacity, utility timelines, and total occupancy costs now influence leasing and development strategy.The conversation explores why power has moved from a background consideration to a front-line requirement, how older buildings are being repositioned, and what tenants should evaluate when selecting their next facility.Learn more about Sonaria here. Follow us on LinkedIn and YouTube.Support the show

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Jeff Bliss notes Governor Newsom promotes high-speed rail despite a nearby fire and no track laid, while facing skepticism about his presidential potential and California's ongoing infrastructure struggles.1908 TULARE COUNTY