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Brad Gerstner sits down with Gavin Baker and Andrew Fox of Atreides Management, alongside Altimeter partner Clark Tang, to break down one of the biggest questions in tech and markets: how should investors think about the SpaceX IPO?They unpack the major levers behind SpaceX's next phase: Starship rapid reusability, Starlink broadband and direct-to-cell, Elon's emerging AI compute business, xAI's model ambitions, the Cursor acquisition, and the long-term promise of orbital data centers. The group also debates whether SpaceX is becoming a new kind of AI hyperscaler — “Elon Web Services” — and what that means for the future of compute, cloud, and frontier intelligence.Then they dive into the latest model race: Fable 5, Mythos, ChatGPT 5.5, long-running agents, open source vs. frontier models, Nvidia vs. ASICs, the AI CapEx boom, and why the market may still be underestimating the scale of AI demand. Enjoy another episode of BG2!Timestamps:(00:40) Intro — SpaceX IPO in Two Days, Mythos Launches, Taiwan Takeaways(03:05) xAI's Google & Anthropic Deals: Highest Operating Profit Per Gigawatt(13:28) "Elon Web Services" — Nobody Had AI Compute in the SpaceX Model(19:08) Data Centers Are Not Commodities: First-Principles Design(26:01) Orbital Compute Economics: $5B Per Gigawatt in Space vs. $25B on the Ground(29:25) The Most Underrated Variable: What Cursor Does for xAI's Model(35:05) Bull & Bear Case — Can SpaceX Really 8X Revenue in 4 Years?(37:00) Post-IPO Drawdowns, Lock-Ups & How to Size the Position(43:56) Fable 5, Mythos & Why Snapshot Benchmarks Are Broken(51:00) Frontier vs. Open Source: 90% of Revenue Accrues at the Frontier(01:04:11) $1.5T in CapEx vs. $300B in AI Revenue — Does the Math Math?(01:18:13) The Next $1 Trillion: Three Companies, Half the TimeProduced by Edward Schmidt & Dan ShevchukMusic by Yung SpielbergAvailable on Apple, Spotify, www.bg2pod.comFollow:Brad Gerstner @altcap https://x.com/altcapGavin Baker @GavinSBaker https://x.com/GavinSBakerClark Tang @_clarktang https://x.com/_clarktangBG2 Pod @bg2pod https://x.com/BG2Pod

Jon Najarian @jonnajarian and @jeffkilburg on @LarryKudlowShow Broke it down perfectly:"Yesterday's selloff was healthy profit-taking — nothing more." $SPX still just 3% off all-time highs, up 7.9% YTD and 24% over the last year. Nasdaq up 10.6% YTD, small caps even stronger. Semiconductors still +80% this year despite the dip.#VIX spiked from super-low levels — traders simply rotating ahead of the massive SpaceX IPO next week at ~$135 post-split. Smart money locking in gains in chips $SMH $NVDA to make room for the big inflow everyone's anticipating. AI compute demand is exploding: Google just signed a $900M+/month deal, Anthropic another $1.25B/month. Earnings are gangbusters (27.7% YoY growth). This is a temporary reset, not a reversal. As Dr. J always says — don't flinch. FOMO comes back fast. Stay long the future. $NDX $GOOG $AMZN $TSLA

Our 247th episode with a summary and discussion of last week's big AI news!Recorded on 06/03/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:Anthropic released Claude Opus 4.8 with improved benchmark scores, discussed eval-awareness findings and welfare/corrigibility themes from its system card, and introduced Dynamic Workflows for long-running multi-agent tasks.Microsoft unveiled the always-on Microsoft Scout assistant built on OpenClaw plus new in-house MAI models (including MAI Thinking 1) and “frontier tuning,” emphasizing enterprise security architecture and model-from-scratch capability.Major business moves included Anthropic's $65B Series H at a $965B valuation alongside an IPO filing, a JPMorgan analysis arguing OpenAI needs major revenue growth to justify infrastructure spend, and Cognition raising $1B at a $25B valuation.Policy and security highlights covered Trump's voluntary pre-release government testing framework for powerful AI, Meta AI support being exploited to hijack Instagram accounts, tightened US Nvidia export controls and China's travel approvals for AI experts, plus expanded Glasswing/Mythos-style cyber and biodefense initiatives.Timestamps:(00:00:10) Intro / Banter(00:04:10) Sponsors(00:07:10) News PreviewTools & Apps(00:07:54) Anthropic releases Opus 4.8 with new 'dynamic workflow' tool | TechCrunch(00:22:37) Microsoft Scout is a new AI personal assistant built on OpenClaw | The Verge(00:26:55) Microsoft launches new MAI family of AI models at Microsoft Build | Mashable(00:37:43) Robinhood now lets your AI agents trade stocks | TechCrunch(00:40:49) OpenAI launches new Codex tools for white-collar work | TechCrunch(00:43:40) ElevenLabs' new music-generation model can switch genres mid-track | TechCrunchApplications & Business(00:44:35) Anthropic Hits $965 Billion Valuation, Surpassing OpenAI - WSJ(00:45:32) Anthropic Files to Go Public, Setting Stage for Huge I.P.O. - The New York Times(00:51:15) China's ByteDance Developing New AI Chips Like Those from Nvidia Partner Groq(00:55:00) Anthropic expands Mythos to 150 additional organizations(00:55:35) OpenAI needs a 26x revenue increase to justify its buildout(00:58:46) AI coding startup Cognition raises $1B at $25B pre-money valuation | TechCrunchProjects & Open Source(01:00:50) MiniMax-M3 debuts, eclipsing GPT-5.5 and Gemini 3.1 Pro on key benchmark performance for just 5-10% of the cost | VentureBeatPolicy & Safety(01:06:08) Trump Signs Executive Order Seeking Oversight of A.I. Models - The New York Times(01:11:45) Hackers Simply Asked Meta AI to Give Them Access to High-Profile Instagram Accounts. It Worked(01:13:058) Chinese AI experts in private firms now required to secure approval before international travel — Beijing enforces policy to secure top-tier talent, expands measures beyond government(01:17:53) U.S. Tightens Controls on Nvidia AI Chip Exports | Let's Data Science(01:21:47) OpenAI launches Rosalind Biodefense, offers federal agencies early access to its life-sciences model(01:24:00) Using LLMs to secure source code(01:26:19) Project Glasswing: An initial update(01:29:30) White House Approves $9 Billion for Spy Agencies to Catch Up on A.I.(01:32:11) US Law Enforcement Warns of ‘Anti-Tech Extremism' as AI Hatred GrowsSynthetic Media & Art(01:35:38) YouTube will now automatically label AI videos | TechCrunchResearch & Advancements(01:36:22) Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention(01:41:26) From Simulation to Enaction: Post-trained language models recognize and react to their own generationsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, Chris sits down with Josh Zegen, Co-Founder & Managing Principal of Madison Realty Capital, a $25 billion real estate private credit firm he started with his college roommate in 2004. They dig into how he built one of the largest private lenders in the country starting from a desk in his dad's law office - and why he still thinks of himself as a businessman first and a real estate guy second. Josh got into lending almost by accident. Laid off from a VC firm at 26 when the dot-com bubble burst, he took one mortgage deal nobody else would do, saw how fragmented and non-institutional the market was, and built a fund around it before "private credit" meant anything. Chris and Josh go deep on surviving '08, reinventing the business when capital dried up, and how Madison grew into a platform that now lends to other lenders. They discuss: How Josh went from a laid-off VC associate living back home to founding a $25B firm Surviving '09 - including giving up 50% of the company for a $50M anchor that collapsed at the last minute Why he built servicing, asset management, and capital raising in-house instead of outsourcing The $10B back-leverage book that makes Madison the lender to ~100 other private lenders The $720M single loan behind the largest office-to-residential conversion in NYC Where he sees real estate credit headed - and why he stays away from office, data centers, and anything "binary" Timestamps:(00:00) Intro(00:52) Rate Volatility and a Stalled CRE Investment Market(09:44) What's Getting Done Today: Construction, Conversions, and Recaps(18:49) Founding Madison: Seeing Opportunity in a Fragmented Market(25:19) The GFC: Gating Investors and Going Vertically Integrated(31:26) The $50M REIT Deal That Nearly Ended Madison—And the Door It Opened(44:28) Why Borrowers Now Prefer Private Credit Over Banks(47:17) In-House Loan Servicing as Madison's Competitive Edge(49:06) The Back Leverage Business: Lending to Private Lenders(55:35) Capital Markets Expansion and Staying True to Real Estate(1:05:55) The Pfizer Deal, Lifecycle Lending, and Madison's Road Ahead(1:15:06) Staying Relevant by Constantly Innovating and Looking for Acquisition Opportunities ----- Presented by Airshare: Trusted across the country for fractional ownership, jet cards, charter, and aircraft management, Airshare gives you a smarter way to fly private - over 25 years of experience, operating their own fleet, with the top safety ratings in the industry. Drive up to the FBO, walk on, and go. Go to flyairshare.com to learn more. ----- Sponsored by: Collateral Partners builds institutional-grade investor materials for private credit, private equity, real estate, and family office firms - the kind of marketing collateral that helps you close capital. Learn more at collateral.com/fort. Relay Human Cloud helps you build a highly skilled global team that operates as a true part of your business - not an outsourced vendor. From accounting to operations, Relay's talent works inside your systems and alongside your local team, unlocking 24-hour productivity and significant cost savings. Learn more at https://www.relayhumancloud.com/powers-podcast/ ----- Chris on Social Media: X: https://x.com/fortworthchris Instagram: https://www.instagram.com/thepowerspodcast LinkedIn: https://www.linkedin.com/in/chrispowersjr/ Visit our website: https://www.powerspod.com/Leave a review on Apple: https://bit.ly/45crFD0Leave a review on Spotify: https://bit.ly/3Krl9jO

Corruption Central has new address: 1600 Pennsylvania Avenue. From Trump's Weaponization slush fund to international negotiations including Trump Towers and sketchy stock trades and Crypto deals, this next gift should come as no surprise: Dell wins a $9.7B Pentagon deal — weeks after Trump publicly urged people to “go out and buy a Dell” and after Michael Dell's family pledged $6.25B to seed the administration's “Trump accounts.” We'll check out the contract (a five‑year Core Enterprise Technology Agreement to supply Microsoft licensing, cloud subscriptions and on‑prem software). We'll drill down into the Pentagon's claim the award followed a competitive process and will save roughly $422M a year, and we'll examine why timing — stock purchases, public praise, and Dell's ties to the Trump administration are now prompting ethics and conflict‑of‑interest scrutiny. We'll roll this story past Michael Shure and Mo Kelly as we lay out the top Stories of the week in ‘This Week in Politics.' Fingers crossed Albert finds a good gator story this week but whether it's meth, machetes, or mayhem, Friday Fabulous Florida never disappoints. Quick set change… and ou tcomes the rainbow. The Culture Blaster Michael Snyder will slide right in to give us the best advice on movies and streaming options worth your time.

Canada Strong Fund vs. Sovereign Wealth Funds: Why Borrowing to Invest at Home Could BackfireHosts Josh Sheluk and Colin White discuss the proposed Canada Wealth/Canada Strong Fund and argue it differs materially from traditional sovereign wealth funds. They explain sovereign wealth funds originated as a response to “Dutch disease,” using commodity windfalls to build large funds (e.g., Norway's) that invest outside the country to diversify and stabilize the domestic economy and currency. By contrast, they say Canada would start with about $25B in borrowed money, likely invest domestically, and overlap with existing vehicles like the Canada Infrastructure Bank and Canada Growth Fund without clear details on governance, cost of capital, returns, or liquidity. They warn government investing can become politically driven, may crowd out private capital, and fear a retail component with capital guarantees would shift risk to taxpayers and repeat past failures like labour-sponsored venture capital funds. Their current verdict is “no.”00:00 Sovereign Wealth Hype00:21 Show Intro and Setup01:26 What Sovereign Wealth Means02:44 Dutch Disease Origins05:03 Norway Model Explained06:59 Canada Strong Fund Basics08:46 Where Will It Invest10:35 Domestic Focus and Diversification11:38 Government Investing Risks14:04 Retail Investor Idea Alarm16:38 EV Subsidies as Warning19:26 What Government Should Do21:09 Labor Fund Cautionary Tale23:04 Guarantees and Liquidity Problems31:06 Best Case vs Worst Case33:43 Verdict and Wrap Up35:17 Disclaimers and Credits

The "Art of the Deal" or the Art of the Fold?

Patrick Moorhead and Daniel Newman return from Dell Technologies World to unpack Google I/O's Gemini-as-operating-system moment, the Blackstone-Google TPU joint venture nobody saw coming, NVIDIA's $81.6 billion quarter with a $91 billion guide, and debate whether or not the "SaaSpocalypse" is finally over. The handpicked topics for this week are: Google I/O 2026: Gemini Becomes the Operating System. Google I/O repositioned Gemini from a product to the operating layer for everything Google does, and the numbers backed it up. 900 million monthly active users, 3.2 quadrillion tokens per month, a 7x jump year over year. Pat's headline: this is about widening distribution, not just model quality. Gemini 3.5 Flash, Antigravity 2.0, Gemini Spark, and Android XR glasses all extend Gemini into surfaces that no competitor can replicate. Daniel's read: the token-cost reckoning is coming, and when enterprise subsidies end, models that can deliver value at a lower cost per token will become the ground zero of the next era. (The Decode) Dell Technologies World 2026: AI Factory Goes Agentic, 1,000 New AI Server Clients. Pat and Dan were both on the ground in Las Vegas and called it the most consequential Dell event in years. Michael Dell and Jensen Huang co-keynoted to launch the next-generation Dell AI Factory with liquid-cooled PowerEdge XE9780 servers, Dell Deskside Agentic AI, and a multi-model ecosystem including Google Distributed Cloud with Gemini 3.0, on-prem OpenAI Codex, and Grok. 1,000 new AI server clients in a single quarter is the cleanest leading indicator of enterprise demand heading into Dell's Q1 print. Pat's biggest takeaway: OpenShell as a control plane for agents spanning from the GB10 all the way to the PowerEdge rack has been the missing orchestration piece. Daniel's read: large enterprises are going to build hybrid AI architectures and want to deliver tokens at the lowest possible on-prem cost, and Dell is ready. (The Decode) Blackstone and Google Launch a $5B TPU Joint Venture. Pat called it the biggest story of the week and the one that went most under the radar. For the first time, a hyperscaler has released its proprietary AI silicon to a third-party distribution entity. The $5 billion deal, up to $25 billion with leverage, targets 500 megawatts of capacity online by 2027. Daniel's framing: Google decided its custom silicon is worth more as a commercially distributed asset than as a captive moat. Pat's note: the proprietary nature of TPU infrastructure means retrofitting existing data centers will require real work, but the sovereign angle gives the JV a natural first market. (The Decode) AMD Helios, $10B Taiwan Investment, and the MI450 Anchor Customer Rumor. AMD dropped a $10 billion Taiwan ecosystem investment alongside confirmation that Helios rack-scale is on track for multi-gigawatt customer deployments beginning 2H 2026. A Citi rumor surfaced Anthropic as the anchor MI450 customer, to be formally announced at AMD's Advancing AI Day in July. Pat's read: Lisa Su has made a commitment and she almost never falls through. The analysts who said AMD would not ship anything in the second half of 2026 are going to be very wrong. (The Decode) OpenAI Guaranteed Capacity: Sam Altman's Moment. OpenAI launched multi-year compute commitment contracts the same week that Anthropic was struggling with capacity outages. Pat called it brilliant and said it makes Sam Altman look like a genius. It's the inference-era analog of cloud reserved instances: guaranteed availability at a locked price for one, two, or three years. Daniel added context: Anthropic's annualized ARR growth is nearly double OpenAI's and is about to lap them, so the model war is far from over. But for enterprises that need reliability, OpenAI just made the most compelling enterprise trust argument of the week. (The Decode) Sovereign AI Crosses $30 Billion at NVIDIA, 14% of Revenue. NVIDIA disclosed sovereign AI as a segment-level line for the first time, at $30 billion in FY26, 3x the prior year. Pat has been tracking sovereign for years and calls this the clearest possible signal that it has moved from marketing term to structural revenue category. Daniel's point: outside of the four or five hyperscalers doing all the major buying, sovereign is where the incremental demand is coming from and it is very real. (The Decode)  The Flip: Is the SaaSpocalypse Over? Daniel took the affirmative and came in loaded. Every earnings report across CrowdStrike, Cloudflare, ServiceNow, Intuit, Salesforce, Atlassian, Notion, and monday.com shows companies growing with the AI tailwind. His core argument: there was a reason SaaS emerged 20 to 30 years ago. Companies do not want to be in the software business. Vibe-coded flat-file apps with no security, no governance, no data lineage look great in a kitchen demo and fall apart at enterprise scale. The SaaSpocalypse is over and he is tired of talking about it. Pat's counter: BofA slapped Salesforce with an Underperform at $160, 8% below where it trades. Snowflake is down 35% year-to-date. A senior Dell executive told him Dell will not buy another SaaS system and is tripling internal software creation. The growth question is real even if the terminal value is not zero. Both agree the tape will tell the real story. (The Flip) NVIDIA Q1 FY27 Results. Record $81.6 billion revenue, up 85% year over year. Data center at $75.2 billion, up 92%. Non-GAAP EPS of $1.87, up 140%. Q2 guide of $91 billion crushed the $86.8 billion consensus by $4 billion at the midpoint. $80 billion buyback authorized, dividend raised 25x. The stock went down after hours for the fifth consecutive time following a massive beat and raise. Pat's read: NVIDIA may be worth $8 to $9 trillion on paper at a sector-average multiple and 75% gross margins held. Daniel's framing: this is the best company in the world, possibly tied with Google, and it is becoming the Apple of this era. He sees a long safe journey of continued growth vs. speculative dollars chasing quantum and space names that can double in a week. (Bulls and Bears) Intuit: Earnings Beat, Revenue Miss. A 17% workforce cut, raised guidance, and $8 billion buyback were authorized. Pat's emerging thesis: these companies are cutting people to afford tokens. Intuit comes at a moment when OpenAI's ChatGPT finance plugin via Stripe is building an intelligence layer that could sit on top of Intuit's products without displacing them directly, at least not yet. (Bulls and Bears) Lenovo: Record $21.6 billion quarterly revenue, up 27% year over year. The company's fastest growth in five years. AI-related revenue is up 84% year over year to 38% of total company revenue. ISG returned to full-year operating profit with a $21 billion AI server pipeline. Pat and Dan both read Lenovo's results as NVIDIA tea leaves, a leading indicator of enterprise AI server demand that directly validates what Dell said on stage about 1,000 new AI server clients. (Bulls and Bears) Analog Devices: Record $3.62 billion revenue, up 37% year over year. EPS up 67%. Q3 guide of $3.9 billion crushed consensus by $270 million. Data center up 90%, industrial up 56%, comms up 79%. The $1.5 billion Empower Semiconductor acquisition adds integrated voltage regulator technology that can reduce AI data center power consumption by 10 to 15% while shrinking the power footprint by up to 4x. Daniel's closing point: you can't build AI servers without players like Analog Devices and Lattice Semiconductor. These essential node companies aren't boring, they're foundational. (Bulls and Bears) Check out all of our Dell Technologies World coverage linked in the show notes including our sit-downs with Michael Dell, Jeff Clark, and key customers. Be part of our community. Hit that subscribe button and see you at Computex.   The Decode Google I/O 2026 — Gemini Becomes the Operating System: 900M MAU, 3.2 Quadrillion Tokens/Month, Gemini Omni, Antigravity 2.0, Gemini Spark, and Android XR Glasses https://blog.google/innovation-and-ai/sundar-pichai-io-2026/ Dell Technologies World 2026 — AI Factory Goes Agentic: Michael Dell + Jensen Huang Unveil PowerEdge XE9780, Dell Deskside Agentic AI, and a Multi-Model Ecosystem; Dell Adds 1,000 AI-Server Clients in the Quarter https://www.dell.com/en-us/blog/dell-technologies-world-a-bright-and-beautiful-road-ahead/ Blackstone + Google Launch $5B (Up to $25B w/ Leverage) JV to Sell Google TPUs Outside Google Cloud — First Time a Hyperscaler Has Released Its Custom Silicon to a Third-Party Distribution Channel; 500 MW Online by 2027, Benjamin Treynor Sloss as CEO https://www.blackstone.com/news/press/blackstone-announces-joint-venture-with-google-to-create-new-tpu-cloud/ AMD Announces $10B+ Taiwan Ecosystem Investment — Helios Rack-Scale Platform With MI450X GPUs and Venice EPYC on TSMC 2nm Targeting Multi-Gigawatt Deployments 2H 2026; the Clearest Second-Source Signal Yet https://ir.amd.com/news-events/press-releases/detail/1286/amd-announces-more-than-10-billion-in-taiwan-ecosystem-investments-to-accelerate-ai-infrastructure OpenAI Launches Guaranteed Capacity — Multi-Year Compute Commitments Turn Inference Capacity Into a New Enterprise Asset Class https://www.cnbc.com/2026/05/19/openai-announces-new-guaranteed-capacity-offering-for-customers-to-secure-compute.html The Sovereign AI Government Investment Wave — NVIDIA Discloses ~$30B Sovereign-AI Revenue (14% of Mix); UAE, Saudi, Japan, Australia, France All in Motion This Week https://finance.yahoo.com/markets/stocks/articles/analog-devices-q2-earnings-beat-153000996.html   The Flip: Is the SaaSpocalypse Officially Over — or Is BofA's Split Call (ServiceNow Buy, Salesforce Underperform) the Real Signal That Platform AI Monetization Is Going to Be Bifurcated, Not Universal? FOR:  BofA Reinstates Coverage of ServiceNow, Salesforce — Barron's (May 18) https://www.barrons.com/articles/servicenow-salesforce-stock-price-ai-7b109396 Embedded workflow + system-of-record stickiness still wins citing ServiceNow Q1 2026 financial results https://newsroom.servicenow.com/press-releases/details/2026/ServiceNow-Reports-First-Quarter-2026-Financial-Results/default.aspx Intuit Q3 revenue up 10%, cuts 17% of staff — SEC 8-K filing (May 20) https://www.stocktitan.net/sec-filings/INTU/8-k-intuit-inc-reports-material-event-b23073259896.html   AGAINST:  BofA Slaps Salesforce With Underperform Rating, $160 Price Target — 24/7 Wall St (May 18) https://247wallst.com/investing/2026/05/18/bofa-slaps-salesforce-with-underperform-rating-160-price-target-is-the-ai-story-falling-flat/ BofA resets Salesforce price target to Underperform — TheStreet (May 19) https://www.thestreet.com/investing/stocks/bofa-resets-salesforce-stock-price-target-to-underperform-at-160 Snowflake -35% YTD heading into May 27 print is the canary that platform stickiness is being repriced https://eciks.org/4640-22295-snowflake-set-to-report-q1-earnings-may-27-with-ai-strategy-in-focus OpenAI Guaranteed Capacity + Dell on-prem Codex create a credible path to displace seat-based SaaS https://www.cnbc.com/2026/05/19/openai-announces-new-guaranteed-capacity-offering-for-customers-to-secure-compute.html Bulls & Bears NVIDIA Q1 FY27 ACTUALS https://www.cnbc.com/2026/05/20/nvidia-nvda-earnings-report-q1-2027.html Intuit Q3 FY26 Actuals https://investors.intuit.com/news-events/press-releases/detail/1312/intuit-reports-strong-third-quarter-results-and-raises-full-year-revenue-guidance Lenovo Q4 FY26 ACTUALS https://www.cnbc.com/2026/05/22/lenovo-shares-jump-15percent-on-record-earnings-as-ai-revenue-nearly-doubles.html Analog Devices Q2 FY26 ACTUALS https://finance.yahoo.com/markets/stocks/articles/analog-devices-q2-earnings-beat-153000996.html  

Hey, Alex here, just got back from the sunny Shoreline Theater in Mountain view, so let me catch you up! This week was definitely Google heavy, we are covering Google's IO conference for the third year in a row, and today we have a special guest, Logan Kilpatrick, is joining to discuss the announced Gemini 3.5 Flash, Google Omni model, and the new Managed Agents offerings. Plus, this week, for the first time, OpenAI announced that AI solved a Math problem that humans couldn't solve for 80 years, Cursor is showing off Composer 2.5 which is partly trained on XAI data, Karpathy joins Anthropic and much more! Let's dive in! P.S - We've announced our upcoming hackathon, Weavehacks-4, June 6-7, I'll be there, we're expecting the seats to run out very soon so register nowThursdAI - We'd love to have your subscription, and if you're already subscribed, please hit that bell on YT to never miss an episode!Google I/O 2026 - Google goes agentic everywhereI went to cover Google I/O for the third year in a row, shoutout to the DeepMind team for inviting ThursdAI again, and folks, this one felt different.Last year, Google I/O was still very model-centric. This year, the story was not “here is another benchmark chart.” The story was: Google is putting Gemini into everything, and the agentic layer is becoming the product layer. Search, Gemini app, Android, Workspace, YouTube, AI Studio, Cloud, Antigravity, Flow, managed agents, smart glasses, all of it is now orbiting around one pretty clear strategy: Gemini is the intelligence, Antigravity is the agent harness, Google's products are the distribution. I saw many reactions that were milquetoast, as in, “we expected more” and those seem to dominate the X feed. But I think the distribution is the part that many folks on X are missing. Yes, we can argue about Gemini 3.5 Flash pricing. Yes, we can argue whether “Flash” still means what Flash used to mean. But when Google says the Gemini app itself has 900 million monthly active users, before even counting Search, Gmail, YouTube, Docs, Drive, Android, and the rest of the Google surface area, that's massive! OpenAI ChatGPT is supposedly stagnated at ~900M, I don't remember them crossing a 1B. Meanwhile Google is gaining traction. And they just updated all those folks with a new model!Wolfram said it really well on the show: his mother is not sitting there reading model cards. She just uses her Pixel, voice unlocks Gemini, asks for help, and suddenly the default intelligence available to her goes up. Antigravity 2.0 - the agent harness takes center stageThe biggest strategic signal from Google I/O for me was Antigravity.Remember, Antigravity was an IDE that came from the Windsurf acquisition saga. Part of the Windsurf team went to Google, part went to Cognition, and now Google is very clearly putting Antigravity in the middle of its agentic future. And I mean very clearly. Sundar mentioned it. Demis mentioned it. Varun Mohan the co-founder was on stage immediately after them! If you've ever watched a Google I/O keynote, you know how carefully every minute is allocated. Google has YouTube, Search, Gmail, Android, Cloud, Ads, Workspace, and a thousand VP-level products that could be on stage. The fact that Antigravity was that prominent should tell you everything.Logan Kilpatrick joined us and framed this in a way I loved: Gemini became the through-line across Google products, and now the Antigravity agent harness is becoming the through-line for agentic experiences.The new Antigravity 2.0 is a complete overhaul, showing only an agentic interface (which was previously just a separate window called Agent Manager) and separating the IDE layer completely into its own app and showing a Codex like agent-first interface, which got a few folks furious. This move may be weird to some folks, but if you follow along where everyone's going, this seems to be the way of the future, coding is no longer about lines of code, it's about managing fleets of agents. The new Gemini 3.5 absolutely shines inside the new Antigravity, the model was trained with this harness in mind, and is currently offered at an incredible speed (12x), so I'm definitely going to try it! Gemini 3.5 Flash - fast, determined, and maybe not the old “Flash”The most debated model release of the week was Gemini 3.5 Flash.Some folks saw the pricing and token usage and immediately went “this is not Flash.” I get that reaction. Flash used to mean cheap, fast, lightweight chat model. But Logan's framing on the show was important: Flash is now being built for the agentic era.In a chat era, you optimize for one user message and one model answer. In an agentic era, the real token volume is in tool loops, intermediate reasoning, retries, file reads, web searches, code execution, and self-correction. That's a different product profile.Wolfram already ran Gemini 3.5 Flash through WolfBench, and the results were fascinating. With the Hermes agent harness, Gemini 3.5 Flash hit an 87% ceiling on Terminal Bench 2.0, meaning across runs it could solve more of the benchmark than even GPT-5.5 extra high in that setup. The variance was higher with the simpler Terminus harness, but with a real agent harness, the model looked much stronger.That tracks with what Nisten saw in his “Martian railgun from Olympus Mons” test. Gemini 3.5 Flash went extremely detailed, almost too determined, kept correcting itself, overcorrecting itself, and built a whole game-like simulation. Logan laughed and basically said: yeah, this model is very determined, possibly an overcorrection from the “Gemini is lazy” feedback. It also tracks with the mismatch in other benchmarks, in some, Gemini 3.5 flash shines (like the above Apex-agents from AA) and in some, it doesn't match the other frontiers. In my tests, it was definitely over-eager to use a million and a half tool calls, read tons of files, to just help me review this draft inside antigravity. It's like a super eager robotic golden retriever! Gemini Omni - Nano Banana for video, but actually more than thatThe biggest update from last year IO was Veo 3! This year, the biggest wow factor was also visual, but it wasn't VEO 4, it was a new model that is multimodal, trained end-to-end they call Omni. Google is calling this their first “create anything from anything” model, and the first version, Gemini Omni Flash, starts with conversational video editing. The easy description is: Nano Banana for video. You upload or create a video, then talk to it. Change this character. Replace this person. Add an object. Make this scene claymation. Keep the scene, but change the environment.I played with it live and showed a few examples. I asked for a claymation explainer of protein folding, then gave it my face and asked it to replace the character with me. It did it. I uploaded pictures of Sonia, my cat, and it generated a talking cat video with the right kind of cat teeth, which is weirdly important because so many pet generations accidentally add human teeth and become nightmare fuel.The failure modes are still there. I asked it to make Sonia a Russian-speaking female cat, and it only partly switched languages and didn't really change the voice. Audio upload support is also not fully productized yet, even though the underlying model is multimodal. But the direction is very clear.This is not just “Veo with a chat model glued on.” I asked Jeff Dean - Google's chief scientist about this at I/O, and he explained that Omni is trained end-to-end. The intelligence and the generative media capabilities are part of the same model family, not a hacky two-model pipeline. He also said the intelligence is around a recent Flash-level model, which is a big deal when you think about video editing as reasoning over physics, identity, scene continuity, and intent.A lot of people compared Omni to Seedance 2.0, and I think that's the wrong comparison. Seedance is amazing at cinematic generation (lkaregly due to lack of copyright concerns from Bytedance). Omni's unlock is iterative editing on real footage and coherent multi-turn creative control. Other Google IO 2026 releases I found notableThis was a concentrated effort of a huge company to insert AI into every product surface they have so of course I can't cover ALL of it here, but the most notable things for me were: * Gemini Spark - a new agentic experience from Google, to help you with tasks across Gmail, Drive and more. It should support skills, and is a de-facto OpenClaw/Hermes alternative from Google for regular folks. It's not “yet” live so we'll talk more about it when I can test it out* Managed Agents in the Gemini API - We chatted with Logan about this one, Google is re-imagining how agents are going to get built, and are offering 1 api call to spin up an agent in a full Linux env, with security and sandboxing in mind. I'll expand more on this in a next episode, as I recorded a complete conversation about this with Ali Çevic, a PM for Google APIs* AI overhaul of Google Search - AI Overviews will not expand into AI mode, and the iconic Google search box itself will change, for the first time in 25 years to include AI mode! * SynthID expantion and OpenAI collab - Google showed off that OpenAI is joining in marking all AI generate imagery and video with an invisible SynthID watermark. I think this is amazing and more companies should adopt this standard* AI Glasses! We got Google Glasses demos - Together with Warby Parker and Gentle Monster, Google finally showed off their answer to Meta Raybans/Oakleys. They look like regular glasses too, but can hear and talk to you, with the full power of Gemini multimodality. Available in the fall sometime! * Demis Hassabis “we're on the cusp of the singularity” closer - CEO and Co-Founder of DeepMind, Demis Hassabis, closed the show with his remarks about the positive future and that we are nearing this Singularity point after which the future is very uncertain. I found it to be very inspiring and closed our show with that clip as well! * Personally, I got to chat to: Demis Hassabis, have breakfast with Jeff Dean, ask Josh Woodward a bunch of questions, and pester about 20 other great folks on a live stream, and had a lot of fun! Huge thanks to the DeepMind folks, Lucie, Dimple, JD and many others for the continued belief in ThursdAI and invite me to cover this great event. OpenAI LLMs solve an 80yo math problem - Erdős Unit Distance ConjectureOutside of Google I/O, the biggest story of the week was OpenAI announcing that a general-purpose reasoning model made progress on the Erdős planar unit distance problem.This problem goes back to 1946. For nearly 80 years, mathematicians believed the best constructions looked roughly like square grids. OpenAI's model found a new family of constructions with a polynomial improvement, using algebraic number theory ideas that humans apparently had not explored in this context. The above is a representation of it! Important caveat: this does not fully solve every version of the asymptotic Erdős conjecture. Some mathematicians are pushing back on the framing, and fair enough. Precision matters. But even with the caveat, this is still a huge moment.The reason it matters is not that I personally understand the math. I absolutely do not. The reason it matters is that this was not a special-purpose IMO model fine-tuned only for math competitions. This was a general-purpose reasoning model exploring a real open problem, generating candidates, verifying them, and finding a path humans hadn't taken. Extrapolate this to other sciences, Physics for example? This means an amazing future. LDJ pointed out that mathematicians have been skeptical because there have been previous false alarms. But this one landed differently. When Fields Medalist-level mathematicians verify the proof, the discourse changes from “lol stochastic parrot” to “wait, what does this mean for my PhD?”My answer is: yes, still study math. Please study math. The mathematicians who use these tools will do much more than people who don't understand the domain. Same with software engineering. Senior engineers with Codex, Claude Code, Hermes, Antigravity, Cursor and other agents are becoming dramatically more effective because they can steer, evaluate, and recover the work.This being published a day after Demis's “foothills of the singularity” is a great conjecture. Cursor Composer 2.5 - Opus 4.7 performance model from Cursor, at 10x better efficiencyCursor dropped Composer 2.5, and folks, this is a serious release.Composer 2.5 is built on Moonshot's Kimi K2.5 base, like Composer 2, but Cursor scaled the post-training dramatically. They used 25x more synthetic tasks and introduced targeted textual feedback during RL rollouts, where the model gets hints inserted at the point of failure instead of only getting a noisy final reward.The benchmark story is strong: around 69.3 on Terminal Bench 2.0, basically neck and neck with Opus 4.7 in Cursor's chart, and strong results on SWE-bench multilingual and CursorBench. The pricing is the part that makes this especially interesting: $0.50 per million input tokens and $2.50 per million output tokens, with a faster variant at $3 / $15. That is much cheaper than the frontier models it is trying to replace for day-to-day coding work.Cursor engineers are reportedly dogfooding Composer 2.5 heavily and rarely switching away. That matters more to me than any single benchmark. If the people building Cursor can use it as a daily driver, that is a very real signal.The wild part is what comes next. Cursor is partnering with SpaceXAI to train a much larger model from scratch using 10x more compute on Colossus 2. Cursor has the workflow data. xAI has enormous compute. If this works, Cursor stops being just the IDE company and becomes a coding-model lab.We've been saying for months that coding agents are the path toward general agents. Anthropic has Claude Code. OpenAI has Codex. Google has Antigravity. xAI has Grok Build. Cursor has Composer. I'm looking forward to seeing how well it performs on our own benchmarks! Anthropic, xAI, Karpathy, and the compute warsThe compute story this week was bonkers.The SpaceX IPO filing reportedly revealed that Anthropic is paying SpaceXAI $1.25B per month for AI compute at the Memphis Colossus facility. Per month. That's about $15B a year, through May 2029, for access to more than 220,000 NVIDIA GPUs including H100s, H200s and GB200s.This is apparently inference compute for Claude Pro, Max and API users, not training. And it explains a lot of the recent quota changes. Anthropic doubled some Claude usage limits, and suddenly the product feels less constrained.Also, can we just acknowledge the comedy here? Elon Musk publicly called Anthropic “misanthropic,”, went off against every competitor to XAI, is now selling spare GPU time to Cursor and Anthropic? Who's next, OpenAI? The bigger point is that the AI capex story is no longer just NVIDIA. It's also whoever owns the data centers, power, cooling, networking, and GPU clusters. Compute is becoming the land under the AI economy.Also, Andrej Karpathy joined Anthropic. Karpathy could work anywhere. He co-founded OpenAI, led Tesla Autopilot vision, taught half the AI world how neural nets work, and now he's going back into frontier LLM R&D at Anthropic.Open source LLMs - Cohere, Qwen, NousOpen source had a strong week too.Cohere released Command A+, a 218B total parameter sparse MoE model with only 25B active parameters per token, under Apache 2.0. This is their first model that unifies reasoning, vision, multilingual, tool use and citations in one package.The hardware story is great: W4A4 quantization can run on 2 H100s or a single B200. Cohere says it supports 48 languages, 128K input context, 64K output, and gets big jumps over Command A Reasoning, including Tau-squared Bench Telecom from 37% to 85% and Terminal-Bench Hard from 3% to 25%.Cohere is one of those labs that doesn't always chase the loudest consumer hype, but they are very serious on enterprise and multilingual. Apache 2.0 makes this one especially useful.Alibaba also dropped Qwen 3.7-Max, positioned as an agentic frontier model. The headline from their testing is wild: 35 hours of continuous autonomous operation with more than 1,000 tool calls. They also showed it controlling a physical robot inside Alibaba offices and finding an umbrella after about 20 minutes of agent interaction.This digital-to-physical bridge is where things start feeling very real. An agent loop that can write code and use tools can also navigate physical tasks if you give it the right robotics stack.And our friends at Nous Research released Lighthouse Attention, a sparse attention method for long-context pretraining. At 512K context, they report a 17x faster forward+backward pass than standard attention on a single B200, and the recovered checkpoints actually beat dense-from-scratch final loss at the same token budget.The clever part is that the selection logic sits outside the attention kernel, so you still use regular FlashAttention on a gathered dense subsequence. No custom sparse kernel nonsense. If this holds up, this could matter a lot for long-context training.Tools and agentic engineering - X subscriptions, Grok Build, Codex MobileOne really practical tool update: Hermes and OpenClaw can now use your X subscription directly.This is more important than it sounds. You can connect your X Premium subscription and get access to semantic X search and Grok-related tooling without using sketchy browser automation or unofficial APIs that might get you banned. Wolfram already used this to have his agent go through his likes and bookmarks from the past week and send me news items for the show. That is exactly the kind of “small but real” agent workflow that becomes addictive.xAI also launched Grok Build, their agentic CLI coding tool, in early beta for SuperGrok Heavy subscribers. Early users are already running parallel Grok Build agents through tmux supervisors and using it for more than coding: fleet data triage, security patching, training label work, and general automation.The pricing being discussed is aggressive, around $1 per million input tokens and $2 per million output tokens for the API. The model version is grok-build-0.1, and folks have already wired it into Hermes with a 256K context window.And then there's Codex Mobile, which OpenAI shipped inside the ChatGPT mobile apps. This is one of those releases that sounds small until you start using it. You can control Codex sessions remotely from your phone, connected to your machine, and because Codex has native connectors to Gmail, Calendar and other surfaces, it sometimes feels faster and more reliable than local CLIs duct-taped to third-party integrations.I ported Wolfred into Codex with skills and everything, and I've been comparing the same tasks in Hermes and Codex. Codex is often faster, not necessarily because the model is always smarter, but because the connectors and harness are cleaner. Harness matters. We keep coming back to this.This Week's Buzz - W&B, CoreWeave, WolfBench and roboticsThis week in the Buzz, Wolfram walked us through a few things from the Weights & Biases / CoreWeave world.CoreWeave is a gold sponsor at ICRA 2026 in Vienna, the International Conference on Robotics and Automation. NVIDIA is also going big there with a keynote on generalist humanoid robots, 17 accepted papers and workshops around sim-to-real, robot foundation models, autonomous driving, manipulation, and physical AI.Wolfram will be there later in the week, after speaking at the AI Developer event in Cologne about WolfBench. If you're in Europe and into robotics or agent evals, find him.We also looked at WolfBench results for Gemini 3.5 Flash, which honestly became one of the more interesting empirical points of the episode. The model looks variable in simple harnesses, but very capable in better agent loops. That's the whole thesis of measuring model + harness together instead of pretending the model card tells the whole story.The water discourse, almonds, and data center realityWe also got into the data center water discourse, because this talking point is everywhere right now.There are real infrastructure questions around AI. Power, land, cooling, grid capacity, permitting, local impact, all of that matters. But the “AI is stealing drinking water” version of the argument is often wildly detached from scale.The stat I brought up on the show: California almonds use roughly 3 to 5.5 million acre-feet of water per year, multiple times more than all North American data centers combined in 2025. Nisten and LDJ added the important cooling nuance: many large data centers use closed-loop cooling, and evaporative cooling is not universal. Some data centers can avoid water use almost entirely, but at the cost of higher electricity usage.This doesn't mean “no concerns are valid.” It means if we're going to regulate or pause data centers, let's be honest about the actual tradeoffs. AI compute is becoming the substrate for medicine, robotics, science, logistics, software, education and every other productivity layer. We should build responsibly, but not based on viral fear math.Closing thoughts - foothills of the singularityDemis closed I/O saying we're in the foothills of the singularity, and I know how that lands when you write it down. But I was in the room, and after the keynote he told me something I haven't been able to shake: he thinks AI is going to be 10x as impactful as the Industrial Revolution, and 10x as fast. Basically 100x. This is the AlphaFold guy. Not someone loose with his words.Then look at the week. A general reasoner cracked an 80-year-old math problem. Cursor is training near-frontier coding models on a fraction of the big-lab budget. Anthropic is paying Elon $15B a year for inference. Karpathy left education to go back into pre-training. Google rolled out an intelligence uplift to a billion people who don't even know a model dropped.If you put that on a whiteboard in 2023, it reads like a sci-fi pitch.LDJ's mathematician friends are asking if they should keep doing their PhDs. My answer hasn't changed: yes, please keep going. The people who combine domain taste with these tools are going to ship more in 5 years than the previous generation did in 50. The tool doesn't replace the taste. It just removes the bottleneck.That's the whole reason ThursdAI exists. Not to hype every drop, not to dunk for engagement, but to give you a shot at being one of the people who knows what's happening, with the receipts.This week, a lot changed.See you next Thursday.TL;DR and Show Notes* Hosts and Guests* Alex Volkov - AI Evangelist at Weights & Biases / CoreWeave, @altryne* Co-hosts: @WolframRvnwlf, @nisten, @ldjconfirmed* Guest: Logan Kilpatrick, MTS at Google DeepMind / AI Studio, @OfficialLoganK* Google I/O 2026* Google went all-in on agents across Search, Gemini, Antigravity, Workspace, Android, Cloud and YouTube (I/O site, Alex thread)* Antigravity 2.0 became the central agentic coding harness across Google (Sundar, Google OS demo)* Gemini 3.5 Flash launched as a fast, determined workhorse model for agentic loops (Logan, Noam Shazeer, Jeff Dean)* Gemini 3.5 Flash is rolling out across the Gemini app, Search AI Mode, Gemini API, Google AI Studio, Antigravity and Gemini Enterprise Agent Platform (Koray Kavukcuoglu)* Google Search is getting new Gemini 3.5 Flash-powered agentic capabilities, including a new AI-powered Search box and background information agents (Sundar)* Gemini Spark was announced as a 24/7 personal AI agent that can proactively work across Google surfaces (News from Google)* Google teased Gemini-powered Android XR smart glasses with eyewear partners Gentle Monster and Warby Parker (Google, Alex live reaction)* Google AI Studio and the Gemini API got major agentic developer updates, including Managed Agents (Google AI Developers)* Vision & Video* Google DeepMind launched Gemini Omni, a “create anything from anything” multimodal model starting with conversational video editing (DeepMind, Google DeepMind on X)* Omni is available in the Gemini app, Google Flow and YouTube, with API support coming soon (Logan, Gemini App, Sundar)* Key distinction: Omni is not just text-to-video, it is an iterative multi-turn video editing model that combines Gemini intelligence, world knowledge, multimodal inputs and generative media (Google)* Big CO LLMs + APIs* OpenAI announced a general-purpose reasoning model made progress on the Erdős planar unit distance problem, challenging an 80-year-old mathematical belief (OpenAI, X)* Cursor launched Composer 2.5, built on Kimi K2.5, with Opus-class coding performance at much lower cost (Cursor blog, X)* Alibaba released Qwen 3.7-Max, an agentic frontier model with long autonomous runs and robotics demos (Qwen blog, X, robot demo)* Andrej Karpathy joined Anthropic to work on frontier LLM R&D (X)* SpaceX IPO filing revealed Anthropic is paying $1.25B/month for AI compute at the Memphis Colossus facility (Axios, Sawyer Merritt)* The jury in Musk v. Altman found Musk's OpenAI claims barred by statute of limitations, with Musk saying he will appeal (Elon Musk, Sawyer Merritt, Max Zeff)* Open Source LLMs* Cohere released Command A+, a 218B MoE model with 25B active parameters under Apache 2.0 (Cohere, Nick Frosst, HF W4A4, HF BF16)* Nous Research released Lighthouse Attention, a sparse attention method for long-context pretraining with major speedups (Blog, X, arXiv, GitHub)* Tools & Agentic Engineering* Google launched Managed Agents in the Gemini API, letting developers spin up hosted Antigravity agents with Linux sandboxes and persistent state (Docs, X)* xAI launched Grok Build, an agentic CLI coding tool in beta for SuperGrok Heavy users (xAI CLI, X)* Hermes and OpenClaw can now use X subscription auth for semantic search and Grok tooling (Alex)* OpenAI Codex Mobile is now available in the ChatGPT mobile apps for remote agent workflows (OpenAI)* Anthropic doubled Claude usage outside peak hours for a limited period, including Claude Code and other Claude surfaces (Claude)* This Week's Buzz - W&B / CoreWeave* Weights & Biases by CoreWeave is at ICRA 2026 in Vienna, with robotics and automation taking center stage (ICRA, W&B event page)* NVIDIA heads to ICRA 2026 with robotics work around generalist humanoids, physical AI and sim-to-real systems (NVIDIA Robotics, NVIDIA ICRA)* Wolfram is speaking about WolfBench at the AI Developer event in Cologne before heading to ICRA in Vienna (Wolfram)* Other Topics* Data center water usage discourse came up again, including why comparisons need real scale and context rather than viral fear math* The broader theme of the week: coding agents are becoming general agents, and the major labs are now competing on the full stack of model, harness, tools, context and compute This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

SpaceX filed publicly for its IPO on Nasdaq, revealing $18.7B in 2025 revenue, billions in losses, and Musk's 85.1% voting control. Anthropic pays SpaceX $1.25B per month for compute. Nvidia beat estimates again, Spotify launches Reserved ticketing, and Waymo suspends service over flooding. SpaceX files publicly for its IPO, choosing Nasdaq to make its debut under the symbol SPCX; Elon Musk's shares give him 85.1% of the voting power in the company (Bloomberg) SpaceX's S-1 reveals Anthropic is paying $1.25B per month through May 2029 under their Colossus compute deal, with a 90-day termination clause (The Verge) Spotify partners with Live Nation to launch Reserved, a new feature that sets aside tickets for the most dedicated fans, starting with Premium users in the US (Hollywood Reporter) Spotify debuts a desktop app for creating personal podcasts, competing with Google's NotebookLM, with support for daily briefings based on email and calendar (TechCrunch) Nvidia reports Q1 revenue up 85% YoY to $81.62B, above $78.86B est., Data Center revenue up 92% YoY to $75.2B, and announces an $80B share repurchase program (Nvidia) Waymo suspends operations in Atlanta and San Antonio as its robotaxis struggle with flooded roads and says it has yet to develop a "final remedy" for flooding (TechCrunch) Learn more about your ad choices. Visit megaphone.fm/adchoices

Story of the Week (DR):Trump is bringing Tim Cook, Elon Musk, and a dozen other CEOs to Beijing for his Xi summitTechnology & AIElon Musk – CEO, Tesla and SpaceXTim Cook – CEO, AppleJensen Huang – CEO, Nvidia (joined as a last-minute addition after a personal call from the President)Cristiano Amon – CEO, QualcommSanjay Mehrotra – CEO, Micron TechnologyDina Powell McCormick – President, MetaJim Anderson – CEO, CoherentFinance & InvestmentLarry Fink – CEO, BlackRockStephen Schwarzman – CEO, BlackstoneDavid Solomon – CEO, Goldman SachsJane Fraser – CEO, CitigroupAerospace & ManufacturingKelly Ortberg – CEO, Boeing (reportedly finalizing a massive 500-jet deal during the trip)Larry Culp – CEO, GE AerospacePayments & ServicesMichael Miebach – CEO, MastercardRyan McInerney – CEO, VisaAgriculture & BiotechBrian Sikes – CEO, CargillJacob Thaysen – CEO, IlluminaPaypal agrees to $30 million settlement with Trump's Justice Department over 'illegal DEI'The company launched a $530M Economic Opportunity Fund in 2020 for Black and underrepresented minority businessesDid not fight this in court, just surrenderedTo make the DOJ happy, PayPal had to ditch its race-based criteria; instead, it now funnels that financial support to veteran-owned businesses and companies in farming, manufacturing, or technology. A direct “black” to “white” transferAny company that launched a race-specific grant or loan program after 2020 is now officially in the DOJ's crosshairs, and "social justice" is being litigated as "civil rights fraud."PayPal board:“Independent” chair David W. Dorman (2015-; 17%)member of the Dell Technologies BoardMichael Dell and Donald Trump are BFFs: Dell pledged $6.25B to Trump AccountsJonathan Christodoro (2015-; 13%): a disciple of billionaire Carl Icahn (former Managing Director at Icahn Capital), one of Trump's oldest and most vocal alliesFounder PayPal Mafia Trump BFFs: Musk (DOGE), David Sacks (AI and Crypto Czar), Peter Thiel (JD Vance creator)Frank Yeary (2015-; 12%): Intel director since 2009 and chair since 2023It Was One of DOGE's Most Absurd Abuses. A Court Finally Exposed ItThis whole saga centers on a major legal showdown between the Trump administration's Department of Government Efficiency (DOGE) and the National Endowment for the Humanities (NEH). The case is a consolidated lawsuit (often called the NEH-DOGE lawsuit) filed in May 2025 by groups including the Authors Guild, the American Historical Association, and the Modern Language Association. On May 7, 2026, U.S. District Judge Colleen McMahon issued a massive 143-page ruling. She essentially nuked DOGE's attempt to defund hundreds of humanities projects, calling their process a "textbook example of unconstitutional viewpoint discrimination."The AI Purge: Instead of a professional review, DOGE staffers (described in court as young "technologists" with no background in humanities) ran thousands of grant descriptions through ChatGPT.DOGE staffers—mostly described as 20-somethings with "zero experience in the humanities"—attempted to dodge government transparency laws by conducting official business on Signal with auto-delete enabled. The court found this was a blatant violation of the Federal Records Act, proving that "efficiency" is often just code for "avoiding a paper trail."The Woke Filter: They told the AI to flag anything related to "DEI." This backfired spectacularly when the AI flagged projects on Holocaust survivors, Appalachian history, and Italian-American archives simply because they used words like "identity," "culture," or "women."DOGE didn't actually read the grants they cut. Instead, they used ChatGPT and basic keyword searches to flag any program containing "incriminating" words like "history," "culture," "identity," or "BIPOC." If the AI thought it sounded "woke," the funding was axed—a move Judge Colleen McMahon called a "textbook example of unconstitutional viewpoint discrimination."In perhaps the most "mask-off" moment of the proceedings, it was revealed that DOGE staffers flagged and canceled a documentary about Jewish women's slave labor during the Holocaust. The reason? Their AI-driven filter decided that focusing on "Jewish cultures" and "female voices" made it an illegal DEI program. Apparently, documenting Nazi atrocities is now "radical identity politics."The ruling highlighted a minor detail the administration seemed to forget: DOGE isn't a real government agency. The judge noted that DOGE had absolutely no lawful authority to terminate congressionally appropriated funds. They were essentially a group of private-sector bros playing President with the NEH checkbookThe Redirect: The court found that the $100 million "saved" wasn't actually returned to the Treasury. Instead, it was being funneled into the administration's own projects, like the "National Garden of American Heroes."Why Two Big Companies Just Cut Paid Family Leave MMFor the last decade, a tight labor market forced companies to compete for talent with generous perks. Now, with the job market cooling and employees having less leverage to quit, companies like Deloitte and Zoom are quietly rolling back benefits.Zoom, the company that became the face of remote work, has slashed its paid parental leave. Birthing parents saw their leave drop from up to 24 weeks to 18 weeks, while non-birthing parents were cut from 16 weeks down to 10.Deloitte is making deep cuts, but not for everyone. The reductions specifically target “Center” employees—the administrative, IT, and finance support staff who generally earn less—rather than the high-earning consultants. Their leave was halved from 16 weeks to just eight.Beyond just time off, Deloitte is axing its $50,000 reimbursement program for adoption, surrogacy, and IVF for these support roles.I Hate Working 5 Days': Zoom CEO Eric Yuan Says AI Could Shrink Workweeks To 3 Days In A Major Future ShiftGoodliest of the Week (MM/DR):DR: Chipotle CEO [Scott Boatwright] tells customers to ‘just ask' if they want bigger portions after downsizing accusations: “You should ask for a little more ... We serve big, beautiful bowls and burritos. Full stop, no questions asked. If you want more, just ask the team member. I promise you there's never a team member on that line that's going to say no.” 886 to 1MM: Oil shortages DR MMBeer demand stumbles as gas prices surge, data showsI mean, isn't this the double best? Less idiots driving drunk AND less idiots DRIVING!Oil shortages are even hitting colored snack bagsUgly snacks, maybe less eating!Assholiest TRIGGERIEST of the Week (MM):Brett BlundyVictoria's Secret unveils allegations against activist investor, loses board directorBlundy, Australian billionaire who launched Bras N Things, a classy establishment sold to Hanes, and currently chairs Lovisa, a fast fashion jewelry business, bought 13% of VS and thinks he can run it betterHe's disappointed with VS acquisition of Adore Me (online retailer) and the drop in earningsMeanwhile, Lovisa's 1Y market returns: -22% vs. ASX +4% TRIGGERED:Blundy, a fucking Australian billionaire blowhard, chairs LovisaLovisa board: Blundy, Mark McInnes (“deputy chair”), John Cheston (CEO), Bruce Carter, Tracey Blundy (wife), John Charlton, Sei Jin Alt (woman, Asian)Brett and Tracey own 40%+ of sharesZero merit directorsExec team: John, Mark, Victor, Chris - zero womenBlundy is targeting VS, whose board is…Donna James, Hillary Super (CEO), Irene Britt, Sarah Davis, Jacqueline Hernandez, Rod Little, David McCreight, Mariam Naficy, Lauren Peters, Anne SheehanExec team: 4 women, 1 manThis is the ultimate mansplain - some chest thumping billionaire walks into a room full of women, pushes them out, takes over… and this from the filing:“On November 13, 2025, members of the Board held a videoconference call with Mr. Blundy to inform him that the Board had determined, in accordance with its fiduciary duties, that appointing Mr. Blundy to the Board would not be in the best interests of VS&Co or its stockholders. In an effort to reach amutually agreeable resolution, the Board proposed collaborating with BBRC and Mr. Blundy on (i) adding one mutually-agreed new independent directornot affiliated with BBRC to the Board, (ii) Mr. Blundy's participation in a review with the Board of the Company's capital allocation, (iii) entering into alonger-term information sharing agreement and, in the context of a negotiated resolution with BBRC and Mr. Blundy, an agreement on customary standstill restrictions, and (iv) taking down the Rights Plan. After this call, the Board delivered to Mr. Blundy the following letter explaining its rationale for rejecting his candidacy and proposing a new framework for a mutually agreeable resolution:“The potential for significant reputational and legal risk to Victoria's Secret arising from (1) your pattern of hiring executives with a history of serious allegations of sexual harassment or other misconduct, and (2) the reported and alleged instances of harassment and highly inappropriate employee policies that occurred under your oversight at companies you controlled or effectively controlled.The proxy should just say, “Australian white male billionaire who is cool sexually harassing women while selling them underwear wants to take over massive underwear store run by women”Elon Musk and Sam AltmanMusk first…Sam Altman Accuses Elon Musk of Laughing at Memes During Important OpenAI MeetingsMusk's China trip during OpenAI trial prompts apology from his lawyer for CEO's absenceTRIGGERED: This is the man child trillionaire we're supposed to take seriously - does his mom fold his socks for him? Does he eat Cheerios out of a frisbee for breakfast? These are our male adult role models?Musk apparently was too busy for the trial, but during talks of absorbing OpenAI into Tesla, he wasn't too busy to spend a long time forcing everyone to look at his fucking dopey idiot manboy memes that made him laughReminder time: Musk is in charge of who gets internet in military conflict (Starlink), gutted the government (DOGE), is trying to implant chips in brains (Neurolink), and used everyone else to get his billions (Tesla was bought, subsidized, SpaceX subsidies, Boring Company steals municipal money to dig holes…)Altman next…Sam Altman faces awkward grilling over 'toxic culture of lying'ChatGPT Told a 19-Year-Old How to Mix Drugs — His Mother Found Him Dead the Next MorningWHEN YOU PUT A SOCIOPATH AND MANCHILD IN CHARGE OF A WORLD DESTROYING DEVICE, IT TURNS OUT IT'S BADWarren Buffett DRPut the folksy “I'm just a guy eating a werther's original candy making money” schtick aside, where he says they pick great management and let them do their thing - this is “their thing”:TRIGGERED: Electric Company Says It's Cutting Off an Entire Town So It Can Sell All Its Power to Data CentersThere is so much to hate here:Tech billionaires building data centers for AI: checkNV Energy is wholly owned by Berkshire Energy which is owned by Warren Buffett: checkTrump appointed asshole running regulatory agency that represented utilities: checkThe town is Lake Tahoe - 50,000 residents have to find a new source of electricity in ONE YEAR because Buffett/Berkshire/NV Energy decided the re-route all energy to data centers for AIGoogle, Apple, MSFT all have facilities, 12 data center projects in Northern NevadaNevada would have to ask woke California to build hundreds of millions of dollars worth of transmission lines in a year to get to Tahoe, FERC would have to approve other changes (Chair Laura Swett, Trump appointee, represented electric utilities and the firm wrote pieces about the glory of data centers - one of the Amicus Briefs they wrote in 2024 was on behalf of… NV Energy)Of the fines issued by FERC this year, 99% are one company: an energy efficiency companySince Trump was elected, FERC has issued fines targeting blue state utilities and renewables at a more than 2:1 rateSo the people are fucked - maybe Warren can tell them to power their town on See's Candy sugar rushesHeadliniest of the WeekDR: Kids with fake mustaches can fool high-tech age verification systemsMM: Waymo recalls 3,800 robotaxis after glitch allowed some vehicles to 'drive into standing water'Who Won the Week?DR: Steve Roth, the CEO of Vornado Realty Trust, expressed his support for fellow billionaire and the Citadel CEO Ken Griffin: “I must say that I consider the phrase tax the rich — quote tax the rich — when spit out with anger and contempt by politicians both here and across the country, to be just as hateful as some disgusting racial slurs”MM: Lawyers - literally everything now is a lawsuit and everyone is a lawyer. PredictionsDR: NYC Mayor Mahmdani asks Steve Roth for “just little more” and Roth replies: “I'm not a fucking Chipotle, commie scum.”MM: Chili's CEO wakes up at 5 a.m., runs daily, and uses that time to generate ideas for the business: On a run next Thursday, May 21, Chili's CEO Kevin Hochman stops short and says out loud, “What if the Big Crispy Chicken Sandwich was BIGGER???”

With Ricky Smith—Founder & Managing Partner, Inspired Wealth Planning Overview Jason Diamond speaks with Ricky Smith of Inspired Wealth Planning about leaving Edward Jones after 30 years, evaluating 12 firms, and building an independent business that grew to $1.25B in assets under care in less than three years. Listen in… > Download a transcript of this episode… NOTE: The views and opinions expressed by the guests on this podcast are their own and do not necessarily reflect the views and opinions of Diamond Consultants. Neither Diamond Consultants nor the guests on this podcast are compensated in any way for their participation. Watch… https://youtu.be/cobAfEl0_To About this episode… What happens when you stop thinking like a renter and start thinking like an owner? Not just in theory, but in how you run your business, make decisions, and show up for clients. For Ricky Smith, that question didn't come at the beginning of his career. It came 30 years later, after building a highly successful practice at Edward Jones and beginning to see the business through a different lens. Today, Ricky is the founder and managing partner of Inspired Wealth Planning, the independent firm he built with Kestra Private Wealth Services. Since launching in March 2023, the firm has grown to over $1.25B in assets under its care across seven locations. What makes this story interesting isn't just the move—it's how intentional it was. Ricky didn't rush into independence. He spent a year evaluating 12 different firms and paths, clarifying what mattered most, and ultimately making a decision based on people and alignment, not just economics. Ricky shares his journey with Jason Diamond, including: His approach to due diligence—and why he dove deeper into the weeds before he was satisfied with his next steps. Reconsidering the wirehouse model—and why he felt independence was the best path forward. The “ownership mindset”—and how that drives his values and processes. The early phase of independence—and why it's less about growth and more about getting the structure right. Growing by 50%—and what “breakthroughs” he had in less than three years. Ricky offers the perspective that making the leap to independence may be “short-term hard,” but you're working toward building a business that's designed to be “long-term easy.” And there's another broader idea worth paying attention to: Most advisors don't lack options; they hesitate to act on them. Listen in for sage advice from an advisor who has lived in the wirehouse world and is now independent—and has realized the value of ownership. Want to learn more about where, why, and how advisors like you are moving? Click to contact us or call 908-879-1002. Related Resources Diamond Consultants Edward Jones Advisor Transition Report 2025This “firm-focused report” seeks to look under the hood at movement to and from Edward Jones from January to June of 2025. The Cost of Clarity: What Advisors Stand to Gain and Lose When Their Firm Shows Its HandWhen firms become explicit about who and what they value, it's time for advisors to read those signals and respond. The Advisor Transition Playbook: The Latest on Due Diligence, the Move, and Everything In Between – Part 2Jason and Mindy Diamond revisit the transition playbook, this time focused on how advisor priorities are shifting. From AI and enterprise value to stability and flexibility, they unpack what's changing in due diligence and what it means for advisors evaluating their next move. Ricky SmithManaging Partner Ricky Smith is the founder and Managing Partner of Inspired Wealth Planning. Inspired Wealth Planning is group of like minded veteran financial advisors who serve their clients and local communities across Georgia and now even Ohio. Before founding Inspired, Ricky worked as a financial advisor for 39 years. Primarily as an employee of a nationwide financial firm. Wanting to have more control over the outcomes for clients, his team and his own career, he left the employee model to join an independent firm – Kestra Private Wealth Services. After opening the Kestra based office, other advisors inquired about joining Inspired. Within the first 36 months, Inspired grew to 7 locations, 10 advisors, 14 support staff and over $1.2 billion in assets under care. In February 2026, Inspired was selected as the Outstanding Business of the Year for Kestra Financial (the parent company of Kestra Private Wealth). This was the first time that any firm from Kestra Private Wealth had ever been selected for that award. In early April the firm was on the cover of Advisor Hub magazine and in mid-April, Ricky was selected for the Forbes/Shook Best in State Wealth Advisors for the state of Georgia. An Honor that he has received 3 times in the past 5 years. Ricky lives in Cordele Georgia with his wife, Patti and their tuxedo cat Oreo. They have a daughter, Brooke, who lives in Maryland. Ricky has been a loyal member and participant with the local Chamber of Commerce for 42 years, serving as chairman in 1999. He and Patti are long-time members of Cordele First Church and supporters of the local chapter of Celebrate Recovery.

4.3 million industrial robots are already deployed globally. Robot costs have dropped 50% in 30 years. Payback periods are now 1 to 3 years. The reshoring of American manufacturing isn't a forecast — it's a buy order.This week on Money On Tap, Ben Brayshaw and Dan Michelon continue the series with The Railroads of Robotics — the picks-and-shovels playbook for physical AI and the next great industrial build-out.What you'll learn:Why three forces — reshoring, labor shortage, and 1–3 year robot payback — make automation inevitableThe four investable layers: robots · AI systems · software · hardwareA walk-through of the public names: Rockwell Automation, Teradyne, Emerson Electric, NVIDIA, Tesla (Optimus), AeroVironment, Applied Materials, AutodeskHow cobots are reshaping skilled-trades work — and what the NVIDIA CEO's "three-day work week" prediction really meansFive robotics-themed ETFs walked through: ROBO, BOTZ, IBOT, ARKQ, ROBTWhat to tell the kids and grandkids about which jobs will actually exist in 10 yearsThe geopolitical risk that could shelve this entire build-out overnightPlus Money In The News:United Airlines hikes fares up to 20% — CEO admits passing 100% of jet-fuel cost to consumersMusk vs. Altman: a $134B suit heading to court while SpaceX ($1.25T) and OpenAI ($850B) IPOs loomAdobe announces a $25B buyback (25% of market cap) while Big Tech keeps laying off — and the buyback nuance most investors missRead the companion blog: brayshawfinancial.com/blogSchedule a free consultation: app.greminders.com/t/9f3ce72e/initialconsultaFull Money On Tap episode library: brayshawfinancial.com/money-on-tapContact UsPhone: 855-226-8551Email: info@yourmoneyontap.comOffice: 116 South River Road, Bedford, NH 03110Web: brayshawfinancial.comWhat is "physical AI" and why does it matter for investors? Physical AI is the application of artificial intelligence to machines that operate in the real world — industrial robots, cobots, autonomous vehicles, drones, and humanoid robots. Unlike AI software that lives only on a screen, physical AI directly performs labor: assembling products, moving materials, inspecting quality, and operating equipment. For investors, it converts the AI thesis into measurable productivity gains and physical reshored capacity.

We admire the mountains of Austria with the co-founder of Curie.Bio, a founder-friendly fund that now manages $1.25B and backs 38 portfolio companies.We discuss Curie.Bio's track record, with its most advanced portfolio candidate Forward Therapeutics in Phase I.We also talk about major bottom-up successes including argenx, Ascendis and Genmab, and how life sciences investing differs from tech investing.Christoph also explains his ‘rebel' mindset and why founders shouldn't accept the status quo.---This episode is brought to you by Avance, where European biotech turns for trusted financial advice. Learn more at https://bit.ly/flot-avance---⭐️ ABOUT THE SPEAKERChristoph co-founded of Curie.Bio in 2022, after serving as Partner at Third Rock Ventures for 6 years. He also had leading roles at Blueprint Medicines for ten years, and at MOMA Therapeutics for one year.He has also been an Adjunct Associate Professor at Johns Hopkins University for more than 20 years, with past roles at Celsius Therapeutics, Sanofi, and Novartis.

Welcome back to the Konfidence in the Klutch Podcast with Donald Nelson (2:15). Konfidence in the Klutch's Deezus gives his Konfident Service Announcement: Be You (2:30).  Deezus gives his thoughts on "Michael", and he doesn't understand the hate towards the movie (5:30).  Deezus then shares his NBA news, including the league releasing anti-tanking measures that will be voted on.  Masai Ujiri named Mavs team president.  Jahmal Mosley fired in Orlando.  Detroit advances. Is Orlando a Giannis destination?  Cavs advance.  Is it now or never for Spida Mitchell?  Knicks advance.  Did they figure it out, and the Hawks should be proud of their season?  Sixers advance.  Who gets traded on the Celtics?  Lakers advance.  Are the Rockets having buyer's remorse knowing they could've used those assets to land Giannis?  Wolves advance.  The Nuggets just had an identity and reality check.  Spurs advance.  Blazers ownership hires Splitter, or you will be cursed by the basketball gods.  Deezy finishes up with series previews and action from the Eastern and Western Conference semi-finals (14:15). Deezy then shares his WNBA thoughts, including A'Ja, Angel, and Paige at the Met.  Deezus shares his KITK WNBA Fantasy roster (32:15). Deezus discusses 'Politics as usual,' including how the U.S. has spent over $25B on a sixty-plus-day exercise.  Does anyone know WTF is going on over there, anyway? (33:50). Deezus shares his Quick Ones: Meg and/or Klay step up for Lexie; Stefon Diggs vs. The Chef is OC.  Doechi at the Met (37:00). The podcast was recorded at 5:00 p.m. CT on Tuesday, May 5, 2026.  Host: Donald Nelson Producer/Engineer: Donald Nelson Music by: Konfidence in the Klutch Productions Subscribe, Stream, or Download:

Training platoon ethnic tensions expose cohesion crisis, Carney unveils $25B sovereign wealth fund, annual May Day protest erupts in clashes, and crime clearance hits historic lows.-Get original articles, extended podcasts, and direct access to Blendr News on our Substack Channel: blendrnews.com-This episode is brought to you by The Tallowed Truth. Use promo code "Blendr" for 15% off:www.thetallowedtruth.com/blendr-In this episode of "The Blendr Report," Liam and Dennis discuss:00:00 — Introduction & Episode Overview00:43 — Canadian Armed Forces Ethnic Infighting Crisis05:35 — Cultural Cohesion & Immigration Policy Failures10:35 — Mark Carney's $25 Billion Sovereign Wealth Fund16:20 — Sovereign Fund Strategy & Political Calculation21:40 — Montreal May Day Protest Violence26:45 — NGO Funding & Organized Protest Strategy-Follow BLENDR News:Twitter - @BlendrNewsInstagram - @blendr.report TikTok - @blendrnews-Follow Jonathan:Instagram - @itsjonathanharveyTikTok - @itsjonathanharvey-Follow Liam:Instagram - @liam.out.loudX - @liam_out_loudYouTube - @liam-out-loud

May 2, 2026, 9 AM ; Trump's claim came on what was the 60-day deadline for him to gain approval from Congress to continue the war with Iran. Meanwhile, in his first Capitol Hill appearances since the start of the war, Defense Secretary Pete Hegseth was grilled about a host of issues, including the cost of the war, which the Pentagon put at $25B dollars over two months. Massachusetts Democratic Rep. Seth Moulton joins The Weekend to discuss Hegseth's testimony to congress and the conflict's mounting price tag. For more, follow us on social media: Bluesky: @theweekendmsnow.bsky.social Instagram: @theweekendmsnow TikTok: @theweekendmsnow To listen to this show and other MS podcasts without ads, sign up for MS NOW Premium on Apple Podcasts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Thursday, April 30th, 2026 Today, the Supreme Court gutted what was left of the Voting Rights Act in a 6-3 ruling over Louisiana maps; Trump's FCC ordered a review of ABC's broadcasting license because Jimmy Kimmel makes jokes; the DOJ is dismissing conspiracy charges against the Broadview six including Kat Abughazaleh; a Pentagon official tells Congress the war in Iran has cost $25B so far; right after Democrats filed contempt charges against Pam Bondi she announced she'll be appearing for a deposition May 29th; a new Colorado conversion therapy ban with a clever mechanism is close to passing in the state; and Allison and Dana deliver your Good News. Thank You, Mint Mobile Make the switch! MINTMOBILE.com/DAILYBEANS Thank You, Coyuchi Get 15% off your first order when you visit Coyuchi.com/dailybeans The Daily beans is donating $10,000 and invites you to give what you can to support their life-affirming work - Donate to It Gets Better / The Daily Beans Fundraiser   Guest: Traci Feit Love - Lawyers for Good Government@tracifeitlove.bsky.social National Law Day of Action 2026: Find an Event Near You National Law Day of Action Lawyers for Good Government - @lawyers4goodgov.bsky.social The Latest Breakdown:Todd Blanche's Unhinged Midnight Ballroom Motion StoriesFlorida Approves Redistricting Map That Could Add 4 Republican House Seats | The New York Times FCC Orders a Review of ABC's Licenses Amid Feud Between Trump and Kimmel | The New York Times Feds to Dismiss Conspiracy Charges Against Remaining ‘Broadview Six' Defendants | Chicago News | WTTW New Colorado Conversion Therapy Ban With Clever Mechanism Close To Passing | Erin In The Morning   Good Trouble National Law Day of Action 2026: Find an Event Near You National Law Day of Action MAY DAY STRONG Find a march or rally in your area:May Day Participation Guide | Indivisible →Letter Carriers' “Stamp Out Hunger“ Food Drive →FieldTeam6.org →Palmetto State Abortion Fund - Midland Gives  →2026 Primary Election Calendar: All the Dates Ahead of Midterms →Standwithminnesota.com →Tell Congress Ice out Now | Indivisible, Defund ICE | 5Calls →Congress: Divest From ICE and CBP | ACLU →ICE List  →iceout.org Good NewsLeavingMAGA.org   →Email Dana LGBTQ Owned eating establishments in your area - hello@mswmedia.com Subject: “Dana's Project” →Share your Good News & Good Trouble - The Daily Beans →Beans Talk audio -beans-talk.simplecast.com Subscribe to the MSW YouTube Channel - MSW Media - YouTube Harry Dunn is running for CongressHarry Dunn for Maryland Our Donation Links The Daily beans is donating $10,000 and invites you to give what you can to support their life-affirming work - Donate to It Gets Better / The Daily Beans Fundraiser Pathways to Citizenship link to MATCH Allison's Donationhttps://crm.bloomerang.co/HostedDonation?ApiKey=pub_86ff5236-dd26-11ec-b5ee-066e3d38bc77&WidgetId=6388736 Join Dana and The Daily Beans with a MATCHED Donation http://onecau.se/_ekes71 More Donation LinksNational Security Counselors - Donate, ActBlue.com/donate/msw-bwc, WhistleblowerAid.org/beans Dr. Allison Gill - The Breakdown | Allison Gill, Mueller, She Wrote @muellershewrote.com - Bluesky, MSW & The Daily Beans Podcast @muellershewrote - Instagram, MSW Media - YouTube →Federal workers - email AG at fedoath@pm.me and let me know what you're going to do, or just vent. I'm always here to listen.  Dana Goldberg - Dana is on Patreon! At Dana's Dugout, @dgcomedy - Bluesky, @dgcomedy - IG, Dana Goldberg - Facebook,  DanaGoldberg.com More from MSW Media - Shows - MSW Media, Cleanup On Aisle 45 pod, The Breakdown | Allison Gill Reminder - you can see the pod pics if you become a Patron. The good news pics are at the bottom of the show notes of each Patreon episode! That's just one of the perks of subscribing! patreon.com/muellershewrote Listener Survey:http://survey.podtrac.com/start-survey.aspx?pubid=BffJOlI7qQcF&ver=shortFollow the Podcast on Apple:https://apple.co/3XNx7ckWant to support the show and get it ad-free and early?https://patreon.com/thedailybeanshttps://dailybeans.supercast.com/https://apple.co/3UKzKt0 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The CEO of Qualcomm joins the show with the stock surging after results. The company saying it has a new mystery hyperscalers customer which it will reveal in the coming months. Plus, the CEO of commercial real estate firm JLL on earnings and the opportunity in data centers. Then CNBC speaking with sources, confirming Meta plans to tap the bond market in an effort to raise $20-25B. The stock falling on concerns over its capex plans. And can consumers expect any relief at the pump with gas prices at 2022 highs? We discuss. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Matt Cornwell — 130+ credits, 20+ years in the Southeast, and the man behind Get Taped's 350+ videos — joins Get Scene host Jesse Malinowski to unpack the real reason most actors aren't booking, the insurance backdoor inside casting offices, and why he refuses to coach during your self-tape. Plus: how Georgia still beats LA by $1.25B in tax incentives, his Steve Martin philosophy on staying in the top 10%, and the celebrity-set booking he believes belonged to another actor.Join Our Newsletter:https://getscenestudios.us7.list-manage.com/subscribe?u=7660af20fdb3c04d6b6516591&id=eecb804958Monthly Promo :Use code BOOKEDPOD for $10 off the 4-week Booking Challengehttps://www.getscenestudios.com/getsceneonline/booked-it-challenge-5zka6Join out Patreon!⁠https://www.patreon.com/cw/GetScenePod⁠

In this episode of Tank Talks, Matt Cohen and John Ruffolo break down one of the biggest economic policy announcements in Canada's innovation economy: Mark Carney's proposed $25 billion Canada Strong Fund, a sovereign wealth fund designed to invest in nation-building projects, strategic industries, Canadian technology companies, and long-term economic sovereignty. John, who previously argued for this type of fund in his Substack piece Canada's Missing Pot of Gold, explains why Canada's biggest structural problem is undercapitalization and why relying on foreign direct investment for critical industries creates serious sovereignty risks.Matt and John dig into the hard questions behind the fund: Where does the money come from? Can Canada borrow at low rates and invest for long-term returns? How should the fund be governed so it does not become a political slush fund? And can this vehicle finally force a more serious conversation around Canadian pension funds, domestic capital formation, and backing companies like Cohere, Kepler, and Xanadu before they are pushed toward foreign capital markets?The episode also covers Cohere's acquisition of German AI firm Aleph Alpha, the rise of sovereign AI alternatives outside the U.S. and China, Xanadu's volatile post-SPAC quantum stock run, SpaceX's reported Cursor acquisition talks, Meta's 8,000-person AI-driven workforce reduction, and Thoma Bravo's massive Medallia equity wipeout. From sovereign wealth and AI infrastructure to quantum financing and private equity pain, this episode asks the real question: can Canada build the capital systems needed to own its future?Canada Strong Fund: Carney's $25B sovereign wealth fund announcement (00:31)Matt opens the episode by laying out the breaking news: Mark Carney has launched the proposed Canada Strong Fund, a $25 billion sovereign wealth fund aimed at giving Canadians a stake in strategic national projects and critical industries.Why John Ruffolo says Canada is dangerously undercapitalized (01:22)John argues that Canada's core economic problem is not a lack of ideas, talent, or companies, but a lack of domestic capital formation. He explains why foreign-controlled capital in sovereign industries is a bad idea and why Canada needs its own funding mechanism.The biggest risk: governance or political slush fund? (03:14)John explains that the Canada Strong Fund will only work if it is independently governed, similar to CPPIB or CDPQ. Without strong governance, he warns, the fund could collapse into politically motivated pet projects.Can Canada borrow at 3.5% and earn 7% long term? (04:59)John breaks down the financial logic behind using Canada's strong credit rating to borrow at lower rates and invest through a professionally managed fund targeting long-term returns similar to major pension funds.Why the fund fails if returns do not materialize (08:15)Matt raises concerns about launching a sovereign wealth fund during a deficit environment. John says the idea only works if the fund is independently managed and capable of generating real long-term returns.No more grants: John's blunt plan for government funding (14:02)John calls for Canada to stop giving grants, especially to foreign-based companies, and instead convert government support into equity investments that create long-term ownership and capital recycling for the country.Cohere acquires Aleph Alpha and makes a sovereign AI play (16:12)Matt breaks down Cohere's acquisition of German AI firm Aleph Alpha, the new Berlin European headquarters, and the reported $600 million financing commitment from Schwarz Group as part of a broader sovereign AI strategy.Xanadu's quantum stock surge and post-SPAC volatility (19:59)Matt explains Xanadu's post-SPAC trading action, including its sharp rise, options activity, and SEC filing registering nearly 300 million Class B shares for sale after the lockup period expires.SpaceX, Cursor, and peak AI paper-deal froth (24:25)Matt and John react to reports that SpaceX could acquire AI coding startup Cursor for $60 billion, with John arguing that SpaceX shareholders should be furious about the growing complexity and governance concerns.Meta layoffs and the real cost of AI capital spending (27:56)Matt highlights Meta's reported 10% workforce reduction tied to massive AI capital spending. John argues the “AI efficiency” explanation often masks bad capital allocation and failed strategic bets.Thoma Bravo's $5.1B Medallia equity wipeout (29:55)The episode closes with Thoma Bravo handing Medallia back to creditors after a major private equity software deal collapses, raising questions about SaaS valuations, debt structures, and exit assumptions in the AI era.Connect with John Ruffolo on LinkedIn: https://ca.linkedin.com/in/joruffoloConnect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

The AI world is moving fast… maybe faster than most people realize.In this episode, I break down 4 major stories that are shaping where AI, software, and business are headed right now:A powerful new model from Anthropic that was built… then locked awayOpenAI hitting $25B in revenue and what an IPO could meanThe $5B vibe coding boom (and why most builders aren't developers)Elon Musk's $250B power move with xAI, SpaceX, and CursorThis isn't just news — these shifts are changing how apps get built, who builds them, and who controls the future of AI.If you're building, investing, or even just paying attention… this one matters.

This week on Ricker and Bon: Tim Cook exits Apple, Tesla raises its 2026 capex to $25B, OpenAI ships GPT-5.5 "Spud," SpaceX eyes a $60B acquisition of Cursor, and consumer sentiment hits a 74-year low.

This week, we discuss agents taking over at Google Cloud Next, Apple's new CEO, and Cursor getting acquired (sort of). Plus, Coté's e-waste has no exit strategy. Watch the YouTube Live Recording of Episode 569 Runner-up Titles I love throwing stuff in the trash. Dillo dirt's a thing. BurgerOps. Thomas opens for Richard. Department of “No” people Starfish Stomach Model Enterprise — come into me The Organization will Assimilate it Gold plaques all around He can let his freak flag fly Take the first billion dollar offer Rundown Google Next Welcome to Google Cloud Next26 Google's AI adoption — Steve Yegge X Post Tanzu Platform 10.4: a private cloud platform for AI harnesses (or, "agentic AI") Apple becomes a $4 Trillion under Tim Cook Cursor Watch Cursor in talks to raise $2B+ at $50B valuation as enterprise growth surges There's No Time for SpaceX to Buy Cursor SpaceX says it can buy Cursor later this year for $60 billion or pay $10 billion for 'our work together' Relevant to your Interests Poland street sees humanoid robot chasing boars in unusual AI showcase Someone planted backdoors in dozens of WordPress plug-ins used in thousands of websites Snapchat owner cuts 16% of global staff in latest round of job cuts Email for agents - Cloudflare Email Service now in public beta DeployBar — Free CI monitoring. Unsolicited platypus included. Let them tinker - hacking developer resistance to sound enterprise architecture and platforms China's DeepSeek is raising funds at $10 billion valuation, The Information reports Sources: Cursor in talks to raise $2B+ at $50B valuation as enterprise growth surges AI chipmaker Cerebras files to go public after scrapping IPO plans last year Amazon to invest up to $25B in Anthropic as part of expanded cloud partnership - SiliconANGLE Amazon to invest up to $25 billion more in Anthropic; Claude developer to spend more than $100 billion on AWS AI technology Amazon and Anthropic expand strategic collaboration Anthropic CPO leaves Figma's board after reports he will offer a competing product OpenAI loses multiple executives in latest leadership shakeup Scoop: NSA using Anthropic's Mythos despite Defense Department blacklist The scientific case for being nice to your chatbot Anthropic's redesigned Claude Code desktop app lets you burn through tokens even faster OpenAI's Codex Mac app adds three key features that go beyond agentic coding Introducing Claude Opus 4.7 Anthropic Sponsors WebRTC.ventures – Real-time communication & Voice AI integration WeAreDevelopers World Congress North America Sept 23–25, San José, CA Use Code DEVPOD26 — 15% off, stacks with group rates for 4+ Listener Feedback Subscribe to Failover New Nonsense Struggling shoe retailer Allbirds makes bizarre pivot from shoes to AI, stock explodes more than 700% Allbirds Stock Now Crashing as Reality Sets in About Its Delusional AI Pivot Conferences DevOpsDays Austin, May 5-6, 2026 DevOpsDays + AI Nashville, May 14-15, 2026 KCD Texas, May 15, 2026, use code MEDIA_THANK_YOU for free pass WeAreDevelopers Europe, July 8-10, 2026 Berlin, Coté speaking. DevOpsDays Graz, Sept 4-5, 2026 DevOpsDays Dallas, Sept 28-29, 2026 DevOpsDays Rockies, Sept. 22 – 23, 2026, Discount Code: 26DODSWEDEFTALK WeAreDevelopers NA, Sept 23-25, 2026, Discount Code: DEVPOD26 DevOpsDays Vilnius, Sep 30 - Oct 1. 2006 DevOpsDays Istanbul, October 24th, 2026 - Coté keynoting. VMware User Groups (VMUGs): Toronto (May 12-14, 2026) Dallas (June 9-11, 2026) Orlando (October 20-22, 2026) SDT News & Community Join our Slack community Email the show: questions@softwaredefinedtalk.com Free stickers: Email your address to stickers@softwaredefinedtalk.com Follow us on social media: Twitter, Threads, Mastodon, LinkedIn, BlueSky Watch us on: Twitch, YouTube, Instagram, TikTok Book offer: Use code SDT for $20 off "Digital WTF" by Coté Sponsor the show Sponsor more podcasts with Failover Media Recommendations Brandon: Claude /team-onboarding The Junior Dev Crisis: Who Inherits the Code When AI Does the Work Matt: Resident Alien Coté: and

Tesla Q1 2026 earnings are hot off the press, and the biggest announcement wasn't in the results but on the conference call. Elon Musk and Tesla are now upping CAPEX guidance to $25B in 2026! That's right, they want to spend $25BILLION this year on investments on AI and robotics. This means they will be losing free cash flow and could mean they will have to raise more capital in the future. All to fund ambitious projects like Cybercab, Robotaxi, Optimus training, Terafab AI Chips & more! What are your thoughts on this massive news!??!My X:   / gfilche  HyperChange Patreon :)   / hyperchange   Disclaimer: Nothing in this show is financial advice I'm long Tesla.

Apple named John Ternus as its next CEO, with Tim Cook stepping up to executive chairman on September 1. Amazon agrees to invest up to $25B more in Anthropic, Bezos' Project Prometheus nears a $10B raise, and SpaceX's IPO prospectus reveals Musk's power moves. John Ternus, senior VP of Hardware Engineering, will become Apple's next CEO on September 1; Tim Cook will become executive chairman of Apple's board (CNBC) Amazon agrees to invest up to $25B in Anthropic, on top of the $8B that it has already invested; Anthropic commits to spend $100B+ on AWS over the next 10 years (CNBC) Sources: Jeff Bezos' Project Prometheus is close to a $10B fundraising deal, which includes an initial $6.2B raise in November, at a $38B post-money valuation (FT) Draft of SpaceX's confidential IPO prospectus: Elon Musk increased his stake in SpaceX last year by purchasing $1.4B of stock from current and former employees (The Information) Learn more about your ad choices. Visit megaphone.fm/adchoices

D.A. Davidson's Gil Luria talks with TITV Guest Host Anita Ramaswamy about Apple's CEO transition and Amazon's $25B investment in Anthropic. We also talk with The Information's Valida Pau and Cory Weinberg about Elon Musk's increased stake in SpaceX and the company's upcoming IPO. On the ground at Adobe Summit 2026, TITV Host Akash Pasricha joins Adobe CMO Rachel Thornton and Amit Ahuja about the launch of new AI agents and enterprise partnerships with Nvidia, Microsoft and Anthropic. Lastly, we get into agentic commerce with DICK'S Sporting Goods' VP of Product Jason Cherok and the macro state of AI adoption with Adobe's Senior Director & Head of Adobe Digital Insights Taylor Schreiner.Articles discussed on this episode: https://www.theinformation.com/articles/musk-bought-1-4-billion-spacex-shares-helping-boost-controlSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/

Netflix beat on revenue and income but dropped 10%+ on weak Q2 guidance as Reed Hastings exits the board. Anthropic launches Claude Design, OpenAI overhauls Codex Desktop with computer control, and DeepSeek seeks its first outside funding at $10B+. Netflix reports Q1 revenue up 16% YoY to $12.25B, vs. $12.2B est., net income up 83% YoY to $5.28B, and forecasts Q2 EPS and revenue below est.; NFLX drops 10%+ (Bloomberg) Anthropic launches Claude Design, a new experimental product that lets users create visuals like prototypes, slides, one-pagers, and more using Claude (TechCrunch) Sources: Dario Amodei is set to meet with WH Chief of Staff Susie Wiles on Friday, a breakthrough in Anthropic's effort to resolve its fight with the Pentagon (Axios) OpenAI updates its Codex desktop app with features like computer control, an in-app browser, image generation, automation memory, plugin support, and more (ZDNet) Sources: DeepSeek is in talks to raise outside capital for the first time, seeking at least $300M at a valuation of at least $10B (The Information) Longreads India produces 1.5M+ CS graduates annually, but AI coding tools are forcing its $315B IT outsourcing industry into an existential reckoning (Bloomberg) Doug Liman's $70M movie Bitcoin: Killing Satoshi uses AI for sets, lighting, and more in post-production, cutting costs from an estimated $300M (The Wrap) Defunct startups are being liquidated for their Slack archives, Jira tickets, and email threads—operational exhaust that AI labs now treat as premium training data (Forbes) Learn more at liquid.trade/techbrew. Disclaimer: ● Initial 3 week subscription and 4 weeks of medication from $79 plus tax and $179 per month plus tax for 12 week subscription thereafter. Final pricing depends on program selection. ● Noom GLP-1Rx Program involves healthy diet, exercise and support. Individual results vary. Meds & personalization based on clinical need. Not reviewed by FDA for safety, efficacy, or quality. No affiliation with Novo Nordisk Inc., the only US source of FDA-approved semaglutide. Not available in all 50 US states ● Based on an analysis of self reported data from 1,254 engaged Noom users. Learn more about your ad choices. Visit megaphone.fm/adchoices

The NIA boys discuss Claude Mythos, The Clipping Economy & Anthropic's $30B vs. OpenAI's $25B (?)Timestamps(00:00:00) - Intro(00:03:20) - Claude Mythos(00:13:27) - The Clipping Economy(00:23:16) - Claude Mythos (pt2) (00:27:09) - Anthropic's $30B vs. OpenAI's $25B (?)What Is Not Investment Advice?Every week, Jack Butcher, Bilal Zaidi & Trung Phan discuss what they're finding on the edges of the internet + the latest in business, technology and memes.Subscribe + listen on your fav podcast app:Apple: https://pod.link/notadvicepod.appleSpotify: https://pod.link/notadvicepod.spotifyOthers: https://pod.link/notadvicepodListen into our group chat on Telegram:https://t.me/notinvestmentadviceLet us know what you think on Twitter:http://twitter.com/bzaidihttp://twitter.com/trungtphanhttp://twitter.com/jackbutcherhttp://twitter.com/niapodcast Hosted on Acast. See acast.com/privacy for more information.

Today we were pleased to welcome Doug Lambert, Co-Founder and Chief Operating Officer of Saronic Technologies. Doug, alongside Dino Mavrookas (CEO), Rob Lehman (CCO), and Vibhav Altekar (CTO), co-founded Saronic in 2022 to advance maritime superiority through intelligent autonomous systems. Saronic's mission is to equip the U.S. and its allies with advanced autonomous surface vessels, enhancing situational awareness and enabling more effective detection, tracking, and response to emerging maritime threats. The company recently closed a $1.75B Series D at a $9.25B valuation and announced a new downtown New Orleans office to support its expanding shipbuilding operations in Louisiana. We were thrilled to spend time with Doug and explore autonomy, maritime innovation, and the future of naval and offshore operations. In our conversation, Doug provides an overview of Saronic, their product range, and rapid growth to ~1,500 employees. We discuss the convergence of enabling technologies (AI, machine learning, edge compute, and advanced sensors) that have made true maritime autonomy possible, why this moment is different from prior attempts, and the combination of technology breakthroughs, market tailwinds, and geopolitical developments that have accelerated adoption. We explore Saronic's approach to designing purpose-built autonomous vessels, as well as their decision to vertically integrate across design, manufacturing, and operations, highlighting how scale production, control of the full system, and a data-driven flywheel are critical to driving down costs and unlocking broader adoption. We examine the strategic implications of autonomy and how these platforms act as force multipliers across defense, offshore energy, and critical infrastructure. Doug shares his perspective on the concept of a hybrid fleet, where autonomous systems augment traditional assets, extend reach, and improve safety, and how this shift could reshape maritime strategy over time. We cover the importance of edge-based decision making versus cloud reliance, and how real-world deployment and data collection underpin both performance and competitive advantage. We also touch on the broader industrial and cultural backdrop, including the reindustrialization of U.S. shipbuilding, the blending of software and skilled trades, and the growing importance of building in the physical world. We discuss workforce dynamics, labor constraints in maritime, adoption challenges, the gap between technical readiness and real-world trust as autonomy moves from concept to scaled deployment, and much more. It was a wide-ranging discussion and we're thankful to Doug for sharing his time and unique insights. Mike Bradley started the show by noting that the 10-year bond yield had moved down to ~4.27% following a softer-than-expected March PPI report (YoY +4%). While still elevated, the print came in well below consensus and remains far below the 11.7% peak seen during the Biden Presidency. On the oil market front, WTI was trading at ~$92/bbl, down $6–$7 on the day and $4–$5 since President Trump announced the Strait of Hormuz blockade over the weekend. He noted that global oil prices also moved lower on optimism around a potential second round of Iranian peace talks, as well as a meaningful downward revision (~730 kbpd) to the IEA's 2026 demand outlook. Traders are now less focused on how high prices could go and more focused on how low they could fall if and when the Strait of Hormuz reopens. On the broader equity market front, the S&P 500 was up ~1% on the day and trading within ~0.5% of its all-time high, highlighting a notable divergence between energy market concerns and broader market optimism. Within equities, Energy was the worst-performing S&P sector on the day and has effectively round-tripped since the onset of the Iran conflict, now down ~11% from its March peak. Looking ahead, Oil Services Q1 earnings begin next

Why can't you just use canola oil from your kitchen to kill spider mites? If you've ever wondered about the true science behind oil-based pesticides, this episode is a deep dive into the physics and chemistry of plant protection. Tad and Julie Graesch explore why oils remain one of the most effective tools for managing resistant pest populations like two-spotted spider mites and thrips. In this episode, we cover: Physical Modes of Action: How oils kill through suffocation and desiccation, and why pests can't develop resistance to being physically smothered. Mineral vs. Essential Oils: The functional differences between horticultural mineral oils (like SuffOil-X) and 25B essential oils (like Epishield). The Power of Formulation: Why inert ingredients, surfactants, and molecular weight are just as important as the active ingredients for preventing phytotoxicity. Dipping vs. Spraying: The high-stakes world of plant dips and how to target specific life stages—including the elusive thrips egg. Tank Mixing & Compatibility: Best practices for combining oils with microbials like Beauveria bassiana to create a synergistic knockdown effect. Whether you are a small-scale hobbyist or a large-scale commercial cultivator, understanding how to master oils will help you maintain a cleaner garden with fewer chemical interventions. You can order Bioworks products through KiS Organics, just reach out if you have any questions. About the Guest: Julie Graesch is the Technical Services Manager for BioWorks. With over 18 years of experience as a biological scientist, Julie has worked in laboratory, greenhouse, and field research, specializing in integrated pest management (IPM) for the horticulture industry. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The Artemis II crew has completed the most significant human spaceflight milestone since 1972 — a historic lunar flyby that took four astronauts further from Earth than any humans in history. In today's episode, Anna and Avery cover every moment of Flight Days 6 and 7, including the far-side blackout, a solar eclipse observed from beyond the Moon, and what comes next on the journey home. Plus: NASA faces another proposed 47% science budget cut, a cargo ship heads to the space station, Europe and China are about to launch a groundbreaking solar shield explorer called SMILE, and Blue Origin reveals its ambitious plan to map the Moon's hidden water ice.   Today's Stories 1. Artemis II Days 6 & 7: The Lunar Flyby •       The crew of Reid Wiseman, Victor Glover, Christina Koch, and Jeremy Hansen completed a 7-hour lunar flyby on April 6 •       Orion reached a maximum distance of 252,760 miles from Earth, surpassing the Apollo 13 record of 248,655 miles •       Closest lunar approach: 4,067 miles above the surface at approximately 7 p.m. EDT •       Christina Koch became the first woman to complete a lunar flyby •       The crew witnessed an Earthset, Earthrise, and a solar eclipse from behind the far side of the Moon •       Day 7 is a rest day; splashdown in the Pacific is targeted for April 10   2. NASA FY2027 Budget Proposal •       White House proposes $18.8 billion for NASA — a 23% overall reduction •       Science Mission Directorate would be cut by 47%, from $7.25B to $3.9B •       More than 40 missions face termination; Mars Sample Return and SERVIR named explicitly •       Exploration/Artemis funding would increase by ~10% •       Congress rejected nearly identical cuts last year   3. Cygnus NG-24 ISS Resupply •       Launch targeted April 8 from Cape Canaveral on SpaceX Falcon 9 •       Named S.S. Steven R. Nagel after four-time shuttle veteran •       Carrying 11,000+ lbs including Cold Atom Lab upgrade and stem cell research hardware •       Also includes Africa's ClimCam AI-powered climate camera from Egypt, Kenya, and Uganda   4. SMILE Mission — Launch April 9 •       Joint ESA / Chinese Academy of Sciences mission; first ever jointly designed, built, launched and operated by both agencies •       Launches April 9 on Vega-C from French Guiana; 3-year science mission •       Will give humanity its first complete, simultaneous view of Earth's magnetosphere reacting to the solar wind •       Four instruments: soft X-ray imager, UV aurora camera, light ion analyser, magnetometer •       Science orbit reaches 121,000 km above North Pole; up to 40 hours continuous observation per orbit •       Critical for understanding and predicting space weather — protecting satellites, power grids and communications   5. Blue Origin Oasis-1: Lunar Water Ice Prospecting •       Introduced at the 2026 Lunar and Planetary Science Conference (LPSC) •       Two-smallsat mission deployed from Blue Origin's uncrewed Mk1 lander; ultra-low 10x50 km polar orbit •       Instruments: neutron spectrometer (water ice to 1m depth), magnetometer (metals), multispectral imager (Helium-3) •       90-day global mapping phase followed by 10-day controlled deorbit — science continues to impact •       Partnership with Luxembourg Space Agency; data licensed commercially, non-commercial data released publicly via ESRIC •       Phase 1 of a 3-phase Project Oasis roadmap: orbit survey, surface mobility, then extraction operations   6. April Skywatching •       Comet C/2025 R3: closest approach April 27, magnitude ~8, binoculars needed •       Lyrid meteor shower peaks April 21–22, look toward Lyra from 10pm •       Mercury at best visibility of 2026 in the eastern pre-dawn sky   Links & Resources •       NASA Artemis II Flight Day 6 updates: nasa.gov •       Planetary Society Artemis II guide: planetary.org •       NASA FY2027 budget: spacenews.com •       Cygnus NG-24 launch: nasaspaceflight.com •       ESA SMILE mission: esa.int/smile •       Blue Origin Oasis-1: blueorigin.com   Connect With Us •       Website: astronomydaily.io •       Twitter/X: @AstroDailyPod •       Instagram: @AstroDailyPod •       TikTok: @AstroDailyPod •       YouTube: Astronomy Daily •       Tumblr: AstroDailyPodBecome a supporter of this podcast: https://www.spreaker.com/podcast/astronomy-daily-space-news-updates--5648921/support.Sponsor Details:Ensure your online privacy by using NordVPN. To get our special listener deal and save a lot of money, visit www.bitesz.com/nordvpn. You'll be glad you did!Become a supporter of Astronomy Daily by joining our Supporters Club. Commercial free episodes daily are only a click way... Click HereThis episode includes AI-generated content.

OpenAI locks in the largest private funding round in history, Google disrupts memory economics with a major efficiency breakthrough, and NVIDIA continues to consolidate control over AI infrastructure. This week, Patrick Moorhead and Daniel Newman unpack the clear shift from model competition to full-stack execution.

The global healthcare system is heading for a catastrophic workforce shortage — 10 to 15 million workers short by 2030. In this episode, financial analyst and Tesla expert Cern Basher (@cernbasher) returns to break down how humanoid robots like Tesla's Optimus could be the only scalable solution, why New Zealand is uniquely positioned to lead a healthcare robotics pilot, and what the Terafab chip factory and Digital Optimus mean for the timeline. From fleet learning to the privacy concerns, from Moxy robots already in 25+ US hospitals to Elon Musk's vision of billions of robots powered by space-based AI — this is the conversation that healthcare, tech, and policy leaders need to be having right now. IN THIS EPISODE: The global healthcare workforce crisis — why no recruitment drive can fix it Robots already in hospitals: Moxy's 1M+ deliveries across 25 US hospitals Tesla Optimus: where the technology is right now The three components: physical body, AI brain, and language model Fleet learning explained — 40 robots learn in 6 months what takes a human 25 years Why New Zealand is the ideal proving ground for healthcare robotics Digital Optimus and Macrohard: the software robot that runs businesses Terafab: Tesla's $25B chip factory with SpaceX and xAI This episode is sponsored by MitoSynergy Copper 1+ Most copper supplements use poorly absorbed oxidised forms that can actually increase free radical damage. MitoSynergy's patented BioCopper1 (Cunermuspir) is a copper-niacin chelate that delivers reduced copper (Cu1+) directly to your mitochondria, supporting ATP production at cytochrome c oxidase. I've been personally testing MitoActivator EX and have noticed a real difference in training power and energy output. Try it: https://mitosynergy.com/lisaTamati (10% off with this link) ABOUT CERN BASHER: NZ-born financial analyst and one of the most influential voices on X at the intersection of AI, Bitcoin, Tesla, and macroeconomics. Follow Cern: @cernbasher on X and YouTube -------------------------------------------- PTL SIGNAL — AI, Tech, Bitcoin and Markets: https://ptlsignal.com Free founding member access to our AI-powered Financial Document Analyzer — earnings transcripts, annual reports, Fed minutes analysed in 60 seconds. Take control of your health and unlock the secrets to a longer, healthier and more vibrant life: https://www.lisatamati.com/healthspan-hacks-course/ SHOP Longevity Supplements: https://shop.lisatamati.com -------------------------------------------- PODCAST — Pushing The Limits: https://www.lisatamati.com/podcast https://podcasts.apple.com/nz/podcast/pushing-the-limits/id1207975008 https://open.spotify.com/show/6mc5BfQispXYMxd4AaYXYL FOLLOW LISA: Instagram: @lisatamati X/Twitter: @lisaytamati YouTube: @LisaTamati Enquiries: support@lisatamati.com Website: www.lisatamati.com Shop: shop.lisatamati.com PTL Signal: ptlsignal.com

This week on Swimming with Allocators, Earnest and Alexa chat with Anthony Giambrone, Partner at StepStone Group. Anthony shares his unconventional path from gas station manager and nightclub worker to leading a major global venture allocation platform. The conversation covers his break into investment banking, the scaling of GreenSpring into StepStone, and why relationships, EQ, and consistency across vintages matter more than market timing in venture. Key takeaways include the power-law nature of VC returns, how emerging managers and spinouts can stand out with a real edge and long-term relationship-building, why asset quality matters more than discounts in secondaries, and how AI, liquidity pressures, and longer private company lifecycles are reshaping the next decade of venture capital. Also, Rebecca Stuart, an employment-focused partner at Sidley, explains how she helps venture-backed companies navigate complex employment and co‑founder separations, equity and vesting pitfalls, evolving worker classification and pay transparency laws, and the fast-changing regulatory landscape around AI in hiring and employment decisions. Highlights from this week's conversation include: Anthony's Background and Humble Beginnings (0:42) Importance of Empathy and Relationships in Venture (4:16) Applying Greenspring/StepStone Experience to Today's Market (6:15) StepStone Venture Team, AUM, and Global Footprint (8:07) Why You Can't Time Early Stage Venture (9:38) Vintage Volatility and Power Law in Venture Outcomes (11:23) How Founder Ambition Affects GP and Fund Diligence (14:28) Insider Segment: Co‑Founder Divorce (18:04) Using New Investments to Clean Up Equity and IP (21:43) Employees Demanding Human Review in AI‑Driven Processes (25:43) Fund Slot Constraints and LP Down‑Selection (28:33) Advice for New LPs on Capturing Upper Quartile Returns (31:36) Is Top Quartile Performance Still Good Enough? (33:08) Secondaries Strategy and Asset Quality Over Discounts (34:31) Liquidity Pressures, DPI, and GP‑Led Solutions (38:37) StepStone's 10‑Year Lifecycle Partner Vision (40:35) StepStone Group is a global private markets firm focused on providing customized investment solutions and advisory and data services to its clients worldwide. The firm's venture capital and growth equity platform, built on the foundation of Greenspring Associates, manages $25B+ in AUM across primary fund investments, secondaries, and co-investments, as of June 30, 2025. Learn more at www.stepstonegroup.com. Sidley Austin LLP is a premier global law firm with a dedicated Venture Funds practice, advising top venture capital firms, institutional investors, and private equity sponsors on fund formation, investment structuring, and regulatory compliance. With deep expertise across private markets, Sidley provides strategic legal counsel to help funds scale effectively. Learn more at sidley.com. Swimming with Allocators is a podcast that dives into the intriguing world of Venture Capital from an LP (Limited Partner) perspective. Hosts Alexa Binns and Earnest Sweat are seasoned professionals who have donned various hats in the VC ecosystem. Each episode, we explore where the future opportunities lie in the VC landscape with insights from top LPs on their investment strategies and industry experts shedding light on emerging trends and technologies.  The information provided on this podcast does not, and is not intended to, constitute legal advice; instead, all information, content, and materials available on this podcast are for general informational purposes only. Learn more about your ad choices. Visit megaphone.fm/adchoices

Welcome to The Daily Wrap Up, an in-depth investigatory show dedicated to bringing you the most relevant independent news, as we see it, from the last 24 hours (3/26/26). As always, take the information discussed in the video below and research it for yourself, and come to your own conclusions. Anyone telling you what the truth is, or claiming they have the answer, is likely leading you astray, for one reason or another. Stay Vigilant. !function(r,u,m,b,l,e){r._Rumble=b,r[b]||(r[b]=function(){(r[b]._=r[b]._||[]).push(arguments);if(r[b]._.length==1){l=u.createElement(m),e=u.getElementsByTagName(m)[0],l.async=1,l.src="https://rumble.com/embedJS/u2q643"+(arguments[1].video?'.'+arguments[1].video:'')+"/?url="+encodeURIComponent(location.href)+"&args="+encodeURIComponent(JSON.stringify([].slice.apply(arguments))),e.parentNode.insertBefore(l,e)}})}(window, document, "script", "Rumble");   Rumble("play", {"video":"v75hm4a","div":"rumble_v75hm4a"}); Source Links (In Chronological Order):  (7) The Last American Vagabond on X: "@DBrozeLiveFree One of MANY obvious and well-documented cases of Americans being illegally detained or abused by ICE. https://t.co/hhurrVqnej" / X DHS Lies About Detaining/Deporting US Citizens & Trump Reportedly Readying To Attack Venezuela New Tab (7) The Last American Vagabond on X: "@DropSiteNews I am glad you guys are covering this, it needs more attention. Here is TLAV's coverage on this story from February: https://t.co/jVJjiONeZa" / X She uncovered a terrifying lab hidden in California, with alleged ties to China - Los Angeles Times Israeli Citizen Charged In The Las Vegas "Biolab" Case As Fort Detrick Investigates Sabotage (7) Las Vegas Locally

In Episode 242 of China EVs & More, Tu Le and Lei Xing break down a pivotal week in the global EV industry — one defined by accelerating innovation, new partnerships, and intensifying competition across China, the U.S., and beyond.  XPeng reaches a major milestone with its first quarterly profit, joining NIO, Li Auto, and Leapmotor in demonstrating that China's EV startups can achieve profitability — even amid one of the most competitive markets in the world.Meanwhile, Rivian secures a $1.25 billion partnership with Uber, signaling a major push into the robotaxi ecosystem and raising questions about whether EV startups can remain viable without tapping into autonomy and mobility platforms.The hosts also dive into Xiaomi's refreshed SU7 launch, the growing wave of EV announcements ahead of the Beijing Auto Show, and how Chinese automakers continue to iterate products 2–3x faster than legacy competitors.Other key topics include:The rise of “physical AI” and next-generation autonomy platforms from XPengNVIDIA's expanding role in global AV ecosystemsThe future of robotaxis and whether margins will hold as competition growsThe coming battle for large electric SUVs in China and globallyHow Chinese EV technology is increasingly influencing global vehicle design and developmentWith Chinese OEMs scaling faster, launching more products, and expanding globally, Tu and Lei highlight a clear shift: the EV race is no longer about catching up — it's about survival and adaptation.

LISTEN TO ADS-FREE Audio of this episode at https://djamgamind.com

What does it take to scale technology across one of the world's largest marketing organizations? In this episode of Technovation, Peter High speaks with Craig Cuyar, Global CIO of Omnicom, about leading technology transformation across a $25B global enterprise. Craig shares how Omnicom is evolving from a holding company to a more centralized operating model, particularly in the wake of its acquisition of Interpublic Group. Key topics include: Managing post-acquisition integration at global scale Leveraging first-party data and proprietary platforms Using AI’s predictive models to agentic workflows, to transform marketing Balancing centralization, outsourcing, and business-aligned IT leadership

Market update for Thursday March 19, 2026Check out the Public app for incredible investing tools and to support the show (LINK)Follow us on Instagram (@TheRundownDaily) for bonus content and instant reactions.In today's episode:Fed Meeting recap: rate cut hopes fadeUber invests $1.25B* in Rivian to launch robotaxisMicron crushes earnings but stock still fallsFive Below jumps, Red Cat drops on earningsMeta shuts down the metaverse (yes, really)

(0:00) Travis Kalanick: Officially exiting stealth mode, what he's been working on (5:52) How to automate the physical world, markets to go after (11:00) Return to self-driving: Tesla, Waymo, and the autonomous race (16:17) Leaving Los Angeles for Austin, the decline of truth and justice in California (25:51) Actuators, robot hands, "Capital as a weapon," Middle East SWF impacted by Iran War (36:00) Michael Dell: Dorm room to $140B in annual revenue, why Texas attracts founders (43:46) Dell's $50B AI infrastructure bet (1:03:50) Invest America: Michael Dell's $6.25B gift - A 401k from birth for 25M kids This podcast was recorded LIVE at Arena Hall in Austin, Texas. Thanks to our partners for making this event possible!: EY: Austin vibes meet AI innovation. Thanks to EY for co‑hosting with us at #SXSW. Discover what executives are saying about AI transformation in the latest AI Pulse Survey. https://ey.com/en_us/insights/emerging-technologies/pulse-ai-survey Forge Global: We're proud to highlight our partners at Forge Global, who are helping the world's most innovative private companies and their teams gain #liquidcourage on their terms. Learn more here: https://forgeglobal.com/who-we-serve/private-companies/ De'Longhi Athena Polymarket

Astronaut time costs $130,000 per hour, yet a significant portion goes to routine maintenance and cargo logistics rather than breakthrough science. Icarus Robotics is building the robotic workforce for commercial space stations, and despite being just over a year old, secured a deployment partnership with NASA and Voyager Space for the International Space Station in 2027. In this episode, we sat down with Ethan Barajas, CEO and Co-Founder of Icarus Robotics, to understand how they positioned teleoperated robotics as the wedge into a horizontal expansion strategy spanning satellite constellation servicing, space infrastructure maintenance, and eventually cislunar operations.Topics Discussed:Why the shift from NASA-funded ISS to commercial stations fundamentally changes the economics of space laborHow optical communications via Starlink reduced latency from 800ms (S-band radio relay through GEO) to 100ms, enabling Earth-based teleoperationThe teleoperation-to-autonomy data flywheel: collecting in-distribution physics data to train high-level movement primitivesFlight Heritage constraints at NASA and why mainline robotics run on chips that stopped production in the early 2000sCollaborating with commercial station developers during design phase to embed robotic-friendly architecture (hatch tabs, fiducials for localization)Horizontal expansion thesis: ISS labor as the corpus for intelligent robotics across multi-thousand satellite constellations and space infrastructureThe biological research unlock: how Keytruda's $25B revenue between 2023-2024 resulted from ISS protein crystallization researchGTM Lessons For B2B Founders:Time market entry to structural cost shiftsStack infrastructure betsBuild the data moat earlyInfluence infrastructure design earlyFrame automation as economic inevitabilityUse distribution to attract technical talentPlan horizontal expansion early// Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.ioThe Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co//Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation.In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire's core bet: that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire's answer is to build a bi-directional interface between humans and models: read what's happening inside, edit it surgically, and eventually use interpretability during training so customization isn't just brute-force guesswork.Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models.We discuss:* Myra + Mark's path: Palantir (health systems, forward-deployed engineering) → Goodfire early team; Two Sigma → Head of Product, translating frontier interpretability research into a platform and real-world deployments* What “interpretability” actually means in practice: not just post-hoc poking, but a broader “science of deep learning” approach across the full AI lifecycle (data curation → post-training → internal representations → model design)* Why post-training is the first big wedge: “surgical edits” for unintended behaviors likereward hacking, sycophancy, noise learned during customization plus the dream of targeted unlearning and bias removal without wrecking capabilities* SAEs vs probes in the real world: why SAE feature spaces sometimes underperform classifiers trained on raw activations for downstream detection tasks (hallucination, harmful intent, PII), and what that implies about “clean concept spaces”* Rakuten in production: deploying interpretability-based token-level PII detection at inference time to prevent routing private data to downstream providers plus the gnarly constraints: no training on real customer PII, synthetic→real transfer, English + Japanese, and tokenization quirks* Why interp can be operationally cheaper than LLM-judge guardrails: probes are lightweight, low-latency, and don't require hosting a second large model in the loop* Real-time steering at frontier scale: a demo of steering Kimi K2 (~1T params) live and finding features via SAE pipelines, auto-labeling via LLMs, and toggling a “Gen-Z slang” feature across multiple layers without breaking tool use* Hallucinations as an internal signal: the case that models have latent uncertainty / “user-pleasing” circuitry you can detect and potentially mitigate more directly than black-box methods* Steering vs prompting: the emerging view that activation steering and in-context learning are more closely connected than people think, including work mapping between the two (even for jailbreak-style behaviors)* Interpretability for science: using the same tooling across domains (genomics, medical imaging, materials) to debug spurious correlations and extract new knowledge up to and including early biomarker discovery work with major partners* World models + “pixel-space” interpretability: why vision/video models make concepts easier to see, how that accelerates the feedback loop, and why robotics/world-model partners are especially interesting design partners* The north star: moving from “data in, weights out” to intentional model design where experts can impart goals and constraints directly, not just via reward signals and brute-force post-training—Goodfire AI* Website: https://goodfire.ai* LinkedIn: https://www.linkedin.com/company/goodfire-ai/* X: https://x.com/GoodfireAIMyra Deng* Website: https://myradeng.com/* LinkedIn: https://www.linkedin.com/in/myra-deng/* X: https://x.com/myra_dengMark Bissell* LinkedIn: https://www.linkedin.com/in/mark-bissell/* X: https://x.com/MarkMBissellFull Video EpisodeTimestamps00:00:00 Introduction00:00:05 Introduction to the Latent Space Podcast and Guests from Goodfire00:00:29 What is Goodfire? Mission and Focus on Interpretability00:01:01 Goodfire's Practical Approach to Interpretability00:01:37 Goodfire's Series B Fundraise Announcement00:02:04 Backgrounds of Mark and Myra from Goodfire00:02:51 Team Structure and Roles at Goodfire00:05:13 What is Interpretability? Definitions and Techniques00:05:30 Understanding Errors00:07:29 Post-training vs. Pre-training Interpretability Applications00:08:51 Using Interpretability to Remove Unwanted Behaviors00:10:09 Grokking, Double Descent, and Generalization in Models00:10:15 404 Not Found Explained00:12:06 Subliminal Learning and Hidden Biases in Models00:14:07 How Goodfire Chooses Research Directions and Projects00:15:00 Troubleshooting Errors00:16:04 Limitations of SAEs and Probes in Interpretability00:18:14 Rakuten Case Study: Production Deployment of Interpretability00:20:45 Conclusion00:21:12 Efficiency Benefits of Interpretability Techniques00:21:26 Live Demo: Real-Time Steering in a Trillion Parameter Model00:25:15 How Steering Features are Identified and Labeled00:26:51 Detecting and Mitigating Hallucinations Using Interpretability00:31:20 Equivalence of Activation Steering and Prompting00:34:06 Comparing Steering with Fine-Tuning and LoRA Techniques00:36:04 Model Design and the Future of Intentional AI Development00:38:09 Getting Started in Mechinterp: Resources, Programs, and Open Problems00:40:51 Industry Applications and the Rise of Mechinterp in Practice00:41:39 Interpretability for Code Models and Real-World Usage00:43:07 Making Steering Useful for More Than Stylistic Edits00:46:17 Applying Interpretability to Healthcare and Scientific Discovery00:49:15 Why Interpretability is Crucial in High-Stakes Domains like Healthcare00:52:03 Call for Design Partners Across Domains00:54:18 Interest in World Models and Visual Interpretability00:57:22 Sci-Fi Inspiration: Ted Chiang and Interpretability01:00:14 Interpretability, Safety, and Alignment Perspectives01:04:27 Weak-to-Strong Generalization and Future Alignment Challenges01:05:38 Final Thoughts and Hiring/Collaboration Opportunities at GoodfireTranscriptShawn Wang [00:00:05]: So welcome to the Latent Space pod. We're back in the studio with our special MechInterp co-host, Vibhu. Welcome. Mochi, Mochi's special co-host. And Mochi, the mechanistic interpretability doggo. We have with us Mark and Myra from Goodfire. Welcome. Thanks for having us on. Maybe we can sort of introduce Goodfire and then introduce you guys. How do you introduce Goodfire today?Myra Deng [00:00:29]: Yeah, it's a great question. So Goodfire, we like to say, is an AI research lab that focuses on using interpretability to understand, learn from, and design AI models. And we really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models. That's our description right now, and I'm excited to dive more into the work we're doing to make that happen.Shawn Wang [00:00:55]: Yeah. And there's always like the official description. Is there an understatement? Is there an unofficial one that sort of resonates more with a different audience?Mark Bissell [00:01:01]: Well, being an AI research lab that's focused on interpretability, there's obviously a lot of people have a lot that they think about when they think of interpretability. And I think we have a pretty broad definition of what that means and the types of places that can be applied. And in particular, applying it in production scenarios, in high stakes industries, and really taking it sort of from the research world into the real world. Which, you know. It's a new field, so that hasn't been done all that much. And we're excited about actually seeing that sort of put into practice.Shawn Wang [00:01:37]: Yeah, I would say it wasn't too long ago that Anthopic was like still putting out like toy models or superposition and that kind of stuff. And I wouldn't have pegged it to be this far along. When you and I talked at NeurIPS, you were talking a little bit about your production use cases and your customers. And then not to bury the lead, today we're also announcing the fundraise, your Series B. $150 million. $150 million at a 1.25B valuation. Congrats, Unicorn.Mark Bissell [00:02:02]: Thank you. Yeah, no, things move fast.Shawn Wang [00:02:04]: We were talking to you in December and already some big updates since then. Let's dive, I guess, into a bit of your backgrounds as well. Mark, you were at Palantir working on health stuff, which is really interesting because the Goodfire has some interesting like health use cases. I don't know how related they are in practice.Mark Bissell [00:02:22]: Yeah, not super related, but I don't know. It was helpful context to know what it's like. Just to work. Just to work with health systems and generally in that domain. Yeah.Shawn Wang [00:02:32]: And Mara, you were at Two Sigma, which actually I was also at Two Sigma back in the day. Wow, nice.Myra Deng [00:02:37]: Did we overlap at all?Shawn Wang [00:02:38]: No, this is when I was briefly a software engineer before I became a sort of developer relations person. And now you're head of product. What are your sort of respective roles, just to introduce people to like what all gets done in Goodfire?Mark Bissell [00:02:51]: Yeah, prior to Goodfire, I was at Palantir for about three years as a forward deployed engineer, now a hot term. Wasn't always that way. And as a technical lead on the health care team and at Goodfire, I'm a member of the technical staff. And honestly, that I think is about as specific as like as as I could describe myself because I've worked on a range of things. And, you know, it's it's a fun time to be at a team that's still reasonably small. I think when I joined one of the first like ten employees, now we're above 40, but still, it looks like there's always a mix of research and engineering and product and all of the above. That needs to get done. And I think everyone across the team is, you know, pretty, pretty switch hitter in the roles they do. So I think you've seen some of the stuff that I worked on related to image models, which was sort of like a research demo. More recently, I've been working on our scientific discovery team with some of our life sciences partners, but then also building out our core platform for more of like flexing some of the kind of MLE and developer skills as well.Shawn Wang [00:03:53]: Very generalist. And you also had like a very like a founding engineer type role.Myra Deng [00:03:58]: Yeah, yeah.Shawn Wang [00:03:59]: So I also started as I still am a member of technical staff, did a wide range of things from the very beginning, including like finding our office space and all of this, which is we both we both visited when you had that open house thing. It was really nice.Myra Deng [00:04:13]: Thank you. Thank you. Yeah. Plug to come visit our office.Shawn Wang [00:04:15]: It looked like it was like 200 people. It has room for 200 people. But you guys are like 10.Myra Deng [00:04:22]: For a while, it was very empty. But yeah, like like Mark, I spend. A lot of my time as as head of product, I think product is a bit of a weird role these days, but a lot of it is thinking about how do we take our frontier research and really apply it to the most important real world problems and how does that then translate into a platform that's repeatable or a product and working across, you know, the engineering and research teams to make that happen and also communicating to the world? Like, what is interpretability? What is it used for? What is it good for? Why is it so important? All of these things are part of my day-to-day as well.Shawn Wang [00:05:01]: I love like what is things because that's a very crisp like starting point for people like coming to a field. They all do a fun thing. Vibhu, why don't you want to try tackling what is interpretability and then they can correct us.Vibhu Sapra [00:05:13]: Okay, great. So I think like one, just to kick off, it's a very interesting role to be head of product, right? Because you guys, at least as a lab, you're more of an applied interp lab, right? Which is pretty different than just normal interp, like a lot of background research. But yeah. You guys actually ship an API to try these things. You have Ember, you have products around it, which not many do. Okay. What is interp? So basically you're trying to have an understanding of what's going on in model, like in the model, in the internal. So different approaches to do that. You can do probing, SAEs, transcoders, all this stuff. But basically you have an, you have a hypothesis. You have something that you want to learn about what's happening in a model internals. And then you're trying to solve that from there. You can do stuff like you can, you know, you can do activation mapping. You can try to do steering. There's a lot of stuff that you can do, but the key question is, you know, from input to output, we want to have a better understanding of what's happening and, you know, how can we, how can we adjust what's happening on the model internals? How'd I do?Mark Bissell [00:06:12]: That was really good. I think that was great. I think it's also a, it's kind of a minefield of a, if you ask 50 people who quote unquote work in interp, like what is interpretability, you'll probably get 50 different answers. And. Yeah. To some extent also like where, where good fire sits in the space. I think that we're an AI research company above all else. And interpretability is a, is a set of methods that we think are really useful and worth kind of specializing in, in order to accomplish the goals we want to accomplish. But I think we also sort of see some of the goals as even more broader as, as almost like the science of deep learning and just taking a not black box approach to kind of any part of the like AI development life cycle, whether that. That means using interp for like data curation while you're training your model or for understanding what happened during post-training or for the, you know, understanding activations and sort of internal representations, what is in there semantically. And then a lot of sort of exciting updates that were, you know, are sort of also part of the, the fundraise around bringing interpretability to training, which I don't think has been done all that much before. A lot of this stuff is sort of post-talk poking at models as opposed to. To actually using this to intentionally design them.Shawn Wang [00:07:29]: Is this post-training or pre-training or is that not a useful.Myra Deng [00:07:33]: Currently focused on post-training, but there's no reason the techniques wouldn't also work in pre-training.Shawn Wang [00:07:38]: Yeah. It seems like it would be more active, applicable post-training because basically I'm thinking like rollouts or like, you know, having different variations of a model that you can tweak with the, with your steering. Yeah.Myra Deng [00:07:50]: And I think in a lot of the news that you've seen in, in, on like Twitter or whatever, you've seen a lot of unintended. Side effects come out of post-training processes, you know, overly sycophantic models or models that exhibit strange reward hacking behavior. I think these are like extreme examples. There's also, you know, very, uh, mundane, more mundane, like enterprise use cases where, you know, they try to customize or post-train a model to do something and it learns some noise or it doesn't appropriately learn the target task. And a big question that we've always had is like, how do you use your understanding of what the model knows and what it's doing to actually guide the learning process?Shawn Wang [00:08:26]: Yeah, I mean, uh, you know, just to anchor this for people, uh, one of the biggest controversies of last year was 4.0 GlazeGate. I've never heard of GlazeGate. I didn't know that was what it was called. The other one, they called it that on the blog post and I was like, well, how did OpenAI call it? Like officially use that term. And I'm like, that's funny, but like, yeah, I guess it's the pitch that if they had worked a good fire, they wouldn't have avoided it. Like, you know what I'm saying?Myra Deng [00:08:51]: I think so. Yeah. Yeah.Mark Bissell [00:08:53]: I think that's certainly one of the use cases. I think. Yeah. Yeah. I think the reason why post-training is a place where this makes a lot of sense is a lot of what we're talking about is surgical edits. You know, you want to be able to have expert feedback, very surgically change how your model is doing, whether that is, you know, removing a certain behavior that it has. So, you know, one of the things that we've been looking at or is, is another like common area where you would want to make a somewhat surgical edit is some of the models that have say political bias. Like you look at Quen or, um, R1 and they have sort of like this CCP bias.Shawn Wang [00:09:27]: Is there a CCP vector?Mark Bissell [00:09:29]: Well, there's, there are certainly internal, yeah. Parts of the representation space where you can sort of see where that lives. Yeah. Um, and you want to kind of, you know, extract that piece out.Shawn Wang [00:09:40]: Well, I always say, you know, whenever you find a vector, a fun exercise is just like, make it very negative to see what the opposite of CCP is.Mark Bissell [00:09:47]: The super America, bald eagles flying everywhere. But yeah. So in general, like lots of post-training tasks where you'd want to be able to, to do that. Whether it's unlearning a certain behavior or, you know, some of the other kind of cases where this comes up is, are you familiar with like the, the grokking behavior? I mean, I know the machine learning term of grokking.Shawn Wang [00:10:09]: Yeah.Mark Bissell [00:10:09]: Sort of this like double descent idea of, of having a model that is able to learn a generalizing, a generalizing solution, as opposed to even if memorization of some task would suffice, you want it to learn the more general way of doing a thing. And so, you know, another. A way that you can think about having surgical access to a model's internals would be learn from this data, but learn in the right way. If there are many possible, you know, ways to, to do that. Can make interp solve the double descent problem?Shawn Wang [00:10:41]: Depends, I guess, on how you. Okay. So I, I, I viewed that double descent as a problem because then you're like, well, if the loss curves level out, then you're done, but maybe you're not done. Right. Right. But like, if you actually can interpret what is a generalizing or what you're doing. What is, what is still changing, even though the loss is not changing, then maybe you, you can actually not view it as a double descent problem. And actually you're just sort of translating the space in which you view loss and like, and then you have a smooth curve. Yeah.Mark Bissell [00:11:11]: I think that's certainly like the domain of, of problems that we're, that we're looking to get.Shawn Wang [00:11:15]: Yeah. To me, like double descent is like the biggest thing to like ML research where like, if you believe in scaling, then you don't need, you need to know where to scale. And. But if you believe in double descent, then you don't, you don't believe in anything where like anything levels off, like.Vibhu Sapra [00:11:30]: I mean, also tendentially there's like, okay, when you talk about the China vector, right. There's the subliminal learning work. It was from the anthropic fellows program where basically you can have hidden biases in a model. And as you distill down or, you know, as you train on distilled data, those biases always show up, even if like you explicitly try to not train on them. So, you know, it's just like another use case of. Okay. If we can interpret what's happening in post-training, you know, can we clear some of this? Can we even determine what's there? Because yeah, it's just like some worrying research that's out there that shows, you know, we really don't know what's going on.Mark Bissell [00:12:06]: That is. Yeah. I think that's the biggest sentiment that we're sort of hoping to tackle. Nobody knows what's going on. Right. Like subliminal learning is just an insane concept when you think about it. Right. Train a model on not even the logits, literally the output text of a bunch of random numbers. And now your model loves owls. And you see behaviors like that, that are just, they defy, they defy intuition. And, and there are mathematical explanations that you can get into, but. I mean.Shawn Wang [00:12:34]: It feels so early days. Objectively, there are a sequence of numbers that are more owl-like than others. There, there should be.Mark Bissell [00:12:40]: According to, according to certain models. Right. It's interesting. I think it only applies to models that were initialized from the same starting Z. Usually, yes.Shawn Wang [00:12:49]: But I mean, I think that's a, that's a cheat code because there's not enough compute. But like if you believe in like platonic representation, like probably it will transfer across different models as well. Oh, you think so?Mark Bissell [00:13:00]: I think of it more as a statistical artifact of models initialized from the same seed sort of. There's something that is like path dependent from that seed that might cause certain overlaps in the latent space and then sort of doing this distillation. Yeah. Like it pushes it towards having certain other tendencies.Vibhu Sapra [00:13:24]: Got it. I think there's like a bunch of these open-ended questions, right? Like you can't train in new stuff during the RL phase, right? RL only reorganizes weights and you can only do stuff that's somewhat there in your base model. You're not learning new stuff. You're just reordering chains and stuff. But okay. My broader question is when you guys work at an interp lab, how do you decide what to work on and what's kind of the thought process? Right. Because we can ramble for hours. Okay. I want to know this. I want to know that. But like, how do you concretely like, you know, what's the workflow? Okay. There's like approaches towards solving a problem, right? I can try prompting. I can look at chain of thought. I can train probes, SAEs. But how do you determine, you know, like, okay, is this going anywhere? Like, do we have set stuff? Just, you know, if you can help me with all that. Yeah.Myra Deng [00:14:07]: It's a really good question. I feel like we've always at the very beginning of the company thought about like, let's go and try to learn what isn't working in machine learning today. Whether that's talking to customers or talking to researchers at other labs, trying to understand both where the frontier is going and where things are really not falling apart today. And then developing a perspective on how we can push the frontier using interpretability methods. And so, you know, even our chief scientist, Tom, spends a lot of time talking to customers and trying to understand what real world problems are and then taking that back and trying to apply the current state of the art to those problems and then seeing where they fall down basically. And then using those failures or those shortcomings to understand what hills to climb when it comes to interpretability research. So like on the fundamental side, for instance, when we have done some work applying SAEs and probes, we've encountered, you know, some shortcomings in SAEs that we found a little bit surprising. And so have gone back to the drawing board and done work on that. And then, you know, we've done some work on better foundational interpreter models. And a lot of our team's research is focused on what is the next evolution beyond SAEs, for instance. And then when it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning. And so went back to the drawing board and we're like, how do we make that not the case and how do we improve it beyond that? And one of our researchers, Ekdeep, who just joined is actually Ekdeep and Atticus are like steering experts and have spent a lot of time trying to figure out like, what is the research that enables us to actually do this in a much more powerful, robust way? So yeah, the answer is like, look at real world problems, try to translate that into a research agenda and then like hill climb on both of those at the same time.Shawn Wang [00:16:04]: Yeah. Mark has the steering CLI demo queued up, which we're going to go into in a sec. But I always want to double click on when you drop hints, like we found some problems with SAEs. Okay. What are they? You know, and then we can go into the demo. Yeah.Myra Deng [00:16:19]: I mean, I'm curious if you have more thoughts here as well, because you've done it in the healthcare domain. But I think like, for instance, when we do things like trying to detect behaviors within models that are harmful or like behaviors that a user might not want to have in their model. So hallucinations, for instance, harmful intent, PII, all of these things. We first tried using SAE probes for a lot of these tasks. So taking the feature activation space from SAEs and then training classifiers on top of that, and then seeing how well we can detect the properties that we might want to detect in model behavior. And we've seen in many cases that probes just trained on raw activations seem to perform better than SAE probes, which is a bit surprising if you think that SAEs are actually also capturing the concepts that you would want to capture cleanly and more surgically. And so that is an interesting observation. I don't think that is like, I'm not down on SAEs at all. I think there are many, many things they're useful for, but we have definitely run into cases where I think the concept space described by SAEs is not as clean and accurate as we would expect it to be for actual like real world downstream performance metrics.Mark Bissell [00:17:34]: Fair enough. Yeah. It's the blessing and the curse of unsupervised methods where you get to peek into the AI's mind. But sometimes you wish that you saw other things when you walked inside there. Although in the PII instance, I think weren't an SAE based approach actually did prove to be the most generalizable?Myra Deng [00:17:53]: It did work well in the case that we published with Rakuten. And I think a lot of the reasons it worked well was because we had a noisier data set. And so actually the blessing of unsupervised learning is that we actually got to get more meaningful, generalizable signal from SAEs when the data was noisy. But in other cases where we've had like good data sets, it hasn't been the case.Shawn Wang [00:18:14]: And just because you named Rakuten and I don't know if we'll get it another chance, like what is the overall, like what is Rakuten's usage or production usage? Yeah.Myra Deng [00:18:25]: So they are using us to essentially guardrail and inference time monitor their language model usage and their agent usage to detect things like PII so that they don't route private user information.Myra Deng [00:18:41]: And so that's, you know, going through all of their user queries every day. And that's something that we deployed with them a few months ago. And now we are actually exploring very early partnerships, not just with Rakuten, but with other people around how we can help with potentially training and customization use cases as well. Yeah.Shawn Wang [00:19:03]: And for those who don't know, like it's Rakuten is like, I think number one or number two e-commerce store in Japan. Yes. Yeah.Mark Bissell [00:19:10]: And I think that use case actually highlights a lot of like what it looks like to deploy things in practice that you don't always think about when you're doing sort of research tasks. So when you think about some of the stuff that came up there that's more complex than your idealized version of a problem, they were encountering things like synthetic to real transfer of methods. So they couldn't train probes, classifiers, things like that on actual customer data of PII. So what they had to do is use synthetic data sets. And then hope that that transfer is out of domain to real data sets. And so we can evaluate performance on the real data sets, but not train on customer PII. So that right off the bat is like a big challenge. You have multilingual requirements. So this needed to work for both English and Japanese text. Japanese text has all sorts of quirks, including tokenization behaviors that caused lots of bugs that caused us to be pulling our hair out. And then also a lot of tasks you'll see. You might make simplifying assumptions if you're sort of treating it as like the easiest version of the problem to just sort of get like general results where maybe you say you're classifying a sentence to say, does this contain PII? But the need that Rakuten had was token level classification so that you could precisely scrub out the PII. So as we learned more about the problem, you're sort of speaking about what that looks like in practice. Yeah. A lot of assumptions end up breaking. And that was just one instance where you. A problem that seems simple right off the bat ends up being more complex as you keep diving into it.Vibhu Sapra [00:20:41]: Excellent. One of the things that's also interesting with Interp is a lot of these methods are very efficient, right? So where you're just looking at a model's internals itself compared to a separate like guardrail, LLM as a judge, a separate model. One, you have to host it. Two, there's like a whole latency. So if you use like a big model, you have a second call. Some of the work around like self detection of hallucination, it's also deployed for efficiency, right? So if you have someone like Rakuten doing it in production live, you know, that's just another thing people should consider.Mark Bissell [00:21:12]: Yeah. And something like a probe is super lightweight. Yeah. It's no extra latency really. Excellent.Shawn Wang [00:21:17]: You have the steering demos lined up. So we were just kind of see what you got. I don't, I don't actually know if this is like the latest, latest or like alpha thing.Mark Bissell [00:21:26]: No, this is a pretty hacky demo from from a presentation that someone else on the team recently gave. So this will give a sense for, for technology. So you can see the steering and action. Honestly, I think the biggest thing that this highlights is that as we've been growing as a company and taking on kind of more and more ambitious versions of interpretability related problems, a lot of that comes to scaling up in various different forms. And so here you're going to see steering on a 1 trillion parameter model. This is Kimi K2. And so it's sort of fun that in addition to the research challenges, there are engineering challenges that we're now tackling. Cause for any of this to be sort of useful in production, you need to be thinking about what it looks like when you're using these methods on frontier models as opposed to sort of like toy kind of model organisms. So yeah, this was thrown together hastily, pretty fragile behind the scenes, but I think it's quite a fun demo. So screen sharing is on. So I've got two terminal sessions pulled up here. On the left is a forked version that we have of the Kimi CLI that we've got running to point at our custom hosted Kimi model. And then on the right is a set up that will allow us to steer on certain concepts. So I should be able to chat with Kimi over here. Tell it hello. This is running locally. So the CLI is running locally, but the Kimi server is running back to the office. Well, hopefully should be, um, that's too much to run on that Mac. Yeah. I think it's, uh, it takes a full, like each 100 node. I think it's like, you can. You can run it on eight GPUs, eight 100. So, so yeah, Kimi's running. We can ask it a prompt. It's got a forked version of our, uh, of the SG line code base that we've been working on. So I'm going to tell it, Hey, this SG line code base is slow. I think there's a bug. Can you try to figure it out? There's a big code base, so it'll, it'll spend some time doing this. And then on the right here, I'm going to initialize in real time. Some steering. Let's see here.Mark Bissell [00:23:33]: searching for any. Bugs. Feature ID 43205.Shawn Wang [00:23:38]: Yeah.Mark Bissell [00:23:38]: 20, 30, 40. So let me, uh, this is basically a feature that we found that inside Kimi seems to cause it to speak in Gen Z slang. And so on the left, it's still sort of thinking normally it might take, I don't know, 15 seconds for this to kick in, but then we're going to start hopefully seeing him do this code base is massive for real. So we're going to start. We're going to start seeing Kimi transition as the steering kicks in from normal Kimi to Gen Z Kimi and both in its chain of thought and its actual outputs.Mark Bissell [00:24:19]: And interestingly, you can see, you know, it's still able to call tools, uh, and stuff. It's um, it's purely sort of it's it's demeanor. And there are other features that we found for interesting things like concision. So that's more of a practical one. You can make it more concise. Um, the types of programs, uh, programming languages that uses, but yeah, as we're seeing it come in. Pretty good. Outputs.Shawn Wang [00:24:43]: Scheduler code is actually wild.Vibhu Sapra [00:24:46]: Yo, this code is actually insane, bro.Vibhu Sapra [00:24:53]: What's the process of training in SAE on this, or, you know, how do you label features? I know you guys put out a pretty cool blog post about, um, finding this like autonomous interp. Um, something. Something about how agents for interp is different than like coding agents. I don't know while this is spewing up, but how, how do we find feature 43, two Oh five. Yeah.Mark Bissell [00:25:15]: So in this case, um, we, our platform that we've been building out for a long time now supports all the sort of classic out of the box interp techniques that you might want to have like SAE training, probing things of that kind, I'd say the techniques for like vanilla SAEs are pretty well established now where. You take your model that you're interpreting, run a whole bunch of data through it, gather activations, and then yeah, pretty straightforward pipeline to train an SAE. There are a lot of different varieties. There's top KSAEs, batch top KSAEs, um, normal ReLU SAEs. And then once you have your sparse features to your point, assigning labels to them to actually understand that this is a gen Z feature, that's actually where a lot of the kind of magic happens. Yeah. And the most basic standard technique is look at all of your d input data set examples that cause this feature to fire most highly. And then you can usually pick out a pattern. So for this feature, If I've run a diverse enough data set through my model feature 43, two Oh five. Probably tends to fire on all the tokens that sounds like gen Z slang. You know, that's the, that's the time of year to be like, Oh, I'm in this, I'm in this Um, and, um, so, you know, you could have a human go through all 43,000 concepts andVibhu Sapra [00:26:34]: And I've got to ask the basic question, you know, can we get examples where it hallucinates, pass it through, see what feature activates for hallucinations? Can I just, you know, turn hallucination down?Myra Deng [00:26:51]: Oh, wow. You really predicted a project we're already working on right now, which is detecting hallucinations using interpretability techniques. And this is interesting because hallucinations is something that's very hard to detect. And it's like a kind of a hairy problem and something that black box methods really struggle with. Whereas like Gen Z, you could always train a simple classifier to detect that hallucinations is harder. But we've seen that models internally have some... Awareness of like uncertainty or some sort of like user pleasing behavior that leads to hallucinatory behavior. And so, yeah, we have a project that's trying to detect that accurately. And then also working on mitigating the hallucinatory behavior in the model itself as well.Shawn Wang [00:27:39]: Yeah, I would say most people are still at the level of like, oh, I would just turn temperature to zero and that turns off hallucination. And I'm like, well, that's a fundamental misunderstanding of how this works. Yeah.Mark Bissell [00:27:51]: Although, so part of what I like about that question is you, there are SAE based approaches that might like help you get at that. But oftentimes the beauty of SAEs and like we said, the curse is that they're unsupervised. So when you have a behavior that you deliberately would like to remove, and that's more of like a supervised task, often it is better to use something like probes and specifically target the thing that you're interested in reducing as opposed to sort of like hoping that when you fragment the latent space, one of the vectors that pops out.Vibhu Sapra [00:28:20]: And as much as we're training an autoencoder to be sparse, we're not like for sure certain that, you know, we will get something that just correlates to hallucination. You'll probably split that up into 20 other things and who knows what they'll be.Mark Bissell [00:28:36]: Of course. Right. Yeah. So there's no sort of problems with like feature splitting and feature absorption. And then there's the off target effects, right? Ideally, you would want to be very precise where if you reduce the hallucination feature, suddenly maybe your model can't write. Creatively anymore. And maybe you don't like that, but you want to still stop it from hallucinating facts and figures.Shawn Wang [00:28:55]: Good. So Vibhu has a paper to recommend there that we'll put in the show notes. But yeah, I mean, I guess just because your demo is done, any any other things that you want to highlight or any other interesting features you want to show?Mark Bissell [00:29:07]: I don't think so. Yeah. Like I said, this is a pretty small snippet. I think the main sort of point here that I think is exciting is that there's not a whole lot of inter being applied to models quite at this scale. You know, Anthropic certainly has some some. Research and yeah, other other teams as well. But it's it's nice to see these techniques, you know, being put into practice. I think not that long ago, the idea of real time steering of a trillion parameter model would have sounded.Shawn Wang [00:29:33]: Yeah. The fact that it's real time, like you started the thing and then you edited the steering vector.Vibhu Sapra [00:29:38]: I think it's it's an interesting one TBD of what the actual like production use case would be on that, like the real time editing. It's like that's the fun part of the demo, right? You can kind of see how this could be served behind an API, right? Like, yes, you're you only have so many knobs and you can just tweak it a bit more. And I don't know how it plays in. Like people haven't done that much with like, how does this work with or without prompting? Right. How does this work with fine tuning? Like, there's a whole hype of continual learning, right? So there's just so much to see. Like, is this another parameter? Like, is it like parameter? We just kind of leave it as a default. We don't use it. So I don't know. Maybe someone here wants to put out a guide on like how to use this with prompting when to do what?Mark Bissell [00:30:18]: Oh, well, I have a paper recommendation. I think you would love from Act Deep on our team, who is an amazing researcher, just can't say enough amazing things about Act Deep. But he actually has a paper that as well as some others from the team and elsewhere that go into the essentially equivalence of activation steering and in context learning and how those are from a he thinks of everything in a cognitive neuroscience Bayesian framework, but basically how you can precisely show how. Prompting in context, learning and steering exhibit similar behaviors and even like get quantitative about the like magnitude of steering you would need to do to induce a certain amount of behavior similar to certain prompting, even for things like jailbreaks and stuff. It's a really cool paper. Are you saying steering is less powerful than prompting? More like you can almost write a formula that tells you how to convert between the two of them.Myra Deng [00:31:20]: And so like formally equivalent actually in the in the limit. Right.Mark Bissell [00:31:24]: So like one case study of this is for jailbreaks there. I don't know. Have you seen the stuff where you can do like many shot jailbreaking? You like flood the context with examples of the behavior. And the topic put out that paper.Shawn Wang [00:31:38]: A lot of people were like, yeah, we've been doing this, guys.Mark Bissell [00:31:40]: Like, yeah, what's in this in context learning and activation steering equivalence paper is you can like predict the number. Number of examples that you will need to put in there in order to jailbreak the model. That's cool. By doing steering experiments and using this sort of like equivalence mapping. That's cool. That's really cool. It's very neat. Yeah.Shawn Wang [00:32:02]: I was going to say, like, you know, I can like back rationalize that this makes sense because, you know, what context is, is basically just, you know, it updates the KV cache kind of and like and then every next token inference is still like, you know, the sheer sum of everything all the way. It's plus all the context. It's up to date. And you could, I guess, theoretically steer that with you probably replace that with your steering. The only problem is steering typically is on one layer, maybe three layers like like you did. So it's like not exactly equivalent.Mark Bissell [00:32:33]: Right, right. There's sort of you need to get precise about, yeah, like how you sort of define steering and like what how you're modeling the setup. But yeah, I've got the paper pulled up here. Belief dynamics reveal the dual nature. Yeah. The title is Belief Dynamics Reveal the Dual Nature of Incompetence. And it's an exhibition of the practical context learning and activation steering. So Eric Bigelow, Dan Urgraft on the who are doing fellowships at Goodfire, Ekt Deep's the final author there.Myra Deng [00:32:59]: I think actually to your question of like, what is the production use case of steering? I think maybe if you just think like one level beyond steering as it is today. Like imagine if you could adapt your model to be, you know, an expert legal reasoner. Like in almost real time, like very quickly. efficiently using human feedback or using like your semantic understanding of what the model knows and where it knows that behavior. I think that while it's not clear what the product is at the end of the day, it's clearly very valuable. Thinking about like what's the next interface for model customization and adaptation is a really interesting problem for us. Like we have heard a lot of people actually interested in fine-tuning an RL for open weight models in production. And so people are using things like Tinker or kind of like open source libraries to do that, but it's still very difficult to get models fine-tuned and RL'd for exactly what you want them to do unless you're an expert at model training. And so that's like something we'reShawn Wang [00:34:06]: looking into. Yeah. I never thought so. Tinker from Thinking Machines famously uses rank one LoRa. Is that basically the same as steering? Like, you know, what's the comparison there?Mark Bissell [00:34:19]: Well, so in that case, you are still applying updates to the parameters, right?Shawn Wang [00:34:25]: Yeah. You're not touching a base model. You're touching an adapter. It's kind of, yeah.Mark Bissell [00:34:30]: Right. But I guess it still is like more in parameter space then. I guess it's maybe like, are you modifying the pipes or are you modifying the water flowing through the pipes to get what you're after? Yeah. Just maybe one way.Mark Bissell [00:34:44]: I like that analogy. That's my mental map of it at least, but it gets at this idea of model design and intentional design, which is something that we're, that we're very focused on. And just the fact that like, I hope that we look back at how we're currently training models and post-training models and just think what a primitive way of doing that right now. Like there's no intentionalityShawn Wang [00:35:06]: really in... It's just data, right? The only thing in control is what data we feed in.Mark Bissell [00:35:11]: So, so Dan from Goodfire likes to use this analogy of, you know, he has a couple of young kids and he talks about like, what if I could only teach my kids how to be good people by giving them cookies or like, you know, giving them a slap on the wrist if they do something wrong, like not telling them why it was wrong or like what they should have done differently or something like that. Just figure it out. Right. Exactly. So that's RL. Yeah. Right. And, and, you know, it's sample inefficient. There's, you know, what do they say? It's like slurping feedback. It's like, slurping supervision. Right. And so you'd like to get to the point where you can have experts giving feedback to their models that are, uh, internalized and, and, you know, steering is an inference time way of sort of getting that idea. But ideally you're moving to a world whereVibhu Sapra [00:36:04]: it is much more intentional design in perpetuity for these models. Okay. This is one of the questions we asked Emmanuel from Anthropic on the podcast a few months ago. Basically the question, was you're at a research lab that does model training, foundation models, and you're on an interp team. How does it tie back? Right? Like, does this, do ideas come from the pre-training team? Do they go back? Um, you know, so for those interested, you can, you can watch that. There wasn't too much of a connect there, but it's still something, you know, it's something they want toMark Bissell [00:36:33]: push for down the line. It can be useful for all of the above. Like there are certainly post-hocVibhu Sapra [00:36:39]: use cases where it doesn't need to touch that. I think the other thing a lot of people forget is this stuff isn't too computationally expensive, right? Like I would say, if you're interested in getting into research, MechInterp is one of the most approachable fields, right? A lot of this train an essay, train a probe, this stuff, like the budget for this one, there's already a lot done. There's a lot of open source work. You guys have done some too. Um, you know,Shawn Wang [00:37:04]: There's like notebooks from the Gemini team for Neil Nanda or like, this is how you do it. Just step through the notebook.Vibhu Sapra [00:37:09]: Even if you're like, not even technical with any of this, you can still make like progress. There, you can look at different activations, but, uh, if you do want to get into training, you know, training this stuff, correct me if I'm wrong is like in the thousands of dollars, not even like, it's not that high scale. And then same with like, you know, applying it, doing it for post-training or all this stuff is fairly cheap in scale of, okay. I want to get into like model training. I don't have compute for like, you know, pre-training stuff. So it's, it's a very nice field to get into. And also there's a lot of like open questions, right? Um, some of them have to go with, okay, I want a product. I want to solve this. Like there's also just a lot of open-ended stuff that people could work on. That's interesting. Right. I don't know if you guys have any calls for like, what's open questions, what's open work that you either open collaboration with, or like, you'd just like to see solved or just, you know, for people listening that want to get into McInturk because people always talk about it. What are, what are the things they should check out? Start, of course, you know, join you guys as well. I'm sure you're hiring.Myra Deng [00:38:09]: There's a paper, I think from, was it Lee, uh, Sharky? It's open problems and, uh, it's, it's a bit of interpretability, which I recommend everyone who's interested in the field. Read. I'm just like a really comprehensive overview of what are the things that experts in the field think are the most important problems to be solved. I also think to your point, it's been really, really inspiring to see, I think a lot of young people getting interested in interpretability, actually not just young people also like scientists to have been, you know, experts in physics for many years and in biology or things like this, um, transitioning into interp, because the barrier of, of what's now interp. So it's really cool to see a number to entry is, you know, in some ways low and there's a lot of information out there and ways to get started. There's this anecdote of like professors at universities saying that all of a sudden every incoming PhD student wants to study interpretability, which was not the case a few years ago. So it just goes to show how, I guess, like exciting the field is, how fast it's moving, how quick it is to get started and things like that.Mark Bissell [00:39:10]: And also just a very welcoming community. You know, there's an open source McInturk Slack channel. There are people are always posting questions and just folks in the space are always responsive if you ask things on various forums and stuff. But yeah, the open paper, open problems paper is a really good one.Myra Deng [00:39:28]: For other people who want to get started, I think, you know, MATS is a great program. What's the acronym for? Machine Learning and Alignment Theory Scholars? It's like the...Vibhu Sapra [00:39:40]: Normally summer internship style.Myra Deng [00:39:42]: Yeah, but they've been doing it year round now. And actually a lot of our full-time staff have come through that program or gone through that program. And it's great for anyone who is transitioning into interpretability. There's a couple other fellows programs. We do one as well as Anthropic. And so those are great places to get started if anyone is interested.Mark Bissell [00:40:03]: Also, I think been seen as a research field for a very long time. But I think engineering... I think engineers are sorely wanted for interpretability as well, especially at Goodfire, but elsewhere, as it does scale up.Shawn Wang [00:40:18]: I should mention that Lee actually works with you guys, right? And in the London office and I'm adding our first ever McInturk track at AI Europe because I see this industry applications now emerging. And I'm pretty excited to, you know, help push that along. Yeah, I was looking forward to that. It'll effectively be the first industry McInturk conference. Yeah. I'm so glad you added that. You know, it's still a little bit of a bet. It's not that widespread, but I can definitely see this is the time to really get into it. We want to be early on things.Mark Bissell [00:40:51]: For sure. And I think the field understands this, right? So at ICML, I think the title of the McInturk workshop this year was actionable interpretability. And there was a lot of discussion around bringing it to various domains. Everyone's adding pragmatic, actionable, whatever.Shawn Wang [00:41:10]: It's like, okay, well, we weren't actionable before, I guess. I don't know.Vibhu Sapra [00:41:13]: And I mean, like, just, you know, being in Europe, you see the Interp room. One, like old school conferences, like, I think they had a very tiny room till they got lucky and they got it doubled. But there's definitely a lot of interest, a lot of niche research. So you see a lot of research coming out of universities, students. We covered the paper last week. It's like two unknown authors, not many citations. But, you know, you can make a lot of meaningful work there. Yeah. Yeah. Yeah.Shawn Wang [00:41:39]: Yeah. I think people haven't really mentioned this yet. It's just Interp for code. I think it's like an abnormally important field. We haven't mentioned this yet. The conspiracy theory last two years ago was when the first SAE work came out of Anthropic was they would do like, oh, we just used SAEs to turn the bad code vector down and then turn up the good code. And I think like, isn't that the dream? Like, you know, like, but basically, I guess maybe, why is it funny? Like, it's... If it was realistic, it would not be funny. It would be like, no, actually, we should do this. But it's funny because we know there's like, we feel there's some limitations to what steering can do. And I think a lot of the public image of steering is like the Gen Z stuff. Like, oh, you can make it really love the Golden Gate Bridge, or you can make it speak like Gen Z. To like be a legal reasoner seems like a huge stretch. Yeah. And I don't know if that will get there this way. Yeah.Myra Deng [00:42:36]: I think, um, I will say we are announcing. Something very soon that I will not speak too much about. Um, but I think, yeah, this is like what we've run into again and again is like, we, we don't want to be in the world where steering is only useful for like stylistic things. That's definitely not, not what we're aiming for. But I think the types of interventions that you need to do to get to things like legal reasoning, um, are much more sophisticated and require breakthroughs in, in learning algorithms. And that's, um...Shawn Wang [00:43:07]: And is this an emergent property of scale as well?Myra Deng [00:43:10]: I think so. Yeah. I mean, I think scale definitely helps. I think scale allows you to learn a lot of information and, and reduce noise across, you know, large amounts of data. But I also think we think that there's ways to do things much more effectively, um, even, even at scale. So like actually learning exactly what you want from the data and not learning things that you do that you don't want exhibited in the data. So we're not like anti-scale, but we are also realizing that scale is not going to get us anywhere. It's not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed in all these sorts of like mission critical contexts. Current life cycle of training and deploying and evaluations is, is to us like deeply broken and has opportunities to, to improve. So, um, more to come on that very, very soon.Mark Bissell [00:44:02]: And I think that that's a use basically, or maybe just like a proof point that these concepts do exist. Like if you can manipulate them in the precise best way, you can get the ideal combination of them that you desire. And steering is maybe the most coarse grained sort of peek at what that looks like. But I think it's evocative of what you could do if you had total surgical control over every concept, every parameter. Yeah, exactly.Myra Deng [00:44:30]: There were like bad code features. I've got it pulled up.Vibhu Sapra [00:44:33]: Yeah. Just coincidentally, as you guys are talking.Shawn Wang [00:44:35]: This is like, this is exactly.Vibhu Sapra [00:44:38]: There's like specifically a code error feature that activates and they show, you know, it's not, it's not typo detection. It's like, it's, it's typos in code. It's not typical typos. And, you know, you can, you can see it clearly activates where there's something wrong in code. And they have like malicious code, code error. They have a whole bunch of sub, you know, sub broken down little grain features. Yeah.Shawn Wang [00:45:02]: Yeah. So, so the, the rough intuition for me, the, why I talked about post-training was that, well, you just, you know, have a few different rollouts with all these things turned off and on and whatever. And then, you know, you can, that's, that's synthetic data you can kind of post-train on. Yeah.Vibhu Sapra [00:45:13]: And I think we make it sound easier than it is just saying, you know, they do the real hard work.Myra Deng [00:45:19]: I mean, you guys, you guys have the right idea. Exactly. Yeah. We replicated a lot of these features in, in our Lama models as well. I remember there was like.Vibhu Sapra [00:45:26]: And I think a lot of this stuff is open, right? Like, yeah, you guys opened yours. DeepMind has opened a lot of essays on Gemma. Even Anthropic has opened a lot of this. There's, there's a lot of resources that, you know, we can probably share of people that want to get involved.Shawn Wang [00:45:41]: Yeah. And special shout out to like Neuronpedia as well. Yes. Like, yeah, amazing piece of work to visualize those things.Myra Deng [00:45:49]: Yeah, exactly.Shawn Wang [00:45:50]: I guess I wanted to pivot a little bit on, onto the healthcare side, because I think that's a big use case for you guys. We haven't really talked about it yet. This is a bit of a crossover for me because we are, we are, we do have a separate science pod that we're starting up for AI, for AI for science, just because like, it's such a huge investment category and also I'm like less qualified to do it, but we actually have bio PhDs to cover that, which is great, but I need to just kind of recover, recap your work, maybe on the evil two stuff, but then, and then building forward.Mark Bissell [00:46:17]: Yeah, for sure. And maybe to frame up the conversation, I think another kind of interesting just lens on interpretability in general is a lot of the techniques that were described. are ways to solve the AI human interface problem. And it's sort of like bidirectional communication is the goal there. So what we've been talking about with intentional design of models and, you know, steering, but also more advanced techniques is having humans impart our desires and control into models and over models. And the reverse is also very interesting, especially as you get to superhuman models, whether that's narrow superintelligence, like these scientific models that work on genomics, data, medical imaging, things like that. But down the line, you know, superintelligence of other forms as well. What knowledge can the AIs teach us as sort of that, that the other direction in that? And so some of our life science work to date has been getting at exactly that question, which is, well, some of it does look like debugging these various life sciences models, understanding if they're actually performing well, on tasks, or if they're picking up on spurious correlations, for instance, genomics models, you would like to know whether they are sort of focusing on the biologically relevant things that you care about, or if it's using some simpler correlate, like the ancestry of the person that it's looking at. But then also in the instances where they are superhuman, and maybe they are understanding elements of the human genome that we don't have names for or specific, you know, yeah, discoveries that they've made that that we don't know about, that's, that's a big goal. And so we're already seeing that, right, we are partnered with organizations like Mayo Clinic, leading research health system in the United States, our Institute, as well as a startup called Prima Menta, which focuses on neurodegenerative disease. And in our partnership with them, we've used foundation models, they've been training and applied our interpretability techniques to find novel biomarkers for Alzheimer's disease. So I think this is just the tip of the iceberg. But it's, that's like a flavor of some of the things that we're working on.Shawn Wang [00:48:36]: Yeah, I think that's really fantastic. Obviously, we did the Chad Zuckerberg pod last year as well. And like, there's a plethora of these models coming out, because there's so much potential and research. And it's like, very interesting how it's basically the same as language models, but just with a different underlying data set. But it's like, it's the same exact techniques. Like, there's no change, basically.Mark Bissell [00:48:59]: Yeah. Well, and even in like other domains, right? Like, you know, robotics, I know, like a lot of the companies just use Gemma as like the like backbone, and then they like make it into a VLA that like takes these actions. It's, it's, it's transformers all the way down. So yeah.Vibhu Sapra [00:49:15]: Like we have Med Gemma now, right? Like this week, even there was Med Gemma 1.5. And they're training it on this stuff, like 3d scans, medical domain knowledge, and all that stuff, too. So there's a push from both sides. But I think the thing that, you know, one of the things about McInturpp is like, you're a little bit more cautious in some domains, right? So healthcare, mainly being one, like guardrails, understanding, you know, we're more risk adverse to something going wrong there. So even just from a basic understanding, like, if we're trusting these systems to make claims, we want to know why and what's going on.Myra Deng [00:49:51]: Yeah, I think there's totally a kind of like deployment bottleneck to actually using. foundation models for real patient usage or things like that. Like, say you're using a model for rare disease prediction, you probably want some explanation as to why your model predicted a certain outcome, and an interpretable explanation at that. So that's definitely a use case. But I also think like, being able to extract scientific information that no human knows to accelerate drug discovery and disease treatment and things like that actually is a really, really big unlock for science, like scientific discovery. And you've seen a lot of startups, like say that they're going to accelerate scientific discovery. And I feel like we actually are doing that through our interp techniques. And kind of like, almost by accident, like, I think we got reached out to very, very early on from these healthcare institutions. And none of us had healthcare.Shawn Wang [00:50:49]: How did they even hear of you? A podcast.Myra Deng [00:50:51]: Oh, okay. Yeah, podcast.Vibhu Sapra [00:50:53]: Okay, well, now's that time, you know.Myra Deng [00:50:55]: Everyone can call us.Shawn Wang [00:50:56]: Podcasts are the most important thing. Everyone should listen to podcasts.Myra Deng [00:50:59]: Yeah, they reached out. They were like, you know, we have these really smart models that we've trained, and we want to know what they're doing. And we were like, really early that time, like three months old, and it was a few of us. And we were like, oh, my God, we've never used these models. Let's figure it out. But it's also like, great proof that interp techniques scale pretty well across domains. We didn't really have to learn too much about.Shawn Wang [00:51:21]: Interp is a machine learning technique, machine learning skills everywhere, right? Yeah. And it's obviously, it's just like a general insight. Yeah. Probably to finance too, I think, which would be fun for our history. I don't know if you have anything to say there.Mark Bissell [00:51:34]: Yeah, well, just across the science. Like, we've also done work on material science. Yeah, it really runs the gamut.Vibhu Sapra [00:51:40]: Yeah. Awesome. And, you know, for those that should reach out, like, you're obviously experts in this, but like, is there a call out for people that you're looking to partner with, design partners, people to use your stuff outside of just, you know, the general developer that wants to. Plug and play steering stuff, like on the research side more so, like, are there ideal design partners, customers, stuff like that?Myra Deng [00:52:03]: Yeah, I can talk about maybe non-life sciences, and then I'm curious to hear from you on the life sciences side. But we're looking for design partners across many domains, language, anyone who's customizing language models or trying to push the frontier of code or reasoning models is really interesting to us. And then also interested in the frontier of modeling. There's a lot of models that work in, like, pixel space, as we call it. So if you're doing world models, video models, even robotics, where there's not a very clean natural language interface to interact with, I think we think that Interp can really help and are looking for a few partners in that space.Shawn Wang [00:52:43]: Just because you mentioned the keyword

Send us a textInvest in pre-IPO stocks with AG Dillon & Co. Contact aaron.dillon@agdillon.com to learn more. Financial advisors only. www.agdillon.com00:00 - Intro00:07 - SpaceX Acquires xAI at a $1.25T Combined Valuation01:39 - SpaceX Seeks Early Index Inclusion Once Public02:50 - SpaceX Files for 1M AI Data-Center Satellites03:37 - Anthropic Lines Up a $350B Employee Tender04:16 - Anthropic Opus 4.6 Ships With 1,000,000 Tokens and “Agent Teams” for Parallel Work04:53 - Goldman Uses Anthropic's Claude for AI Agents05:49 - Waymo Nears a $16B Round at $110B as Alphabet Writes Most of the Check06:34 - Cerebras Jumps to $23B Post-Money on a $1B Raise and a 750MW OpenAI Compute Deal07:15 - ElevenLabs Raises $500M at $11B and Targets a 2x ARR Step-Up07:59 - Clay's New Employee Tender - $5B Tender After ARR Hits $100M08:52 - Lotus Health Raises $35M to Build a Free AI Primary-Care Practice Across All 50 States09:46 - Goodfire Raises $150M at $1.25B to Make Black-Box Models Debuggable10:42 - Accrual Raises $75M to Deliver AI to Slower-Adopting Industries/Sectors11:28 - Fundamental Emerges With $255M and a $1.2B Valuation to Own Structured Data AI12:33 - OpenAI Launches Standalone Coding App 13:29 - OpenAI Frontier Pitches the Enterprise Agent Control Plane as B2B Revenue Targets 50% of Total Rev

When an AI agent makes a decision that costs your company millions in a lawsuit, who do you fire?Agility requires both the speed to adopt new technologies like AI agents, as well as the foresight to build the guardrails that prevent that speed from driving your brand off a cliff.Today, we're going to talk about the hidden crisis brewing behind the AI revolution: the accountability gap. As companies race to replace roles with autonomous AI agents, a critical question is being ignored: when an agent makes a biased, unethical, or simply wrong decision that harms a customer or an employee, who is actually responsible? This isn't a future problem; it's happening right now, and it poses a massive threat to brand trust, customer relationships, and legal standing.To help me discuss this topic, I'd like to welcome, Albert Castellana, Co-Founder & CEO at GenLayer. About Albert CastellanaAlbert Castellana is Co-Founder & CEO at GenLayer. A serial crypto entrepreneur since 2013, Albert has co-founded and led major blockchain projects including Radix DLT, NEM.io, BadgerDAO, and StakeHound, reaching over $25B in combined market value. Albert brings extensive experience in decentralized finance and governance. Albert's leadership is driven by firsthand insight into how existing legal systems fall short for digital assets, fueling his passion to create a trustless, global arbitration layer. Albert Castellana on LinkedIn: https://www.linkedin.com/in/acastellana/ Resources GenLayer: https://www.genlayer.comTake your personal data back with Incogni! Use code AGILE at the link below and get 60% off an annual plan: ⁠https://incogni.com/agile⁠  The Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://www.teksystems.com/versionnextnow Drive your customers to new horizons at the premier retail event of the year for Retail and Brand marketers. Learn more at CRMC 2026, June 1-3. https://www.thecrmc.com/ Enjoyed the show? Tell us more at and give us a rating so others can find the show at: https://ratethispodcast.com/agileConnect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstromDon't miss a thing: get the latest episodes, sign up for our newsletter and more: https://www.theagilebrand.showCheck out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.com The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company Hosted on Acast. See acast.com/privacy for more information.

Interviewing Larry Goldberg (aka Tesla Larry) about SpaceX's upcoming IPO. We discuss the company's proposed $1.5T valuation and if thats over or under valued. SpaceX is currently operating it's launch and Starlink businesses at a ~$25B revenue run-rate, Larry believes the new V3 Starlink satellites could expand this significantly. Datacenters in space are coming, but may not add to the bottom line for another 4 or 5 years. And everything hinges on the success of Starship to enable these new businesses. 0:00 SpaceX IPO at $1.5T Valuation2:49 Starship Enables New Businesses4:08 Starlink's Military Potential & Strategic Value5:54 New Satellites From Starlink Are Gamechangers7:25 AI Datacenters In Space11:48 Elon Musk's Focus on Tesla's AI Chips13:09 When Does SpaceX Profit From Datacenters in Space14:17 Will Datacenters In Space Work?16:33 Everything Relies On Starship's Success18:48 SpaceX IPO: Under or Overpriced?Tesla Larry on X: https://x.com/TeslaLarryMy X:   / gfilche  HyperChange Patreon :)   / hyperchange   Disclaimer: Tesla Larry and I are long Tesla and SpaceX stock, this show is not financial advice.

SUBSCRIBE TO JORDAN'S FREE NEWSLETTER. PEACE TALKS: Want Jordan's advice on how to navigate relationships amid the polarizing political climate? ⁠SUBMIT YOUR DILEMMA HERE⁠. Get the facts, without the spin. UNBIASED offers a clear, impartial recap of US news, including politics, elections, legal news, and more. Hosted by lawyer Jordan Berman, each episode provides a recap of current political events plus breakdowns of complex concepts—like constitutional rights, recent Supreme Court rulings, and new legislation—in an easy-to-understand way. No personal opinions, just the facts you need to stay informed on the daily news that matters. If you miss how journalism used to be, you're in the right place. In today's episode: What We Know About the Follow-Up Strike on the Alleged Drug Boat in the Caribbean (1:12) Trump Threatens to Void All Biden Actions Signed With Autopen, But Can He? (13:42) ICE to Target Somali Migrants in Minnesota Amid Accusations of Fraud; Here's What We Know (~21:27) White House Launches New 'Media Bias' Webpage (~44:13) Quick Hitters: Dell Family Donates $6.25B to Trump Accounts, New DoD Inspector General Report on Hegseth's Signal Chat, Trump Pardons Democratic Representative (~47:29) Rumor Has It: Did the DOJ Spend Nearly $1M in Overtime Pay for Agents to Redact Epstein Files? Does Kamala Harris Want the Voting Age Lowered to 16? (~50:02) Critical Thinking Segment (~53:01) SUBSCRIBE TO JORDAN'S FREE NEWSLETTER. Watch this episode on YouTube. Follow Jordan on Instagram and TikTok. All sources for this episode can be found here.  Learn more about your ad choices. Visit podcastchoices.com/adchoices

The real scandal in the Hegseth narco-terrorist strikes? Democrats are more furious about U.S. strikes on drug boats poisoning America than an Afghan national ambushing National Guardsmen in D.C. – exposing their depravity. Stefanik blasts Speaker Johnson for blocking deep-state reforms; Trump teases 2028 bench including Vance and Rubio; DOT threatens Minnesota's highway funds over fake Somali trucker licenses; Minneapolis mayor vows to shield Somali fraudsters from feds; Michael and Susan Dell pledge $6.25B to seed 25M kids' Trump investment accounts; Colorado dad heroically shoves armed burglar down stairs to protect sleeping children; and Scott Jennings nails why 2026 must laser-focus on domestic wins to avoid leftist court-packing nightmare.   The AM Update, Aaron McIntire, Pete Hegseth narco strikes, Afghan ambush, Somali fraud Minnesota, Trump accounts children, Michael Dell donation, home invasion Colorado, Elise Stefanik Mike Johnson, Scott Jennings 2026, deep state reform, Steve Deace Show

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