POPULARITY
Categories
56 episodes with 7B
21 episodes with 7B
9 episodes with 7B
13 episodes with 7B
13 episodes with 7B
13 episodes with 7B
7 episodes with 7B
8 episodes with 7B
10 episodes with 7B
7 episodes with 7B
5 episodes with 7B
6 episodes with 7B
13 episodes with 7B
19 episodes with 7B
6 episodes with 7B
14 episodes with 7B
8 episodes with 7B
9 episodes with 7B
6 episodes with 7B
4 episodes with 7B
18 episodes with 7B
6 episodes with 7B
12 episodes with 7B
4 episodes with 7B
10 episodes with 7B
6 episodes with 7B
2 episodes with 7B
8 episodes with 7B
4 episodes with 7B
3 episodes with 7B
5 episodes with 7B
4 episodes with 7B
4 episodes with 7B
3 episodes with 7B
6 episodes with 7B
8 episodes with 7B
3 episodes with 7B
3 episodes with 7B
9 episodes with 7B
4 episodes with 7B
12 episodes with 7B
3 episodes with 7B
3 episodes with 7B
4 episodes with 7B
6 episodes with 7B
7 episodes with 7B
6 episodes with 7B
8 episodes with 7B
3 episodes with 7B
7 episodes with 7B
4 episodes with 7B
9 episodes with 7B
8 episodes with 7B
3 episodes with 7B
Tuuliki Peil on hüpnoteraapia praktik, kunstnik ja pikaajaline terapeutilise paastumise entusiast, kelle huvi inimese alateadvuse ja sisemaailma vastu on saatnud teda kogu elu. Tema teekond on ühendanud kunsti, filosoofia, psühholoogia, vaimsed praktikad ja hüpnoteraapia, et aidata inimestel jõuda püsivate muutusteni otse alateadvuse tasandil. Paastumine on olnud tema elu oluline osa juba aastakümneid ning ta usub, et teadlikult juhendatud paast võib toetada nii füüsilist tervist kui ka vaimset selgust. Riita Jaup on Shindo juhendaja, Lomi Lomi massaaži õpetaja ja kehatöö praktik, kelle missiooniks on aidata inimestel jõuda suurema elususe, eheduse ja kontaktini iseendaga. Tema teekond on viinud läbi erinevate kehatöö, hingamise, šamanistlike praktikate ja toitumise teemade kuni paastumiseni, millest on saanud tema jaoks üks terviklikumaid viise inimese tervise ja sisemise tasakaalu toetamisel. Koos Tuuliki Peiliga loob ta teadlikku paastulaagrit, kus kohtuvad turvaline juhendamine, kehatöö ja kogukonna tugi. Osta pileti paastulaagrisse siit: https://www.facebook.com/events/1485972836352945/?acontext=%7B%22event_action_history%22%3A[%7B%22surface%22%3A%22search%22%7D%2C%7B%22mechanism%22%3A%22attachment%22%2C%22surface%22%3A%22newsfeed%22%7D]%2C%22ref_notif_type%22%3Anull%7D --- SHOWNOTES "Paast on selline imeline tööriist, mis ükskõik mis su probleem on." "See ei ole mingi piirang, see on selline vabadus, mida sa lubad endale teha." "Paastuga sa ei saa endal midagi viga teha. Sa ei saa ennast ära tappa." "Inimesed kardavad muutust üleüldiselt." "Toit on üks kõige suuremaid inimeste tüliajajaid." "Kui valik on su sees päriselt toimunud, on pühendumus palju lihtsam tulema." "Miks ma üldse olen kunagi elus pidanud sööma, kui ma olen juba 20 päeva söömata ja mitte midagi ei tunne?" "See paastuprotsess lihtsalt pühib ära." "Ma lihtsalt ikkagi elan tänases ja elan ka nagu homses." "Väga ohtlik on tulla valesti paastust välja." "Me ei ole loodud siia ellu selleks, et lihtsalt tarbida." "Paast võiks olla terviklik tööriist, kuidas saadagi kontakt päriselt iseendaga, enda jõu ja väega." "Kui sa kahtled, siis parem ära tee." "Sa oled ise oma paastumullis." "See ei ole selline asi, et ma piinan ennast." "Elu üldse ei liikunud." "Mul on neli päeva, viis päeva, kuus päeva, seitse päeva ainult vee peal. Ma sain sellega kõik hakkama." "Meil on need teised kehad, mida me ei näe." "See toit ei ole lihtsalt toit — ma söön selleks, et ennast rahustada." "Läksin paastulaagrisse ja tee peal ikka Statoilist väike kohv ja muffin sisse — see viimane pidusöök." ---
The Twenty Minute VC: Venture Capital | Startup Funding | The Pitch
AGENDA: 05:00 Jensen's Open-Weights Letter Puts Anthropic on an Island 10:00 OpenAI's Model Targets Hugging Face in a Cyber-Security Scare 18:00 America's Open-Model Push Accelerates as China Looms Large 30:00 Etched Raises $300M to Take on Nvidia 34:00 Google Cloud Grows 82%—So Why Did the Market Panic? 41:00 Travis Kalanick Raises $1.7B for Atoms and Physical AI 51:00 Francisco Partners Raises $21B: Can Private Equity Still Save SaaS? 1:00:00 Mark Pincus Says Quit When It's Too Hard—Jason Lemkin Erupts 1:07:00 Stripe vs Revolut: Which $100B+ Fintech Would You Own?
Brands used to pay influencers thousands for a single post. Now they're paying regular, everyday people $500+ per video — filmed on a phone, from home, with zero following required. Here's why.✅ Join my free live masterclass:https://ugcmasteryacademy.com/webinarsignup?funnel=Youtube&utm_source=youtube&utm_medium=organic&utm_campaign=sydney&utm_content=s09-why-brands-pay-everyday-people-500&utm_term=long-form
Dan Nathan and Guy Adami break down a wild week across markets — memory stocks in freefall, Google's brutal two-day selloff, and why China's AI models are closing the gap without top-tier Nvidia chips. Micron cratered from its all-time high of $1,255 to $800 in a matter of weeks, and the DRAM ETF whipsawed right along with it. Guy and Dan dig into whether this is a routine flush or something bigger. Google sold off 10% after Gemini news broke the hyperscaler rotation narrative, and they debate whether the moat still holds given decelerating margins and ballooning capex. On the China front: DeepSeek raised $7B at a $52B valuation, Alibaba announced $50B in capex, and Moonshot's new model is narrowing the gap with US labs — all without access to Nvidia's most advanced chips. Dan and Guy also revisit the Nvidia-vs-component-suppliers margin debate, unpack Apple's surprising rally into earnings, and call the technical line in the sand on Netflix after its cash flow miss cut the stock in half. Plus: a quick check on crude, refiners hitting new highs, and what's next for energy names like Exxon and ConocoPhillips. Show Notes Google Gemini Launch Delayed as Tech Falls Short of Internal Goals (Bloomberg) Chinese Models vs. Frontier Models (The Daily Spark) China's Moonshot Unveils AI Model That Narrows Gap With US Firms (Bloomberg) —FOLLOW USYouTube: @RiskReversalMediaInstagram: @riskreversalmediaTwitter: @RiskReversalLinkedIn: RiskReversal Media The financial opinions expressed in Risk Reversal content are for information purposes only. The opinions expressed by the hosts and participants are not an attempt to influence specific trading behavior, investments, or strategies. Past performance does not necessarily predict future outcomes. No specific results or profits are assured when relying on Risk Reversal. Before making any investment or trade, evaluate its suitability for your circumstances and consider consulting your own financial or investment advisor. The financial products discussed in Risk Reversal carry a high level of risk and may not be appropriate for many investors. If you have uncertainties, it's advisable to seek professional advice. Remember that trading involves a risk to your capital, so only invest money that you can afford to lose. Derivatives are not suitable for all investors and involve the risk of losing more than the amount originally deposited and any profit you might have made. This communication is not a recommendation or offer to buy, sell or retain any specific investment or service.
Trevor McGregor lost his parents' home on one bad bet. Then he helped a client scale to $2.7B.Trevor McGregor spent over five years coaching for Tony Robbins, has invested in real estate for two decades, and has helped more than 45,000 people work through their business and their portfolios. Before any of that, he lost his family's home on a failed business bet and had to rebuild from nothing. In this episode, Trevor breaks down the mindset framework he now teaches investors, why most people jump straight to strategy and stay stuck, and how one client went from four single family homes to $2.7 billion in assets under management. If you feel capped on income, deals, or momentum, this episode is the mindset work underneath the strategy.Key topics:The 4S framework: state, story, standards, and strategyHow one client scaled from four homes to $2.7B in AUMWhy high performers have a "tax problem" real estate solvesThe three positions every investor sits in: maintenance, growth, or scaleFinding your zone of genius and building the right team around itGuest bio:Trevor McGregor is a high performance master platinum coach who spent over five years coaching for Tony Robbins. He's invested in real estate for two decades and has helped more than 45,000 people navigate their business, real estate, and franchise decisions. His book, Rich Beyond Belief, releases in August.Links:Trevor McGregor: trevormcgregor.com
Last week Christian called Vista's rumored bid for Criteo "cheap" and left it at a throwaway line: three to four times.. what? A few people texted him afterward and said he could have done better; he agreed.So this week, solo from an undisclosed location while Ayelet celebrates her 30th in LA, Christian goes deep. A full side-by-side of Criteo and LiveRamp, a walkthrough of why the multiple gap between them makes almost no sense on the financials, and a concrete bull case: pay $58 a share, a 200%+ premium, then run an M&A play to build the agentic commerce OS for brands and retail.The thesis isn't buy it cheap. It's buy it decisively.What we cover: Who actually leaked the Vista story (and why Criteo's repeated phantom-deal leaks are a comms problem), the Criteo vs. LiveRamp side-by-side on revenue growth, revenue mix, EBITDA, and free cash flow, why LiveRamp's 107% net retention is at real risk once Publicis owns it, why Criteo's transactional model might be the safer bet in an agentic era where subscription pricing is under fire, the AI option value nobody's pricing in, and three specific M&A targets that would fix Criteo's biggest gap: no Amazon, no Walmart.Plus two deals worth flagging: Podean's fifth acquisition (Social Commerce Club) and Brunner buying AdSkate.⏱️ TIMESTAMPS0:26 — Solo episode, life changes, and happy 30th to Ayelet 0:50 — Why we're revisiting Criteo/Vista: "you really could have done better" 1:30 — The backstory: Bloomberg, Reuters, and a 50% premium at ~$3.7B implied 2:00 — Who leaked it? Why back channels point at Criteo, not Vista 2:30 — Criteo's leak engine: Microsoft, Walmart, Skai — deals that never materialized 3:00 — The headline thesis: pay 2.5x revenue ex-TAC, then run an M&A play 4:00 — Side-by-side setup: Criteo vs. LiveRamp 4:30 — Revenue growth: LiveRamp at 9%, Criteo at 1% (and why that's misleading) 5:15 — Growth quality: the Roundel and Uber Eats churn, and 16% underlying retail media growth 5:45 — Why LiveRamp's 107% net retention is at risk under Publicis ownership 6:30 — Revenue type: true SaaS vs. transactional media economics 7:00 — Why subscription models are under fire in the agentic era 7:45 — EBITDA: Criteo at $407M vs. LiveRamp at $185M, at a quarter of the multiple 8:30 — Free cash flow: both are cash compounders with clean balance sheets 9:15 — Strategic buyers pay up, financial buyers don't — but Vista usually pays 10-20x 9:45 — The AI option value nobody's pricing: OpenAI's ChatGPT ads pilot, 2x AI-referred conversions 10:30 — The real asset: 4,100 brands, 225 retail media networks, $1B in quarterly activated spend 11:15 — The bull case: $58/share, $2.9B equity value, a 203% premium 12:00 — Why no board can responsibly ignore an offer like this 12:30 — M&A target #1: Skai — solves Amazon and Walmart, and they already know each other 13:30 — M&A target #2: Pacvue (Advent) — Amazon, Walmart, Instacart muscle (and the Helium 10 problem) 14:15 — Why The Trade Desk's April integrations create urgency 14:45 — M&A target #3: digital shelf analytics — and the Profitero/Publicis precedent 16:00 — The Christian math, summarized 17:00 — Deal hit: Podean acquires Social Commerce Club (deal #5) 17:45 — Deal hit: Brunner acquires AdScape — creative intelligence as an AI play 18:30 — Why more deals are moving to our Substack, and what's coming next
During Episode 36 of Biotalk, Geoff Meyerson, CEO of Locust Walk, unpacks our 2026 Q2 Report: Global Trends in Biopharma Transactions, covering capital markets, strategic deals, and regional trends. Market Overview: 2026 Q2 confirmed that 2025's momentum was durable: strategic activity stayed exceptionally strong, public markets kept reopening, and Europe's venture market rebounded sharply. China cemented its lead in global licensing while the U.S. dominated M&A and public markets. Strategic Transactions: Licensing held near record levels at ~$72B across 43 deals, with average deal size reaching a record ~$1.7B as pharma concentrated capital on fewer, earlier-stage assets. China drove ~60% of first-half value, anchored by the $15B+ Bristol Myers Squibb–Hengrui collaboration, even as upfronts fell to just ~5% of deal value. M&A hit a record ~$80B across 34 transactions, up 222% year-over-year, with U.S. sellers accounting for ~93% of value and oncology leading at ~40%. Capital Markets: U.S. markets kept reopening: seven IPOs raised ~$3.1B (a five-year high), venture reached ~$4.5B, and layoffs fell to a three-year low. Europe's venture market rebounded more than fivefold to ~$3.3B, led by Isomorphic Labs' $2.1B Series B. Outlook: Dealmaking is strong and markets are opening, but structures will keep favoring milestone-heavy economics. China's licensing dominance and U.S. leadership in M&A and financings will shape strategy through the rest of 2026.
49% of Americans under 30 live with their folks… But now it's a financial flex #StayAtHomeSonDriscoll's invented the year-round fresh berry… But this $7B berry brand doesn't grow berries.The US gave Ukraine instructions to DIY Patriot Missiles… It's the Ikea-fication of Defense.Plus, the hot new bachelorette party trend is… The 1-night local blowout.$LMT $FDP $SPYGrab your Tickets to the IPO Tour: Our In-Person OfferingSan Francisco 9/23: https://www.ticketmaster.com/event/1C0064AFB5F688BDBoston 10/14: https://tickets.citywinery.com/event/tboy-the-ipo-tour-in-person-offering-8cdhupSeattle 11/4 (21+): https://www.axs.com/events/1446394/the-best-one-yet-ticketsNEWSLETTER:https://tboypod.com/newsletter OUR 2ND SHOW:Want more business storytelling from us? Check our weekly deepdive show, The Best Idea Yet: The untold origin story of the products you're obsessed with. Listen for free to The Best Idea Yet: https://wondery.com/links/the-best-idea-yet/NEW LISTENERSFill out our 2 minute survey: https://qualtricsxm88y5r986q.qualtrics.com/jfe/form/SV_dp1FDYiJgt6lHy6GET ON THE POD: Submit a shoutout or fact: https://tboypod.com/shoutouts SOCIALS:Instagram: https://www.instagram.com/tboypod TikTok: https://www.tiktok.com/@tboypodYouTube: https://www.youtube.com/@tboypod Linkedin (Nick): https://www.linkedin.com/in/nicolas-martell/Linkedin (Jack): https://www.linkedin.com/in/jack-crivici-kramer/Anything else: https://tboypod.com/ About Us: The daily pop-biz news show making today's top stories your business. Formerly known as Robinhood Snacks, The Best One Yet is hosted by Jack Crivici-Kramer & Nick Martell. Hosted on Acast. See acast.com/privacy for more information.
Eight years ago, a broken heart and a bar called Sugars turned into the GZ Chop Shop. This week, Greg, Ty, and Uly celebrate 8 years on air by diving into PlayStation's plan to kill physical games in 2028, the billion-dollar UK lawsuit that could reshape the PlayStation Store, dynamic pricing, Xbox's leadership shakeup, and a real console-wars history lesson — plus the origin story of the show itself.KEY TAKEAWAYSSony is ending physical disc production in January 2028 — every future PlayStation game will be digital-only, which strips away the one built-in price comparison consumers had against the PlayStation Store.Sony faces a UK monopoly lawsuit (reported near $2.7B) alleging the PlayStation Store's 30% commission and lack of retail competition make digital prices "excessive and unfair" — on top of an already-settled $7.85M U.S. case over lost voucher competition.Dynamic pricing is coming to PlayStation Store — the same game could cost you more or less depending on your purchase history and genre preferences, with no outside storefront to compare against once physical is gone."Owning" a digital license isn't the same as owning a game — Yakuza Zero and the Kingdom Hearts All-in-One collection have both been delisted from PS Store, and physical PS Vita purchases have a hard shutoff date looming.Xbox's leadership is in flux, and the crew digs into what that means for whoever ends up being blamed if Game Pass's economics don't hold up long-term.A real console-wars history lesson: why the Xbox 360 outsold the PS3 lifetime (and why that one-year head start matters more than people remember), and how Nintendo profits off pure brand loyalty (Labo, we're looking at you).Square Enix is moving upcoming titles to "game key cards" on Switch — a cartridge that's really just a license key, not the actual game data.8 years, 241+ episodes, zero hiatuses — the crew looks back at how a bad breakup and a bar conversation turned into a full media brand.CHAPTERS00:00 — Intro: 8 years of the Chop Shop + anniversary housekeeping01:00 — G FUEL Giveaway details (prize pack + $50 Steam Gift Card)02:00 — PlayStation's censorship baggage, revisited03:41 — Sony's billion-dollar UK monopoly lawsuit04:41 — Dynamic pricing, explained plainly06:22 — GTA 6's price tag and the industry domino effect07:03 — Digital "ownership" is a rental — the GamesRadar take that broke us11:26 — The Crew (Yakuza Zero) delisting precedent13:14 — Kingdom Hearts All-in-One quietly disappears from PS Store20:12 — How to actually protect your physical/digital library before the cutoffs hit22:57 — Xbox's "we told you so" moment29:00 — Is Microsoft trying to sell off Xbox?31:00 — Xbox 360 vs. PS3 vs. Wii — the real lifetime sales numbers41:41 — Nintendo's untouchable formula (yes, we all bought the Labo)44:52 — Days Gone, Sunset Overdrive, and games we'll never get sequels for45:26 — Square Enix's game key card move — bad news for physical Switch collectors53:54 — [Redacted for decency — you'll know it when you hear it]54:22 — 8 years of GZ Chop Shop: the actual origin story1:08:00 — The wildest guest requests we've ever landed1:14:16 — Where we want the show to go next1:19:15 — Outro + how to enter the anniversary giveaway⚡ G FUEL affiliate link
AI companies absorbed $259 billion in venture capital last year — 61% of every VC dollar on Earth — and three-quarters of it landed in the United States. So what happens to everyone else? Dr. Vivian Atud, PhD economist and international consultant, follows the money from Silicon Valley to Lagos, Accra, and Luanda — where a quiet revolution in pension funds, development finance, and venture debt is building something more durable than a funding boom: capital sovereignty. Featuring the Capital Sovereignty Stack™, a three-layer framework for nations, founders, and households — and the surprising way it maps onto Ownership Friday. Evidence sources: OECD, Bloomberg, AVCA, Partech, Reuters, StartupBlink.AI investment 2025, venture capital Africa, emerging markets, capital concentration, African startups, development finance, pension funds, venture debt, economic sovereignty, Ownership Friday, AI boom, global capital flows, Dr. Vivian Atud, The Clarity Mandate0:00 — Cold open: the $259 billion verdict1:10 — Act I: What the data actually says (OECD, AVCA, Partech)4:00 — Act II: Capital gravity — the quiet geopolitics7:30 — Act III: The inward turn — what Africa is building11:00 — Act IV: The Capital Sovereignty Stack™13:30 — The faith lens: the little-by-little economy14:20 — Close and mandate“Africa didn't lose its investors. It lost the assumption that global capital would always show up.”“Markets don't only price fundamentals. They price narratability.”“Dependency is a business model with a single point of failure.”“Nobody funds your future with more conviction than you do.”Bloomberg (May 28, 2026): “Africa Startups Turn Inward as US AI Boom Drains Venture Capital” — anchor report; local investors ~47% of commitments, US under 25%.OECD (Feb 2026): AI VC at $258.7B in 2025 = 61% of global VC; ~75% to US companies.AVCA: African equity funding $2.1B (−21% YoY); debt up sharply year over year.Partech 2025 Africa Tech VC Report: $4.1B combined equity + debt (+25%); record venture debt ≈ 41% of capital deployed.Reuters (Q3 2025): AI = 46% of global venture funding.StartupBlink: Angola +70.8%, Algeria +38.7%, Uganda +32.5% ecosystem growth; Big Four ≈ 72% of continental capital.Confidence note: figures cross-verified across five independent outlets reporting the same underlying OECD/AVCA/Partech datasets. AVCA and Partech use different methodologies (equity-only vs. combined equity + debt); the script flags this on-air to preserve institutional credibility.Keywords / TagsChapter MarkersSocial Pull-Quotes (short-form clips)Source Register (for show notes)
Will Bitcoin dip below $50K? On today's Markets Outlook, 10x Research Founder and CEO Markus Thielen tells CoinDesk's Jennifer Sanasie why he sees Bitcoin dropping even more before recovery, with $7B in ETF outflows since mid-May and MicroStrategy stepping back as the last real buyer. Plus, why he stays bearish on Ethereum, why he's calling Hyperliquid overvalued, and why nothing turns around until the Fed goes dovish. - Timecodes: 00:00 Bitcoin Could Dip to $46K Before Year-End Rally 01:20 Bitcoin Falls Below $60K: What's Driving the Selloff? 02:37 $4.5B in ETF Outflows, Saylor's Bitcoin Sale, and Missing Catalysts 03:14 Why the Genius Act and CLARITY Weren't Real Catalysts 05:00 When Does the Fed Turn Dovish? The Macro Case for a Rally 05:19 Kevin Warsh Nomination Was the Turning Point for Bitcoin 07:27 Why Bitcoin Could Break Below $50K to $46–47K 09:41 The 2022 Parallel: Grayscale's SEC Win as the Sentiment Shift 11:06 Brand New Rails: Securitize Lists on NYSE 12:40 Where Does Bitcoin End the Year? 13:51 Why Markus Is Still Bearish on Ethereum 15:20 Stablecoin Activity Isn't Accruing to ETH Holders 16:40 The Bull Case for ETH Has Broken Down 17:28 Hyperliquid Overvalued at $72? 18:33 Why HYPE Is Unlikely to Reach $100 19:46 What to Do Now: Covered Calls, Shorts, and Watching for the Low - This episode is brought to you by RealFi, a smarter stablecoin, backed by real-world assets. Find out more at realfi.co. - Ledn provides a secure and transparent way to access liquidity while maintaining your bitcoin holdings. Perfect 8 year track record of keeping clients assets safe. Don't sell your bitcoin. Get a bitcoin-backed loan. Check out your rate by using their loan calculator at ledn.io - JPEG Trading is a global proprietary trading firm specializing in cryptocurrency and decentralized finance markets. From market structure and liquidity provision to quantitative trading strategies, JPEG Trading operates across the full spectrum of blockchain-based assets. Follow @jpegtrading on X to stay ahead of the latest developments in digital asset markets: https://x.com/jpegtrading - This episode was hosted by Jennifer Sanasie.
Will Bitcoin dip below $50K? On today's Markets Outlook, 10x Research Founder and CEO Markus Thielen tells CoinDesk's Jennifer Sanasie why he sees Bitcoin dropping even more before recovery, with $7B in ETF outflows since mid-May and MicroStrategy stepping back as the last real buyer. Plus, why he stays bearish on Ethereum, why he's calling Hyperliquid overvalued, and why nothing turns around until the Fed goes dovish. - Timecodes: 00:00 Bitcoin Could Dip to $46K Before Year-End Rally 01:20 Bitcoin Falls Below $60K: What's Driving the Selloff? 02:37 $4.5B in ETF Outflows, Saylor's Bitcoin Sale, and Missing Catalysts 03:14 Why the Genius Act and CLARITY Weren't Real Catalysts 05:00 When Does the Fed Turn Dovish? The Macro Case for a Rally 05:19 Kevin Warsh Nomination Was the Turning Point for Bitcoin 07:27 Why Bitcoin Could Break Below $50K to $46–47K 09:41 The 2022 Parallel: Grayscale's SEC Win as the Sentiment Shift 11:06 Brand New Rails: Securitize Lists on NYSE 12:40 Where Does Bitcoin End the Year? 13:51 Why Markus Is Still Bearish on Ethereum 15:20 Stablecoin Activity Isn't Accruing to ETH Holders 16:40 The Bull Case for ETH Has Broken Down 17:28 Hyperliquid Overvalued at $72? 18:33 Why HYPE Is Unlikely to Reach $100 19:46 What to Do Now: Covered Calls, Shorts, and Watching for the Low - This episode is brought to you by RealFi, a smarter stablecoin, backed by real-world assets. Find out more at realfi.co. - Ledn provides a secure and transparent way to access liquidity while maintaining your bitcoin holdings. Perfect 8 year track record of keeping clients assets safe. Don't sell your bitcoin. Get a bitcoin-backed loan. Check out your rate by using their loan calculator at ledn.io - JPEG Trading is a global proprietary trading firm specializing in cryptocurrency and decentralized finance markets. From market structure and liquidity provision to quantitative trading strategies, JPEG Trading operates across the full spectrum of blockchain-based assets. Follow @jpegtrading on X to stay ahead of the latest developments in digital asset markets: https://x.com/jpegtrading - This episode was hosted by Jennifer Sanasie.
Rubrik (RBRK) stock is down near $70 after peaking above $89 — but its subscription ARR is still growing over 30%. Here's the reverse DCF and margin math behind why the recovery has stalled.Rubrik (NYSE: RBRK) is one of the smaller names in the cybersecurity sector, operating in the data storage and backup security niche — a category distinct from network security players like Palo Alto Networks and Fortinet, or endpoint security leaders like CrowdStrike. In this excerpt from our Semiconductor Insider Live session, Nick and Kasey Rossolillo break down why Rubrik has lagged the broader cybersecurity recovery even as subscription ARR growth stays above 30% and reported revenue grows nearly 40% year-over-year.We dig into the free cash flow story to explain why it has stalled sequentially even as it grows year-over-year. We also explore what that margin compression means for a smaller, AI-infrastructure-dependent company, and how Rubrik's balance sheet ($1.7B cash vs. $1.1B long-term debt) holds up. Finally, we run a reverse discounted cash flow (DCF) analysis to see what growth assumptions are baked into the current stock price and discuss how small and mid-cap software stocks fit into a diversified, long-term portfolio strategy.This is educational content for self-directed investors evaluating semiconductor-adjacent software and cybersecurity plays in 2026.Join Semi Insider: Get access to CSI's research platform, tools, and deeper research as it happens at chipstockinvestor.comSpecial Discount: Get 15% off your membership with our special link: fiscal.ai/csiRelated Episode: We called this back in June — Listen/Watch hereIf you found this episode useful, please make sure to follow the podcast and leave us a rating! Let us know in the Spotify Q&A below: Do you think Rubrik's margin compression is temporary, or a longer-term concern?Content in this podcast is for general information or entertainment only and is not specific or individual investment advice. Forecasts and information presented may not develop as predicted and there is no guarantee any strategies presented will be successful. All investing involves risk, and you could lose some or all of your principal.CSI owns shares of Rubrik.
On MoneyFM 89.3’s Diplomatic Dispatch, H.E. Paul Thoppil, High Commissioner for Canada in Singapore joins Saturday Mornings Show host Glenn van Zutphen to discuss one of the most quietly successful bilateral relationships in the region. Singapore and Canada share deep alignment on multilateralism, the rule of law, open trade and global security and expanding cooperation. We explore: • $3.7B in merchandise trade and $4.6B in services trade • Why Singapore is Canada’s largest investment source in Southeast Asia • The CPTPP and the push for an ASEAN–Canada Free Trade Agreement • Long‑standing defence ties, including RSAF training in Canada On the lighter side, we also find out about his go-to places and eats in Singapore and what he loves most about living here.See omnystudio.com/listener for privacy information.
Vinny Lingham warned 18 months ago that Michael Saylor would harm Bitcoin more than FTX. Now he maps how the Strategy empire breaks and the one move that could slow the bleed. ======================================================== Thank you to our sponsor! Fidelity: Fidelity has been building in crypto and DeFi since 2014 — now they're hiring. Explore career opportunities at one of the most forward-thinking names in finance here: crypto.fidelitycareers.com. Cape: Your biggest crypto vulnerability isn't your wallet, it's your phone number. Cape is America's privacy-first mobile carrier that rotates your SIM identity daily and blocks SIM swaps before they happen. Get 33% off your first six months at cape.co/unchained (use code: UNCHAINED). ======================================================== Strategy's stock has fallen over 80% from its November 2024 high, its STRC preferred trades well below par, and a fresh $335 million raise has done nothing to restore confidence. Vinny Lingham, co-founder of Praxos Capital, tweeted in October 2024 that Michael Saylor would do more damage to Bitcoin than FTX. On Unchained, he argues the collapse was always predictable, and that this is not a Ponzi but what he calls a 'Saylor scheme.' Lingham maps how the empire breaks once MSTR trades at a discount to mNAV, why the 32-Bitcoin sale and the $1.5 billion buyback of 2029 converts blew Saylor's runway, and why $6.7 billion in convertible notes raises default risk by 2028. He also weighs a Soros-style attack theory and the switch to bimonthly dividends. His fix is the one thing Saylor won't do: stop buying, stop diluting, wait it out. The question is who removes the biggest buyer of Bitcoin, him or the market. Host: Laura Shin, Host / Unchained Guests: Vinny Lingham - Co-founder of Praxos Capital Timestamps
Micron (MU) stayed in focus on earnings, with AI-driven demand continuing to provide support. Crude oil fell below $70 for the first time since early March, boosting airline stocks, while Onsemi (ON) came under pressure after announcing its $7B acquisition of Synaptics.======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about
Take America Back with Steven Eugene Kuhn | The Devil Doc Talk Show | Veteran Leadership, Humble Alpha & Citizen Servant Leader
We're excited to have Databricks join us at AIEWF, among hundreds of the top companies in the AI Engineer ecosystem. LS subscribers can use their discount to get past the late bird pricing and access over $50k in sponsor offers! Everyone is still talking about Satya's Frontier Ecosystems post, but few have actually built a (now $175 billion) frontier ecosystem and cloud like our guests today.From open-sourcing the layer above coding agents to rethinking databases for the agent era, Databricks cofounders Matei Zaharia and Reynold Xin are pushing the company beyond the lakehouse into a full data-and-AI operating system. In this episode, Matei and Reynold join swyx at the 2026 Data + AI Summit to unpack Omnigent, LTAP, Lakebase, agent security, open formats, Mosaic, and why databases may matter more than ever once AI agents start doing real work.We go deep on Omnigent: Databricks' open-source meta-harness for combining, controlling, and sharing agents across Claude Code, Codex, Cursor, Pi, custom agents, and internal tools. Matei explains why coding agents and enterprise agents run into the same problems: portability, collaboration, session history, security, spend controls, and the need for a common API above every harness.Then Reynold walks through Databricks' database dream: why CDC is brittle enough to joke that it means “continuous data corruption,” why HTAP has been the holy grail of database engineering, and why Databricks thinks LTAP gets most of the benefits by unifying the storage layer instead of collapsing every query engine. We also cover Databricks' infrastructure scale, the culture behind rapid prototyping, the difference between tech and enterprise customers, Databricks vs Snowflake, whether vector databases should have ever existed, the Mosaic model strategy, Genie, AI Runtime, RL fine-tuning, and the thesis that traditional software gets rewritten once the data is in the right place and agents sit on top.Databricks began as a company for the big data era. The origination of Spark from the Berkeley AMPLab which eventually turned into the product Lakehouse convinced enterprises that they didn't need a separate data lake, warehouse, ML platform, and governance layer. They just needed one open foundation where all of their data could live and be reasoned over.Since then a lot has changed, but data has only become more important. Data is no longer something you keep track of and analyze ad hoc, it's the necessary context agents need in order to act. So the framing has shifted from “where do we put all of our data?” to “how do we expose the right slice of state, history, permissions, and business logic to an AI system at the exact moment it's doing work?”If frontier model performance becomes commoditized, the durable advantage then becomes the company-specific context around them: proprietary data, governed access, operational state, transaction logs, workflows, and feedback loops. Which makes Databricks positioned perfectly.Now coming fresh off the Data + AI Summit 2026, the company is moving just as fast to keep up, announcing Genie One, Omnigent, LTAP, and many more, indicating a central mission in its newer work: Databricks is trying to become the operating system for enterprise agents.Models are getting good enough, but agents are only useful if they have the right context, permissions, memory, state, cost controls, and access to live business data. Fundamentally it appears that significantly better model performance in production is a systems problem, one that data guys like us are remarkably well prepared to solve!We discuss:* Why Databricks built Omnigent as a meta-harness above existing AI agents* Why coding agents and custom enterprise agents need the same infrastructure* The common API for agent sessions, files, streams, tool calls, and cancellation* Why persistent sessions, cloud sandboxes, sharing, search, and collaboration matter* Why Databricks open-sourced Omnigent instead of keeping it proprietary* Databricks' internal agent usage, cloud sandboxes, and coding workflows* The scale of Databricks: 50–60 million virtual machines a day and exabytes before breakfast* Why agent security needs contextual and stateful policies* How an agent could read confidential docs, install a compromised npm package, and leak data* Why spend control matters when an agent can burn $500 reading logs* Startup opportunities around coding-agent analytics, quality, skills, and spend* LTAP, Lakebase, and why Databricks wants to rethink the database stack* OLTP vs OLAP, CDC, and why data pipelines break at 3 a.m.* Why HTAP has historically been the holy grail of database engineering* Why Databricks thinks LTAP is “HTAP done right”* How writing transactional data into column-oriented formats changes analytics* Why agents need live operational context from databases, not just telemetry* How Databricks prototypes strategic systems without endless process* Enterprise vs tech customers, governance, procurement, and DIY culture* The “second system syndrome” risk of rewriting a database engine* Building a database engine from a decade of traces and quadrillions of data points* Why vector databases should never have been a separate category* Why open formats and AI changed the race with Snowflake* The Mosaic story, DBRX, Genie, document parsing models, and specialized model training* Why model customization and RL fine-tuning may become mainstream* Why “get the data there, slap some agent on top” may rewrite traditional softwareMatei Zaharia* LinkedIn: https://www.linkedin.com/in/mateizaharia* X: https://x.com/matei_zahariaReynold Xin* LinkedIn: https://www.linkedin.com/in/rxin* X: https://x.com/rxinDatabricks* Website: https://www.databricks.com* X: https://x.com/databricksTimestamps00:00:00 Introduction00:02:22 Omnigent and the Agent Infrastructure Layer00:08:39 Agent Clouds, Common APIs, and Open Source00:16:52 Databricks Scale and Internal AI Workflows00:18:03 Agent Security, Governance, and Spend Controls00:27:34 LTAP and the Database Dream00:30:30 CDC, HTAP, and Why Data Pipelines Break00:34:05 Lakebase, Parquet, and Live Data for Agents00:36:47 Databricks' Culture of Fast Prototyping00:43:40 The Dream Engine and Rewriting the Database Stack00:51:02 Vector Databases, Query Engines, and LTAP00:52:36 Databricks vs Snowflake00:57:48 Mosaic, DBRX, Genie, and Specialized Models01:03:11 Context, AI Runtime, and RL Fine-Tuning01:06:15 Why Data + Agents May Rewrite Software01:07:09 Closing ThoughtsTranscriptIntroduction: Databricks, Data + AI Summit, and Founder DynamicsSwyx [00:00:00]: Matei and Reynold from Databricks, welcome to Latent Space.Reynold Xin [00:00:06]: Hey, thanks for having us.Swyx [00:00:07]: Yeah.Matei Zaharia [00:00:08]: Yeah, thanks so much.Swyx [00:00:09]: thanks for taking time out. You have your Databricks, Data AI Summit going on. You were just telling me how the first summit that you guys ran was just 50 peopleReynold Xin [00:00:17]: Yeah, it wasSwyx [00:00:17]: in BerkeleyReynold Xin [00:00:18]: little meetup at Berkeley, I thinkMatei Zaharia [00:00:19]: YeahReynold Xin [00:00:19]: put togetherMatei Zaharia [00:00:20]: We were doing these tutorials and, yeah, just teach people Spark.Swyx [00:00:23]: Yeah. obviously now it's like, I think like the headline number's like 100,000 people around the world, 30,000 in person.Swyx [00:00:30]: it's a crazyMatei Zaharia [00:00:31]: AmazingSwyx [00:00:31]: community. Well, I just saw the keynote.Swyx [00:00:35]: Ali's just. Did was it obvious or that back when that Ali would be, like, such a great, like, CEO? LikeReynold Xin [00:00:42]: OhSwyx [00:00:42]: such a great presenter?Reynold Xin [00:00:43]: What do you think?Matei Zaharia [00:00:44]: I think among our group of founders it was clear that, I think he'd be the best at this.Swyx [00:00:50]: Yeah.Matei Zaharia [00:00:50]: And yeah, it turned out great. And he's, he's ramped up on so many topics growing a company. He would just go in and, like, study it and, be talk to all the experts. Like, even if he can't hire the person, learn enough about, like, finance and sales and whatever it was, and, and go from there. Yeah.Swyx [00:01:09]: Yeah.Reynold Xin [00:01:10]: he's obviously very high IQ and a very high EQ, but it wasn't. Like, Ali today is quite different from Ali from, like 10 years ago. I think there's a lot of work that he put in to, get to this point.Swyx [00:01:20]: Yeah. no, to me the most appealing thing about him is that he's funny. And like, it, it's, it'Matei Zaharia [00:01:26]: It's true, yeahSwyx [00:01:26]: it's hard to make jokes about, data warehousesReynold Xin [00:01:30]: About serious topicsSwyx [00:01:31]: securityMatei Zaharia [00:01:32]: YeahSwyx [00:01:32]: what have you.Matei Zaharia [00:01:33]: Oh, yeah. That's for sure.Swyx [00:01:34]: Yeah. So you guys launched a whole bunch of things. I'll, I'll just name check briefly, the stuff because we're not gonna cover everything. Omnigentt, your baby. LTAP, your baby, your dream engine.Swyx [00:01:47]: we're also gonna cover Genie, cover CustomerLake, you acquired PantherMatei Zaharia [00:01:52]: YeahSwyx [00:01:52]: Open Sharing, and there's Unity AI Gateway. A lot of these, I think, like, are things that you would expect a Databricks to do. It's, it's like part of the roadmap. Everyone in your category has similar things. But I think, probably the two of you are leading the two most unique and differentiated initiativesOmnigent and the Agent Infrastructure LayerSwyx [00:02:09]: on, in the landscape. Maybe we'll start with, Omnigentt we'll, we'll, we'll, we'll go into it. I do think that a lot of people are exploring this meta harness concept.Matei Zaharia [00:02:21]: Yeah, totally.Swyx [00:02:21]: What led you to it?Matei Zaharia [00:02:22]: Yeah. There were a couple of, like, converging lines, which I think is a good sign that you need something new. So on the one hand, there's all the coding agent info internally. We have really great, dev infra team. they built something called Isaac, that's like a wrapper on Claude Code and Codex, and, lets you use them either on the web in, like, sandboxes or, just on your dev machine or on your laptop or whatever. And then, they were adding all kinds of stuff there. And we saw all the more advanced engineers like, were building their own workflows with tons of agents, and they were building their own UIs and stuff on top or even on top of that. And then the other one was, like, us building agents. We ship this, like, data science agent called Genie on the research team, which I lead. We also build a lot of internal ones for various things, and then we have all the customer ones. And all of them running into this thing of like, “Oh, I need to switch model and harness and so on,” every few months. Plus the agent is, like, completely useless if you can't share sessions with someone and have history and have search and all this, like, layer on top of it for collaboration. I thought a bit about it from both contexts and, at first people thought it was weird. They're like, “Why are you doing coding agents and custom agents in the same thing?” But I said it's, it's the same problems and, you just wanna build the stuff that lets you deliver the agent, maybe control it if you care about security, and, make it portable across things. And then we prototyped some things as experiments. We saw, yeah, we can make it work, and then we built that for real.Swyx [00:04:06]: I'm wondering if this let's call it architectureMatei Zaharia [00:04:11]: YeahSwyx [00:04:11]: maps to anything in your careers in the past. like I always think about how a lot of things just tie back to operating systems.Swyx [00:04:18]: A lot of operatingMatei Zaharia [00:04:19]: YeahSwyx [00:04:20]: systems tie back to databases,Matei Zaharia [00:04:21]: SoSwyx [00:04:21]: or the other way aroundMatei Zaharia [00:04:22]: so the thing, I do think it ties a lot to, like, network protocols, internet protocol. we alsoSwyx [00:04:29]: Communication between entities.Matei Zaharia [00:04:30]: Yeah. We did stuff with, like, data sharing also, which is probably, most viewers probably won't know unless they'Swyx [00:04:36]: Yeah, open protocol is the term.Matei Zaharia [00:04:37]: Yeah.Swyx [00:04:38]: Open sharing. Open sharing.Matei Zaharia [00:04:38]: Open sharing.Swyx [00:04:39]: Yes.Matei Zaharia [00:04:39]: Yeah. So it's like you have a company, you maintain some table, like let's say like a Walmart or something. They have like the, inventory and what's been sold in each store. And then you also have suppliers, and they would love to produce more things and ship them, like, exactly the moment you need them. So they would love, like, real-time access to your table. So instead of like sending emails around or Excel sheets or phone calls, why can't you share like a view of that table in real time with them? Then they query, they, join it with their data, and they decide what to send. So it's one of these things where you, like you might ask like today since we can vibe code anything so fast, why do we even need to design like protocols or APIs or software? Why can't you just vibe code things on demand? But for this type of interoperability where multiple parties that are moving at different speeds are building stuff and you still want some layer on top to coordinate, you do wanna design it and build it. So it reminds me of that, like agents talking to each other and, users talking to agents and tools.Agent Clouds, Cloud Sandboxes, and Keeping Sessions AliveSwyx [00:05:42]: Reynold, any other comments alternative viewpoints?Reynold Xin [00:05:46]: I think, by the way, we had a debate on exactly which set of benefits would, matter a lot, and I think around the time we decided to do this thing I was telling Matei, “Hey,” it just happened to be there's a particular week that I was coding nonstopSwyx [00:06:00]: from the moment I woke up to, like, the moment I went to bed, I was, like, looking at my Claude sessions, my Codex sessions. And one of the things that was particularly annoying was having to keep my laptop open.Swyx [00:06:12]: I was driving to a doctor's appointment, and I remember because I wanted to make sure the whole thing continues working.Matei Zaharia [00:06:18]: But by the way, it's so comforting to hear you say that because I'm like, “I don't know if I'm a clown and I'm doing this or like.”Swyx [00:06:25]: Yeah. Like honestly, I was driving and I was tethering my laptop to my phone.Matei Zaharia [00:06:29]: huh.Swyx [00:06:29]: Keeping it on the side. Whenever I hit a red light, I started looking at what's going on my laptop.Matei Zaharia [00:06:35]: Yeah.Swyx [00:06:35]: And I just felt that was ridiculous.Matei Zaharia [00:06:37]: Yeah.Swyx [00:06:37]: It felt like we went back to the dark agesMatei Zaharia [00:06:39]: YeahSwyx [00:06:40]: programming. the productivity you gain from all this coding age is amazing, but, yeah.Matei Zaharia [00:06:45]: Have you heard of cloud?Swyx [00:06:47]: Yeah.Swyx [00:06:48]: It was crazy to me.Matei Zaharia [00:06:49]: Oh, the thing you were working on was the sandboxes or was this before that?Swyx [00:06:52]: It was a sandbox.Matei Zaharia [00:06:53]: Okay.Swyx [00:06:54]: I was workMatei Zaharia [00:06:54]: So you were inSwyx [00:06:55]: So I was approaching from a very different angle. I wanted to, “Hey, we're gonna have cloud sandboxes that doesn't shut down. You can get one very quickly,” but not just for running agentic sessions.Matei Zaharia [00:07:06]: Yeah.Swyx [00:07:06]: It's also for running development. So I was personally building that week, and through building that, I ran into all these issues, and then I wroteMatei Zaharia [00:07:15]: YeahSwyx [00:07:15]: a document for Matei, it's like, “Here's my wish list of what the actual environment should do.” And I think he ended up almost implementingMatei Zaharia [00:07:22]: YeahSwyx [00:07:22]: every single one of them.Matei Zaharia [00:07:23]: Yeah, I remember Reynolds saying, ‘cause my first prototype of this had just chats with your agent and he said, “I have to be able to open a shell, like my own shell and like list files and like tail them and stuff.” SoSwyx [00:07:36]: So SSH into a mainframe.Matei Zaharia [00:07:37]: Yeah. it has that now.Swyx [00:07:39]: Tailing my log.Matei Zaharia [00:07:40]: Yeah.Matei Zaharia [00:07:41]: Yeah.Swyx [00:07:41]: And also another thing I think I asked was, I had. I still use cursor for the sole purpose of rendering markdown files.Matei Zaharia [00:07:48]: huh. Yes.Swyx [00:07:49]: So I said, “If you just give me a way to see my markdown files and renderMatei Zaharia [00:07:53]: YeahSwyx [00:07:53]: them properly, I don't need a separate tool anymore.”Matei Zaharia [00:07:55]: Yeah.Swyx [00:07:56]: And I think you also built that in.Matei Zaharia [00:07:57]: Yeah, we, yeah, we did that, yeah. Yeah, we had a lot of engineers building, their own vibe coding setup. But then the other thing they all said is like, “Hey, I built something that's amazing for me, but, like, no one else on the team can use it ‘cause I don't have a server to collaborate.” And this is why we tried to set up, Omnigent, so you can have a server and have the security, set up in there. So, like log in with Google or whatever and, like securely share stuff. which. And that's where we've seen a lot of other agents like hit things. Like people think they prototyped an awesome agent, but it's not allowed to connect to like some really important data or whatever because of the security team.Omnigent Architecture, Open Source, and Common APIsSwyx [00:08:38]: Yeah.Matei Zaharia [00:08:38]: So yeah.Swyx [00:08:39]: Yeah. At this point, so for those watching along on YouTube, we're gonna putting up a image of the structure here, and we can talk a little bit of the architecture. I think I just want to have people understand, ‘cause like when we're talking about software, it can be very abstract and like here is what we're talking about. You've worked out in open source this entire platform and there's a runner component and server component with a uniform API that you've, you've figured out. any other element and obviously you can plug in all this, persistence layers and compute layers. This is a whole cloud. It's an agent cloud.Matei Zaharia [00:09:12]: Yeah. It's, it's got these components to work with it. The, a lot of the action happens like on the machine where you deploy your agent too. So whatever you've got on there, you can run. But yeah, it's, I think it's the minimal thing you want to have hosted, like collaborative agents and to have that server. And one of the reasons we open sourced it is, anyone building agents, this gives them an app they can start with and customize, which we were seeing in Databricks too. Like someone would make a nice, agent app and then other teams would ask, “Oh, can I just use yours for my agent?”Swyx [00:09:45]: Yeah, I think we had like five or six different agentic frameworksMatei Zaharia [00:09:48]: YeahSwyx [00:09:48]: built by every different team. They do all do more or less the same thing. Yeah, you need to. people wanna take something that works in Forkit, and you might as well have something open source. Yeah, which also was another question, which is interesting for Databricks. Like what do you choose to open source? What do you choose to make it proprietary? It's in. this goes back to Spark, right?Matei Zaharia [00:10:05]: Yeah.Matei Zaharia [00:10:06]: One, so one of the reasons to open source something is if you think it's a layer that will there'll be some network effect, it'll benefit from many, people collaborating, on it. So, for example, with Spark, I don't know if when Spark came out, we also focused a lot on letting you have libraries on top. So like there used to be differentSwyx [00:10:28]: EcosystemMatei Zaharia [00:10:28]: distributed computing engines for like machine learning and graph computation. We said they should all be libraries that you can compose. And we made it super easy to add connectors to data sources too. And then we benefit because, we don't have the time to write like connectors to like, 1,000 like different databases and file formats, but we can just use the ones people make, and of course they benefit from joining, this thing. So that's like one of these as it. Another way to think about it is like imagine, we our thing wasn't open. We had some agent hosting thing, but it's not open and then there is an open one. if you're. Which one's gonna win in the long run? So like here, because there is this benefit from like people writing integrations, it'll be, it'll be that. And then there are other things that like you just can't, even deliver as open source that are things the company does. Like for example, how do you make sure you're like streaming, jobs or your Lakebase database doesn't like, lose all your data at night? Well, that requires an operational team that's gonna sit there. There's no way it has to be a service. So like we wanna make sure as a company we're really good at those infra services and then we're as open as we can in terms of like what you build on top.Swyx [00:11:42]: speaking from a benefits, I think we are already seeing pull requestsMatei Zaharia [00:11:45]: YeahSwyx [00:11:45]: of all kinds of ecosystem integration, even though it was only released on Saturday.Matei Zaharia [00:11:50]: Yeah, Saturday. Yeah. So someoneSwyx [00:11:51]: Let's see, let's see what's going on. Yeah, you can look at the merge ones. I asked Sam Nigon this morning aboutMatei Zaharia [00:11:59]: 400 merge already?Matei Zaharia [00:12:00]: Yeah. I think Recent quite, I would guess around half are not from our team. but for example, someone added support for running it on Kubernetesrnetes. people added, many cloud sandboxes, so this can launch a cloud sandbox and run your agent in there, which is great for sharing too, ‘cause it's not, like, on your laptop and someone's, like, running scary code on there. so yeah, many startups have put those in, and, we expect to see more of them. We also have more agent harnesses already. Cursor, CLI, and Antigravity also.The Modern Data Stack and the Emerging AI StackMatei Zaharia [00:12:34]: Yeah. That's all, beautiful. And I, I feel like the last time this happens, there was the rise of the modern data stack.Matei Zaharia [00:12:42]: I don't know if it's that useful. I'm, I'm curious in your postmortem.Matei Zaharia [00:12:46]: I think most peopleSwyx [00:12:47]: AgreeMatei Zaharia [00:12:47]: will agree that it is finally dead. but maybe this arises to a new modern AI stack that, like, does the same thing.Matei Zaharia [00:12:52]: I don't know.Reynold Xin [00:12:54]: I think the modern data stack was a pretty useful thing, probably even up until this day. I think what, maybe for the audience who don't understand the history, I think the modern data stack is effectively decomposed into you need a layer to ingest the data in, you need a layer to transform your data, and then all of this are run, and then you need a layer to maybe visualize your data. And all of this runs on some data warehouse, or later on, as we're doing data warehouse or lakehouse.Reynold Xin [00:13:21]: I think that concepts are all very powerful and very useful. They enable a lot of workloads. What people eventually run into is a question of unification and consolidation is, hey, do you really need to chop all this into different pieces and work with so many different vendors and platforms in order to get, like, a very simple visualization done, right? So I think, like, over time, everybody started realizing that customers are pushing us. We started, we can realize that, so we started building more and more capabilities and trying to consolidate. And at the end of the day now, customers don't have to worry about having me hook up five different systems in orderMatei Zaharia [00:13:55]: YeahReynold Xin [00:13:55]: produce a chart. But the. I think, honestly, something like this is probably happening, in how many different frameworks do you want to hook up together in order to produce, like do a very simple agent.Matei Zaharia [00:14:06]: Just to be clear, I would say the core of this is this common API on top of all the harnesses. So the API is like, you've got an agent session, and you can send in a message or, like, a file. That's what you can send in, and then you get out, these streams as it's streaming text or as it's doing tool calls. And, or the other thing you can send in is you can, like, tell it to cancel a turn. So that's the API. Now, the thing we did is we could get you that on top of, like, cloud code running in a terminal, Codex, Py, OpenAI SDK, all that stuff. We map them all to that same interface. So that is something that you'd have to maintain yourself if you built your own, like, agent orchestrator, and then whenever cloud changes its API, you gotta, tweak your thing or it's gonna lose some messages. So that's the thing that's valuable to maintain. Then on top of that, like, we built a few apps. I think we built a pretty cool UI and stuff, but that's, And we built a security and control piece, which I'm excited about. But it's that common interface, so we don't. We. That doesn't try to be a stack. And in fact, you could plug in your own UI on top of this, server. That, and that's one of the use cases we care a lot about, ‘cause we want to use this in our own products.Compute, Sandboxes, and Databricks ScaleSwyx [00:15:20]: Yeah. It should be everywhere.Matei Zaharia [00:15:22]: Yeah.Swyx [00:15:22]: I think one of those things that is really interesting to me is, like, well, first of all, I'll, I'll endeavor to do everything and not call it the modern AI stack because like it needs a different name.Matei Zaharia [00:15:32]: Yeah.Swyx [00:15:32]: But like, yes, like, so one of the first people that told me about compute, sandboxing was Nikita from Neon.Swyx [00:15:39]: Because a lot of people think about Neon as like, well, it's serverless Postgres with, like, the separation of compute and storage and, instant branching and all those things. But every database company is also a compute company.Matei Zaharia [00:15:51]: Yeah. Yeah.Swyx [00:15:52]: And so he was showing to me his whole, his sandboxing solution. I don't think he have ever launched it.Matei Zaharia [00:15:57]: So our sandbox solution, the reason we could build it so quickly was because we realized if you just take the actual Lakebase architectureSwyx [00:16:05]: YeahMatei Zaharia [00:16:05]: and remove the database from it, by the coming from NeonSwyx [00:16:08]: Exactly, rightMatei Zaharia [00:16:09]: you have this sandboxSwyx [00:16:09]: Every database company has it already, yeah.Matei Zaharia [00:16:11]: Now, there are some differences. For example, in the one to support this particular workflow, it's important to have local persistence,Swyx [00:16:19]: YeahMatei Zaharia [00:16:19]: because you want your state to persist. Your libraries, you don't have to install your library every time, right?Matei Zaharia [00:16:24]: whereas the Neon architecture, because of the separation of storage from compute, you don't need persistent local disk.Swyx [00:16:30]: Yeah.Matei Zaharia [00:16:30]: So there's some differences.Swyx [00:16:32]: Yeah.Matei Zaharia [00:16:32]: But the, at the end of the day, yeah, it's, Yeah, so this is when you run, like, a coding sandbox. Like, if I use it, yeah, we have the dev env internally at Databricks. There's, like, many, like, tens of gigabytes of data just for, like, all the source code and, like, artifacts and stuff that I built, and I want that to come back next time, so.Matei Zaharia [00:16:51]: Yeah.Matei Zaharia [00:16:51]: But yeah.Matei Zaharia [00:16:52]: Before the show, we was talking about some statistics that might be surprising at the adoption.Matei Zaharia [00:16:56]: It could be internal, it could be external, whatever comes to mind, just to impress people the scale this is happening.Swyx [00:17:02]: So we, on the analytics side, I think we launchedReynold Xin [00:17:06]: Maybe 50 or 60 million virtual machines a day across all three clouds, so we're one of the biggest compute orchestrators out there.Reynold Xin [00:17:13]: Stuff for sure for CPU compute.Swyx [00:17:14]: Yeah.Matei Zaharia [00:17:14]: Yeah.Reynold Xin [00:17:15]: the. And all of this process, I think exabytes of data, I joked about depending on which time zone you are, typically before you have breakfast, Databricks would have processed exabytes of data already on that day. and on Neon, it's pretty interesting, too. It's launching, I think, 13 million databasesSwyx [00:17:34]: YeahReynold Xin [00:17:34]: a day now.Swyx [00:17:35]: Yeah, to me that was, like, aReynold Xin [00:17:36]: And that's just likeSwyx [00:17:37]: Like, what do you mean?Matei Zaharia [00:17:38]: Yeah. And that's the point.Reynold Xin [00:17:40]: And a lot of those were thanks to agent- agents and branching experimentationSwyx [00:17:44]: YeahReynold Xin [00:17:44]: because we made it so easy and so quickly, and thanks a lot to Nikita's team, to launch databases. It's, the. So it's changing the way people use databases.Swyx [00:17:54]: Yeah. Okay, we're gonna go into more database talk in a bit, but I wanna make sure we close up anything on Omnigentt. you mentioned, you were excited about the securityOmnigent Security, Contextual Policies, and Spend ControlsSwyx [00:18:03]: control side.Matei Zaharia [00:18:04]: Yeah.Swyx [00:18:04]: a lot of companies are figuring that out right now, as well as the spend side.Matei Zaharia [00:18:08]: Yep.Swyx [00:18:09]: what have you found there?Matei Zaharia [00:18:11]: Yeah, so I spent quite a bit of time talking to internal users, developers, security team, managers, and also lots of customers, and there's a few things. Like, first of all, one thing, that immediately was. became obvious is for security, there's this tension between, like, usability and security. And, the way people do. Like, a lot of coding agents today have very basic things like you can tell me which tool patterns I'll allow or disallow or whatever. It's like yes or no. But that puts you in a very tough spot. So just as an example, like, should my agent be able to read, some confidential documents, or let's say, should it be able to install new packages from npm, which, maybe it's compromised. Yes or no? Like, maybe I wanna allow it. Should my agent be able to publish stuff to the company website? Well, if I'm using it to code on the website, yes. But should it be able to do both, so it can, like grab a confidential document and be prompt injected and leak it? Probably not. So the thing we decided we need is stateful or what we call contextual policies where you keep track of the state of that session. It's not like is it allowed to push to the marketing site or not, but, like, hey, if it did a risky thing, like it installed, a old package from npm, or it read, like, 1,000 confidential docs, then no. Then don't, don't do it. Otherwise, maybe it's okay. That's one example of, like, moving that trade-off so it's both more secure and more useful by having a more powerful engine, essentially. This requires tracking sessions. The other piece that was interesting there is, like, there are these very level events it's doing, and you want some libraries on top that parse them. Like, for example, we have a, MCP server on Google Drive internally. It's got 60 API calls. like, how do I know which of those, like, will share a document with stuff on the internet and which ones won't? It's, it's annoying. So we designed in Omnigentt the policy layer so that it's functions and you can have libraries. Like, someone can make something that maps the level events to high-level ones, and then you write a policy about the high-level things that came out. so and thatSwyx [00:20:25]: This is related to the Panther,Matei Zaharia [00:20:27]: Yeah, Panther is. will help with that. PantherSwyx [00:20:30]: YeahMatei Zaharia [00:20:30]: a similar idea on the event processing side, and it's Python-based versus a weird custom language. this is more, as in realSwyx [00:20:39]: I didn't even know we were good yeah.Matei Zaharia [00:20:41]: Those things are happening, yeah.Swyx [00:20:42]: Yeah.Matei Zaharia [00:20:42]: So yeah, but these are the cool things. I think the contextual or stateful part, and then the way it can be libraries, and that was another reason to make it open source because others will write libraries and, like, we and our customers can use them. And the final thing, because it's stateful, one of the states we track is how much you spent in that session. So I can. I've had, like, I ask an agent to debug something, and it spent $500 because it decided to read a lot of log files and burn a lot of tokens. but I can literally say, “Okay, launch a agent to do this and cap it to spending $5.” Like, ask me for permission if it needs more. And because we're counting that within that session, it'll pop up and tell me, “Okay, you spent five, $5. Do you wanna go on?”Reynold Xin [00:21:27]: So important context here. Matei spent the last five years, a lot of his time was architecting Unity Catalog at DatabricksMatei Zaharia [00:21:34]: YeahReynold Xin [00:21:34]: which is the governance layer for data.Matei Zaharia [00:21:35]: That's right, yeah.Reynold Xin [00:21:36]: And he's combining expertise at that layer together with all the AI governance he knows.Matei Zaharia [00:21:41]: Yeah.Swyx [00:21:41]: DoMatei Zaharia [00:21:41]: But I also spent a lot of time being annoyed by coding agents and getting prompts.Matei Zaharia [00:21:46]: And also as theReynold Xin [00:21:48]: All the aboveMatei Zaharia [00:21:48]: I don't want to end up on the front page as, like, I installed some weird npm package and leakedSwyx [00:21:53]: YeahMatei Zaharia [00:21:53]: all the code, so I'm especially paranoid. But also I have very little time, so I don't want to sit there approving, like, do you want to run a 20-line, bash script, yes or no? so that's why I spend a lot of time figuring out, like, how can I make it as safe as possible and not annoying?Swyx [00:22:10]: Yeah. Is safety and mmm, let's call it security a bigger concern than token maxing or token budgets? which one is, likeMatei Zaharia [00:22:19]: Oh, yeah, they're both there. I don't know. I guess it depends on the type of company you are. So I think, some companies, like, the budget is, limited and, they really care about thatSwyx [00:22:34]: you can be Uber and still be concerned?Matei Zaharia [00:22:36]: Yeah. Oh, yeah, totally. Yeah. If you haveReynold Xin [00:22:38]: for us, securityMatei Zaharia [00:22:39]: YeahReynold Xin [00:22:40]: super paramount.Matei Zaharia [00:22:40]: For us, security is absolutely critical as a, cloud provider. It's, it's the most important thing, and, token maxing, we're not so worried about it yet, but I've seen the Like, for example, I talked to some consulting companies. They have, like, 100,000 employees who are all coding for customers. If those each spend, like, an extra $1,000 a month, that's, that's not fun.Swyx [00:23:04]: YeahMatei Zaharia [00:23:04]: we have, like, only a few thousand engineers.Swyx [00:23:06]: What's the policy in Databricks? Is it just unlimited or what'Matei Zaharia [00:23:08]: It's, it's unlimited, but we do. we use our own product to, like, analyze the traces and stuff, and we have a team that'looking to optimize and to see if anyone's doing something weird. And, we had some really cool insights just from analyzing current traces, like whichSwyx [00:23:24]: YeahMatei Zaharia [00:23:25]: models are better at, say, Rust versus like TypeScript or whatever. So yeah, at least in our code base.Swyx [00:23:31]: Yeah. Amazing. Obviously, I have to ask the token question, obviously.Matei Zaharia [00:23:34]: Yeah.Swyx [00:23:34]: I think it'sReynold Xin [00:23:34]: YeahSwyx [00:23:34]: it's a key thing. But yes, security and control above that, and figuring out a sane layer there you can have some autonomy, but, not too much.Matei Zaharia [00:23:43]: Yeah. Yeah, and we wanna make it super easy. As a engineer, you should set a thing. So in Omnigentt, you can ask your agent, “Set a policy on yourself to do this.” So it can likeSwyx [00:23:52]: But if there's something I should be showingMatei Zaharia [00:23:53]: YeahSwyx [00:23:53]: I don't, I don't see it on the GitHub, but,Matei Zaharia [00:23:55]: Oh, yeahSwyx [00:23:56]: there's justMatei Zaharia [00:23:56]: Well, in the docs there's something.Swyx [00:23:57]: Yeah, this is it.Matei Zaharia [00:23:58]: You can look at it later.Swyx [00:23:59]: Okay. Yeah.Matei Zaharia [00:23:59]: Just look in the docsSwyx [00:24:00]: YeahMatei Zaharia [00:24:00]: contextual policies if you wanna see.Swyx [00:24:04]: I just like to point peopleMatei Zaharia [00:24:05]: look at the built-in policies.Swyx [00:24:06]: Yeah.Reynold Xin [00:24:06]: Yeah.Swyx [00:24:06]: If you want to, follow up on this is exactly where to look, right?Reynold Xin [00:24:10]: Yeah.Matei Zaharia [00:24:10]: Yeah. yeah, and the story of these is, like, I just wrote, like, I wrote a doc with like 10 ideas for things before as you were working on them. Well, that was, like, my wish list of things people asked, and I told the team, like, “Hey, can you do like at least five of these for the launch?” And then they just got back with all of them, so.Swyx [00:24:29]: Oh, wow.Matei Zaharia [00:24:29]: so you can come up with more, but them- some of them are just meant to be examples. really you can intercept, like, any event the agent is making, and you can then either block or force it to ask the user or, like, allow, and you can update state to keepSwyx [00:24:45]: YeahMatei Zaharia [00:24:45]: track stuff.Swyx [00:24:46]: Yeah, ‘cause ultimately you're, I think of you as, like, a systems designer.Swyx [00:24:50]: You let people plug in, right? That's the wholeMatei Zaharia [00:24:51]: YeahSwyx [00:24:52]: modus operandi of what you do.Matei Zaharia [00:24:53]: Yeah.Swyx [00:24:54]: It's likeMatei Zaharia [00:24:54]: And we care a lot about also composab- like, can someone else write a library that others use, whichSwyx [00:24:59]: YeahMatei Zaharia [00:24:59]: this is meant to.Reynold Xin [00:25:00]: There's also a batteries included philosophy hereMatei Zaharia [00:25:03]: YesReynold Xin [00:25:03]: probably very similar to how you did Spark, which is you could just start using.Swyx [00:25:06]: Yeah.Matei Zaharia [00:25:06]: Yeah, that's right. It has to be good out of the box at certain things, and then you can build your own things on top that, like, we don't wanna do. But in Spark, if you just wanna like, I don't know, like read a table or do, like, a aggregation, it should be awesome at that out of the box.Building on Omnigent: Contributions, Startups, and AnalyticsSwyx [00:25:23]: Yeah. People wanna catch up on Omnigentt, they should watch your keynote.Swyx [00:25:26]: they should go through the GitHub and the docs. If they wanted to contribute, or they want to build on this ecosystem what would you call out as the most high-leverage places get involved?Matei Zaharia [00:25:36]: Yeah, do get involved in the Discord and in GitHub. Our team is there, is monitoring, and, some of the things people ask for we just built ourselves. Some of them, we're, we're collaborating with them to build it. and also tell us, likeSwyx [00:25:49]: Yeah, they're gonna be veryMatei Zaharia [00:25:49]: how you would like to use it because I think especially for developers, like, everyone wants it to work their own way, and a really good developer tool, like you have to hear the feedback on all the ways and figure out the abstractions and how to let people customize. So we'd love to hear, like, if you think, “Hey, I, I don't want it to work this way,” tell us. We really just wanna get that compatibility layer across agents and then let you do stuff on top.Swyx [00:26:14]: Yeah. is there any, in terms of like the startup side, I'm, I'm a founder.Swyx [00:26:18]: I wantMatei Zaharia [00:26:18]: YeahSwyx [00:26:18]: I see an opportunity, I wanna get in front of you. What's your request for, like, a startup that, like, I wish someoneMatei Zaharia [00:26:23]: Oh, like you wanna integrate with us?Swyx [00:26:24]: someone was working on this.Matei Zaharia [00:26:26]: Oh, for a startup?Swyx [00:26:27]: Yeah.Swyx [00:26:28]: Like, your, you got your own startup. It's doing well.Matei Zaharia [00:26:30]: Yeah.Swyx [00:26:30]: But like, if you weren't working on your own startup, what is, like, obvious that you should You advise many startups too, obviously.Matei Zaharia [00:26:37]: I do think, just as a company with a lot of engineers, like anything that helps me make sense of how people are usingSwyx [00:26:46]: SpendMatei Zaharia [00:26:46]: coding agents and,Swyx [00:26:48]: Yeah. AnalyticsMatei Zaharia [00:26:48]: spend, but also quality or like you should write, you should add this skill, or you should write this thing, or your agents are really horrible at tasks involving this service, so I go spend time. That would be nice. yeah.Swyx [00:27:00]: Yeah. The closest I've found is, this team, GitAI.Matei Zaharia [00:27:03]: Oh, cool. Yeah.Swyx [00:27:04]: They started with, like, we will just do, code and human attribution, but they're building the analytics layer on top of that.Matei Zaharia [00:27:12]: Yeah.Swyx [00:27:12]: I do think, like, there are a bunch of, like, artificial analysis is obviously,Matei Zaharia [00:27:18]: Yeah, they have their benchmarksSwyx [00:27:18]: doing super wellMatei Zaharia [00:27:19]: YeahSwyx [00:27:19]: with their stuff. so there's, there will be people. I think this is like the domain of consultants first, but then peopleMatei Zaharia [00:27:26]: YeahSwyx [00:27:26]: will build software that, let's say, it's kinda like the management planeMatei Zaharia [00:27:29]: YeahSwyx [00:27:30]: for coding agents.Matei Zaharia [00:27:30]: Yeah, I think there'll be a lot of insights there. You have it in other areas.Swyx [00:27:34]: Okay. Well, and then the other, big thing is your dream engine.LTAP: Lake Transactional/Analytical ProcessingSwyx [00:27:39]: maybe you wanna tell the story of, LTAP.Reynold Xin [00:27:45]: So, and background with. I'm, I'm gonna make people listen to our Ankur Goyal episode where we talked about SingleStore, HTAPMatei Zaharia [00:27:52]: YeahReynold Xin [00:27:52]: and all that history.Matei Zaharia [00:27:52]: Yeah. The LTAP idea is pretty simple. so if people have heard of the, Ankur's, talk about HTAP, it's effectively the world of databases. Sorry, there's like maybe a lot of context needs to be injected here. The world of databasesSwyx [00:28:06]: I am happy to be the database podcast that I'm forcing people to, like, learn your databases, guys.Swyx [00:28:11]: You cannot vibe code with just markdown files.Reynold Xin [00:28:13]: Yeah.Swyx [00:28:13]: Like,Reynold Xin [00:28:14]: It's one of the most important fundamental systems technologies out there. But the world of database effectively split into roughly two halves. There's what we call OLTP databases, which are transactional, and think of your Postgres, your MySQL, your Oracle databases, and the other side is what we call analytics, and sometime might refer to term OLAP. And the difference is on OLTP, you typically have maybe run some transaction on some event that looks up at one specific row. We update that row, right? It's a very oriented data structure. And on analytics, you're trying to reason on the data. You're trying to compute, “Hey, what's my revenue per store? What's my. How's my website doing every day?” And then you, eventually want to probably end up running anal- machine learning on it to predict, “Hey, how will my maybe sales be going in the future?” they are so very different architecture, and everybody start with OLTP databases. Every app, when you become serious enough, that needs more than markdown files, you need to have a database. You want to lose your data, you want to have some transactional consistency. But once you want to reason on the data, if you only have like- A hundred rows, it's probably okay to run it on your Postgres or your own, your MySQL database. But once you have more data and want to run more complicated analysis, the very analysis might crush your Postgres database. So you start doing, getting data out of the OLTP databaseSwyx [00:29:35]: Replication.Reynold Xin [00:29:36]: Replicate them into the analytic systems and just startSwyx [00:29:39]: Yeah, which for people, Elasticsearch is, like, aReynold Xin [00:29:42]: Yeah. So some of them get into Elasticsearch for, like, blocked analysis. A lot of our customers obviously get into Databricks to run more sophisticated things.Swyx [00:29:51]: Yeah.Reynold Xin [00:29:51]: And there's this term called CDC, whichMatei Zaharia [00:29:54]: Change data captureReynold Xin [00:29:55]: change data capture. and what it does, it reads the binlog of the database, and if you don't understand what binlog is, it's fine. The, but it's a little delta of the data, and it reconstructs based on the delta, the state of the database, on the analytics side. But CDC is, like, a very painful thing. It's how standard in the industry, everybody uses it, but, it ends up being. I think many data engineers ends up being waken up at, like, 3:00 a.m, because there's some pipeline thing.Swyx [00:30:22]: my explanation is, like, Airbyte is like a, became a $5 billion company just doing CDC.Reynold Xin [00:30:27]: Yeah, exactly.Reynold Xin [00:30:28]: CDC is, like, a veryMatei Zaharia [00:30:30]: It's hard.Reynold Xin [00:30:30]: It's one of the most boring but one of the most fundamental operations, like, powering modern society.Matei Zaharia [00:30:37]: huh.Reynold Xin [00:30:37]: But it's so brittle that, we joke that it's, should be called continuous data corruption, because you might change your schema on your OLTP database, and then the CDC pipeline fails to handleSwyx [00:30:48]: YeahReynold Xin [00:30:48]: the schema change.Swyx [00:30:49]: Yeah.Reynold Xin [00:30:49]: And then everything goes out.Swyx [00:30:51]: And there's all sorts of tricks that you can do, like, you add in, like, some versioning or whatever, but yeah.Reynold Xin [00:30:55]: Yeah, but it's a very, in general, very complicated. Like, I think at my keynote, I asked the audience put up their hand if they love their CDC pipeline. Only, like, maybe two people put it up. So if single store, like, about maybe a decade ago, I think the industry had this idea, hey, what if I built a single database that can handle both workloads? Now I don't.Swyx [00:31:12]: Which, like, by the way, every database person ever has ever always dreamed about this.Reynold Xin [00:31:15]: Yes. Yes.Reynold Xin [00:31:16]: This is the holy grail of database engineering is why not build a single system that can do both of this? But it ends up just being a lot of compromises. one, I think one of the first issue is that, hey, each. they say Postgres has a massive ecosystem, right? You want to be using the tools that's built for Postgres. And Spark, for example, had a massive ecosystem. There's a lot of libraries you want to use. If you were to create now a new thing, you don't have a ecosystem. You tend to create a new, smaller proprietary API, and you're lacking both, and it's also very difficult to make it performance-wise to be, comparable on either side. So it ends up being sucking on both. And our whole idea of LTAP, it's obviously a wordplay on the term HTAP, is that we think this is HTAP done right. HTAP wants to build a single engine for both. We think you can get 99% of what you need by unifying the storage, and just have a single storage layer. And once you have the single storage layer, if your Postgres databases are writing data in a column-oriented format, everything analytics can just go read that data directly without any delay, right? There's no pipeline in between, so all the data will immediately be available for reasoning analytics. I think I was telling some customers earlier, hey, when we talked about this is gonna be super useful for agents, I at first didn't really believe in it myself, even though we wrote that positioning.Lakebase, Agents, and Live Operational DataMatei Zaharia [00:32:39]: Yeah.Reynold Xin [00:32:40]: But then last night I was having dinner with a Australian customer, and they told me, “Oh, hey, one of the big issue we have is we have all these logs from our services, and we see SLA dips and want to investigate. But then there's no way for those agents to even understand what's going on in the actual databases themselves. All we see is just, like, product telemetry of the database and the services.” It would make those agents 10 times more powerful if understand, for example, who's placing those orders, what is happening, what exactly are they doing. So now I'm sold on our own message.Swyx [00:33:13]: Yeah.Reynold Xin [00:33:14]: I think it's really. It gets you the almost all of the benefits of the HTAP holy grail, which is, hey, make the data available immediately for reasoning analyticsSwyx [00:33:26]: Yeah, I think,Reynold Xin [00:33:27]: without compromiseSwyx [00:33:28]: in the way that humans are generally intelligent and want to have the ability and access to query anythingReynold Xin [00:33:34]: YeahSwyx [00:33:35]: while they do the work, they also need history and need context.Swyx [00:33:38]: And, like, where else does they get context? That's it's an analytical workload.Reynold Xin [00:33:41]: Exactly.Matei Zaharia [00:33:42]: Yeah. Yeah. And I remember when we had incidents with our databases and engineers said, “Well, I can't just run a giant query on it to see what's going on because that's gonna bring down the database and hoard it even more.” Like, that's the stuff that this gets rid of, because you spin up a whole separate fleet of machines that's doing the analytics. You're not overloading, like, the main databaseReynold Xin [00:34:02]: RightMatei Zaharia [00:34:02]: that's still trying to serve stuff.Reynold Xin [00:34:04]: Yeah.Matei Zaharia [00:34:04]: Yeah.Why LTAP Works Now: Parquet, Postgres, and LakebaseSwyx [00:34:05]: So this has been a dream for a while. what had to get done in order to get to today? Like,Reynold Xin [00:34:11]: Yeah.Swyx [00:34:11]: I feel like, you have announced variants of this several times, but it wasn't as clear as LTAP.Reynold Xin [00:34:18]: Yeah.Swyx [00:34:18]: I think LTAP is like Like, okay, we've got it, guys.Matei Zaharia [00:34:21]: This thing, yeah.Reynold Xin [00:34:21]: I was talking to somebody at Meta, and then he was asking me, “Hey, what's the catch? Why is it possible now?” And I think the reality is we took a lot of time to work on the Lakebase architecture. obviously a lot of it came from the Neon team, which is a separation of storage from compute. And it turned out it was just a tiny little step away going from that to this LTAP idea, which is, hey, we just. in the Neon architecture and in Lakebase architecture, we're writing data in oriented format to the open data lake, but in there we're writing in Postgres pages. Ali and I were spending a lot of time debating, hey, can we just change that to write in column-oriented format? And we're just debating, and one day, one of our engineers who's, like, super smart came in, he's like, “Hey, I just prototyped it. It works.”Swyx [00:35:07]: Wait, it's, prototype what?Reynold Xin [00:35:09]: Prototype, instead of storing the data in the data lake in the oriented formatSwyx [00:35:15]: ColumnReynold Xin [00:35:15]: like Postgres pagesSwyx [00:35:15]: YeahReynold Xin [00:35:16]: write them in Parquet.Swyx [00:35:17]: Yeah.Reynold Xin [00:35:18]: and he just made the observation that, hey, our storage fleet has a lot of extra idle CPUs And we could use those CPUs to do the transcoding from row to column, where row is good for OLTP, but column is good for analytics. so let's do that transcoding at that time. And as a matter of fact, once you transcode the data compresses better. So from those services writing to, for example, S3 or other data lake, like object stores, you can write them faster ‘cause now they are now smaller.Matei Zaharia [00:35:49]: Yeah.Reynold Xin [00:35:49]: So there's no overhead, it's no compromise in performanceMatei Zaharia [00:35:52]: Some CPU overhead.Swyx [00:35:54]: Yeah, because,Matei Zaharia [00:35:55]: YeahSwyx [00:35:55]: we had extra CPUs anyway.Matei Zaharia [00:35:56]: We had that fleet anyway, yeah.Swyx [00:35:57]: so the debate ended. it's one of the classics of, tech, issue of a lot of debate, but then somebody went ahead and just tried to prototype it and it worked.Matei Zaharia [00:36:06]: But, like, something this strategicSwyx [00:36:07]: That's rightMatei Zaharia [00:36:07]: and important to the company, I expect there to be, like, a kickoff thing, like a design doc. Nothing like that.Swyx [00:36:13]: Nothing like that.Swyx [00:36:14]: He just. We were debating in many meetingsMatei Zaharia [00:36:17]: Yeah.Swyx [00:36:17]: and then we're just debating whether it's possible or not from first principle.Matei Zaharia [00:36:20]: YeahSwyx [00:36:20]: and then, somebody just did it.Matei Zaharia [00:36:23]: Yeah, if you set yourself up so people do that'll be great. And that happened a bit with Omnigentt too. I think if I just had a doc on, like, we can make these together, everyone would, would think, “Oh, what about this? What about this?” But then you. if you try it out, it helps. And then if you have real users and they bash it and, like, it's still working, or in this case, if you have the workload, what the workload looks like, you can just test the same pattern then.Databricks' Culture of Fast PrototypingSwyx [00:36:47]: Yeah.Matei Zaharia [00:36:47]: Yeah.Swyx [00:36:47]: Tech aside, which is very cool, this is, like, the most important thing, the culture of innovation, and you don't have to ask my permission, you don't have like, do a whole form- formal process, just do it?Matei Zaharia [00:36:59]: Well, especially these days, I think withSwyx [00:37:01]: YeahMatei Zaharia [00:37:01]: AI, it's easier to buildSwyx [00:37:02]: But so, likeMatei Zaharia [00:37:03]: a prototypeSwyx [00:37:03]: I think you are very I made a lot of suite of, like, large companies and, like, I think that at scale, things slow down, and I'm sure you felt it already, but somehow you have this core of people that, like, are exempt. How? I think we hire and we work with really good people, and that's a very important part of it, and empowering them, but also spending a lot of time, maybe us in the trenches matter a lot also.Matei Zaharia [00:37:28]: Yeah, I think, I think first, people can adapt to being in the larger company, so that helps. And we wanna make sure they know that they can try stuff and settle debates and have a lot of examples of how it was done before, or launch a thing in beta or whatever. and then the other thing I do think as a company, like despite the size, we don't launch that many, like, products. We try to keep it pretty coherent. That's, that was the whole, like, theory of the company, was like instead of having, like, 20 Amazon services you need to set up, like a analytics and machine learning stack, you just have one, and it's, like, the same API, the same semantics across all of them, the same copy of the data. So that requires, like, unification. And then we added one more thing at a time. Like, we added storage with Delta Lake. We didn't used to do any storage. Then we added SQL, we added, machine learning platform stuff. So, but yeah, don't, don't do too many, but do those things well and, that also helps, it helps keep it manageable.Reynold Xin [00:38:33]: Yeah. The other thing we encourage a lot is instead of building, boil the ocean for everything, let's figure out how do we do it incrementally, how do we do it very quickly. Like, many of our productsMatei Zaharia [00:38:43]: YeahReynold Xin [00:38:43]: they're built in the span of weeks, and then we go to, hey. Like, usually my first question to whoever team is building is who's the target customer? Who are you working with? Are you on a first-name basis with them? Are you texting with them? I think having that very tight loop,Matei Zaharia [00:38:59]: Can you bring up another launch that comes to mind when, in this thing? I just want to give examples.Reynold Xin [00:39:04]: Omnigentt itself happened that way.Reynold Xin [00:39:05]: Yeah.Matei Zaharia [00:39:06]: Who's the customer? That's a good oneReynold Xin [00:39:34]: storage layer we did. we had, our largest customer at the time said like, “Okay, I need some. I want something in the cloud ‘cause, I. if the rest of our network is compromised, like this thing needs to be separate to store and query the events.” And then, talked to us, he said, “Okay, this is the rate of events per second. This is, like, the freshness I want. Can you do it?” So that was, like, way larger than any workload we had, and we had our, engineer, working on that, Michael Armbrust, and he worked just to make this work. And once it worked for them, it worked for everyone else. Yeah. This was early in the company, probably like four years in or something.Matei Zaharia [00:40:24]: 20- 2018?Swyx [00:40:26]: Yeah, ‘17, ‘18.Matei Zaharia [00:40:28]: Few companiesSwyx [00:40:28]: Do you have other examples?Matei Zaharia [00:40:30]: there'Swyx [00:40:31]: Maybe you have othersMatei Zaharia [00:40:31]: yeah, Clean Room, which is how you share data in a way without sharingSwyx [00:40:35]: YeahMatei Zaharia [00:40:35]: underlying data, but you allow specific operations. Those were done effectively initially just for two customers. I think the industry has a sense of, hey, maybe if you overfit to, like, one or two customers, it's gonna be really bad for you. But I think the, downside of overfitting is much smaller than the upside itself. And if you try to be too ambitious and boil the ocean, it's a much bigger problem.Swyx [00:40:58]: Yeah. Yeah.Matei Zaharia [00:40:58]: ‘Cause you might end up having no customer.Swyx [00:41:00]: Yeah, that's more, that's the more likely outcome.Matei Zaharia [00:41:02]: Yeah.Tech Companies vs. EnterprisesSwyx [00:41:03]: than you can pivot from there. I do think there is such a thing as a bad customer that sometimes you should fire. Yeah.Matei Zaharia [00:41:08]: They could exist sometimes if you drive. well, one of the challenge I think we probably see, and maybe many AI, so newer generation companies are seeing is, so tech companies are very different from tech companies or traditional enterprises.Swyx [00:41:22]: Yeah.Matei Zaharia [00:41:22]: And, if you optimize everything just for tech companies, you might have various challengesSwyx [00:41:27]: OhMatei Zaharia [00:41:27]: scaling them outside of tech companies.Swyx [00:41:28]: Okay, what likeMatei Zaharia [00:41:30]: YeahSwyx [00:41:30]: what like top three differences that you always think about?Reynold Xin [00:41:33]: Governance is a big oneMatei Zaharia [00:41:34]: I think, yeah, a big one is like, yeah, security, data privacy, governance, all that stuff. So usually if you're building some kinda like B2B or developer tool, like your biggest market is gonna be enterprises, but it's just very different. A company that's existed for like, it's had some form of IT for like 30 years, they have so many legacy systems or they operate in a regulated space. whereas a startup or, even like a, like sorta more recent tech company, all the. everything is new and pristine. So yeah, it's just different, and if you've never worked with enterprises or been in one, you just won't know about it.Reynold Xin [00:42:13]: Yeah.Matei Zaharia [00:42:13]: Yeah.Reynold Xin [00:42:13]: And the procurement process is probably quite different. There's far more stakeholders.Matei Zaharia [00:42:17]: Yeah, that is one. Yeah.Matei Zaharia [00:42:18]: Another piece that's interesting is I think some tech companies, people, will say, “Oh, I can build that myself,” right? I'll just build that myself.Matei Zaharia [00:42:27]: So then you go,Reynold Xin [00:42:28]: I don't think people say that about Databricks, butMatei Zaharia [00:42:31]: yeah, it dependsReynold Xin [00:42:32]: They do.Matei Zaharia [00:42:32]: They do?Matei Zaharia [00:42:32]: Yeah, the. Yeah, and it depends on the teams and things. So, but, on the other hand, like many of the enterprises say, “I don't, I never wanna be in the business of building that.” Like, I don't want my, whatever, I'm a retailer or something, I never wannaReynold Xin [00:42:45]: Yeah, sell clothes,Matei Zaharia [00:42:46]: be down because like some weird like nerd like couldn't get streaming pipelines working.Matei Zaharia [00:42:51]: That is not what I'm doing.Reynold Xin [00:42:53]: Yeah.Reynold Xin [00:42:53]: Yeah. This makes them great customers, to be honest, right?Matei Zaharia [00:42:55]: Yeah. But you have to understand that it's hard without having worked there and stuff, like you may not appreciate.Reynold Xin [00:43:01]: Look, I think they're all great. don't get me wrong, they have different challenges. But the, many of the tech companies, for sure there's a lot, far more DIY.Matei Zaharia [00:43:10]: On the flip side, you have people who are. they're very much experts in their domain, like they're building airplanes, they're, designing medicines, whatever, and they just want to bridge the technology, where like they don't wanna learn, databases or whatever. As cool as we think it is, even as interesting as the average software engineer might think it is to read a little bit, like they just never wanna know. They just say, “I have a, giant like, matrix or whatever with my, clinical data, like how do I, how do I like cluster it or whatever?” So yeah.The Dream Engine and Rewriting the Database StackReynold Xin [00:43:40]: Yeah. That's true. Okay, so and then I wanted to build out the dream engine, vision. where does this all lead? So one of the thing we, realized maybe a couple years back is that every single database engine out there, especially on the analytics side, are a decade old. pretty much everything that have reasonable traction are about a decade old. And they all started targeting some very specific narrow use cases, and then over time it's become more and more successful. They have grown in their ambition, and then they try to support more and more use cases. But the fastest way to support those use cases tend to be hacked around the abstractions that were initially created, that were not for those use cases.Matei Zaharia [00:44:23]: Yeah.Reynold Xin [00:44:23]: And then, but you can support them more or less okay. And before it, after 10 years of organic evolution that way, it becomes a gigantic pile of s**t.Reynold Xin [00:44:31]: the. And, but that includes Databricks. And very few company or very few systems, I think, have the gut to say, let's go start from scratch. Let's go back to the drawing board and design, knowing everything we know today after a decade of workloads and probably billions in revenue, let's attempt to rewrite it from scratch and make sure it will work and it can support all of these use cases. So we started doing that, but it's a very ambitious project. by the way, you can search on Wikipedia, there's this thing called second system syndrome.Matei Zaharia [00:45:08]: Yeah, I know that. Yes.Reynold Xin [00:45:09]: Or second system effect.Matei Zaharia [00:45:11]: Every developer must know what a second syndrome is.Reynold Xin [00:45:12]: It's you built your first thing and it works out great, and the second one's bound to fail because you become too ambitious.Reynold Xin [00:45:19]: And then you ask so many requirements.Matei Zaharia [00:45:20]: Or like you think everythingReynold Xin [00:45:21]: YeahMatei Zaharia [00:45:21]: and then you're likeReynold Xin [00:45:22]: You justMatei Zaharia [00:45:22]: you're, “I'm gonna design the perfect system this time.”Reynold Xin [00:45:24]: Yeah. And it turned out it's not perfect, and then it start failing and you're too ambitious, never launch, and you get killed. The, and the engineering team that started this, they were brilliant. I think we hired some of the best database engineers, on the planet into Databricks, and they were brilliant. Thank God it's not their second system. Many of them have built more than two in the past.Matei Zaharia [00:45:44]: Ah, nice.Reynold Xin [00:45:45]: But they were still worried about this, hey, building a database engine from scratch, I think the conventional wisdom is gonna take like five years to mature. This would be a very long-term project. It could fail. I think one of the engineers jokingly said, “Hey, maybe we just call it Reynolds Stream Engine.” If we name after a founder, maybe we then may get canceled or killed. But I think they built something pretty remarkable. they went back to. They changed the way the database engines were built from a paradigm point of view. Usually when y
Anders Jones is the CEO and co-founder of Facet, a fintech company built to bring high-quality, flat-fee financial advice to an underserved market: the mass affluent. Facet has raised over $250M and manages more than $7B. Anders has seen the real finances of tens of thousands of households.In this episode of Summation, Anders and Auren discuss:why the percentage-of-assets fee is the greatest heist in financethe retention data showing people stick 3x longer when you help them act, not just advisethe horizontal wealth transfer to spouses that nobody is planning forwhy most companies should never raise venture capitalYou can find Auren Hoffman on X at @auren and Anders Jones on LinkedIn
AI Unraveled: Latest AI News & Trends, Master GPT, Gemini, Generative AI, LLMs, Prompting, GPT Store
SpaceX is acquiring Cursor for $60B days after its blockbuster IPO, as its stock soared 20% on day one. Anthropic's standoff with the Trump administration churned on without resolution, OpenAI's 2025 spending hit $34B, and OpenRouter's Fusion claims to beat frontier models. SEC filing: SpaceX agrees to acquire Anysphere in a merger valuing the Cursor-developer at $60B, expected to close in Q3 2026 (Reuters) SpaceX has agreed to acquire AI coding startup Cursor in a $60B stock deal, less than two months after announcing a tie-up, to help its AI division catch up to the major labs (TechCrunch) SpaceX's stock closed up 19.6% on June 15, its first full day of trading; Musk said it "might be able to reach" ~$1T in revenue in 2030, up from $18.7B in 2025 (CNBC) Anthropic says its senior leaders met Trump admin officials on June 15, but no resolution was reached and both sides are working to resolve things quickly (The Information) A senior White House official says easing Friday's action on Anthropic will likely take more than a few days, but leaves the door open to a quick resolution (Politico) Ben Thompson argues Anthropic has near-perfect alignment between talent, mission, and business — a "safety superpower" he says he both respects and fears (Stratechery) Sources: audited financial figures show OpenAI spending hit $34B in 2025, including $19B on research and development and nearly $6B on sales and marketing (FT) OpenRouter debuts Fusion, a tool that prompts multiple AI models in parallel, claiming it can "reach and surpass Fable-level performance on deep research tasks" (OpenRouter) How OpenRouter Fusion works and how it beats frontier models — a "panel of models" approach that scored 69% on Perplexity's DRACO deep research benchmark (Digit) ResumeWriting.com On Product Hunt Learn more about your ad choices. Visit megaphone.fm/adchoices
Today I speak with Dr. Nina Schwalbe, a public health scientist and former senior leader at UNICEF and Gavi who helped lead a Biden-appointed $7B global COVID vaccine effort, about why she's running for Congress in New York's District 12 in a crowded, money-dominated primary (June 23). We unpack how fundraising, media coverage, Democratic club endorsements, and super PACs shaped by Citizens United create a self-fulfilling "arms race," and she proposes reforms like campaign finance limits, matching funds, and equal-time standards. We also discuss evidence-based, systems-oriented policy priorities: expanding community health centers, lowering drug prices via pooled purchasing and single-payer, restoring CDC/FDA capacity, strengthening Medicare/Medicaid/ACA, investing in public housing, improving transparency and constituent services, and rebuilding trust in science through listening and primary care.(01:59) Why She Ran(05:55) Money And Primaries(08:26) Minimum Viable Campaign(12:08) Machine And Super PACs(16:13) Fixing Campaign Finance(17:06) Public Health Mindset(20:52) Transparency And Accountability(23:45) Street Level Messaging(26:08) NYC Infrastructure Priorities(27:49) Hyperlocal Transit Problems(28:06) What Congress Controls(30:46) Abundance Agenda Debate(32:46) Fixing Public Housing(33:23) Trust in Institutions(35:38) Rebuilding Health Trust(37:12) Community Health Centers(38:13) Why Drugs Cost More(39:42) Restore Public Health Agencies(41:35) Economics Shapes Health(43:39) Single Payer and Prevention(45:58) Chronic Illness Care Gaps(48:58) Working With Paul Farmer(52:23) Vision for Healthcare JusticeNina's campaignFollow Nina on Instagram
What if the thing limiting AI growth isn't chips or power, but wastewater treatment capacity?In this episode of KP Unpacked, KP Reddy and Nick unpack why water infrastructure is the next bottleneck. Jacobs has a $22.7B backlog weighted toward water. AECOM intends to double its water business in three years. Stantec's water practice is its single largest vertical. Meta just built a $70M wastewater plant in Idaho. TSMC broke ground on a 15-acre water reclamation facility in Phoenix targeting 90% recycling. The CHIPS Act, EV gigafactories, and hyperscaler water-positive commitments are pulling wastewater treatment capacity onto private campuses at a scale AEC hasn't seen since the petrochemical buildout of the 70s.KP and Nick reveal Shadow's bet in the space: Western Chemicals, which uses duckweed (a plant that doubles in size every 24 hours) grown on wastewater to filter nitrogen and phosphorus while producing ethanol fuel. The insight? Wastewater treatment consumes 2% of global electricity using heavy machinery to do what biology does for free. Then they pivot to why big ideas need big capital (raising $1M for pre-con AI versus $100M for modular wastewater plants), why college grads complaining about no job offers have recency bias ($250K signing bonuses for 22-year-olds was never normal), and why skepticism from engineering firm LPs is actually an anti-signal Shadow should lean into.Key questions answered:Why is water the next infrastructure constraint after data centers and power?What's Shadow's water infrastructure bet, and what is duckweed?How does duckweed double in size every 24 hours and filter wastewater for free?Why does wastewater treatment consume 2% of global electricity?Why are private companies building their own wastewater plants now?Should founders raise $1M seed rounds or $100M for big infrastructure ideas?Is the college grad job crisis real, or just recency bias from the 2010s?Why is skepticism from engineering LP firms an anti-signal for Shadow?What's the difference between alpha (non-consensus bets) and beta (consensus with upside)?How does Founders Fund operate with only 4 partners managing billions?What happened with the Vinod Khosla/Cloudflare co-founder drama?Why do co-founder breakups kill more startups than bad products?If you're wondering where infrastructure investment flows after data centers, trying to understand why wastewater suddenly matters, or deciding whether to raise incrementally or swing for $100M on a big idea, this episode will show you why the next constraint is already visible, and capital is moving faster than you think.Listen now.
SpaceX priced the biggest IPO ever at $135/share, raising $75B and debuting at $1.77T. ShinyHunters exploited an unpatched Oracle PeopleSoft flaw hitting 100+ organizations, Mistral seeks €3B at €20B, MrBeast hit 500M subscribers, and SBF lost his appeal. SpaceX raises $75B in the biggest-ever IPO, pricing 555.6M shares at $135 each, giving it a market value of $1.77T (Bloomberg) Founders Fund's ~3% SpaceX stake is worth $50B+, Sequoia's ~1.5% is worth $20B+, and a16z will see its biggest return ever at $10B+ (Bloomberg) Some investors question SpaceX's valuation, citing its $4.3B loss on $4.7B in revenue in Q1, as well as concerns over space data centers (NYT) Oracle warns customers of a critical PeopleSoft flaw after ShinyHunters claimed breaches of 100+ organizations using PeopleSoft; Oracle has not issued a patch (TechCrunch) Sources: French startup Mistral AI is in talks to raise ~€3B at a ~€20B valuation; it was last valued at €11.7B during a funding round in September 2025 (Bloomberg) MrBeast hits 500M subscribers on YouTube, a record for the platform (The Wrap) Sam Bankman-Fried loses his bid to overturn his fraud conviction and 25-year prison sentence over the collapse of FTX (Reuters) Longreads As companies are hit by rising AI costs, they are increasingly using tools that tap cheaper models, including some from China, putting price pressure on OpenAI and Anthropic (WSJ) Sixteen economists weigh in on what AI will mean for the US economy, workers, and workplaces; only two expect AI to actually create more jobs (WSJ) Learn more about your ad choices. Visit megaphone.fm/adchoices
Crypto News: Michael Saylor lies saying Strategy never said it would sell its Bitcoin. Visa says it has moved $7B annually in stablecoins through its network. Stellar Development Foundation has unveiled a quantum preparedness plan to migrate all XLM accounts to quantum-resistant signatures by end of 2027. Ripple and Bitso expand their partnership, bringing Bitso's MXN-backed stablecoin MXNB to the XRP Ledger. Brought to you by
Welcome to Omni Talk's Retail Daily Minute, sponsored by Duvo and Mirakl.In today's Retail Daily Minute, Omni Talk's Chris Walton discusses:Macy's reports Q1 net sales of $4.7B, up 1.8% YoY, with comps rising 3% overall and 2.4% at its 200 reimagined stores.Ulta Beauty beats top and bottom line estimates with EPS of $7.74 vs. $6.86 expected and revenue of $3.16B, raises full-year EPS guidance, and credits its TikTok Shop launch, Rare Beauty addition, and fragrance strength for a standout fiscal Q1.Amazon expands AI-powered visual search in its shopping app with real-time generative image suggestions, "Shop by Style" shoppable collages, and visual filter capabilities.The Retail Daily Minute has been rocketing up the Feedspot charts, so stay informed with Omni Talk's Retail Daily Minute, your source for the latest and most important retail insights.
Caroline McAuliffe is Senior Vice President, Head of Corporate Finance (FP&A and Procurement) at Fannie Mae. In its Q1 2026 results, the government-owned mortgage giant boasted 33 consecutive quarters of profitability and $3.7B in net income in the quarter—delivered by a team of 7,000 employees. In this episode Caroline reveals the FP&A mindset and processes behind this success. The career progression from audit to controllership, and FP&A Combining procurement and FP&A Shifting from an annual budget cycle to a 2-year rolling forecast How AI is transforming repetitive low value work including AI “flash reports needed supporting 50 officers at Fannie Mae Secrets to being a CTA (Challenging Trusted Advisor) at Fannie Mae
We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,
A top-5 mobile game with 60-70 million daily active users just disappeared from Google Play for 24 hours and came back. Nobody from Hungry Studios is saying anything. That's the headline.Jakub Remiar flies solo this week with nine stories from the May 23-29 news cycle. The Block Blast Android outage is the biggest shock — a game that big going dark on a major platform for an entire day with zero public explanation. The Monopoly Go licensing story is the most quietly important: Scopely paid Hasbro $41M last quarter alone, confirming the $168M/year run-rate that puts Monopoly Go in genuinely different stratosphere from everything else in social casino. And Valve raised the Steam Deck OLED 1TB from $649 to $949 — a 40% hike that prompted Tim Sweeney to publicly joke about Gabe Newell's $500M super-yacht.Plus: Playtika layoffs as social casino keeps declining, Unreal Engine 6 teased via Rocket League, NetEase posts 7% YoY growth, CD Projekt Red announces a new Witcher 3 expansion called Songs of the Past, IO Interactive's James Bond game hits 1.5M copies in 24 hours, Fortnite returns to iOS to a 3.4M download spike, and the GDC 2026 trend report confirms generative AI is the only thing anyone in the industry is talking about.━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⏱️ TIMESTAMPS00:00 Cold open — the Steam Deck mega-yacht joke00:30 Block Blast vanished from Google Play (and came back)02:00 Playtika layoffs + Monopoly Go's $41M Hasbro license fee03:30 Unreal Engine 6, NetEase Q1, Witcher 3 + James Bond05:24 Sponsor — Potensus06:32 GDC 2026 trend report — generative AI dominates07:30 Fortnite iOS return drives 3.4M downloads in 8 years08:30 Steam Deck +40% price hike + the mega-yacht punchline
Former U.S. Capitol Police officer and author of the book, “Standing My Ground: A Capitol Police Officer's Fight for Accountability and Good Trouble After January 6th,” Harry Dunn talks about his new lawsuit to block the Trump administration's $1.7B payout fund and why he believes it's “putting a retainer on a mob.”Become a supporter of this podcast: https://www.spreaker.com/podcast/tavis-smiley--6286410/support.
Trump's corruption scandals are exploding, Republicans are panicking, gas prices are surging, Congress is rebelling over Iran, and even conservative insiders are warning this could become a political disaster for the GOP.In this episode of Political Rehab, Matt Robison and Matt Wylie break down:Trump's $1.7 billion settlement slush fundThe hidden theory behind the payout schemeJeff Bezos and the collapse of media independenceWhy Republicans are privately terrified about the midtermsTrump's sinking approval ratingsThe revenge tour against Massie, Cassidy, and CornynCongress finally pushing back on Trump's Iran warThe end of Stephen Colbert and what it says about modern mediaWhy corruption may become the defining political issue of 2026PLUS:A powerful Dose of Hope segment on the FDA's breakthrough Alzheimer's blood test rollout and why government-funded science still matters.⏱️ TIMESTAMPS00:00 Intro — “Historic Butt-Kicking”00:34 Trump Dump — Congress abandons oversight03:20 Bezos, Trump, and media corruption06:10 Bye Bye Ballroom08:15 Corruption becomes central political issue09:05 Trump polling collapse10:00 Is Trump giving up on the midterms?11:15 Republicans panic over Ken Paxton endorsement12:05 Trump revenge tour backfires15:05 Senate GOP rebellion over Iran war18:05 Trump stock trades and media silence20:00 Should Democrats run on corruption?21:15 Deep Dive — The end of Colbert and late-night TV24:00 Political satire in the Trump era27:00 Why Colbert worked on Comedy Central29:15 “That's Bullshit” — Trump's $1.7B settlement fund30:30 Theory #1 — Trump chaos and greed31:40 Theory #2 — Ending the IRS audit32:50 Theory #3 — The RICO / mafia model34:10 Cassidy Hutchinson and loyalty payouts35:10 Why the settlement fund may reveal weakness35:45 Dose of Hope — Memorial Day and democracy37:10 FDA Alzheimer's blood test breakthrough38:20 Why early diagnosis matters39:00 NIH-funded science and America's future
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
Vote-a-rama could get underway in the Senate today on the Republicans' second reconciliation bill. Playbook's Jack Blanchard and Dasha Burns dig into Republican anxiety over Trump's ballroom, the $1.7B "weaponization" fund, the Iran war's rising costs, and what the CIA director's Havana trip really means. And how the Democrats could use all this for their midterm messaging.
Wednesday, May 20th, 2026 Today, The Senate finally advances a War Powers Resolution after eight tries; Todd Blanche refuses to block slush fund payouts for convicted sex offenders and rioters that assaulted police on January 6th; the DOJ adds a stipulation to the slush fund that ends the IRS audits into Trump's taxes; Donald has endorsed Ken Paxton for Senate and Republicans are livid; Trump is pressuring John Thune to fire the parliamentarian over his $1B Ballroom budget provision; the top lawyer at the US Treasury has resigned after DOJ established the $1.7B slush fund; and Allison and Dana deliver your Good News. Thank You, Coyuchi Get 15% off your first order when you visit Coyuchi.com/dailybeans Thank You, HomeServe Go to HomeServe.com to find the plan that's right for you. Not available everywhere. Most plans range between $4.99 to $11.99 a month your first year. Terms apply on covered repairs. California Rising - It was a powerful night to launch the fight to win back the House! The show is over but you can still help us reach our fundraising goal! bluewavecalifornia.org/concert Guest: Oliver Larkin Democratic Socialist running for U.S. House FL-25https://www.oliverforcongress.com/ The Latest Breakdown:Retired Judge Blasts Trump's $1.7B Slush Fund for Allies | The Breakdown StoriesThe IRS Thought it Could Fight Trump's Lawsuit, but it Struck a Deal Anyway | NYT Trump Is Pressuring John Thune to Fire the Parliamentarian Over Ballroom Funding - NOTUS | News of the United States Trump shows off White House ballroom construction as funding stalls in Congress | Washington Post Republican Senators Are Livid at Trump's Endorsement of Paxton | The New York Times Senate advances bill aimed at ending Iran war as Cassidy, after primary loss, flips to support | AP News Good Trouble ⭑ 5 Calls → Stop Trump's $1.8B Political Slush Fund →Dump Data Centers MAY 23, UTAH STATE CAPITOL · Indivisible →Recall Gov. Jeff Landry - Louisianadeservesbetter.com →STOP the deportation proceedings against Mohsen Mahdawi - Action Network →SusanRogan - how-to-help-win-the-midterms →detentionwatchnetwork.org →FieldTeam6.org →Standwithminnesota.com →Tell Congress Ice out Now | Indivisible, Defund ICE | 5Calls →Congress: Divest From ICE and CBP | ACLU →ICE List →iceout.org Good NewsSome Colorado Democrats seek to censure Governor Polis over Tina Peters clemency See Dana Sept 23 in Chicago →Share your Good News & Good Trouble - The Daily Beans →Beans Talk audio -beans-talk.simplecast.com →Email Dana LGBTQ Owned eating establishments in your area - hello@mswmedia.com Subject: “Dana's Project” 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 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 in support of Human Rights Campaign 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.
DOJ announced a $1.7B fund to pay insurrectionists using our taxpayer money. Trump is planning to pardon 250 people on America's 250th anniversary. Jamie Raskin has proposed a bill to compensate law enforcement officers who defended the Capitol on January 6th. And Governor Jared Polis commuted the sentence of Tina Peters in Colorado. Allison Gillhttps://muellershewrote.substack.com/https://bsky.app/profile/muellershewrote.comHarry DunnHarry Dunn | Substack@libradunn1.bsky.social on BlueskyWant to support this podcast and get it ad-free and early?Go to: https://www.patreon.com/aisle45podTell us about yourself and what you like about the show - http://survey.podtrac.com/start-survey.aspx?pubid=BffJOlI7qQcF&ver=short Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
0:30 - Massie concession: they couldn't buy my vote so they bought the seat 13:00 - Paxton endorsement 31:59 - San Diego mosque shootings 48:17 - Newly elected chair of the Illinois Republican Party, Bob Grogan, says communication is the key to reshaping the Illinois GOP - "we need to be louder and clearer with what we have to offer" 01:09:17 - President of Center of the American Experiment and contributor to Powerline, John Hinderaker, follows the Minnesota fraud money trail and asks whether it leads back to Ilhan Omar. Get John’s latest at powerlineblog.com 01:29:11 - Noted economist Stephen Moore weighs in on voter ID, calling it an “80/20 issue” and saying, “Let Democrats explain why they don’t want it.” Get more Steve @StephenMoore 01:47:41 - Jack Roth Senior Fellow in American Politics at the Claremont Institute and former director of policy planning at the Department of State, Michael Anton, looks back at "The Flight 93 Election" 10 years later. Michael has two books coming out this summer! Dispatches from the Late Republic – available 6/30 & Studies in Machiavellian Political Philosophy – available 8/18 02:02:44 - UFO files 02:09:48 - $1.7B lawfare fundSee omnystudio.com/listener for privacy information.
Tuesday, May 19th, 2026 Today, Trump has unilaterally dropped his $10B lawsuit against the IRS and has set up a $1.7B slush fund to pay his criminal co-conspirators; the district attorney in Hennepin County Minnesota has charged ICE officer Christian Castro with assault and lying in the shooting of Julio Cesar Sosa-Celis; a jury has dismissed Elon Musk's claims against Open AI CEO Sam Altman; the House Oversight Committee will interview one of the prison guards on duty when Epstein died; and Allison and Dana deliver your Good News. Thank You, IQBAR Text DAILYBEANS to 64000 to get 20% off all IQBAR products, plus FREE shipping. Message and data rates may apply. California Rising - It was a powerful night to launch the fight to win back the House! The show is over but you can still help us reach our fundraising goal! bluewavecalifornia.org/concert Guest: Adam KlasfeldAll Rise News@allrisenews|Bluesky, @klasfeldreports.com|BlueSky, @KlasfeldReports|Twitter, @senecaprojectus - Instagram The Latest Breakdown:Retired Judge Blasts Trump's $1.7B Slush Fund for Allies | The Breakdown StoriesLive updates: Three killed, two suspects dead in shooting at San Diego mosque | NBC 7 San Diego DOJ sets up $1.8B ‘anti-weaponization' fund after Trump drops IRS lawsuit | NBC News House Oversight Committee to interview prison guard on duty when Jeffrey Epstein died | ABC7 New York Minnesota county charges ICE officer in shooting during immigration crackdown | PBS News Jury dismisses all claims in Elon Musk's lawsuit against OpenAI CEO Sam Altman | NPR Good Trouble The next protest here opposing the "deathstar" 'Stratos' data center is by Indivisible, Saturday, 23 May at the Utah State Capitol, 11 am. Dump Data Centers · Indivisible →STOP the deportation proceedings against Mohsen Mahdawi - Action Network →SusanRogan - how-to-help-win-the-midterms →detentionwatchnetwork.org →FieldTeam6.org →Standwithminnesota.com →Tell Congress Ice out Now | Indivisible, Defund ICE | 5Calls →Congress: Divest From ICE and CBP | ACLU →ICE List →iceout.org Good NewsV Spehar (@underthedesknews) - Instagram LouisianaDeservesBetter.comgeauxvote.com/ElectionsAndVoting.html No Detention Centers in Michigan Conserve Ohio →Share your Good News & Good Trouble - The Daily Beans →Beans Talk audio -beans-talk.simplecast.com →Email Dana LGBTQ Owned eating establishments in your area - hello@mswmedia.com Subject: “Dana's Project” 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 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 in support of Human Rights Campaign 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.
It's News Day Tuesday on The Majority Report. On today's program: Donald Trump has secured an illegal $1.7B slush fund from the Treasury that can be used however the president chooses and comes with ZERO oversight. At a Senate Budget Hearing for the DOJ, Senator Chris Van Hollen (D-MD) gets under acting Attorney General Todd Blanche's skin simply by pointing out that he was Donald Trump's personal lawyer less than two years ago. Adam Serwer, author and staff writer at The Atlantic, joins the program to discuss the dismantling of the Voting Rights Act. In the Fun Half: More from Senator Chris Van Hollen's exchange with acting AG Todd Blanche at the Senate Budget Hearing. Carl Quintanilla mentions that Donald Trump has been day trading which sends Jim Cramer in to an apoplectic shock, I guess out of fear of reprimand from his Supreme Leader. Donald Trump claims the requested $1B for his ballroom is actually not for the ballroom, it's for the security around the ballroom. The ballroom is of course at no expense to the taxpayer, unless you count the $1B of tax dollars that are being used to build the ballroom. Zohran Mamdani takes a jab at Ronald Reagan during his speech announcing the construction of a city-run grocery store in the south Bronx. It appears that Artificial Intelligence is really doing a number on Jim Breuer's brain. Meghan McCain proves again that she is an awful person as she attacks Nicholas Kristoff's piece in the New York Times on the horrible abuse that Palestinian prisoners were subjected to at the hands of Israeli guards. All that and more. To connect and organize with your local ICE rapid response team visit ICERRT.com The Congress switchboard number is (202) 224-3121. You can use this number to connect with either the U.S. Senate or the House of Representatives. Follow us on TikTok here: https://www.tiktok.com/@majorityreportfm Check us out on Twitch here: https://www.twitch.tv/themajorityreport Find our Rumble stream here: https://rumble.com/user/majorityreport Check out our alt YouTube channel here: https://www.youtube.com/majorityreportlive Gift a Majority Report subscription here: https://fans.fm/majority/gift Subscribe to the AMQuickie newsletter here: https://am-quickie.ghost.io/ Join the Majority Report Discord! https://majoritydiscord.com/ Get all your MR merch at our store: https://shop.majorityreportradio.com/ Get the free Majority Report App!: https://majority.fm/app Go to https://JustCoffee.coop and use coupon code majority to get 10% off your purchase Check out today's sponsors: WILD GRAIN: Get $30 off your first box + free Croissants in every box. Go to Wildgrain.com/MAJORITY to start your subscription. SUNSET LAKE CBD: Starting today, you can save 35% on your favorite CBD Oil Tinctures with the coupon code Memorial26 at SunsetLakeCBD.com Follow the Majority Report crew on Twitter: @SamSeder @EmmaVigeland @MattLech On Instagram: @MrBryanVokey Check out Matt's show, Left Reckoning, on YouTube, and subscribe on Patreon! https://www.patreon.com/leftreckoning Check out Matt Binder's YouTube channel: https://www.youtube.com/mattbinder Subscribe to Brandon's show The Discourse on Patreon! https://www.patreon.com/ExpandTheDiscourse Check out Ava Raiza's music here! https://avaraiza.bandcamp.
Monday, May 18th, 2026 Today, a former judge weighs in on Trump's $1.7B slush fund for January 6th rioters in a court filing; the Senate parliamentarian has stripped the $1B ballroom provision from Republican's budget reconciliation bill; Colorado Governor Jared Polis has commuted the sentence of voter data thief Tina Peters; the Federal Aviation Administration is going to sharply cut the number of air traffic controllers; a Texas hospital will create a detransition clinic as part of a settlement with AG Ken Paxton; Senator Bill Cassidy lost his primary as Democrat Jamie Davis advances in Louisiana; and Allison Delivers your Good News. Thank You, Helix 27% Off Sitewide when you go to HelixSleep.com/dailybeans Thank You, BoxieCat For a limited time, get 30% off your order when you head to Boxiecat.com/DAILYBEANS and use code DAILYBEANS. California Rising - It was a powerful night to launch the fight to win back the House! The show is over but you can still help us reach our fundraising goal! bluewavecalifornia.org/concert The Latest Breakdown:Retired Judge Blasts Trump's $1.7B Slush Fund for Allies | The Breakdown StoriesTina Peters, Colorado Election Denier, Will Be Freed by Gov. Jared Polis | The New York Times FAA cuts target for air traffic control staffing | Reuters Texas Children's Hospital to develop ‘detransition clinic,' fire physicians as part of settlement, AG says | Houston Public Media Sen. Bill Cassidy loses GOP primary in Louisiana as two rivals advance to runoff | NBC News Senate parliamentarian rejects Trump's ballroom fund in budget bill | NBC News Good Trouble STOP the deportation proceedings against Mohsen Mahdawi - Action Network →SusanRogan - how-to-help-win-the-midterms →detentionwatchnetwork.org →Deliver Mother's Day to the Moms of Dilley →Letter Carriers' “Stamp Out Hunger“ Food Drive →FieldTeam6.org →Standwithminnesota.com →Tell Congress Ice out Now | Indivisible, Defund ICE | 5Calls →Congress: Divest From ICE and CBP | ACLU →ICE List →iceout.org Good News →Share your Good News & Good Trouble - The Daily Beans →Beans Talk audio -beans-talk.simplecast.com →Email Dana LGBTQ Owned eating establishments in your area - hello@mswmedia.com Subject: “Dana's Project” 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 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 in support of Human Rights Campaign 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.
On this week's Red State Update, Jackie & Dunlap yell about corruption (Trump), conmen (Trump), and d*ck doctors. Trump Goes to China, coins "Dumocrats" Trump doesn't worry about american's financial pain "not even a little bit. I don't think about americans' financial situation." Trump settled (with himself?) to drop 10 billion lawsuit against the IRS to instred launch a 1.7B fund for his buds, including January 6thers and other criminals, conmen, and reprobates. Trump's reflecting pool remodel reflects corruption, ineptitude and dumbassery. Vance's anti-fraud task force shuts down Medicaid funding to Blue States, says there's a lot of fraud in the federal government. "It's unbelievable how much you've been fleeced by your own government." LOL! Trump's executive assistant Natalie Harp brings in stacks of printed-out pro-Trump and racist memes for Trump to approve before she posts it all online, with no approval from staff or national security officials. Obama apes? Trump Jesus? Blame her. Mike Johnson says congress only makes $223k a year, let 'em do some insider trading so they can buy shoes for their kids. Knoxville Bans Roots. Tennessee author Alex Haley's book Roots, cultural juggernaut and winner of the Pulitzer winner, banned by Knox County Schools. Chud the Builder: Racist murderous online "personality" arrested in Clarksville, TN. Tennessee Speaker Cameron Sexton removed Democrats from committees and subcommittees for protesting redistricting. Andy Ogles: My kid has nightmares that dad is going to be taken away by big bad Biden FDA Commisioner Marty Makary resigns? over flavored vapes "1 in 3 Americans is underbabied," says Dr. Oz, as he and RFK Jr. obssess over teen sperm counts. Kash Patel watching George Strait on either your dime or some crooks' dime that the FBI won't never investigate now Plus Howard Lutnick, New ICE Leader, TUBERVILLE: "ASSIMILATE OR GO HOME;" says Muslims "here to kll us all" and Trump official who leading Hantavirus response is a penile implant specialist. Dr. Brian Christine is an Alabama urologist and fake admiral who spouts crazy far-right talking points and hosts a YouTube show called Erection Connection. Get 20 Extra Minutes with Jackie and Dunlap at http://www.patreon.com/redstateupdate Art by Yoni Limor Photos by Robyn von Swank Music by William Sherry Jr. Follow us on Instagram, TikTok, Facebook, YouTube, BlueSky
Friday, May 15th, 2026 Today, Donald Trump is poised to steal $1.7B from the Treasury to pay his allies prosecuted under Biden including the January 6th insurrectionists; the Supreme Court restores mail access to mifepristone pending appeal with Thomas and Alito dissenting; Trump Border Patrol Chief Mike Banks has abruptly quit amid reports that he traveled abroad to solicit sex workers; emails show that FBI Director Kash Patel's Hawaii trip included a ‘VIP snorkel' at the USS Arizona; Trump's Reflecting Pool repairs are garbage, over budget, and behind schedule; the Trump administration has paused Medicare enrollment for hospice providers; a Trump-appointed judge says the DOJ has ‘proven unworthy' of trust in a blistering trans care case ruling; and Allison Delivers your Good News. Thank You, Fast Growing Trees Get 20% off your first purchase FastGrowingTrees.com/dailybeans Thank You, OneSkin Get 15% off OneSkin with the code DAILYBEANS at https://www.oneskin.co/dailybeans #oneskinpod California Rising - It was a powerful night to launch the fight to win back the House! The show is over but you can still help us reach our fundraising goal! bluewavecalifornia.org/concert Guest: Ezra LevinIndivisibleBlack Voters MatterEzra Levin | Indivisible@ezralevin - Bluesky Guest: John FugelsangTell Me Everything|John Fugelsang, The John Fugelsang Podcast, John Fugelsang|Substack, @johnfugelsang|Bluesky, @JohnFugelsang|TwitterSeparation of Church and Hate by John Fugelsang The Latest Breakdown:Epstein Survivor Reveals More Docs Hidden by Trump DOJ | The Breakdown Stories Trump poised to drop IRS suit, launch $1.7B 'weaponization' fund for allies: Sources - ABC News Emails show FBI Director Kash Patel's Hawaii trip included 'VIP snorkel' at a Pearl Harbor memorial | AP News Trump Border Patrol Chief Abruptly Quits After Report He Solicited Sex Workers Abroad | HuffPost Latest News Reflecting Pool Repairs Appear Uneven and Behind Schedule, Officials Say | The New York Times Trump administration pauses Medicare enrollments for hospice providers amid fraud investigations | CBS News Trump-appointed judge says DOJ ‘proven unworthy' of trust in blistering trans care case ruling | The Advocate Good Trouble Saturday, May 16All Roads Lead to the South Nationwide Protest 9 AM | Selma — Faith leaders gather at the Edmund Pettus Bridge for prayer 1–5 PM | Montgomery — National Mass Rally at the Alabama State Capitol Actions across the country in support of actions in Montgomery and Selma →Email Dana LGBTQ Owned eating establishments in your area - hello@mswmedia.com Subject: “Dana's Project” →SusanRogan - how-to-help-win-the-midterms →detentionwatchnetwork.org →Deliver Mother's Day to the Moms of Dilley →Letter Carriers' “Stamp Out Hunger“ Food Drive →FieldTeam6.org →Standwithminnesota.com →Tell Congress Ice out Now | Indivisible, Defund ICE | 5Calls →Congress: Divest From ICE and CBP | ACLU →ICE List →iceout.org Good NewsTrevor Project @ruthlesslyhandmaderuthlesslyhandmade.com Minocqua Brewing Companyhttps://www.facebook.com/photo?fbid=1400373722121174&set=a.474813974677158 Cowlitz Beaver Kit Cam Live - YouTube Kern County Animal Services - Bakersfield →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 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 in support of Human Rights Campaign 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 Accenture agency acquisition is still in progress. Five AI tuck-ins closed this week across fintech, crypto, process mining, hardware, and spend management. And three deals that tell you everything about where the lower middle market is heading right now.Christian and Ayelet are back for Deal Review Friday — and this one is packed.Three deals. Five AI tuck-ins. One major tease still in progress. Running a little over 15 minutes. Worth it.⏱️ TIMESTAMPS0:00 — Welcome, May 15th 2026, and what's on the agenda0:45 — Accenture update: deal still in progress, silence is golden1:42 — AI tuck-in #1: Carta acquires Avantia — AI-native legal services + UK international play3:47 — AI tuck-in #2: MoonPay acquires Dawn Labs — autonomous AI trading agents5:38 — AI tuck-in #3: Celonis acquires Ikigai Labs — MIT spin-out, AI professor joins as chief scientist7:30 — AI tuck-in #4: Nominal acquires Fid Labs — AI agents connecting to dev environments and physical hardware8:20 — AI tuck-in #5: Coupa acquires Rossum — document ingestion layer completes source-to-pay stack8:39 — Deal #1: Brands at Work acquires Chorus — two London independents bet on integrated model9:45 — Why experiential has shifted from discretionary to core marketing strategy11:53 — Two independents, no banker, no PE: why this deal is worth celebrating13:05 — Deal #2: Smartly finalizes acquisition of INCRMNTAL — LOI to close in 7 weeks13:30 — What INCRMNTAL actually does and why Smartly needed it15:26 — Smartly manages $7B in media spend — and now has the measurement layer to match16:00 — Props to the INCRMNTAL founders and Smartly's Head of Corp Dev17:16 — Deal #3: OpAd Media acquires Broad Agency — two women-owned independents join forces18:30 — How Carrie Kerpen brought the two teams together at dinner19:30 — Ayelet was at the table when it happened20:30 — Same theme as Brands at Work / Chorus: independents on their own terms21:01 — Girl dinner confirmed. Christian not invited.21:57 — Wrap + episode 60 reminder
Trump's nexus of personal corruption with UAE and Gulf states becoming increasingly clear as latest deals between his Crypto company, 'World Liberty Financial', and UAE's sheik MGX company strike deals. How Trump personally benefits. How Trump's Iran war announcements create volatility in energy stocks sales worth $7B since war began. Update on why 'Project Freedom' collapsed in 36 hours, Trump's targeting of Cuba after Iran, US courts reject Trump's 2nd Tariff plans. Latest on US credit card debt and why AI bubble won't produce real profits for most companies.
Corning and Nvidia partnered to open three optical manufacturing plants in the US, with Nvidia investing up to $2.7B. Morgan Stanley launched crypto trading on ETrade, Google tests a personal agent called Remy, and Meta builds an OpenClaw-inspired agent called Hatch.* Corning and Nvidia partner to open three advanced manufacturing plants in North Carolina and Texas dedicated to optical tech for Nvidia, creating 3,000+ jobs (CNBC) Morgan Stanley rolls out a crypto trading pilot on E*Trade, charging less than Coinbase, Robinhood, and Charles Schwab, ahead of a wider launch later in 2026 (Bloomberg) Sources and a document: Google is testing a "personal agent" codenamed Remy in the Gemini app that integrates with Google services to take actions for users (Business Insider) Sources: Meta is building an OpenClaw-inspired agent, internally called Hatch and powered by its Muse Spark model, and an agentic shopping tool in Instagram (The Information) OpenAI partners with Microsoft, AMD, Broadcom, Nvidia, and Intel researchers to detail the Multipath Reliable Connection (MRC) protocol to help scale compute (The Deep View) Learn more about your ad choices. Visit megaphone.fm/adchoices
7B, Pink Tax & again i'm the most H8ted podcaster/livestreamer which makes me so happy! Thank you
What happens after you “make it”… and realize money was never the point?In this episode, Jyoti Bansal, Founder & CEO of Harness and founder of AppDynamics (sold for $3.7B), sits down with Alisa Cohn for a conversation that cuts deeper than typical startup playbooks.This is not just about building companies. It's about what happens when the finish line disappears… and you have to decide who you are without it.Jyoti shares the unexpected identity crisis that hit after his exit, why he chose to build again anyway, and what most founders misunderstand about success, sales, and staying relevant in a world that's changing faster than your roadmap can keep up.From the brutal reality of startup milestones to the urgency of the AI transformation, this episode is a masterclass in how to think, move, and lead when the stakes are real.You'll learn:Why a worse product can still beat you (and how to prevent it)The hidden identity crisis founders face after a big exitHow to think of entrepreneurship as a craft, not a one-time eventWhy startup growth is about milestones, not perfectionThe real reason most companies fall behind during major tech shiftsHow to operate in “founder mode” when speed is everythingThe difference between product differentiation and go-to-market dominanceHow to build a scalable sales machine (not just hire “charismatic closers”)The hiring framework Jyoti uses to spot elite sales talentHow to align a company fast during high-pressure transformationThe “startup within a startup” model that creates ownership at scaleWhy transparency in numbers builds accountability across the entire orgWe talk about:00:00 The uncomfortable truth after a billion-dollar exit02:00 Why Jyoti came back to build again05:00 The founder identity crisis no one prepares you for08:00 Entrepreneurship as a craft, not a single shot11:00 The milestone framework for building successful companies15:00 The AI transformation and why most companies will lose18:00 Founder mode, speed, and making decisions in real time22:00 Aligning teams when everything is changing fast26:00 Why revenue is the only truth in business29:00 Sales as a competitive advantage 32:00 The myth of “relationship-based” selling35:00 Building a scalable, structured go-to-market machine38:00 How to hire great sales leaders (and avoid getting sold in the interview)41:00 The onboarding mistake most founders make44:00 Transparency, numbers, and company-wide accountability47:00 The “startup within a startup” model explained52:00 Ownership, incentives, and building multiple winning productsFollow Jyoti onLinkedIn: https://www.linkedin.com/in/jyotibansalWebsite: https://www.harness.io/Connect with Alisa!Follow Alisa Cohn on Instagram: @alisacohnTwitter: @alisacohnFacebook: facebook.com/alisa.cohnLinkedIn: https://www.linkedin.com/in/alisacohn/Website: http://www.alisacohn.comDownload her 5 scripts for delicate conversations (and 1 to make your life better) Grab a copy of From Start-Up to Grown-Up by Alisa Cohn from Amazon
In this episode of Molecule to Market, you'll go inside the outsourcing space of the global drug development sector with Steve Favaloro, Chairman and CEO of Genezen. Your host, Raman Sehgal, discusses the pharmaceutical and biotechnology supply chain with Steve, covering: His deep passion for operations, and how early lessons in hard work and customer service shaped his leadership Learning from Mark Bamforth and the entrepreneurial journey at Brammer Bio, culminating in a $1.7bn exit to Thermo Fisher Why gaining exposure to every seat at the table was critical in preparing him for the CEO role Building Genezen with a focus on strong values, hiring the right people and investing in differentiated capabilities How the cell and gene therapy market has evolved over the past decade, and why he remains optimistic about the future of curative medicines Why things inevitably go sideways in innovative drug development, and the importance of being prepared to navigate it Steve Favaloro is an experienced biotech executive, board member, and investor. He is currently Chairman and CEO of Genezen, a best-in-class gene and cell therapy CDMO with specialized expertise in viral vector manufacturing. Steve joined Genezen in 2023 and has built up a team of almost 300 employees, supporting gene and cell therapy innovators from early-stage, growth-oriented biotechs to established industry leaders. He is an executive advisor at Ampersand Capital Partners, a leading healthcare investor, and serves on the board of Biologos. He also serves on the board of advisors of Life Science Cares Boston. Prior to Genezen, Steve was CFO at Arbor Biotech, a next-generation gene editing therapeutic company. Steve also served as CFO at Arranta Bio, a leading CDMO for mRNA, from its founding in 2019 to its successful exit to Recipharm in February 2022. Prior to this, Steve was a finance leader and ultimately CFO at Brammer Bio, where he oversaw a period of rapid expansion and capital deployment from 2016 to 2019 – leading up to its successful sale to Thermo Fisher Scientific in May 2019 for $1.7B. Before joining Brammer, Steve held finance roles of increasing responsibility at MilliporeSigma, Merck KGaA, and Bruker Corporation. Steve received his MBA and Master of Science from the Carroll School of Management at Boston College. Steve also received his Bachelor of Arts degree in Economics from Boston College. Molecule to Market is also sponsored by Bora Pharmaceuticals and supported by Lead Candidate. Please subscribe, tell your industry colleagues and join us in celebrating and promoting the value and importance of the global life science outsourcing space. We'd also appreciate a positive rating!
Nepal just experienced one of Asia's most dramatic recent political upheavals. A former rapper and Kathmandu mayor, Balen Shah, swept to power in a landslide election, winning 182 of 275 parliamentary seats and wiping out every established political party. With half of Nepal's 30 million people under 25, this “Gen Z Revolution” could signal a trend for young democracies worldwide.In this episode, Sujeev Shakya - Chair of the Nepal Economic Forum and senior advisor for Nepal and Bhutan at BowerGroupAsia - explains what happened, why it matters, and what comes next for this small Himalayan country sandwiched between India and China.We explore:• How a youth-led anti-corruption movement toppled the government and formed an interim administration on Discord in just five days• Why Nepal's new PM is focused on public service delivery rather than grand promises, and whether he can actually end decades of entrenched corruption• Nepal's remarkable economic transformation: GDP growth from $7B to $44B in 20 years, fueled by $15B in annual remittances and a booming IT export sector• How Nepal navigates its position between India and China - aiming to be an economic “bridge” rather than a geopolitical buffer• The impact of the Iran war and the Strait of Hormuz closure on Nepal's fuel supply and its two million workers in the Gulf• Why thousands of Nepali soldiers are fighting for Russia in Ukraine - and the new government's challenge of bringing them home• Investment opportunities in hydropower, agriculture, technology, tourism, and infrastructureWhether you follow South Asian politics, India-China competition, or youth-led political movements, Nepal's story offers insights into how small states survive and thrive between great powers.
Why November 30, 2022 Created Winners and Losers (And How Parents Can Still Win Big) Episode Overview AI reshaped the side hustle landscape—but not equally. In this episode, we break down how online entrepreneurship split into two distinct paths: those leveraging AI to build sustainable, scalable income without burnout, and those caught in commodity hell. Discover which side you're on, what shifted for ai entrepreneurs, and the one strategic choice that determines your path forward. Essential listening for parents building flexible side hustles in 2025. Discover how ChatGPT's November 30, 2022 launch permanently divided the side hustle economy into winners and losers. Learn why traditional freelancers lost 5.2% of earnings overnight, while AI-savvy parents are building $5,000-$10,000 monthly income streams. Get the exact framework to transform from competing with AI to collaborating with it, plus the three new side hustle personas dominating the $214 billion AI market. https://DarkHorseEntrepreneur.com Key Moments & Timestamps 00:00 The invisible line splitting 1.57 billion freelancers 01:45 Three key takeaways preview 02:45 The uncomfortable truth about 2021 strategies 03:10 Kitchen table scenario - The freelancer's dilemma 06:45 The New AI Side Hustle Framework begins 08:15 The three AI-era side hustler personas 10:10 The parent advantage in AI economy 11:15 New high-value skills 12:45 Essential AI tools breakdown 14:25 4-week implementation strategy 17:15 Macro-level economic transformation 19:00 Whiskered Wisdom Key Topics Covered
What if the key to scaling your business wasn't more people, but ruthless focus combined with relentless speed?Cameron Herold teams up with Sean Kim, the former President and Chief Product Officer at Kajabi (ex-Amazon, ex-TikTok) and the current Chief Product Officer at HighLevel, to crack open the playbook that turns chaos into proven, compounding wins. Inside this conversation: why leaders who say “no” more often grow faster, how to make data the backbone of every decision, and the real story behind powering a $1.7B creator platform without burning out your team or chasing shiny objects.Listen now to dodge the trap of feature bloat, hiring sprees, and slow, clunky execution. These are unfiltered insights and backed-up frameworks from the inside that you simply won't get anywhere else, unapologetically blunt, deeply actionable, and designed for COOs and founders ready to scale, not just survive.Timestamped Highlights00:23 – Why Sean Kim said “hell no” to TikTok… at first. The surprising conversation that changed everything01:10 – “Discovery” is the real unlock… how it built TikTok's domination and sparked Sean Kim's obsession08:18 – What happens when you leave Amazon's scale for pure startup chaos… scrappy desks, no process, and surviving the LA office09:24 – The secret power of the “doc writing” culture… how writing, not slides, became TikTok and Amazon's unfair advantage12:24 – Ruthless speed… fail fast, double down faster, and outmaneuver every competitor. This is how TikTok really operates14:45 – Why Kajabi never bloats its teams… and how knowing exactly when to hire is a massive competitive edge17:13 – The impact calculator… predicting revenue, retention, and customer wins before a single feature ships25:40 – How to crush “feature creep” and avoid turning your SaaS into a Frankenstein's monsterAbout the GuestSean Kim was previously the President and Chief Product Officer of Kajabi, the all-in-one platform powering $1.7B+ in annual creator revenue. He previously led product teams at TikTok and Amazon Prime, shaping global growth strategies and a customer-obsessed culture. With a reputation for world-class execution and a bold, systems-driven mindset, Sean stands out as a top operator for scale-minded founders and COOs. He is currently the Chief Product Officer at HighLevel.
© 2020-2026 Ivy Podcast Discovery LLC
