Podcasts about Adobe

What happens when the thing evaluating your brand isn't a person — but an agent shopping on their behalf, one that doesn't respond to a great story and doesn't care how your homepage makes anyone feel?Agility has usually meant adapting faster than the market. This is a different kind of adaptation: the audience itself is changing shape, and the brands that handle it well will be the ones that adapt without becoming unrecognizable in the process.Today, we're going to talk about:- What actually changes for a brand when autonomous agents enter the discovery and purchase path- Creating content that holds up to an agent's evaluation without flattening into sameness- Closing the AI capability gap inside marketing teams — and what that unlocksTo help me discuss this topic, I'd like to welcome, Rachel Thornton, CMO Enterprise at Adobe.About Rachel ThorntonRachel Thornton is committed to helping businesses create amazing customer experiences at the intersection of marketing, creativity and AI. As Chief Marketing Officer for Adobe's enterprise business, Rachel is responsible for Adobe's global enterprise Creativity & Productivity and Customer Experience Orchestration marketing.Rachel develops marketing, messaging, positioning strategies and activations that expand awareness of Adobe as the world's best marketing and AI platform for delivering brand-changing customer experiences. Her team amplifies Adobe's unique enterprise story in ways that energize, celebrate and empower CMOs and experience makers worldwide.Rachel has more than 25 years of experience in B2B technology marketing, having held leadership roles at Amazon/AWS, Salesforce, Cisco Systems and Microsoft. She has deep experience building and scaling enterprise marketing, customer acquisition, and self-service revenue contributions.Rachel Thornton on LinkedIn: https://www.linkedin.com/in/rhthornton/---------- Resources ---------- : adobe.comThe Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://aglbrnd.co/r/2868abd8085a9703Reach your customers with Reddit. Spend $500 in ad spend, get $500 back in ad credit! Learn more: https://advertalize.com/r/491818c79fb1873fEnjoyed the show? Tell us more at and give us a rating so others can find the show at: https://aglbrnd.co/r/faaed112fc9887f3Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstromDon't miss a thing: get the latest episodes, sign up for our newsletter and more: https://aglbrnd.co/r/35ded3ccfb6716baCheck out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.comThe Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company Hosted on Acast. See acast.com/privacy for more information.

John Toon is joined by Ian Gregory of Advancetrack and Billie McLoughlin to work through July's accounting tech news, and it turns into a run of arguments about what a general ledger is actually for. Billie opens on the batch newly certified for the Xero App Store. Garfield, the UK's first SRA-regulated AI law firm, reads your Xero data, chases overdue invoices and drafts small claims paperwork for amounts up to £10,000. Autohive is a no-code AI agent that works inside Xero itself. That second one sets up her argument for the episode: firms may be about to stop shopping for tools and start shopping for workers, where you pick the task and the most qualified agent surfaces against it. John is not convinced that reduces the number of apps you end up running, and Ian asks the question nobody has answered yet, which is whether we adapt to Xero's workflows or Xero adapts to ours. Then the leadership news. Xero's CTO Rick Carragher leaves after 16 months, weeks after chief people officer Jeff Ryan went after 15, with Madhuri Dhulipala arriving from BlackRock as SVP of Engineering, Payments and AI Transformation and Maninder Sawhney joining from Adobe as chief business officer. Ian reads the payments hire as a signal about where Xero wants to sit in agentic payments, and makes the point that the future of receipts is the future of bookkeeping. Billie's concern is more practical. If the people who promised you a roadmap leave, does the promise leave with them? Then the ledger layer. FreeAgent now connects directly to Joiin for group consolidation, Acumatica has bought Vertrax to get into fuel and energy distribution, and Crunchafi has launched FRS 102 lease accounting for the UK and Ireland. Ian's line on the Vertrax deal is the sharpest of the episode: this is the operational detail a generic ledger does not understand, and a generic AI agent will not magically invent. Which leads to the argument the episode was always heading for. Someone vibe coded their way off premium accounting software over a weekend and wrote it up on AccountingWEB. Billie is not making her own butter just because butter has gone up, and she puts a number on the Saturday it cost him. Ian reckons it goes the way of open source, a niche for the dabblers and nothing mission critical. John has vibe coded a product himself and is still paying outside experts to check it before anyone touches it. Also covered: the Social Prosperity Network's plan to replace six taxes with a single national contribution, and the progress update on HMRC's Transformation Roadmap, where digital engagement is up and so, awkwardly, is the tax gap. This episode is brought to you by FreeAgent and Suitefiles: freeagent.com suitefiles.com   00:00 Intro 02:17 Xero's July app store intake: an AI law firm and no-code agents 08:38 Xero loses its CTO, and what the BlackRock hire signals 12:33 The chief people officer exits too, and a new chief business officer arrives 17:10 FreeAgent users can now connect straight to Joiin 20:55 Acumatica buys Vertrax and moves deeper into fuel distribution 23:27 Crunchafi brings FRS 102 lease accounting to the UK and Ireland 26:28 Someone vibe coded their way off premium accounting software 34:11 Replacing six taxes with a single national contribution 45:21 Outro

What happens when the thing evaluating your brand isn't a person — but an agent shopping on their behalf, one that doesn't respond to a great story and doesn't care how your homepage makes anyone feel?Agility has usually meant adapting faster than the market. This is a different kind of adaptation: the audience itself is changing shape, and the brands that handle it well will be the ones that adapt without becoming unrecognizable in the process.Today, we're going to talk about:- What actually changes for a brand when autonomous agents enter the discovery and purchase path- Creating content that holds up to an agent's evaluation without flattening into sameness- Closing the AI capability gap inside marketing teams — and what that unlocksTo help me discuss this topic, I'd like to welcome, Rachel Thornton, CMO Enterprise at Adobe.About Rachel ThorntonRachel Thornton is committed to helping businesses create amazing customer experiences at the intersection of marketing, creativity and AI. As Chief Marketing Officer for Adobe's enterprise business, Rachel is responsible for Adobe's global enterprise Creativity & Productivity and Customer Experience Orchestration marketing.Rachel develops marketing, messaging, positioning strategies and activations that expand awareness of Adobe as the world's best marketing and AI platform for delivering brand-changing customer experiences. Her team amplifies Adobe's unique enterprise story in ways that energize, celebrate and empower CMOs and experience makers worldwide.Rachel has more than 25 years of experience in B2B technology marketing, having held leadership roles at Amazon/AWS, Salesforce, Cisco Systems and Microsoft. She has deep experience building and scaling enterprise marketing, customer acquisition, and self-service revenue contributions.Rachel Thornton on LinkedIn: https://www.linkedin.com/in/rhthornton/---------- Resources ---------- : adobe.comThe Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://aglbrnd.co/r/2868abd8085a9703Reach your customers with Reddit. Spend $500 in ad spend, get $500 back in ad credit! Learn more: https://advertalize.com/r/491818c79fb1873fEnjoyed the show? Tell us more at and give us a rating so others can find the show at: https://aglbrnd.co/r/faaed112fc9887f3Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstromDon't miss a thing: get the latest episodes, sign up for our newsletter and more: https://aglbrnd.co/r/35ded3ccfb6716baCheck out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.comThe Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company Hosted on Acast. See acast.com/privacy for more information.

In der heutigen Folge sprechen die Finanzjournalisten Lea Oetjen und Holger Zschäpitz über Freud und Leid bei Amazon und Apple, die enttäuschenden Zahlen von Coinbase und den historischen Fall von Adidas. Außerdem geht es um Microsoft, Roblox, Reddit, Monolithic Power Systems, AXT, Marvell Technology, Live Nation Entertainment, Western Union, Infineon, Siemens Energy, Siemens, Hochtief, Siltronic, Nemetschek, TeamViewer, SAP, IREN, Nebius, Sandisk, Micron Technology, SK Hynix, Bloom Energy, CoreWeave, Core Scientific, Nvidia, AMD, Broadcom, Adobe, Arm Holdings, Lam Research, Schneider Electric, Ahold Delhaize und Walmart. Mit dem Code „AAAFRIENDS“ sparst du 50 Prozent auf dein Ticket – aber nur unter folgendem Link. https://veranstaltung.businessinsider.de/event/financesummit26/summary?rp=c6dc55d6-6f4f-4fb4-b75f-3f3501d84859 Wir freuen uns an Feedback über aaa@welt.de. Noch mehr "Alles auf Aktien" findet Ihr bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts. Hier bei WELT: https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html. Hier könnt ihr den AAA-Newsletter abonnieren: https://www.welt.de/newsletter/article232797673/Alles-auf-Aktien-Der-taegliche-Boersen-Newsletter-fuer-WELTplus-Abonnenten.html Und – ganz neu: AAA gibt es jetzt auch auf Instagram: https://www.instagram.com/alles_auf_aktien/ Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte! https://linktr.ee/alles_auf_aktien Anzeige: Eight Sleep: Der Pod 5 reguliert die Temperatur im Bett automatisch, trackt Schlaf- und Gesundheitswerte ohne Wearable und kann so zu besserem Schlaf beitragen. Mit dem Code ALLESAUFAKTIEN erhaltet ihr auf https://www.eightsleep.com/allesaufaktien bis zu 350 Euro Rabatt. Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

Those Long-Term Chip Deals May Not Be as Secure as Investors Are Led to Believe When you listen to memory chip companies like Samsung Electronics, SK Hynix, and Micron Technology discuss their businesses, they often make it sound like customer contracts—some extending as long as five years—are essentially set in stone. Unfortunately, that's not entirely true. Yes, these companies have long-term agreements in place, but contracts in this industry are often renegotiated when market conditions change. If demand for memory chips weakens significantly, chip manufacturers have a strong incentive to work with their customers rather than strictly enforce every contractual commitment. The reason is simple: preserving long-term customer relationships is often far more valuable than maximizing short-term revenue. Imagine a customer that suddenly doesn't need as many chips because its own sales have slowed. If a supplier forces that customer to accept unwanted inventory, those chips may simply sit in a warehouse until demand recovers. By the time the customer needs additional chips, it may choose to reduce future orders or move business to a competitor that proved to be more flexible during difficult times. Competitors are always looking for opportunities to gain market share. If one supplier refuses to work with its customers, another is usually willing to offer better pricing or more favorable terms. Losing a major customer over a rigid interpretation of a contract can cost far more in future profits than making temporary concessions during a downturn. This isn't just theory and it has happened before. During the COVID-era, many long-term agreements were adjusted as demand shifted. Rather than forcing customers to take products they no longer needed, suppliers often renegotiated delivery schedules and purchasing commitments to preserve long-term partnerships. The same principle applies across many industries. Companies frequently modify or delay large commercial agreements when business conditions change. While contracts provide a framework, successful businesses understand that maintaining trust with key customers is often more important than enforcing every clause to the letter. Investors should remember that a signed contract does not necessarily guarantee future revenue will be recognized exactly as originally planned. Management teams often emphasize the value of their long-term agreements during earnings calls, but those agreements can evolve if market conditions deteriorate. At the end of the day, great businesses understand that customer relationships are built over years but can be damaged in a matter of weeks. In many cases, giving a customer flexibility during a downturn is a much better investment than insisting on strict contract enforcement. That's why investors should view long-term chip contracts as valuable, but not invincible.   Why Index Investing Could Leave You Disappointed Long Term I often hear people say, "Just buy the S&P 500 and forget about it. You'll be fine." While that sounds simple, investing is rarely that easy. Many investors don't fully understand how an index works or why it has performed so well in recent years. The S&P 500 has been driven largely by a handful of technology and AI companies. By blindly investing in the index, many people are simply participating in a momentum strategy without realizing it. Very little thought is given to what those 500 companies are actually worth. There is no effort to trim positions that have become extremely expensive or overly concentrated. As valuations climb, the index simply gives those companies an even larger weighting, leaving investors with greater exposure to the stocks that have already gone up the most. Some people respond by saying, "I won't put everything in the S&P 500. I'll diversify into other index funds." But once you go down that road, investing becomes much more complicated and you'll likely underperform the S&P 500. Should you own an international index? A European index? A bond index? A growth index? A value index? Small-cap funds? REITs? There are hundreds of ETFs and mutual funds to choose from. Now you have another challenge: deciding how much to allocate to each one. When your portfolio declines will you understand why? More importantly, will you know what to do next? Many investors don't, and that uncertainty often leads to emotional decisions at exactly the wrong time. This is why I prefer managing a portfolio of individual value-oriented stocks, combined with money market funds and selected real estate investment trusts (REITs). That approach still provides diversification, but I understand what each investment is worth and why I own it. In my view, that's a much better foundation than owning five or ten different index funds without truly understanding what's inside them or how they're valued. Another common argument for index investing is lower fees. While fees certainly matter, they shouldn't be the only factor. The number that ultimately matters is your total return after all fees and expenses. A lower fee doesn't automatically translate into better long-term performance. If you own index funds, take some time to look under the hood. Do you really understand what you own? Do you know which sectors dominate your portfolio, which companies make up the largest holdings, and how expensive those businesses are today? If the answer is no, don't assume you'll be comfortable when the market experiences its next major decline. Investors who don't understand what they own are often the first to panic, and that confusion can lead to costly investment mistakes.   The U.S. economy is still in much better shape than many people think. This week brought three major events for investors: GDP, PCE inflation, and the Federal Reserve meeting. While the headlines may have sounded mixed, the underlying data still paints a healthy consumer. Second-quarter GDP grew at a 1.5% annualized rate, below economists' expectations. At first glance, that may seem disappointing. But when you look under the hood, the economy continues to show resilience. Consumer spending, which accounts for nearly 70% of U.S. GDP, increased 3.2% after a weak first quarter where it only climbed 0.5%. That tells me the American consumer is still in good shape, and that's one of the biggest reasons the economy continues to avoid the recession that so many have been predicting. Major drags on the headline GDP figure included government spending, which reduced growth by 0.14 percentage points, as well as the more volatile components of trade and the change in private inventories, which subtracted 1.01 and 0.67 percentage points, respectively. Inflation remains the biggest challenge. The Fed's preferred inflation measure, core PCE, increased 3.3% over the past year. While that's an improvement from where we've been, it's still well above the Federal Reserve's 2% target. I continue to believe inflation will remain sticky until energy prices become more stable. Energy impacts transportation, manufacturing, and virtually every supply chain, so it's difficult to see inflation falling sustainably while energy costs remain volatile. The Fed, as expected, left interest rates unchanged. What stood out wasn't the decision, it was the growing disagreement among policymakers. The 3 dissents that voted for a 25-basis point increase highlight just how uncertain the economic outlook remains. When inflation is still elevated but the economy continues to grow, there isn't an easy policy answer. One thing I do like so far is Kevin Warsh's changes at the Fed. I like the simplified statement, the encouragement of differing viewpoints, and rather than projecting absolute confidence in economic forecasts, he has acknowledged the uncertainty surrounding them. That's a refreshing change. Economic forecasting has never been an exact science, and I would rather have a Fed Chair who recognizes the limitations of those projections than one who pretends they are precise. What's surprising is how quickly some of the talking heads have claimed Warsh already has a credibility problem. I don't see it that way. Credibility isn't about making bold predictions that later need to be revised. It's about being honest about what we know, what we don't know, and allowing incoming data to guide policy. The takeaway for investors is simple: don't let one headline drive your investment decisions. The economy continues to expand, consumers are still spending, inflation remains stubborn, and the Fed is navigating a difficult policy environment. Looking beneath the surface is often where you'll find the real story.   Leverage Is Fuel... Until It Becomes the Fire The last few weeks have been a reminder that leverage looks like a wonderful tool on the way up... but it's a devastating one on the way down. FINRA's new margin rules have effectively replaced the 25-year-old Pattern Day Trader rule, allowing traders with as little as $2,000 to make unlimited day trades using intraday margin. While this opens the door for more retail participation, it also means more investors have access to leverage, something that has historically magnified both gains and losses. This is a big problem considering FINRA margin debt climbed 49% year over year to another record in June of roughly $1.5 trillion. This comes as investor net credit balances have fallen to a record negative $1.06 trillion. In other words, investors collectively owe more on margin than they have sitting in cash accounts. For comparison's sake, in March 2000 this measure stood at a negative $0.13 trillion. That's an aggressive setup if volatility returns. We also saw this past week the spectacular collapse of Leopold Aschenbrenner's AI-focused hedge fund, Situational Awareness, which shows what can happen when conviction is paired with excessive leverage. The near 25-year-old Aschenbrenner was painted as a genius with strong credentials like being Columbia University's valedictorian at age 19. His fund was launched in July 2024 and he had no experience managing money before that. Before this month's decline the fund had gains of more than 1,000% since inception. The fund used tons of leverage with some saying as much as 400% to build massive positions in AI and semiconductor stocks while shorting stocks in the software space like Adobe. The problem is when names like Coreweave, Nebius, and Sandisk fell more than 50% from their highs and the software stocks rallied, margin calls forced the liquidation of most of its public equity portfolio. The result was staggering considering the fund peaked at above $45 billion in assets and with the selloff they plunged to around $10 billion. This forced a fire sale of assets at a discount to Ken Griffin's Citadel. Some speculate that the forced selling may have helped create the bottom. Once one of the market's largest leveraged sellers had finished liquidating, the selling pressure eased and many AI stocks staged a sharp rebound. Others believe the selling is not over as Michael Burry reportedly used Thursday's powerful rally as an opportunity to increase several of his bearish positions in Micron, Nvidia and the VanEck Semiconductor ETF. Whether he's ultimately right or wrong remains to be seen, but it's a reminder that some experienced investors still believe AI-related valuations and leverage remain stretched.   Here Come the Robots! Robots have been making their way into manufacturing for decades. The first industrial robotic arm, called Unimate, was installed in 1961 on the assembly line at a General Motors plant in Trenton, New Jersey. But today's robots are very different. They're no longer just stationary robotic arms bolted to the factory floor, they're starting to look and move like humans. That reality is beginning to make workers uneasy. At a Hyundai Motor plant in South Korea, employees have gone on a partial strike, with concerns over automation playing a role. Hyundai recently unveiled its humanoid robot, Atlas, which stands 6'2", weighs about 200 pounds, can lift up to 110 pounds, and can continuously carry nearly 70 pounds. It's easy to understand why workers are wondering what these machines could mean for their jobs. South Korea is already the world leader in industrial robot adoption, with approximately 1,220 industrial robots for every 10,000 manufacturing employees. By comparison, the United States has around 307 robots per 10,000 workers. One statistic that surprised me was China, which currently has only about 166 industrial robots per 10,000 manufacturing workers. If Elon Musk has anything to say about it, those numbers could change dramatically over the next several years. Tesla is aggressively developing its humanoid robot, Optimus, with the goal of having it help build vehicles in its factories before long. If that vision becomes reality, other manufacturers will almost certainly follow. The idea of humanoid robots can be unsettling, but the transition is likely to be slower than many people expect. Industry forecasts suggest that global annual production of humanoid robots could reach roughly 1.2 million units by 2030. While that sounds like a large number, it's still a tiny fraction of the global workforce. So, we're probably still a few years away from living like The Jetsons. If you're not familiar with the cartoon, it debuted in September 1962 and imagined a future filled with flying cars and household robots. I guess I will have to wait a few more years to get a maid like the Jetsons had named Rosie the robot.   Financial Planning: Tax Relief Coming for Older Home Sellers? The federal home sale capital gain exclusion has remained unchanged since 1997, allowing homeowners to exclude up to $250,000 of gain if single or $500,000 if married filing jointly when selling a primary residence. With home values rising significantly over the past three decades, particularly in high-cost areas like California, many long-time homeowners now face substantial capital gains taxes when downsizing. A new proposal, the Nest Egg Protection Act, would increase the exclusion to $1 million for homeowners age 65 and older who have owned and lived in their home for at least 25 years. This would allow more seniors to keep the equity they've built over a lifetime. In addition to providing tax relief, the proposal could encourage more older homeowners to sell, increasing housing inventory and making homeownership more attainable for first-time buyers. While the legislation has not yet been enacted and homeowners should continue planning under current law, the proposal reflects a growing recognition that the existing exclusion no longer aligns with today's housing market.   Companies Discussed: International Business Machines Corporation (Ticker: IBM)

Allan Gungormez is a brand strategist, and social culture expert based in Los Angeles, California. He is the Chief Strategy Officer at MDRN Logic, a premier creative agency and strategy studio that specializes in social-first branding, internet culture, and entertainment marketing for global brands like Adobe, Crocs, DoorDash, Manscaped, Bad Bunny, and FX.Career & PhilosophyBefore MDRN Logic alongside Erica Coates, Allan spent over a decade leading strategy and creative direction for high-profile agencies, including senior roles at Mocean. Throughout his career, he has focused on helping brands transition away from traditional, top-down advertising models toward real-time, culture-led engagement.Allan is known for a zero-fluff approach to strategy that rejects generic industry platitudes and white-paper theories.

In dieser Folge nehme ich dich mit nach München und spreche mit Martin Brösamle von eggs unimedia über die echte Welt des PreSales, diesmal nicht aus Sicht des Softwareherstellers, sondern aus der Partnerperspektive. Wir diskutieren, wie erfolgreiche Zusammenarbeit zwischen Partnern und Herstellern aussieht, wo es knirscht, und was einen Trusted Advisor wirklich ausmacht, wenn nicht das Quartalsende, sondern langfristiges Vertrauen zählt. Martin gibt tiefe Einblicke, wie sein Team PreSales, Delivery und Account Management aufstellt und welche Rolle Ehrlichkeit und Partnerschaft spielen. Außerdem beleuchten wir, wie sich Preismodelle durch KI und neue Technologien verändern und warum Beratung und Enablement wichtiger werden als klassische Implementierung. Wenn du wissen willst, wie sich die Rolle des PreSales Consultants gerade wandelt und welche Strategien jetzt wirklich zum Erfolg führen, dann ist diese Folge für dich gemacht. Martin bei LinkedIn - https://www.linkedin.com/in/martin-broesamle/ ----------

Casa Navarro State Historic Site in San Antonio is offering families a morning of fun from 10 a.m. to noon every Wednesday through August. Each week features a different hands-on activity, craft, or demonstration designed to help visitors explore history through creativity and play. Activities are geared toward children but are suitable for all ages. Activities are included with general site admission, and no advance registration is required. Scheduled activities include: •July 29 — Corn-grinding demonstrations •Aug. 5 — Paraph, a signature flourish, activity •Aug. 12 — Ojos de Dios craft •Aug. 19 — Adobe brickmaking •Aug. 26 — Tin... Article Link

Peter Eastway has been photographing for more than 50 years, from film era long exposures and surf photography to becoming one of Australia's most decorated and internationally recognised photographers. He's also spent four decades in publishing, running Better Photography magazine since 1995. His concept of The New Tradition and invisible Photoshop sits at the heart of how he works, subtle, seamless post production that expands creative control without drawing attention to itself. It's an approach that's earned him a Grand Master of Photography title and ambassadorships with Phase One, Canon, Adobe and others. In this episode Peter talks about using projects to guide shooting, building a database of ideas by studying other artists, and why he makes work for himself rather than for judges. He's also refreshingly candid about the business realities of professional photography, his practical view of AI as a tool, and the difference between running workshops and photo tours. I hope you enjoy the show! You can find Peter's work here: Websites: https://www.petereastway.com/ https://www.betterphotography.com/ Instagram: https://www.instagram.com/petereastway/ Facebook: https://www.facebook.com/peter.eastway   Listen to this and other episodes wherever you find your podcasts or on https://grantswinbournephotography.com/lpw-podcast Or subscribe to my YouTube channel https://youtube.com/@grantswinbournephotography   Theme music: Liturgy Of The Street by Shane Ivers - https://www.silvermansound.com #landscapephotography #australianphotographer #photoshoptechniques #betterphotography #finartphotography #photographybusiness #digitalphotography #photographypodcast

Heiko Thieme bleibt im Gespräch mit Börsenradio-Host Peter Heinrich trotz zahlreicher geopolitischer Baustellen optimistisch: Zum Jahresende hält er neue Rekorde für möglich. Für den DAX nennt er ein Ziel von 27.500 Punkten. Gleichzeitig warnt er vor einer Sommerkorrektur von 5 bis 10 % und empfiehlt rund 20 % Liquidität. Seine Strategie bleibt strikt antizyklisch: gefallene Qualitätswerte kaufen, in Tranchen vorgehen und Gewinne diszipliniert sichern. Ab einem Kursplus von 25 % soll das erste Drittel verkauft werden, ab 35 % das zweite. Der Rest läuft mit nachgezogenem Stopp weiter. China gewinnt für Thieme an Gewicht. Wegen der technologischen Aufholjagd und der Stärke bei Batterietechnik, Solarenergie, Windkraft und seltenen Erden hält er einen Depotanteil von bis zu 10 % für vertretbar. Bei Novo Nordisk und Adobe rät er Clubmitglied Julius zu Geduld statt zu einem Panikverkauf: Beide Positionen halten und den Kauf einer weiteren Tranche erst bei deutlich tieferen Kursen prüfen. Auch Mercedes, SAP und gefallene Nebenwerte wie Adesso sieht Thieme als Chancen für geduldige Anleger. Seine Kernbotschaft: Nicht den Kursen hinterherlaufen, sondern dort hinschauen, wo andere längst das Interesse verloren haben. Hier geht es direkt zur Clubausgabe: https://www.heiko-thieme.club/2026/07/28/heiko-thieme-die-boerse-liebt-gefallene-engel-aber-keine-schlafenden-anleger/ Werden Sie Clubmitglied

KI-Agenten sollen vor allem Kosten senken – und werden damit unter Wert verkauft. Der eigentliche Hebel liegt woanders: im Wachstum. Julian Kramer ist AI Evangelism Leader EMEA bei Adobe und bringt Unternehmen bei, wie sie KI sinnvoll einsetzen. In dieser Folge bricht er mit Joel herunter, was es wirklich braucht, damit KI-Agenten dein Unternehmen hebeln – jenseits von Prompts und Tool-Tutorials. Wir sprechen darüber, warum du Fähigkeiten aufbaust statt Stellen zu streichen, wie du KI-Autonomie mit Explainability by Design kontrollierbar machst, welche Teamprofile (Stichwort: W-Shaped People) du künftig brauchst – und wie deine Marke in der KI-Suche überhaupt auffindbar bleibt. Du erfährst... … warum KI-Agenten kein Sparprogramm sind, sondern dir Fähigkeiten schenken, die dein Unternehmen nie hatte … wie du KI-Autonomie zulässt, ohne die Kontrolle zu verlieren … warum du künftig W-Shaped Personalities brauchst … wie deine Marke in der KI-Suche auffindbar bleibt und warum Chatbot-Traffic 34 % besser konvertiert __________________________ ||||| PERSONEN |||||

Sometimes in life, a dream or an idea fades, and you come to the realization that it may never happen. It's ok. Krysta talks about her coming to peace with her thoughts!I apologize for the earlier audio something went wrong with Adobe it has now been fixed.linktr.ee/discontinuedgravy

Series: Tech & AI Series Ep 4Title: Content, Tech & AI Tools in Sports Business Guest: Domenick DiMinni, Adobe Sports & Live Entertainment

In this episode, Mark Jones sits down with Emmanuel Cruz, Assistant Vice President of Digital Marketing at Philippine Airlines, to explore how the 85-year-old airline is transforming for a digital-first future. Cruz paints a fascinating picture of the Philippines' social landscape, where Facebook, Instagram and TikTok aren't just channels, they're part of everyday life, shaping how people discover, connect and buy.  He shares how Philippine Airlines has shifted to a platform-first strategy, creating different content for different audiences: Facebook for the mass market, Instagram for premium travellers, and TikTok for younger consumers. The conversation reveals how social media is collapsing the traditional marketing funnel, with awareness, consideration and conversion increasingly happening in the same place.  Drawing parallels to the kind of cultural fandom that powers global stars like Bad Bunny, Cruz explains why brands today need to participate in culture, not just advertising. He also discusses AI-generated content, personalisation, viral campaigns, and the airline's award-winning safety video, which tapped into Filipino culture to drive remarkable engagement.  It's a masterclass in how brands can stay relevant by combining heritage, technology and social-first storytelling in an AI-powered world.    This episode is brought to you by impact advisory, communications and events agency, ImpactInstitute in partnership with Adobe.  www.impactinstitute.com.au | https://business.adobe.com/au

Bienvenidos a un episodio crítico de Fotógrafo Pro, nuestra última línea de defensa contra una industria que parece preferir coleccionistas de lo retro antes que autores de lo real. Hoy no celebramos lanzamientos; analizamos una historia de supervivencia en un mercado que ha decidido que lo humano y lo técnico ya no son prioridad.En este episodio exploramos:El Fenómeno Kodak E35: Desmontamos las entrañas de este nuevo dispositivo de $35. ¿Es una puerta de entrada al arte o un juguete con velocidad de obturación bloqueada que eleva los costos para los verdaderos profesionales?.La Trampa de la Estética Retro: Canon se une a la tendencia nostálgica con sensores de 32.5 MP en cuerpos de "cuero sintético". Debatimos si vale la pena pagar un "impuesto al estilo" a cambio de sacrificar la ergonomía y la salud de tus manos en jornadas de 8 horas.Guerra de IAs (Meta vs. Adobe): Analizamos la victoria moral de los artistas frente a Meta y la agresiva ofensiva de Adobe, que ha recortado hasta un 30% sus precios de créditos generativos, devaluando el capital creativo del post-productor.Seguridad Nacional y tu Equipo: Advertencia sobre las investigaciones de la FCC del 14 de julio de 2026. Descubre por qué comprar marcas blancas o imitaciones podría significar la pérdida total de tu inversión por restricciones legales inmediatas.El Último Clavo en el Ataúd de DSLR: El desabastecimiento deliberado de lentes Nikon para montura espejo. ¿Te quedarás atrincherado por nostalgia o aceptarás que mantener esta inversión es un riesgo financiero frente al sistema Z?.Conclusión: ¿Qué nos define como autores cuando apagamos la cámara? No permitas que nadie le ponga precio a tu identidad a través de algoritmos o lentes que desaparecen por decreto corporativo.

Sean Peche has spent 2026 doing the opposite of the crowd. While the market piles into AI, memory chips and Elon Musk's every pivot, the Ranmore founder has been selling Tesla, walking away from SpaceX and quietly buying into Adobe and Tencent instead. In this conversation with Alec Hogg, the South African-born, UK-based fund manager breaks down why Alphabet's capex bill worries him, why Britain's new Prime Minister just handed him a trade, and why getting rich slowly beats chasing the next shiny thing.

In this Production Expert Podcast Special Edition, Mike Thornton, co-founder of Production Expert invites Orfeas Boteas, founder and CEO of Krotos, and Matthew Collins, head of product, to address significant community concerns following the release of Video to Sound integration for Adobe Premiere Pro and DaVinci Resolve.This candid conversation tackles difficult questions about the company's strategic direction, the role of AI in sound design, misleading social media practices, and Krotos' commitment to the professional audio community. Orfeas and Matthew respond directly to concerns from industry professionals and clarify misconceptions about generative AI, product positioning, and future roadmap.In This Episode:Why Video to Sound Released for Adobe Premiere Before DAWs — Technical constraints preventing DAW video track access, with integrations coming later in 2024Addressing 'Abandonment' Concerns from Audio Professionals — Krotos is expanding markets, not abandoning professionalsThe Core Mission: Making Sound Important Earlier — Bringing sound into creative workflows as an equal partner rather than afterthoughtAI is Not Generative: The Truth About Krotos Technology — All sounds are professionally recorded; AI is assistive, not generativeAI as Automation, Not Replacement — Automating tedious tasks like auditioning thousands of sounds, not replacing human creativityEvolution from Specialised Tools to Krotos Studio Platform — Journey from Dehumanizer, Reformer, Weaponizer to unified Krotos StudioMisconceptions About Social Media Posts and Credits — Addressing posts implying Krotos involvement in Jurassic World, House of the Dragon, and Nolan filmsSound Redesign vs. Official Production Credits — Distinguishing creative sound redesign exercises from official product involvement claimsSocial Media Clarity and Labelling Issues — Acknowledging unclear messaging in fast-scrolling social feeds, committing to better labellingIndustry-Wide Social Media Challenges — Platforms encouraging content reuse without explicit permission creates attribution confusionMarketplace and Collaboration Possibilities — Vision for professionals to contribute assets and create ecosystem marketplaceIndustry Change with New Technology — Technological disruption requires community to work as united front, not adversariallyCommunication Missteps on Product Roadmap — Acknowledgement of moving too quickly without explaining technical constraints and timeline to communityAbout Our Guests:Orfeas Boteas:Founder and CEO of Krotos, leading sound design software company. Founded Krotos with a passion for sound, guiding the company through evolution from specialised tools to the broader Krotos Studio platform. Deeply committed to understanding and supporting the audio production community.Matthew Collins:Head of product at Krotos, responsible for product development and strategy. Works with both the professional audio community and content creators to develop streamlined sound design tools. Actively communicates technical roadmap and product philosophy to industry stakeholders.About Our Host:Mike Thornton has been involved in the broadcast audio industry for all his working life, some 45 years. Mike has worked with Pro Tools since the mid-1990s, recording, editing, and mixing documentaries, comedy, and drama for both radio and TV, as well as doing the occasional music project. He was the co-founder of Pro Tools Expert and has now retired, taking up the role of Chairman of Production Expert Ltd., bridging innovative companies and the professional audio industry.

This week on AwesomeCast 788, Sorg, Katie Dudas, and Dave Podnar explore the growing appeal of retro technology, screen-free devices, customizable gadgets, and digital tools that give users more control. Katie begins with Mama's Night Off, a browser game inspired by Dungeon Crawler Carl and the Maeve Chocolate Dirty Shirley bar. Dave introduces Garmin's new screenless fitness tracker, an alternative to smartwatches and subscription-heavy wearables. Sorg shares his experience setting up RetroArch on Apple TV, including its wide range of supported systems, open-source games, ROM management, controllers, and the tinkering required to make everything work. The conversation expands into the tension between digital convenience and physical ownership. The hosts discuss GameStop's response to Sony's physical-media decisions, the rapid progress of PlayStation 5 emulation, and why consumers are returning to CDs, vinyl, cassettes, retro games, and other physical formats. They also have an extended discussion about generative AI, creative production, AI-assisted image editing, automation tools, corporate responsibility, electrical-grid pressures, and the rapid construction of data centers. The hosts examine the difference between rejecting AI entirely and learning how to use it responsibly while still demanding accountability from businesses and government. Later, the show looks at NASA's Psyche spacecraft capturing a Mars flyby, Pittsburgh movie history through the Pastfinders app, a minimalist flip phone for people who want fewer distractions, a development board that turns old Nintendo Wii Remotes into customizable controllers, and Google Photos restoring a traditional search option alongside Ask Photos. Stories and Gadgets Discussed Awesome Things of the Week Mama's Night Off and the Maeve Chocolate Dirty Shirley Bar Katie shares a browser-based game connected to Dungeon Crawler Carl. Players fire magical projectiles at cherries to help create Mama's Dirty Shirley. The hosts discuss the game's intentionally retro presentation and voice work by Jeff Hays. Play the game and view the chocolate bar: https://maevechocolate.com/products/virgin-dirty-shirley/?game Garmin's Screenless Fitness Tracker Dave introduces Garmin's wearable fitness tracker designed without a screen. The device offers health and activity tracking without requiring users to wear a full smartwatch. The hosts discuss its approximately ten-day battery life, Garmin Connect compatibility, optional Garmin Connect+ subscription, and Garmin's reputation for durable hardware and direct fitness feedback. Engadget: https://www.engadget.com/2219713/garmin-finally-made-a-screenless-fitness-tracker/ Garmin product information: https://www.garmin.com/en-US/p/1989182/#specs RetroArch on Apple TV Sorg explains how RetroArch can turn an Apple TV into a multi-system retro-gaming device. The software supports numerous systems, including Nintendo, Sega, arcade, PlayStation-era platforms, and older computer games. The hosts discuss Bluetooth controllers, open-source games, shareware versions of Doom and Wolfenstein, storage limitations, organizing ROM libraries, and troubleshooting incompatible files. Apple App Store: https://apps.apple.com/us/app/retroarch/id6499539433 Supported platforms: https://www.retroarch.com/?page=platforms Awesome Person of the Week Ralph Teetor and the Invention of Cruise Control Dave highlights engineer Ralph Teetor, who became blind as a child and later earned engineering degrees. Frustrated by the repeated acceleration and deceleration of drivers, Teetor developed an early version of cruise control. The hosts discuss the evolution from basic speed control to adaptive cruise control and its relationship to modern driver-assistance systems. Teetor later served as president of the Society of Automotive Engineers, and an SAE award for educators bears his name. Automotive Hall of Fame: https://automotivehalloffame.org/honoree/ralph-r-teetor/ Chachi Says Video Game Minute Playing GTA III and Vice City Inside GTA: San Andreas A PC mod allows players controlling CJ in Grand Theft Auto: San Andreas to walk up to an in-game television and launch GTA III or Vice City. The hosts compare the concept to a video-game version of Inception. https://www.tomshardware.com/video-games/pc-gaming/gta-3-and-vice-city-are-now-playable-inside-san-andreas-a-mod-lets-you-revisit-liberty-city-and-vice-city-without-leaving-san-andreas GameStop's CEO Responds to the Decline of Physical Games GameStop CEO Ryan Cohen argues that Sony reducing physical-disc support will not significantly hurt the company because new video-game sales represent a relatively small portion of its business. The discussion includes GameStop's continued interest in eBay and the changing economics of physical game retail. https://www.ign.com/articles/gamestop-ceo-ryan-cohen-insists-sony-killing-physical-discs-doesnt-matter-at-all-because-video-game-sales-make-up-so-little-of-his-business PlayStation 5 Emulation Progress Developers are making progress booting PlayStation 5 titles through multiple emulation projects. Two-dimensional games are advancing more quickly, while fully rendering complex three-dimensional games remains a significant challenge. The story sparks a broader conversation about game preservation, physical ownership, digital licensing, and consumer distrust. https://www.tomshardware.com/video-games/playstation/ps5-emulation-ramps-up-in-wake-of-sonys-end-to-physical-media-ps5-titles-now-booting-across-different-emulators-with-rapid-community-development-for-both-2d-and-3d-games AI, Creative Work and Data Centers The hosts discuss the growing use of AI-assisted tools in image editing, video production, business documents, email, captions, background extension, and design mockups. Sorg explains how AI can help reformat posters for different social-media dimensions without replacing the original creative work. The discussion distinguishes generative AI from other machine-learning tools, such as automated transcription, rotoscoping, search, and image organization. Dave raises concerns about social-media challenges potentially being used to gather free voice-training data. The group discusses how Google, Adobe, Canva, and other platforms increasingly build AI functions directly into existing software. The hosts argue that professionals cannot simply ignore the technology, but they should evaluate where it is appropriate and disclose its use when needed. The discussion also addresses the electrical demands of new data centers, corporate incentives, local government decisions, environmental protections, grid capacity, and the disproportionate placement of industrial infrastructure near lower-income communities. Their central argument is that rapid technological growth must be accompanied by accountability, regulation, public representation, and responsible development. NASA Psyche Mars Flyby NASA's Psyche spacecraft used a Mars flyby as a gravitational assist while traveling deeper into the solar system. The mission captured detailed images and a time-lapse view of Mars during the maneuver. Dave highlights the value of scientific exploration driven by curiosity and the desire to understand more about the universe. https://science.nasa.gov/blogs/psyche/2026/07/17/nasas-psyche-mission-delivers-mars-flyby-data-time-lapse-video/ Pittsburgh Bridges on Film and Pastfinders Sorg highlights a collaboration between Pastfinders and the Pittsburgh Film Office focused on Pittsburgh bridges featured in movies and television. Examples discussed include locations connected to Mayor of Kingstown, Sweet Girl, Jack Reacher, The Perks of Being a Wallflower, The Dark Knight Rises, Dogma, The Silence of the Lambs, and Mindhunter. The hosts discuss how the Pastfinders app sends location-based notifications about historical sites, movie locations, landmarks, and unusual local stories. Pastfinders: https://www.pastfinders.app/ Pittsburgh Film Office bridge-tour post: https://www.facebook.com/photo/?fbid=1458603939638716&set=pcb.1458603999638710 The Light Flip Minimalist Phone The hosts examine a modern minimalist flip phone inspired by classic designs. It uses physical buttons, T9-style typing, a small screen, and a dedicated operating system rather than standard Android. The group discusses the appeal for people who want calls, messages, navigation, and limited apps without carrying a high-powered smartphone. They also consider whether younger users might adopt simpler phones to reduce social-media use and screen time. https://9to5google.com/2026/07/21/light-flip-minimalist-phone-announcement/ OpenMote: Repurposing the Nintendo Wii Remote Sorg introduces OpenMote, a drop-in development board that turns a Nintendo Wii Remote into a customizable controller. Demonstrations include pointing the remote at lamps to control them. The hosts brainstorm uses for smart-home controls, games, production equipment, lighting effects, custom 3D-printed controllers, and event technology. OpenMote: https://openmote.io/?utm_source=ig&utm_medium=social&utm_content=link_in_bio Instagram demonstration: https://www.instagram.com/reels/Da8p5B7PNKP/ OpenMote Instagram: https://www.instagram.com/openmote.io/ Google Photos Restores Classic Search Google Photos is adding an option that lets users choose between the newer Gemini-powered Ask Photos experience and a more traditional search interface. The hosts discuss why some users prefer entering straightforward terms rather than asking an AI-generated question. The change becomes an example of why providing a choice can reduce frustration when companies add AI to familiar products. https://9to5google.com/2026/07/20/google-photos-classic-search-toggle/ Also Discussed The return of CDs, vinyl records, cassettes, retro games, and physical media. The difference between owning digital media and licensing access to it. Consumer distrust of large technology companies. The space required to store large physical-media collections. Using Gemini to interpret business acronyms and generate a first draft of a statement of work. Severe-weather alerts and Katie briefly leaving the podcast during a tornado warning. Dave's upcoming races, including the Two-Face Race and Trick or Trot 5K. The next AwesomeCast interview with Spotter Global and its technology for detecting drones and identifying operators. Support the show at: https://www.patreon.com/awesomecast Discover more from the Sorgatron Media Podcast Network: https://sorgatronmedia.com

This week on AwesomeCast 788, Sorg, Katie Dudas, and Dave Podnar explore the growing appeal of retro technology, screen-free devices, customizable gadgets, and digital tools that give users more control. Katie begins with Mama's Night Off, a browser game inspired by Dungeon Crawler Carl and the Maeve Chocolate Dirty Shirley bar. Dave introduces Garmin's new screenless fitness tracker, an alternative to smartwatches and subscription-heavy wearables. Sorg shares his experience setting up RetroArch on Apple TV, including its wide range of supported systems, open-source games, ROM management, controllers, and the tinkering required to make everything work. The conversation expands into the tension between digital convenience and physical ownership. The hosts discuss GameStop's response to Sony's physical-media decisions, the rapid progress of PlayStation 5 emulation, and why consumers are returning to CDs, vinyl, cassettes, retro games, and other physical formats. They also have an extended discussion about generative AI, creative production, AI-assisted image editing, automation tools, corporate responsibility, electrical-grid pressures, and the rapid construction of data centers. The hosts examine the difference between rejecting AI entirely and learning how to use it responsibly while still demanding accountability from businesses and government. Later, the show looks at NASA's Psyche spacecraft capturing a Mars flyby, Pittsburgh movie history through the Pastfinders app, a minimalist flip phone for people who want fewer distractions, a development board that turns old Nintendo Wii Remotes into customizable controllers, and Google Photos restoring a traditional search option alongside Ask Photos. Stories and Gadgets Discussed Awesome Things of the Week Mama's Night Off and the Maeve Chocolate Dirty Shirley Bar Katie shares a browser-based game connected to Dungeon Crawler Carl. Players fire magical projectiles at cherries to help create Mama's Dirty Shirley. The hosts discuss the game's intentionally retro presentation and voice work by Jeff Hays. Play the game and view the chocolate bar: https://maevechocolate.com/products/virgin-dirty-shirley/?game Garmin's Screenless Fitness Tracker Dave introduces Garmin's wearable fitness tracker designed without a screen. The device offers health and activity tracking without requiring users to wear a full smartwatch. The hosts discuss its approximately ten-day battery life, Garmin Connect compatibility, optional Garmin Connect+ subscription, and Garmin's reputation for durable hardware and direct fitness feedback. Engadget: https://www.engadget.com/2219713/garmin-finally-made-a-screenless-fitness-tracker/ Garmin product information: https://www.garmin.com/en-US/p/1989182/#specs RetroArch on Apple TV Sorg explains how RetroArch can turn an Apple TV into a multi-system retro-gaming device. The software supports numerous systems, including Nintendo, Sega, arcade, PlayStation-era platforms, and older computer games. The hosts discuss Bluetooth controllers, open-source games, shareware versions of Doom and Wolfenstein, storage limitations, organizing ROM libraries, and troubleshooting incompatible files. Apple App Store: https://apps.apple.com/us/app/retroarch/id6499539433 Supported platforms: https://www.retroarch.com/?page=platforms Awesome Person of the Week Ralph Teetor and the Invention of Cruise Control Dave highlights engineer Ralph Teetor, who became blind as a child and later earned engineering degrees. Frustrated by the repeated acceleration and deceleration of drivers, Teetor developed an early version of cruise control. The hosts discuss the evolution from basic speed control to adaptive cruise control and its relationship to modern driver-assistance systems. Teetor later served as president of the Society of Automotive Engineers, and an SAE award for educators bears his name. Automotive Hall of Fame: https://automotivehalloffame.org/honoree/ralph-r-teetor/ Chachi Says Video Game Minute Playing GTA III and Vice City Inside GTA: San Andreas A PC mod allows players controlling CJ in Grand Theft Auto: San Andreas to walk up to an in-game television and launch GTA III or Vice City. The hosts compare the concept to a video-game version of Inception. https://www.tomshardware.com/video-games/pc-gaming/gta-3-and-vice-city-are-now-playable-inside-san-andreas-a-mod-lets-you-revisit-liberty-city-and-vice-city-without-leaving-san-andreas GameStop's CEO Responds to the Decline of Physical Games GameStop CEO Ryan Cohen argues that Sony reducing physical-disc support will not significantly hurt the company because new video-game sales represent a relatively small portion of its business. The discussion includes GameStop's continued interest in eBay and the changing economics of physical game retail. https://www.ign.com/articles/gamestop-ceo-ryan-cohen-insists-sony-killing-physical-discs-doesnt-matter-at-all-because-video-game-sales-make-up-so-little-of-his-business PlayStation 5 Emulation Progress Developers are making progress booting PlayStation 5 titles through multiple emulation projects. Two-dimensional games are advancing more quickly, while fully rendering complex three-dimensional games remains a significant challenge. The story sparks a broader conversation about game preservation, physical ownership, digital licensing, and consumer distrust. https://www.tomshardware.com/video-games/playstation/ps5-emulation-ramps-up-in-wake-of-sonys-end-to-physical-media-ps5-titles-now-booting-across-different-emulators-with-rapid-community-development-for-both-2d-and-3d-games AI, Creative Work and Data Centers The hosts discuss the growing use of AI-assisted tools in image editing, video production, business documents, email, captions, background extension, and design mockups. Sorg explains how AI can help reformat posters for different social-media dimensions without replacing the original creative work. The discussion distinguishes generative AI from other machine-learning tools, such as automated transcription, rotoscoping, search, and image organization. Dave raises concerns about social-media challenges potentially being used to gather free voice-training data. The group discusses how Google, Adobe, Canva, and other platforms increasingly build AI functions directly into existing software. The hosts argue that professionals cannot simply ignore the technology, but they should evaluate where it is appropriate and disclose its use when needed. The discussion also addresses the electrical demands of new data centers, corporate incentives, local government decisions, environmental protections, grid capacity, and the disproportionate placement of industrial infrastructure near lower-income communities. Their central argument is that rapid technological growth must be accompanied by accountability, regulation, public representation, and responsible development. NASA Psyche Mars Flyby NASA's Psyche spacecraft used a Mars flyby as a gravitational assist while traveling deeper into the solar system. The mission captured detailed images and a time-lapse view of Mars during the maneuver. Dave highlights the value of scientific exploration driven by curiosity and the desire to understand more about the universe. https://science.nasa.gov/blogs/psyche/2026/07/17/nasas-psyche-mission-delivers-mars-flyby-data-time-lapse-video/ Pittsburgh Bridges on Film and Pastfinders Sorg highlights a collaboration between Pastfinders and the Pittsburgh Film Office focused on Pittsburgh bridges featured in movies and television. Examples discussed include locations connected to Mayor of Kingstown, Sweet Girl, Jack Reacher, The Perks of Being a Wallflower, The Dark Knight Rises, Dogma, The Silence of the Lambs, and Mindhunter. The hosts discuss how the Pastfinders app sends location-based notifications about historical sites, movie locations, landmarks, and unusual local stories. Pastfinders: https://www.pastfinders.app/ Pittsburgh Film Office bridge-tour post: https://www.facebook.com/photo/?fbid=1458603939638716&set=pcb.1458603999638710 The Light Flip Minimalist Phone The hosts examine a modern minimalist flip phone inspired by classic designs. It uses physical buttons, T9-style typing, a small screen, and a dedicated operating system rather than standard Android. The group discusses the appeal for people who want calls, messages, navigation, and limited apps without carrying a high-powered smartphone. They also consider whether younger users might adopt simpler phones to reduce social-media use and screen time. https://9to5google.com/2026/07/21/light-flip-minimalist-phone-announcement/ OpenMote: Repurposing the Nintendo Wii Remote Sorg introduces OpenMote, a drop-in development board that turns a Nintendo Wii Remote into a customizable controller. Demonstrations include pointing the remote at lamps to control them. The hosts brainstorm uses for smart-home controls, games, production equipment, lighting effects, custom 3D-printed controllers, and event technology. OpenMote: https://openmote.io/?utm_source=ig&utm_medium=social&utm_content=link_in_bio Instagram demonstration: https://www.instagram.com/reels/Da8p5B7PNKP/ OpenMote Instagram: https://www.instagram.com/openmote.io/ Google Photos Restores Classic Search Google Photos is adding an option that lets users choose between the newer Gemini-powered Ask Photos experience and a more traditional search interface. The hosts discuss why some users prefer entering straightforward terms rather than asking an AI-generated question. The change becomes an example of why providing a choice can reduce frustration when companies add AI to familiar products. https://9to5google.com/2026/07/20/google-photos-classic-search-toggle/ Also Discussed The return of CDs, vinyl records, cassettes, retro games, and physical media. The difference between owning digital media and licensing access to it. Consumer distrust of large technology companies. The space required to store large physical-media collections. Using Gemini to interpret business acronyms and generate a first draft of a statement of work. Severe-weather alerts and Katie briefly leaving the podcast during a tornado warning. Dave's upcoming races, including the Two-Face Race and Trick or Trot 5K. The next AwesomeCast interview with Spotter Global and its technology for detecting drones and identifying operators. Support the show at: https://www.patreon.com/awesomecast Discover more from the Sorgatron Media Podcast Network: https://sorgatronmedia.com

Kavian Mojabe is the Founder and CEO of MediScan AI, the AI back office for independent medical evaluators. These are the licensed physicians whose opinions decide the outcome of workers' compensation, personal injury, and disability cases. MediScan's best-in-class record review engine does in minutes what used to take weeks, helping physicians optimize chart reviews to help patients faster and more effectively. Before MediScan, Kavian was a founding engineer at two startups acquired by Amazon and Adobe. But this one is personal—his father is an independent medical evaluator in Southern California, and Kavian grew up watching the toll the busy work took on a doctor who just wanted to practice medicine. MediScan is his answer: give that time back and let people like his father stay independent.See omnystudio.com/listener for privacy information.

Sean Emory of Avory & Co. explores how AI could change software competition if agents become buyers, not just users, shifting value from platform suites to composable, standalone capabilities.He frames three eras of software: individual tools, integrated platforms, and an emerging era where AI orchestration acts like a new operating system that selects the best capability for a task regardless of UI or vendor.In this model, winning depends less on distribution, brand, and long contracts, and more on reliability, latency, accuracy, security and governance, API quality, cost per call, and task success rates.Sean argues platforms won't disappear. They may become command centers for permissions, compliance, and governance, while features "escape" suites. He walks through examples from Shopify, Shop Pay, Stripe, Twilio, Zoom, Box, Salesforce, Adobe, and HubSpot.Chapters:00:00 AI changes software buying01:44 Three eras of software03:10 Composable software era03:55 Agents versus humans05:06 Orchestrator as new OS07:46 Capabilities escaping platforms08:33 Examples: Shopify, Stripe, Zoom12:28 New scorecard for winners15:32 Two questions for companies17:22 Where this could be wrong19:48 Big picture takeaways23:22 Closing and subscribeListen on:Apple Podcasts: https://podcasts.apple.com/us/podcast/avory-markets-and-investing/id1504555573Spotify: https://open.spotify.com/show/3A8acTyfhhxpUFoFLEKeMJYouTube: https://youtube.com/@avorycoFollow Sean and Avory & Co.:X: https://x.com/avorycoX: https://x.com/_SeanDavidLinkedIn: https://www.linkedin.com/company/avory-coWebsite: https://avoryfunds.comDisclaimerThe content on this channel is for informational and educational purposes only. It does not constitute personal investment advice, a solicitation, or an offer to buy or sell any security. Views expressed are those of Sean Emory and Avory & Co. as of the recording date and are subject to change without notice.Avory & Co. and Sean Emory may hold positions in the securities and companies discussed. Any references to specific companies, products, or services are for illustration only and should not be interpreted as recommendations.Investing involves risk, including possible loss of principal. Past performance does not guarantee future results. Consult a qualified financial advisor before making any investment decision.

Have you ever felt like having a 'job' alongside your photography means you're not serious about it? That real photographers go all-in, or not at all? This episode is going to gently, thoroughly dismantle that idea.We're joined by Katrin Eismann: photographer, artist, educator, Sony Artisan of Imagery and someone who has spent over thirty years working with Adobe while maintaining a rich, evolving creative practice of her own.Her career is living proof that stability and creativity aren't in competition. And that building a sustainable life around your work doesn't have to mean compromising either. In this conversation, we talk about financial sustainability, creative identity, where inspiration really comes from, and why editing is far more creative than most photographers give it credit for.Where you can find Katrin:Instagram: https://www.instagram.com/katrin_eismann/Focus & Flourish, Photography Business Talk is a podcast for food photographers and creative entrepreneurs who want to build structured, sustainable, and profitable businesses. Hosted by Marta Grabowska and Linda Hermans, the show combines real-life experiences with practical strategies around marketing, pricing, workflows, and mindset — helping creatives step into their CEO role and grow with clarity and confidence.

Die Wall Street startet mit deutlichen Kursgewinnen, angeführt von Technologie- und Halbleiterwerten. Rückenwind liefern überraschend starke Exportaufträge aus Taiwan, die im Juni um knapp 60 Prozent gestiegen sind, sowie Berichte, wonach Taiwan Semiconductor die Preise für moderne und ältere Fertigungsverfahren 2027 um bis zu zehn Prozent anheben will. Nvidia sorgt zusätzlich für Fantasie, nachdem der Konzern seine Beteiligung am KI-Infrastruktur-Anbieter Nebius auf 9,3 Prozent erhöht hat. Während Halbleiter gefragt sind, bleibt der Softwaresektor unter Druck: Morgan Stanley hat Adobe, Salesforce, Intuit und Workday abgestuft und verweist auf wachsende Unsicherheit bei der Monetarisierung von KI sowie auf zunehmenden Wettbewerbsdruck und hohe Bewertungen. Dagegen wurden Microsoft und Meta von Analysten erneut positiv hervorgehoben. Bei den Quartalszahlen überzeugen General Motors, 3M, Hasbro und Northrop Grumman, während DR Horton trotz besserer Gewinne den Jahresumsatzausblick senkt. Gleichzeitig wird der Anstieg der Kosten für Speicherchips und KI-Infrastruktur zu einem zentralen Thema der Berichtssaison. Wells Fargo erwartet für die vier größten Hyperscaler bis 2027 Investitionen von rund 1,1 Billionen US-Dollar, geht aber davon aus, dass ein Teil der höheren Kosten an die Kunden weitergereicht werden kann. Abonniere den Podcast, um keine Folge zu verpassen! ____ Folge uns, um auf dem Laufenden zu bleiben: • X: http://fal.cn/SQtwitter • LinkedIn: http://fal.cn/SQlinkedin • Instagram: http://fal.cn/SQInstagram

Die Wall Street startet mit deutlichen Kursgewinnen, angeführt von Technologie- und Halbleiterwerten. Rückenwind liefern überraschend starke Exportaufträge aus Taiwan, die im Juni um knapp 60 Prozent gestiegen sind, sowie Berichte, wonach Taiwan Semiconductor die Preise für moderne und ältere Fertigungsverfahren 2027 um bis zu zehn Prozent anheben will. Nvidia sorgt zusätzlich für Fantasie, nachdem der Konzern seine Beteiligung am KI-Infrastruktur-Anbieter Nebius auf 9,3 Prozent erhöht hat. Während Halbleiter gefragt sind, bleibt der Softwaresektor unter Druck: Morgan Stanley hat Adobe, Salesforce, Intuit und Workday abgestuft und verweist auf wachsende Unsicherheit bei der Monetarisierung von KI sowie auf zunehmenden Wettbewerbsdruck und hohe Bewertungen. Dagegen wurden Microsoft und Meta von Analysten erneut positiv hervorgehoben. Bei den Quartalszahlen überzeugen General Motors, 3M, Hasbro und Northrop Grumman, während DR Horton trotz besserer Gewinne den Jahresumsatzausblick senkt. Gleichzeitig wird der Anstieg der Kosten für Speicherchips und KI-Infrastruktur zu einem zentralen Thema der Berichtssaison. Wells Fargo erwartet für die vier größten Hyperscaler bis 2027 Investitionen von rund 1,1 Billionen US-Dollar, geht aber davon aus, dass ein Teil der höheren Kosten an die Kunden weitergereicht werden kann. Ein Podcast - featured by Handelsblatt. ► Entdecke den exklusiven NordVPN Deal! Jetzt risikofrei testen mit einer 30-Tage-Geld-zurück-Garantie: https://nordvpn.com/wallstreet * ► Erhalte einen exklusiven 15% Rabatt auf Saily eSIM Datentarife! Lade die Saily-App herunter und benutze den Code wallstreet beim Bezahlen: https://saily.com/wallstreet * ► Direkt an der Börse handeln mit tradegate.direct: https://bit.ly/WallStreet_Juni * +++ Alle Rabattcodes und Infos zu unseren Werbepartnern findet ihr hier: https://linktr.ee/wallstreet_podcast +++ ► Mehr Einblicke: https://bit.ly/360wallstreetpc * Impressum: https://www.360wallstreet.de/impressum *Werbung

"There is no single way to deploy OpenTelemetry at scale—and that's exactly the challenge."As organizations adopt OTel across teams and environments, they face tough questions around standardization, configuration, and operating resilient observability pipelines.To address these challenges, the OpenTelemetry community has introduced Blueprints and Reference Implementations—practical guidance on topics like data standards, consistent agent and collector configuration, pipeline resilience, and intelligent sampling.In this episode, we're joined by Dan Gomez Blanco, maintainer of the OpenTelemetry End-User SIG, to explore real-world reference architectures from organizations like Skyscanner, Adobe, and Mastodon.Tune in to learn how the community is turning OTel complexity into shared best practices—and how you can contribute your own blueprint

Ryan Summers (Sarofsky) joins us to discuss how the center of the motion design community was lost and thoughtfully responds to the debate on creativity vs AI based on his personal experience. 

Adobe Express and Canva are packed with AI tools that save teachers hours — Amy Storer shares the features her teachers love most. Innovative learning specialist Amy Storer (Montgomery ISD, TX) walks through the AI-powered tools inside Adobe Express and Canva that teachers and students are getting most excited about — from studio-style student podcasts to interactive games you can build without writing a line of code. She also shares a favorite bonus tool for creating step-by-step how-to guides, and closes with a word of encouragement for any teacher feeling buried by "all the things." In this episode, you'll learn: How Adobe Express's Create a Podcast and one-click Enhance make student podcasting simple Why Animate a Character is an easy win for your youngest learners and GT projects How Canva Code lets you build interactive, self-grading activities with no coding A hidden Canva presentation timer and how Scribe turns clicks into a how-to guide Amy's "pick one tool" philosophy for teachers who feel overwhelmed Full show notes and links: https://www.coolcatteacher.com/e948 If this one gave you an idea you can use, share it with a teacher friend who could use a time-saver.

On this episode of Christopher Lochhead: Follow Your Different, Bruce Cleveland, legendary entrepreneur and venture capitalist, joins us to discuss his powerful new book, “Market Engineering: Because Markets Don’t Build Themselves.” The conversation brought together two former competitors who have since become allies in a shared mission: helping founders and executives understand that markets, like products, can be deliberately designed and engineered. Cleveland’s insights are drawn from decades of operating experience at companies like Siebel and Apple, as well as his work as a venture capitalist guiding early-stage startups. The core argument is simple but often ignored. Over 90% of startups fail not because their products are bad, but because they never take responsibility for shaping the market around those products. Cleveland and Lochhead agree that the companies who teach the market how to think about a problem, and then how to solve it, are the ones who become category kings and queens. You're listening to Christopher Lochhead: Follow Your Different. We are the real dialogue podcast for people with a different mind. So get your mind in a different place, and hey ho, let's go.   Bruce Cleveland on What Market Engineering Actually Means Bruce Cleveland defines market engineering as a five-part discipline that includes category design, positioning, messaging, storytelling, and thought leadership. When these elements are combined intentionally and consistently, they create gravitational pull. Customers seek you out, attend your events, and associate your brand with the future they want to be part of. Cleveland draws a sharp distinction between marketing and market engineering. He uses the analogy of a short-order cook at Denny’s versus a chef at a Michelin-star restaurant. Both have the same basic ingredients, but the outcomes are vastly different. The difference is knowing how to combine those ingredients with precision, purpose, and craft.   The Book as an Instruction Manual, Not Just Inspiration One of the most refreshing aspects of Bruce Cleveland’s approach is his insistence on practicality. He openly criticizes business books that fire readers up but leave them with no clear path forward. “Market Engineering” was written as a prescriptive guide, walking readers through specific frameworks like the Market Blueprint, Messaging Matrix, and Market Charter. To take this even further, Cleveland built an AI-powered platform called the Market Engineering Virtual Studio, trained on his own methodology using a neural symbolic recursion model named Finn. The platform allows users to actually build the documents and artifacts described in the book, turning static ideas into dynamic action. Cleveland and Lochhead both agree this model, combining a book, an AI companion, and a community, represents the future of business education.   Why Former Competitors Are Now Building the Same Category Together Perhaps the most telling moment in the conversation is when Lochhead reflects on the fact that he and Bruce Cleveland spent years as direct competitors, yet now champion nearly identical ideas. Rather than seeing this as a conflict, both men view it as validation. A category only exists when multiple credible voices contribute to defining it. Their combined efforts have helped make category design and market engineering part of the mainstream business conversation. Cleveland also speaks candidly about why he works primarily with pre-seed and early-stage companies that have limited capital. He prices his tools and services accessibly on purpose, takes small equity positions, and focuses on creating real economic impact. His philosophy is that helping startups succeed contributes more to society than any check he could write to a traditional charitable cause. For Bruce Cleveland, market engineering is not just a framework. It is a form of giving back. To hear more from Bruce Cleveland on the benefits of Market Engineering, download and listen to this episode.   Bio Bruce Cleveland's career in Tech spans more than 40 years as a venture investor and operating executive. He was a first investor and a board member of Marketo, which held an IPO in 2013 and was acquired in 2018 by Adobe for $4.75B. He was an early-stage investor in other notable companies such as C3.ai, Doximity, Vlocity, and Workday. Bruce also held senior executive roles in engineering, product management and product marketing at Apple, AT&T, C3.ai, Oracle and Siebel Systems. His book, Traversing the Traction Gap, is a prescriptive guide for startups and new product initiatives within larger companies helping teams to use ‘market engineering' techniques to successfully transition from Ideation to Scale. He attended the US Military Academy, West Point, New York, and received a BS in business administration from CSU, Sacramento. He lives in the San Francisco Bay Area.   Connect with Bruce Cleveland! LinkedIn | X (Formerly Twitter) | Website Check out his book here: Market Engineering: Because Markets Don’t Build Themselves   We hope you enjoyed this episode of Christopher Lochhead: Follow Your Different™! Christopher loves hearing from his listeners. Feel free to email him, connect on Facebook, X (formerly Twitter), Instagram, and subscribe on Apple Podcast / Spotify!

This week on the podcast, Zach Kazan and Kat Shoulders are joined by Georgia Benjamin, who by day works in design at Adobe, but by night (and probably all around the clock, frankly) has become quite a force in the watch world. By the numbers, she is among the most followed female watch personalities on social media, and has become a fixture in the broader enthusiast and collecting community. Georgia talks to us about what drew her to watches, how her career at Adobe informs her interest in watches, the growing influence of women in the watch enthusiast space, and why degendering watches is so important in the watch space.  To stay on top of all new episodes, you can subscribe to The Worn & Wound Podcast on all major platforms including Apple Podcasts, Stitcher, Spotify, and more. You can also find our RSS feed here. If you like what you hear, then don't forget to leave us a review. If there's a question you want us to answer you can hit us up at info@wornandwound.com, and we'll put your question in the queue. Show Notes  Georgia on Instagram Georgia Benjamin and Courtney Bachrach on the Collectability podcast Georgia's article on WatchPro Introducing the Anoma A1 Prehistoric Toledano & Chan Introduces the b/1.3r, with a Solid Gold “Ripple” Dial Yves Saint Laurent exhibit at the International Center of Photography

Joy Osinloye is the Vice President of Customer Success, North America at CreatorIQ, where she leads Customer Success across enterprise, mid-market, and agency segments. She oversees retention, expansion, and long-term value creation and realization for a diverse portfolio of high-growth global brands. Partnering closely with revenue, marketing, and executive stakeholders, Joy equips the CreatorIQ customers with the necessary tools and insights to ensure their creator programs drive true measurable business impact.Before joining CreatorIQ, Joy led influencer and integrated marketing initiatives for global brands including Airbnb, Huggies, L'Oréal, Adobe, and Kellogg's. That brand-side experience now informs her approach to customer success by helping organizations operationalize creator marketing as a scalable, revenue-aligned growth channel rather than a series of one-off campaigns.Joy is particularly focused on building resilient customer relationships, strengthening adoption at scale, and aligning creator investments to durable revenue outcomes. She is also a passionate advocate for people-first leadership and mentors emerging leaders across the marketing and technology ecosystem.

Lara Balazs, CMO of Adobe, joins us to discuss what it takes to lead one of the world's most influential creative brands through a period of enormous change.From Adobe's presence at Cannes to the rapid rise of AI, Lara shares her vision for the company and why she believes technology should be used in service of creativity, rather than simply to drive efficiency. We explore why creativity matters more than ever in the age of AI, the skills Adobe looks for in marketers, and the traits that separate the most successful CMOs.Lara also shares the practical leadership principles she uses to run her team, from creating a company plan on a page and harnessing the power of repetition to prioritising progress over perfection and using the “two-way door” approach to make faster decisions.This episode is brought to you by System1. Download their creator effectiveness report here: https://system1group.com/the-creator-effectiveness-playbookTimestamps00:00 - Start01:23 - The most surprising thing about Lara03:11 - How Adobe have stood out at Cannes05:50 - What makes an influential CMO?06:59 - How to manage a business through change09:54 - Lara's vision for Adobe13:33 - Finding inspiration from a brand's history15:13 - Where AI has been used in service of creativity18:03 - AI to enhance creativity vs increasing efficiency20:42 - Why creativity still matters in the AI age23:41 - What skillsets does Adobe hire for in marketing?25:22 - The traits of the most successful CMOs28:55 - The company plan on a page30:49 - The power of repitition32:05 - Progress over perfection at Adobe33:14 - The two way door approach37:19 - The best advice Lara has ever recieved

Can graphic designers become so obsessed with design that they completely miss what actually makes brands successful?In this special live episode of The Angry Designer Podcast, we sit down with Chris Do (The Futur) at Creative South for one of our most thought-provoking conversations yet.What started as a discussion about logos quickly turned into a passionate debate about branding, creativity, business, Adobe, design education, storytelling, and why so many graphic designers focus on the wrong things.We challenged Chris on everything from whether Adobe owes designers anything... to why "blanding" has taken over... to whether great design actually wins in today's market.And Chris challenged us right back.In this episode, you'll discover:- Why great design doesn't always win.- Why branding is bigger than logos.- The biggest mistake graphic designers still make.- Why clients buy brands—not design.- How storytelling creates value.- Why designers become emotionally attached to companies.- What separates good designers from truly valuable ones.If you're a graphic designer, creative professional, brand strategist, freelancer, agency owner, or design student, this conversation will make you question what you think you know about design.Whether you agreed with Chris Do or not, one thing is certain...you'll never look at branding the same way again.Stay Angry our Friends –––––––––––Join Anger Management for Designers Newsletter at https://tinyurl.com/mr4bb4j3Want to see more?  See uncut episodes on our YouTube channel at youtube.com/theangrydesigner Read our blog posts on our website TheAngryDesigner.comJoin in the conversation on our Instagram Instagram.com/TheAngryDesignerPodcast

Had an AHA or Insight? Share it:Why That Number Isn't What You Should Be Asking?What does the market bear? What does your competition charge? What do you think you can get away with? These are the three questions most founders ask when they set a price, and Ed Lee has spent the last decade watching them lead people straight into underpricing themselves.Ed is the author of The Last Mile of Trust and has worked with over two thousand companies on how they price what they sell. For years he watched founders carry the same insecurity into every pricing conversation, shrinking the number the moment a client hesitated. Then he started to see that insecurity eroding the very thing that made his clients worth paying for.In this conversation, Ed makes the case that what someone pays is directly tied to what they believe they are getting, not to what the comps say. A consultant pricing at $3,000 because that is what the market shows, while delivering $50,000 worth of transformation, does not read as a deal. It reads as a mismatch, and mismatches make buyers walk away, not the quoted price. Ed shares how to find the right number for your business. It's not the number he says, it's how much they trust you.Ed and I have worked together for the last year, and what stands out about him is that he is unapologetic about needing time with his family. His business supports his life, not the other way around, and I watched him live with that friction and resolve it one step at a time. The friction he feels when charging what he is worth is the same friction he faces every time he puts his family first. It's an investment for him. If you are tempted to price something by asking what everyone else charges instead of what you are worth, this episode is worth a listen. You will walk away with a way to find your number, and proof that facing friction instead of running from it is what makes the number hold.Chapters:00:00 Cold open — Pricing builds trust, not just numbers.00:46 Intro — Beate welcomes Edward Lee, author of The Last Mile of Trust.03:32 The friction myth — How “frictionless” can erode your secret sauce.04:52 Price as identity — Your number signals value, impact, and trust.08:39 Adobe case — Subscriptions, AI add‑ons, and when features backfire.12:40 Pricing as a filter — Qualify ideal customers; repel discount‑seekers.14:41 Values and balance — Family, presence, and the workout analogy.23:33 Evolving the offer — How Ed reshaped Hello Advisor's pricing and model.27:14 Book + key takeaway — Where to get it, toolkit bonus, and why price is the last mile of trust.About  Ed Lee Ed Lee is the founder and CEO of HelloAdvisr, a pricing strategy consultancy, and the author of The Last Mile of Trust. He has advised more than 100 high-growth companies on pricing and monetization, with a combined valuation exceeding $1.4 billion. He teaches at UCLA, serves as expert-in-residence at Oxford University's Saïd Business School, and hosts the Margin for Error podcast.Connect with Ed LeeWebsite | LinkedIn | X | Instagram |Instagram _____________________We appreciate you, thank you for listening. Let us know in the comments what resonated in this episode, we want to hear from you. Leave a comment, like, share with one person who needs to hear the message our guest shared. Take our QUIZ and find out what your talent is worth in this market:  What's Your Talent Worth (http://WhatsYourTalentWorth.com)Follow us on Instagram:Check us out on Tik Tok: Work With Us

Don’t let the AI wave crush you. Subscribe to our Newsletter: https://theultimatepartner.com/ebook-subscribe/ Check Out UPX: https://theultimatepartner.com/experience/ Dive into the seismic shifts happening within the AWS Marketplace and discover how AI, self-service product-led growth (PLG), and advanced co-selling strategies are redefining partner success. Matt Yanchyshyn, VP of Marketplace at AWS breaks down the recent announcements from the summit, illustrating how agility and adaptation are crucial to surviving the new agentic future. From lowering professional services fees to the explosion of business applications like ServiceNow, this conversation reveals the hidden mechanics of modern cloud procurement and how you can position your organization to capture massive enterprise opportunities before your competitors do. https://youtu.be/gaWxU1kgCLk Key Takeaways Adapting to the new agentic future requires agility rather than fighting the influx of AI tools. Lowering the listing fee for professional services from 2.5% to 0.5% drastically improves partner economics. Organizations without a self-service or PLG motion on the marketplace are literally leaving money on the table. Millennial buyers increasingly initiate complex enterprise procurements through self-service and AI-driven research. New AI-powered opportunity scoring empowers partners to prove their value internally and to AWS. Marketplace success hinges on optimizing metadata for AI agents, not just traditional SEO. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Key Tags: AWS Marketplace, agentic workflow, med pick scoring, phoenix.ai, Cara Cloud, branded storefronts, product-led growth strategy, intrinsic value boost, SaaS evolution, self-service motion, Databricks credit model, Trend Micro companion app, MCP servers, opportunity score tracking, PPA drawdown, concurrent agreements, AAMI structural debt, CXML procurement Transcript: Matt Y Audio Podcast [00:00:00] Matt Y: The ability to adapt with change and kind of roll with punches. ’cause a lot of people are saying like, agents are gonna destroy everything. And, and the opposite has been true. [00:00:08] Vince Menzione: You can feel it happening. The ecosystem is shifting beneath us, the way hyperscalers are partnering, how AI is remaking the channel and what it means to win in 2026. [00:00:19] Vince Menzione: Welcome to the Ultimate Partner Podcast. I’m Vince Menzi, own your host. And each week I sit down with leaders at the intersection of technology, partnerships and outcomes. The voices shaping how ecosystems actually work. We talk about what’s real, what’s changing, and what it takes to lead in this era where the partner channel isn’t just part of the strategy. [00:00:42] Vince Menzione: It is the strategy because being in the room changes everything. [00:00:46] Matt Y: Let’s start. [00:00:50] Vince Menzione: And now on to the really important stuff. So, Matt, I don’t wanna butcher it ’cause I, a couple people have told me how to pronounce your last name and they said use the word magician and you’ll get close to it. But I’m just gonna introduce you as Matt Wy and I’m gonna ask you to pronounce your name on stage, but I want to have you join us. [00:01:08] Vince Menzione: So excited to have Matt wy. After a super busy day and night last night, come over from Brooklyn and join us today. Matt, so great to have you. Thanks. Thank you so much. Thank you so much. Alright, so pronounce your name for us. [00:01:23] Matt Y: Anyone wanna guess? Ian’s? It’s like magician. [00:01:27] Vince Menzione: It’s not that hard, [00:01:28] Matt Y: it’s not that [00:01:28] bad, [00:01:28] Vince Menzione: but I don’t wanna butcher. [00:01:29] Vince Menzione: I wanted to let you do it. Good. [00:01:30] Matt Y: What calls me Matt White. [00:01:31] Vince Menzione: That’s great. [00:01:32] Matt Y: Yeah. [00:01:32] Vince Menzione: So 13 years. [00:01:34] Matt Y: Four coming up on 14 next month. Yeah. [00:01:36] Vince Menzione: Wow. Congratulations. Yeah. So you’ve been there, you’ve been there since the early days. And we, we had a conversation. I had some Microsoft, former Microsoft colleagues. Uh, Theresa Carlson, for those of you who knew the public sector business. [00:01:48] Vince Menzione: Yeah. Who started, I mean, Andy came out, it was so funny because I was there and she was hosting Andy for a dinner and with all the CIOs of the federal government. [00:01:57] Matt Y: Yeah. [00:01:58] Vince Menzione: And she was still at Microsoft and it was actually kind of an interesting time. And she came over and did a lot of great things for a number of years. [00:02:04] Matt Y: Yeah. She [00:02:05] Vince Menzione: and a lot of great [00:02:05] Matt Y: business. [00:02:06] Vince Menzione: Yeah. She really like, it went from employee number one to 7,000. [00:02:09] Matt Y: Yeah. [00:02:09] Vince Menzione: And you, you were, you’ve been there all that whole time. Pretty much. [00:02:12] Matt Y: Yeah, I guess when I started in New York, just down the road, we were, uh, in a Regis facility. There were like 11 of us in, uh, just sitting around a table and we had to speak quietly sometimes because there was a, um. [00:02:21] Matt Y: Some type of a financial services organization down the hall and they’d listen to try and get stock tips on Amazon. Yeah, [00:02:28] Vince Menzione: I love it. [00:02:29] Matt Y: Never leaked. That’s [00:02:29] Vince Menzione: good. I love it. [00:02:30] Matt Y: Yeah, [00:02:30] Vince Menzione: you probably got some great stories and, um, we won’t have time for today ’cause I wanna leave some room for conversations on marketplace end questions. [00:02:38] Matt Y: Yeah. [00:02:38] Vince Menzione: But I would love to invite you back for a real, like, in-depth podcast and I would love to get the whole genesis story. [00:02:44] Matt Y: Let’s do it. [00:02:45] Vince Menzione: We’ll do it. Okay, so let’s talk about, let’s talk about yesterday for you. Uh, some, some really big announcements as well. I thought maybe you could recap a little bit of what’s been going on in the marketplace business and it’s an, it’s been an exciting time. [00:02:58] Matt Y: Yeah. Yeah. You know what’s, I think what was really nice yesterday is it was sort of the combination of bringing, uh, our partner services like Partner Central and all those other services together closer to marketplace. We’ve been doing that over, over several years. So Marketplace has some of its own. [00:03:12] Matt Y: Big announcements, like, uh, we have a, we formalized our list and sell initiative. For example. We have a new, so it we essentially reducing the cost, uh, to list on marketplace through a partner program. [00:03:22] Vince Menzione: Yep. [00:03:22] Matt Y: And incentives associated with that. We have a new AI powered listing experience, which I think is particularly important ’cause I think many of you are like me and watching your SEO numbers go down and watching your agent traffic go up. [00:03:33] Matt Y: And so having, uh, an AI assistance in marketplace to optimize your listings for not just to, you know, retain what you can of your SEO, but prepare for the newent future and improve your GEO as we’re calling it. So that, [00:03:45] Vince Menzione: so it’s GEO now? [00:03:46] Matt Y: Yeah. You know, there’s a little debate right now in the acronym Moral A A EO versus GO I’m going, I’m on the G team, so, yeah. [00:03:52] Vince Menzione: Alright. GEO [00:03:54] Matt Y: It’s like the, the, yeah, they’re gonna win. They’re like the Knicks, but the, um, [00:03:57] Vince Menzione: yeah, yeah, exactly. [00:03:57] Matt Y: But yeah, so AI assisted, uh, I mean, making. The most of, like, essentially marketplace is an excellent conversion engine. And so using AI to help improve that conversion engine in the form of your PDPs for both humans and agents. [00:04:08] Matt Y: So that was an exciting launch. Um, I got the most applause when I announced that. We lowered, we made the economics better for, uh, consulting offers professional services, nice to marketplace. We lowered the listing fee from 2.5 to, to 0.5% and wow, it goes even lower in certain circumstances. So just improving the economics. [00:04:24] Matt Y: I’m really excited to. Really partner with a lot of you to reinvent services through, through the marketplace like we did with SAS and other areas. Uh, and we’re doing with agents right now. So that was a big one. And then a whole series of announcements around, um, how we’re making it easier and more cost effective and more efficient to partner with AWS. [00:04:41] Matt Y: So using AI to, uh, using med pick scoring to automatically progress opportunities so you don’t have to kind of wait on a human. To, to click and progress, you know, that that can take days. And, uh, if you, if you wanna have an opportunity and have that be cos sold with AWS, that can be through a mix of agents for the long tail and with humans in the, in the sort of top end and more complex. [00:05:00] Matt Y: And allowing AI to help all the partners improve their opportunity quality so that we can better co-sell together. So. Yeah, I said AI a lot intentionally. Um, [00:05:09] Audience Guest: yeah, [00:05:10] Matt Y: AI sort of in the whole cycle for buyers, for sellers, uh, for operational efficiency, cost of sales. So a lot of announcements. I think I hit the big ones, so yeah. [00:05:18] Matt Y: I’m Might have missed something there. There we go. [00:05:21] Vince Menzione: George. [00:05:21] Matt Y: Oh, and storefront. Yeah. Thanks George. See, I look at George to see what I missed. Uh, we, we acquired a great company called phoenix.ai late last year. Okay. And you, you actually were said Caresoft and Yeah. Be down. Uh, [00:05:30] Vince Menzione: yeah. [00:05:30] Matt Y: So if you’re familiar with Cara Cloud, they have a procurement portal. [00:05:33] Matt Y: It’s heavy use by the US government, and they, um. Uh, we, we acquired them, uh, the really great growth company. They have over 70 logos now, and they help you build a branded storefront on marketplace, which obviously is important in the government space. If you’re procuring on a certain contract with a certain reseller, um, you know, there’s a certain set of products you’re allowed to buy. [00:05:51] Matt Y: But what we’re finding is even down on Wall Street, you hear, um, enterprises are, are using storefronts for internal procurement and they wanna have a curated collection of, of partner products and, and your own ecosystems internally. So we’re selling to both customers. And also to channel partners to build custom storefronts, branded storefronts for, and [00:06:07] Vince Menzione: it makes total sense, right? [00:06:08] Vince Menzione: Yeah, because you wanna li you wanna limit the, the viewing and, uh, and get, because I mean, how many different listings do we have? Like over 30,000? [00:06:16] Matt Y: Yeah. Yeah. There’s, I think the official numbers over th we have over 36,000. I was checking from over 6,000 vendors. Um, it’s a lot. And, and that’s gonna explode with the AI powered, uh, listing, uh, experience that we launched. [00:06:26] Matt Y: We’re gonna make it easier. And I guess what I’ve been telling partners is. You know, customers aren’t clicking through categories anymore. They’re using AI to search. And so it doesn’t matter how big our catalog is, what matters is being found. And what matters is converting that buyer. So if you have a. [00:06:39] Matt Y: If you’re running a demand gen campaign for say, like, you know, life sciences in, in Jersey and there’s a specific buyer at j and j, you wanna capture, that person doesn’t wanna be just dropped onto a generic marketplace, 30,000 listings. They wanna be dropped in a very specific place where they’re seeing like life sciences offers from Accenture, for example, coupled with a life sciences power thing with Elastic, you know, like, but a solution. [00:07:00] Matt Y: And that they want to land in a curated place where that highly intention buyer can be converted effectively. So that, that’s what we’re doing with all this. [00:07:06] Vince Menzione: And that’s where the GEO comes in because [00:07:09] Matt Y: Yeah. ’cause that buyer might be an agent That’s right. With, and that agent has is even more fickle, honestly. [00:07:14] Matt Y: And you know, what used to be milliseconds for the human before they kind of click away is, is now perhaps microseconds. Yeah. And so, uh, you know, having the right metadata and, and the right positioning, uh, the right story that an agent or a human can pick up to ultimately. Uh, complete their product research and choose your product is, is critical. [00:07:30] Vince Menzione: Very cool. Very cool. So before I, I, I’ve been asked to ask you this because I, I’ve had this con, people have brought come to me and said, you gotta ask Matt about music. He’s a big music guy. And, uh, so what are your favorite bands? [00:07:49] Matt Y: So, I mean, the, the real answer is, uh. I, I go to about a show about every week. [00:07:54] Matt Y: As, as Mike Trill knows, uh, we heard a show last night. Um, we were, uh, just a few hours ago, really? And, uh, um, favorite band, uh, well, I’ll tell, I’ll tell a story. I, I had a side hustle with MTV for years. Um, I used to run a music website. Um, oh, that’s cool. I didn’t know that. It got, it got kind of popular. It got sponsored by, if, if anyone’s into like early hip hop. [00:08:16] Matt Y: It got sponsored by a group called Jurassic Five. ’cause he, one of them reached out to me and said, nice. Hey, uh, you know, I’ve been, I like your website. And he ended up paying for a web, hosting a Dream host, if you remember, of cost back then. [00:08:26] Vince Menzione: Oh, Jesus. [00:08:26] Matt Y: Because I was broke and couldn’t afford it. And then, uh, and then this band sent me like a, a single and said, Hey, you know, trying to get the word out about our little band, can you help us out? [00:08:35] Matt Y: And I put their, uh, I put their, you know, single up on my, on my website and it blew up. And that band is Vampire Weekend. So they’re kind of big now. Wow. Yeah. Um, and uh, that got picked up by like Vanity Fair and all these other guys. And then I got sponsored by MTV to essentially write. Music reviews for years on the side. [00:08:51] Matt Y: So I was working for the Associated Press, laying cable in sports and war and, and, uh, yeah. So Vampire Weekend was good to me that, that they, they kind of paved a way to go to a lot of free shows over the years and a lot of bands and see a lot of great music. But yeah. [00:09:03] Vince Menzione: That is very cool. And that, and how did that get your day? [00:09:05] Vince Menzione: WS It was just a, it was just the technology path that was like, [00:09:09] Matt Y: I mean, it’s a, it’s a, I guess it’s a bit of a long story, but, um, the. There’s many versions of this story. I’ll tell the, tell the one quickly. I was living for free in a Fulbright scholarship house in West Africa. You, we can talk about how that happened another time. [00:09:23] Matt Y: And, uh, a guy had sort of fallen down on the floor ’cause he’d had too much to drink. And I, I sort of lay down beside and be like, Hey man, are you all right? And, um, he, uh. He worked, he, he worked for the Associated Press and next day I had the job, um, being West Africa, head of technology for West Africa. [00:09:37] Matt Y: And because of that, um, and as I learned years later, the AP didn’t have dr they had no disaster recovery. Yeah. And I, I can tell you that now ’cause um, you know, 16 years since I worked there, but they, uh, I put the DR in, um, on AWS and we’re talking like, yeah, 16, 17 years ago. This is early. It was early days. [00:09:56] Matt Y: And I, I swear to God, I paid for. Uh, our AWS bill using, um, taxi receipts, fake taxi receipts that I bought in on Nigerian market, um, because there was no budget and so, you know, it was like 30 bucks. [00:10:08] Vince Menzione: I was gonna say swipe a credit card, but they didn’t [00:10:09] Matt Y: knew that this is the entire press this before. [00:10:11] Vince Menzione: This is before, yeah. [00:10:12] Matt Y: Yeah, like the entire ap. Um, and, uh, so AWS called me like, who are you? Like, why, why are you paying on like this like low limit credit card for like the ap? Like, who are you? And, uh. Next day I had the job. Well, a week later I had the job with aw WS. That so cool. So that’s the story’s [00:10:29] Vince Menzione: cool thing. [00:10:29] Matt Y: Yeah. [00:10:30] Vince Menzione: Very cool. [00:10:31] Vince Menzione: Uh, sports teams. So Knicks fan. [00:10:34] Matt Y: Yeah, I mean, I like the Knicks. Um, they’re h hockey, I’m not allowed to say anything different. No. I appreciate them. Uh, I’m a Raptors fan. I grew up in Toronto mostly. Yeah, yeah. Uh, so, and you know, when they won, uh, that was very exciting as well. So no, Nicks are great. I like the Knicks. [00:10:49] Matt Y: Nothing against the Knicks. Um. They’re fine. Yeah. [00:10:54] Vince Menzione: Hockey, hockey fan. Favorite hockey teams? [00:10:56] Matt Y: Oh yeah. Itron. Maple leaf. Maple leaf. Yeah. They’re gonna, they’re gonna win. Of course. Of course. Yeah. Um, like every year they’re actually, we [00:11:02] Vince Menzione: have some Canadians laughing in the sand. [00:11:03] Matt Y: Well, the leaf are, are, are the Knicks of hockey? [00:11:05] Matt Y: Like Yes, they are. You know, it’s 67 years out, coming up on 68 since they won, so That’s crazy. 53 is nothing. I know. Pain. So. Yeah, definitely the least. Yeah. [00:11:15] Vince Menzione: I love it. I love it. It’s so cool. Yeah. So what was the, uh, what was the, what was the last concert you went to? [00:11:22] Matt Y: Well, literally last night. Oh, it was last, [00:11:23] Vince Menzione: oh, that [00:11:24] Matt Y: was actually concert were my favorite bar in the world. [00:11:26] Matt Y: This place called Sunny’s. Uh, it’s, you know, I, I took Mike and, and Matt from, from Texas and from TGS down there to sort of see my neighborhood and they’re like, where are we? And I’m like, yeah, I live here. Uh, sort of an industrial part of Brooklyn. And, and we went to see, um, I dunno what you would call it, like. [00:11:40] Matt Y: I guess it’d be like roots music. There was a woman with an accordion and a guy with a big cowboy hat. Yeah, it was, it was fun. Yeah. [00:11:47] Vince Menzione: That is so funny. Alright, we’re gonna shift back years. Um, important time right now for partners. What, what should partners be looking out for the most? What would you say to them in terms of what’s the, what’s their real headline for them? [00:11:59] Matt Y: Well, I, I, you know, to borrow from you actually, you know, I liked, uh, the, the principles you had up there and, and with agility, um, you know, there’s a lot of fud flying around right now. You know, people. People were like, oh, it’s the demise of sis with the arrival of ai, you know, everyone’s gonna be using agents. [00:12:13] Matt Y: And then it turns out it’s been a huge boon for most, uh, you know, system integrators and consulting companies that I work with. They all have, you know, the, the good ones especially have vibrant consulting practices now, and everyone is deploying fds, uh, you know, um, the new, the new cool acronym. But it’s, it’s essentially created a huge opportunity for the consulting space. [00:12:31] Matt Y: Uh, and similarly, uh, you know, there there’s this narrative around the sa sa apocalypse, which I really hate, you know, ’cause it was, uh, premature and kind of a trigger reaction from the stock market. And, you know, just look, look what Snowflake did. And, you know, they did what a lot of SaaS companies are doing, but they, they added a nice sort of glaze of positioning and, and, you know, their stock popped and they did pretty well. [00:12:50] Matt Y: And so I think the ability to adapt with change and kind of roll with the punches. ’cause a lot of people are saying like, agents are gonna destroy everything. And, and the opposite has been true. For the more successful consulting companies and software companies who have become agentic. But SaaS hasn’t gone away, you know? [00:13:05] Matt Y: No. Look at our own marketplace. We have this agent marketplace, but people aren’t buying atomic agents at scale. They’re buying ified SaaS solutions with sort of agent sidecars, which has created new opportunities for candidly additional licenses, [00:13:16] Vince Menzione: right? [00:13:16] Matt Y: Um, as customers sort of want to consume more AI services on top of their. [00:13:20] Matt Y: On top of their SaaS solutions. So I think being agile, you know, you see like ServiceNow as part of our billionaires club. Yes. They’re not going anywhere. They’re, yeah. They’re gentrifying. You know, Salesforce has pivoted to this headless model, um, along with Asian Force and using sort of Slack as the operating system. [00:13:34] Matt Y: And, you know, you said like a lot of companies from the seventies aren’t around anymore. They’re gonna be winners and losers. Yeah. Um, but the winners are gonna win even more. And so I, I think what’s so important right now for partners is to not, not bite too hard at the, the latest trend. You know, models are changing and everyone’s like, oh, you know, philanthropics really in the world and they’re wonderful, great to work with, amazing technology. [00:13:55] Matt Y: That’s what people are saying about open AI six months ago. That’s right. And before that, you know, and it, I, I was with Fireworks AI yesterday, a great company and they have some really cool stuff with sort of, um, they believe in more cost effective, uh, open source models essentially, that you can find tune. [00:14:08] Matt Y: Maybe that’s gonna win. I don’t know. Um, is it gonna be sort of domain specific models? Is it gonna be highly capable LLMs? Are LLMs gonna level off as soon as Fable and Mythos are allowed to launch? Maybe. I, I don’t think anyone can predict the future right now. So you have to be agile and you have to kind of seize the opportunities and take a couple punches. [00:14:25] Vince Menzione: Yeah. [00:14:26] Matt Y: You know, and marketplace too, like we’re, you have to be unafraid to experiment right now. Um, you know, that’s hard if your stock’s taking a beating. Um, but this is, it’s a, it is a disruptive time, uh, but it’s creating actually enormous opportunities for growth for partners and, and we really see that, you know, in marketplace specifically within AWS. [00:14:45] Vince Menzione: It, it, it does still feel like the deer in the headlights moment. Right. Would you agree? Like you’re probably taking a lot of meetings and, and calls from ISVs specifically? [00:14:54] Matt Y: Well, [00:14:54] Vince Menzione: that are still trying to figure it out. [00:14:56] Matt Y: Yeah. But it’s everyone. Yeah. I think what’s really interesting, I had a meeting [00:14:58] Vince Menzione: with, it’s not just one. [00:14:59] Matt Y: Yeah. I, well, I had a meeting with one of the leading AI companies, like one of the biggest ones. And they, uh, they demonstrated how they work and they were really proud. They were like, you know, look at our agentic workflow. And I came out at me. I’m like, that’s it. Ours is way better. Like really like, you know, ’cause we we’re, we’re using quick desktop with MCP servers and connectors and all this, and you know, we, we have our own sort of ecosystem of partners, a mix of homegrown software and third party. [00:15:20] Matt Y: And I kinda walked out there and, and looked at, you know, my phone, which has been populated by agents this morning with all the, and I was like, I have a way better agent workflow than this world’s leading supposedly AI company. And I think, um, that really, so during, I, I would, during the headlights, you can call it deer in the headlights, I call it chaos. [00:15:36] Matt Y: And in times of chaos there are people who create. Opportunity again. And so, yeah, there are some people who are stuck and who don’t know what to do, who are over worried about token costs, um, who are not experimenting. But there are a lot of companies, uh, taking this opportunity to kind of pivot their business. [00:15:53] Matt Y: Um, I think, I think we’re in a moment and, uh, yeah, I, I candidly I see more of the latter. I see more experimenting. [00:15:59] Vince Menzione: You mentioned ServiceNow. Any other great examples of that? Organizations that really embraced it? [00:16:04] Matt Y: Uh, yeah. Well, you know, ServiceNow is part of this business applications category, as we call it, in marketplace. [00:16:09] Matt Y: That outside of AI, I think is the fastest growing category in marketplace, which is wild when you think about it. ’cause we’ve historically been an infrastructure partner marketplace with security and data and analytics and, you know, security with channel partners, et cetera. But Salesforce, ServiceNow, Workday, Adobe, you know, I could go on. [00:16:23] Matt Y: They, they are actually. You know, our fastest growing category and yeah, ServiceNow, obviously reinventing itself for ai, Salesforce, but Workday, you know, the workday’s done some, who knows if it’s gonna work, but they, they’re experimenting with essentially like a Databricks, uh, credit style model for like, units of work, uh, which I think is fascinating. [00:16:41] Matt Y: Like everyone’s talking about value-based, outcome-based pricing and meter. And, and you have companies that are ERP companies, you know, like traditional business applications, experimenting with effectively like a metered pay as you go, value based credit model. Again, like who knows if it’s gonna work. [00:16:54] Matt Y: But I think that’s really amazing to see and we need more ISVs experimenting. I, I was talking about trend ai and I know they’re, they’re, they’re one of the sponsors yesterday. You know, many of you know them as Trend Micro back in the day. They’ve successfully reinvented themselves. They built that companion app. [00:17:10] Matt Y: Um, you know, that I think we’re seeing. Just a ton of experimentation in the market across categories. Uh, I could go on and on about partners. Um, yeah, there, I I wouldn’t pick a winner right now. Yeah. [00:17:24] Vince Menzione: You, you, we’ve talked about ai. We’ve talked, talk more about the buying journey and how that’s changing, because again, it feels, it feels like that’s also [00:17:33] Matt Y: Yeah. [00:17:33] Matt Y: So, you know, one of, one of the core, uh, strategic objectives, or we’ll say like the philosophy marketplace is that. Um, financial incentives are important, you know, EDP or PPA drawdown, uh, credits. Like we need to act as an efficient and effective vehicle for allowing buyers to exercise their discounts for, and, and sort of partners to exercise their credits, et cetera. [00:17:55] Matt Y: That, that’s actually important. But what, what a lot of people over rotate on that, and we’re really, one of the things we say a lot inside at Amazon or at AWS marketplace is we want to continue to boost the intrinsic value of marketplace beyond the financial incentives. And well over a quarter of all private offers, private pricing, private, uh, custom terms, et cetera. [00:18:14] Matt Y: Um, begin with a self-service or PLG motion. And partners who don’t have a PLG or self-service motion are literally leaving money on the table. Like if you look at like a Databricks for example, and they did a good job integrating buy with a WS within their SaaS application. They have free trials, they have really strong pego and, and, uh, and PLG motion. [00:18:33] Matt Y: They’re making, I can’t share their numbers obviously, but they’re making a ton of money. On purely self-service motions. And importantly, they’re acquiring new business, new logos that they nurture, you know, really like not just leads but closed opportunities, right? That they lead, they’re growing, uh, at a reasonable conversion rate or or success rate into the next big logos. [00:18:50] Matt Y: And these are over multi-year horizons. They’re patient, you know, they bring in these new logos with PLG, and they’re also bringing banking, a lot of large enterprises. Through self-service. I, I was with data Mask. There’s this great little startup from New Zealand. They’re a New Zealand based company. Um, super nice guy. [00:19:06] Matt Y: And, and, uh, they, they got huge logos. I think they got, what was it? A DP and some huge American logos. Okay. And this like logo in, I think it was Chile, or no, it was Peru. They’ve never been to Peru. They don’t have sales in Peru. Um, and they. Buyers were discovering them self-service and they, they, I think they got something like 13 logos entirely through a self-service motion. [00:19:26] Matt Y: One password will tell you the same thing. I was just with them in Toronto and companies big and small startups and the largest are getting enterprise wins in addition to net new small logos through that PLG. Buyer motion. And that’s because you have a whole generation of CFOs, CTOs, CROs, whatever. The C is [00:19:43] Vince Menzione: millennial [00:19:43] Matt Y: who grew up on their phones. [00:19:45] Vince Menzione: Yeah. [00:19:45] Matt Y: And, and it sounds like, you know, hyperbole, but it’s true. They, they want immediate apps, immediate access. And that actually, you’re like, oh, that never translates to business applications. Turns out it does. It does. And they might not be buying on their phone, but what they are doing is researching and we see the numbers, the amount of customers who are doing their research, and then eventually landing on the page from chat, GPT. [00:20:06] Matt Y: From major financial, like Fortune 500 companies is extremely high. Yeah. Uh, you have procurement team, sourcing team, uh, developers who are starting the research increasingly, like in clawed in chat, GPT, and then, you know, building a proposal and then handing it to their enterprise procurement team. Yeah. [00:20:22] Matt Y: Which is still largely unchanged. So buyer behavior is on the front end, on the research side is really changing. So the [00:20:29] Vince Menzione: discovery is happening through PLG. [00:20:32] Matt Y: Yeah. [00:20:32] Vince Menzione: And then the backend work on private offers and things like that sometimes still happens the old way. [00:20:36] Matt Y: Yeah. Well, and so, you know, it’s [00:20:37] Vince Menzione: fax machine, [00:20:38] Matt Y: some people Yeah, sure. [00:20:39] Matt Y: They’re bringing the deal directly to Marketplace last minute. But even if that deal goes direct, sometimes they’re still beginning their research journey and increasingly using Marketplace as a research vehicle, which is why we launched Agent Mode, um, to help you sort of help you and agents do research. [00:20:51] Matt Y: But that I think if, if I have one piece of device for any partner consulting or ISV is. Don’t leave those leads and that money on the table by not having a PLG self-service strategy like you’re fooling yourself. Uh, and it’s, it’s a huge, it’s a huge, huge business for us. The, the majority of all customers by far on marketplace don’t even have a PPA, uh, and a huge percentage of even those with PPA spend beyond the p. [00:21:17] Matt Y: And so if you’re just think if you’re just using marketplaces as like BPA retirement, you are literally losing money. [00:21:22] Vince Menzione: Yeah. [00:21:22] Matt Y: Yeah. [00:21:23] Vince Menzione: We have a session with Vinod. We’re gonna talk a little bit about that right after. Great. So good. Um, so I, yeah, I think, um. We talked about, we talked about agents, we’ve talked about the millennial buyer, the change in buying behavior. [00:21:40] Vince Menzione: What other, what other areas of aspect I, I, I, I do wanna think about like opening it up though for a second. I think that maybe with maybe nine minutes left. Sure. I just want to get a read from the people in the room. People have questions for Matt that we weren’t able to ask them. Yeah, I think, I think we probably have a few of those. [00:21:57] Vince Menzione: I think that would probably be great. [00:21:58] Matt Y: I can sense the hardball coming. [00:22:00] Vince Menzione: You’ve known each other [00:22:00] Matt Y: a long time. [00:22:01] Vince Menzione: Yeah. No, no. Hardball. We have a mic back here. Okay. I’ll just, we’ll, we’ll, we’ll get you a mic as we are recording. So good. Thank you. [00:22:11] Audience Guest: Uh, Boris Geller with a, a Click PLG is near and dear to my heart. [00:22:17] Audience Guest: We’ve been doing a lot of business in marketplace and I’m still struggling to sell my vision internally on, on, uh, on PLG. Uh, I think. Ag Agent AI is gonna be one of the drivers, and we are already on, uh, agent Marketplace, but I would appreciate guidance on, uh, best practices. How do we kind of, uh, operationalize it? [00:22:41] Audience Guest: It’s, it’s on us, not on you. [00:22:43] Matt Y: Well, no, I think it’s on both of us. You know, we, uh. One thing that we’re trying to do is give you more data to, to sell to your internal stakeholders in your executive suite. The value of co-sell with AWS all up, like finally with what we launched at, uh, the summit yesterday, you now get an opportunity score. [00:23:02] Matt Y: You, you get a number. People have been asking for this for years, so, so you can say when we do this and we, when we give AWS this information. The score goes up and we have a higher propensity to be cos sold by humans or agents before you had to kind of, it was like this mystery you had to guess. And similarly with marketplace, um, we, we have new dashboards that you can use to sort of, you used to have to sit down with us and go through spreadsheets to trace sort of lead to trace the funnel to sort of a close opportunity. [00:23:28] Matt Y: And we’re gonna continue to launch more there. But you now have more data that you can show. You can be like, listen, these are our inbound leads, this how’s converting, and now we have PRM, the partner revenue measurement where we can say like, this is what it’s translating into in terms of. AWS service revenue driven by our product. [00:23:41] Matt Y: And so that being able to tie from that inbound lead from your demand gen campaign through to a converted opportunity to what you actually drive from an AWS impact perspective, so you can, and then what your opportunity score is that data you can use to sell. Not only internally, but to us as well. Yeah, to a skeptical sales team or whatever who’s not maybe, you know, hype on partners in the, in the US West. [00:24:03] Matt Y: You can be like, listen, I don’t care what you think about my business. This is what I’m gonna drive for you with your quarter retirement from an AWS perspective, and this is how the shape of your customer accounts are gonna change. And this is why you should pay attention to my opportunities. ’cause my opportunity score is, is crazy high and I’m giving you insights into business that AWS would not otherwise have. [00:24:19] Vince Menzione: That’s your brand story we’re talking about. [00:24:21] Matt Y: Yeah. [00:24:22] Vince Menzione: Building your story up with within [00:24:25] Matt Y: So it’s, it’s about the data, I guess. And, and you should, you know, you should all actually be [00:24:28] Vince Menzione: Yeah. [00:24:29] Matt Y: Asking me for more data, so, you know, and tell me like, what do you need to sell to your internal stakeholders? ’cause if I can draw a clear line. [00:24:35] Matt Y: From your demand chain campaign that lands on a marketplace, which I know is a conversion machine, it has way better than industry levels of, of conversion rates. And then you can show, hey, if we have a PLG strategy and we land those leads on marketplace, we will convert them with high efficiency, low cost of sales and, and, and have sort of a bifurcated where we can close some through self service, some through express private offers and some through private offers, depending on deal size. [00:24:57] Matt Y: Like you tell A CFO that, and they’re my number one customer now and they love it ’cause they see cost of sales going down, cost of operations going down and business going up. Um, so I think we have more data than we used to use that data. And let me know what other data do you need to make that pitch and make that pitch to the CFO go around the head of sales, all those other people. [00:25:15] Matt Y: Honestly, the CFO is where we get the best leverage. [00:25:18] Vince Menzione: Awesome. Great question. [00:25:23] Matt Y: Gonna bring your mic. [00:25:23] Vince Menzione: We’re, we’re gonna get your mic here. There you go. Oh, [00:25:25] Audience Guest: thank you. So my name’s Jody Cheval and I’m a consultant now, but I was at Workday during when they adopted AWS and it, a sales organization needs propensity to buy data. [00:25:34] Audience Guest: To really drive the sales team to realize the opportunity kind of makes them visualize it. We didn’t struggle, but it was challenging to get that data because at that time we’re getting spreadsheets. So does AWS have a vision of making that API based data that our client, my clients, can get at and bring into a tool to start building account hypothesis based on that data? [00:25:57] Audience Guest: ’cause it really is important to an enterprise sales guy to have the sense that OAWS can help me close this deal. [00:26:03] Matt Y: Yeah. I mean. Part of that. So we, we launched, we’ve been launching part of that in stages and we’re not done. There’s, there’s more coming. Um, part of that is embedded really within the new, uh, partner agent workflows. [00:26:13] Matt Y: We are giving sort of more, uh, information back to you, not just about like what funding programs you’re eligible for, but like, you know, and when, when we will co-sell this deal with you, which is effectively a signal like we, we see this as a high value opportunity, that you have a likelihood of winning internally. [00:26:28] Matt Y: We, we have this solution matching engine that we’re using and we announced. That, that that ties you the partner to a customer specific opportunity that you have a high propensity or the partner has a high propensity to assist with and ultimately win. And now we’ve tied that to our express private offers, which we announced this week. [00:26:44] Matt Y: So it’s an indirect answer to what you’re asking, but a rep can essentially say. Send a private priced offer to the customer on behalf of the partner without having to ring up the partner because they have a high propensity to win this deal with the customer. So we’re progressively launching features like that. [00:26:59] Matt Y: In addition to the propensity to buy data that we do now share. It used to be kind of, again, manual magic depending on who you knew we could share. Now we do share that programmatically, and there’s more to come specifically in that space. Uh, I’d say watch that space. In the next few months, there’s gonna be more data coming away, but we do have the APIs, we have the agent. [00:27:16] Matt Y: We have things like express private office solution matching, and we have been sort of in that space progressively launching features over the last six to 12 months. And, and you should expect to see some more there soon, not just from us or from our partners. [00:27:27] Vince Menzione: Nice. Any announcement dates? [00:27:30] Matt Y: I can’t commit to a date or else my engineers will get mad at me. [00:27:33] Vince Menzione: It looks like we Another question number. Is the mic still back there? Okay. There’s a gentleman over here [00:27:40] Audience Guest: first Go leaves. Um, it’s awesome. I’m right next to. I was right next. [00:27:46] Vince Menzione: We’ve got a lot of great plants here, so, [00:27:49] Audience Guest: um, so this may be a little bit myopic or, or a challenge that we run into, but I love a lot of the innovation that’s looking forward and all the future things that we’re doing. [00:28:00] Audience Guest: One of the things that we’re struggling with is a little bit of almost like tech or structural debt. How do you think about bringing flexibility to the core pieces that underpin all of the innovation, which is. We are self-hosted. So one of our listings is an a MI. You can’t amend an a MI, you have to cancel and start over. [00:28:18] Audience Guest: So a lot of the building blocks, when you think about PLG, if somebody wants to add to that in an a MI listing, it’s, it’s sort of broken. So how are you thinking about taking all of the, the rapidly changing buyer behavior and then looking back at the structural foundation that underpins all of those things, like offers and, and amendments and changes and all of that? [00:28:39] Matt Y: Yeah. I, I promise I didn’t seed that question, but that, that’s a great one. Um, so not to get too in the weeds, but fundamentally, marketplace was built up, um, a bit like AWS like a set, a series of services somewhat independently. And each product type was effectively its own service, SaaS, server images, ais. [00:28:59] Matt Y: Um, what we’ve done recently is now we, we have, we got rid of product types basically on the backend. You, you don’t see it, but what that means, for example, like another thing AAMIs don’t support today, future data agreements. Um, or concurrent agreements, uh, they will all be supported by amis before the end of the year. [00:29:14] Matt Y: ’cause what we’re doing, this fundamental thing that you won’t even see called product offer decoupling. Uh, and it’s a fundamental piece of things that we need to unwind. ’cause we built up, we were moving very quickly over the years. We had a distributed engineering model and we built each product type independently. [00:29:28] Matt Y: And so yeah, if you’re a seller and you’re selling containers, agents, SaaS, amies, um, we’re breaking down the silos between those so that each of them will get the same benefits. And, and by the way, we’re taking the same approach to international. Hopefully you’ve noticed now that. It’s not like a feature launches in the US only and then takes five years to launch in either public sector or another country. [00:29:48] Matt Y: We, we’ve taken a global approach to feature launch and increasingly a product type neutral approach to feature launches. Uh, that’ll be largely resolved before the year’s out. We’re working on it right now. So again, it’s, it should be transparent to you, like you shouldn’t actually see any difference in the, in the experience. [00:30:05] Matt Y: Except that all of those features will be available. So, so that is, uh, actively under work. And that’s actually something if you’d like to try, um, you’re, you’re welcome to. So, yeah, [00:30:17] Vince Menzione: we have time for maybe one more question and we we’re actually gonna have you up here with a couple partners. [00:30:24] Matt Y: Sounds [00:30:24] Vince Menzione: good. Kind of fun. [00:30:32] Audience Guest: Hey, Matt, uh, met Natasha from Dondo. Uh, quick. So great announcements. And you know, you talked about the million, multi-billion dollar, uh, club, and, uh, that’s all great. Uh, in terms of the. Propensity data. I think that’s coming at the center of a lot of things, right? You know, for enterprises, oh, there’s an investment and you tap into that investment. [00:30:53] Audience Guest: But also there’s the other side of the procurement where a lot of customers, sometimes we work with, they’re like, they still wanna go direct for whatever reason, right? So I think there’s an education piece there, but also trying to understand like how we can work together to, you know, get some of that side of the things sorted out as well. [00:31:11] Audience Guest: You know? ’cause a lot of times it’s not about. Just, you know, retiring the, uh, the, the spend comets, but also like, Hey, I’m used, I’m already used that for something else. So maybe that’s not an, uh, something that applies here. And in also in tying that the PLG motion, uh, you know, for the customers you said, you talked about, you know, if there is. [00:31:34] Audience Guest: Leads on the TA table, like where the, it’s not the enterprise, but you know, the others. Um, I feel like it’s more to do, changing the business model at some times. Like with the enterprises, you have the revenue stream coming through, say large deals, right? And all of a sudden you tap into this, you know, PayGo. [00:31:51] Audience Guest: Where it flips the whole equation with, you know, the financing and the, and the, and the revenue measurement. So I think there’s two aspects of how do you kind of cons reconcile those things in terms of, you know, the revenue measurements going forward. [00:32:05] Matt Y: Yeah. So, so two things real quick on the procurement. [00:32:07] Matt Y: Um, yeah, like, yeah, I sort of alluded to this earlier, but, uh. Procurement is a bit late to the AI ag agentic transformation. They’re trying, and there’s a lot of great new incumbents in this space. And the big leaders like, you know, Coupa and Ariba and Oracle are, are, are evolving their products, albeit a bit slowly. [00:32:26] Matt Y: Um, but the, I think, uh, it’s still the long pole in the tent. You know this. And so like, there are two reasons why deals tend to go direct, because it kind of hits a wall of. Legal, uh, you know, procurement, governance, like all that kind of after the selection’s been made, et cetera, or, or they’re, you know, we can’t change. [00:32:44] Matt Y: People are gonna optimize for, for finance, you know, they’re, they’re going to, if they’re getting big discounts. I mean, that is life. I always say it’s like sellers at the most agented company are still gonna chase quota no matter how, you know, crazy. Uh, your, your company is, and it’s the same with, um, with the chief, uh, financial officer and chief procurement officer. [00:33:01] Matt Y: They are going to, they’re literally. Paid to find discounts. And so we’re not, we’re not gonna get rid of financial engineering. That’s a, that’s a thing. What we can do is reduce the friction for procurement. So we launched, for example, like mandatory purchase orders. That was a big thing. We, we have buyer notifications, now we’re making other procure to pay enhancements. [00:33:17] Matt Y: I mean, procurement systems still use like CXML. It’s like, that was, that was cool when I worked for the ap. And like I, I have teenagers that are old, like older than, so they, I, I think, um. Procurement needs to evolve and we’re gonna help it evolve. We’re gonna push it forward and, and we need to make it more seamless for procurement teams so that we remove those objections. [00:33:38] Matt Y: Uh, I can’t remove the financial engineering objection, like, you know, that’s just life. Um, but I can make it irresponsible not to use marketplace ’cause it’s so easy to use. And, uh, that, that’s kind of the approach we’re taking on, on the front end. Uh, you, you know, I think you, you, again, I didn’t see this question. [00:33:52] Matt Y: You, you stepped into a trap. Un unwittingly, um, PLG is not just is for enterprise. And, and PLG doesn’t necessarily mean pego or self-service. Uh, doesn’t necessarily like, uh, most of our self-service efforts are actually focused on private offers. And not necessarily for pego. Uh, when, when I say self-service and, and PLG, uh, it, it can mean all kinds of things like it. [00:34:14] Matt Y: We have requested private offer, requested demo call to actions, buttons that you can put on your listing. For example, you don’t necessarily need a free trial or a metered pay as you go listing to take advantage of those inbound self-service leads. So, and those inbound self-service leads are often massive enterprise deals, like I mentioned specifically, uh, the data mask. [00:34:31] Matt Y: Those giant enterprise deals that they launched came from an enterprise like Fortune 1000 Enterprise in the US that organically discovered their solution on the marketplace using our AI search. And that was a massive enterprise. And so I, I think yes, there is the long tail, you wanna capture a new logo acquisition, but you should think of your product like growth in your self-service strategy as a way to, um, acquire all kinds of leads, including large enterprise. [00:34:54] Matt Y: And so when I say leave money on the table, I’m not just talking about things that are gonna mature over two years or tiny little deals. These could be massive deals. Uh, and, and you’ll accelerate those deals by accelerating their discovery and, and research so that I think that, so, and my advice is don’t, you don’t have to go all in if you don’t have, if you don’t have metering, if you don’t have PayGo, that’s cool. [00:35:13] Matt Y: Start with something simple. Start with a public listing, with a request to private offer like that. That is a, a huge step. That doesn’t take much, and, and it kind of blows my mind still that a lot of companies aren’t doing that yet. [00:35:25] Vince Menzione: Great answer. Well, it’s now time we’re gonna bring, we’re gonna bring, it’s time. [00:35:29] Vince Menzione: We, we’ve got some great partners coming up here, Nvidia Elastic, Accenture gonna all join us for a conversation. Great. And I’m glad that you’re gonna stay with us. And let’s, let’s, well, let’s thank Matt, by the way, for that session. [00:35:41] Matt Y: Thanks. [00:35:42] Vince Menzione: And [00:35:42] Matt Y: thanks for listening to the Ultimate [00:35:44] Vince Menzione: Partner Podcast. If today’s conversation resonated, share it with a partner leader in your network. [00:35:51] Vince Menzione: Subscribe where you listen. And head over to the ultimate partner.com. For show notes related content and the resources for this episode. And if you haven’t already, now’s the time to register for the Ultimate Partner Live Event in Reston, Virginia, October 26th through October 28th. Until next time, keep showing up in the rooms that matter because being in the room changes everything.

Valve offers Windows drivers now for the Steam Machine, Intel feels like they need to join to price rising brigades, Micron is fab, MiniPC's with last gen gear, and Nvidia will lend you money on easy terms!  Adobe has another vulnerability and Microsoft has a couple fresh ones too!So much more in the actual show ... especially the live version, which you probably missed.Timestamps:00:00 Intro01:22 Patreon02:53 Food with Josh 05:41 Steam Machine coverage continues (again)08:52 Intel Nova Lake and AVX-51210:04 Intel raises MSRPs on their best processors11:02 Intel restarts 13th and 14th Gen Core production - for China12:23 Lian Li B4 mATX makes another appearance15:01 Micron breaking ground on a new fab expansion16:01 Get ready for Mini PCs with previous-gen Intel CPUs and DDR417:33 Internal sound cards in 2026?20:25 Cover story - RTX 3060 12GB returns in 202623:54 An NVIDIA story about AI cloud26:24 Jeremy leads us into a story about Samsung's obscene profits28:22 Josh's Racing Corner32:38 (In)Security Corner50:07 Gaming Quick Hits57:12 Picks of the Week1:08:22 Outro ★ Support this podcast on Patreon ★

Charles is joined by Victoria Fernandez, Chief Market Strategist at Crossmark Global Investments, to discuss why even the biggest Wall Street fund managers are struggling right now, why a legendary investor is dumping stocks to chase short-term trends, and whether software giants like Fortinet and Adobe are still safe bets for your money. Learn more about your ad choices. Visit podcastchoices.com/adchoices

This is a book launch-week conversation. David Pearlman joins to mark the release of his first book, Lead by Design: The Human Side of Leadership in an Age of Change. Rather than a standard author interview, Bill and David walk through how the book actually came together over two years — the writing process, the developmental editing partnership between two people who'd never met in person, but walked the same paths, and the personal story at the book's center. They close with reflections on what happens after a book ships: reader reactions, speaking invitations, and what David calls "the riches" that follow publishing something honest. David Pearlman spent roughly 23-24 years across Great Plains Softward and Microsoft, moving through about sixteen roles — people strategy, product, and large-scale fielding — with his last 18 years focused on the public sector: healthcare, K-12, and higher education. Along the way he served as chief of staff and spent time in the Army, active duty and reserves. He's currently at Adobe, working at the intersection of education, healthcare, and AI. Lead by Design is his first book. Visit: www.thedavidpearlman.com to order his book.

Dan Nathan and Guy Adami open the podcast by framing the day's key market story as sharp weakness and heightened volatility in memory, chips, and semi equipment, citing rapid reversals in names like Micron and Applied Materials and opaque guidance from Samsung. They discuss whether the AI build-out is increasingly debt-funded—highlighting Amazon and Oracle debt issuance and private credit deals—and reference a “Groundbreaker” piece shared by Jim Chanos arguing investors miss second-derivative slowdowns and that AI resembles a credit-driven real estate cycle more like 2008 than 2000's patient equity bubble. They note a rotation bounce into software (Microsoft, Palantir, Salesforce, Adobe, ServiceNow) but question sustainability, and debate SpaceX's fast-tracked inclusion in the Nasdaq 100 after its IPO, the stock's poor trading, possible broader IPO implications, and an upcoming insider share unlock —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.

We've been running a bit of an Agent Cloud series surveying all the top inference/compute/cloud providers, from Databricks to Daytona to Railway and, even further back, E2B, but we're excited to conclude this series returning to Modal, which has just raised a monster $355M Series C.The cloud was built for developers. But agents are now changing that.The old infra stack was designed for a human who could read docs, reason through YAML, and understand dashboards to figure out what they need when something broke. While this was painful for developers, it worked since they could fill in missing context in their heads.However, agents don't have that luxury. Now in this new era of agents, everything has to be tighter.They need a place to write code, run it, inspect the output, change the environment, debug failures, and try again. Fast iteration and feedback loops with all the necessary context are crucial for agents to operate properly. Furthermore, sandboxes are a clear representation of this shift as agents can easily spin up isolated environments. This programmatic infra even extends to research:Two years ago, we were one of the first to cover Modal with CEO Erik Bernhardsson and Alessio designed our favorite LS thumbnail of all time:At the time, Modal was just a teeny little company with a $17M Series A.Today, fresh off their $355M Series C, Modal is one of the clearest examples of the agent cloud future being built in real time: a cloud platform moving past traditional web app assumptions toward the workloads AI actually creates such as elastic inference, sandboxes, GPU burst, post-training, background agents, and infrastructure that agents themselves can operate.In this episode, Modal CTO Akshat Bubna joins swyx and Vibhu to unpack why AI applications don't fit traditional cloud assumptions, why Kubernetes was never designed for bursty compute-heavy workloads, and why Modal is now shifting from developer experience to agent experience.We go deep on Modal's AI infra stack: serverless functions, decorator-based infrastructure, elastic inference for custom models, GPU snapshotting, DeFlash, speculative decoding, Auto Endpoints, sandboxes, persistent storage, networked containers, private IPv6, RDMA, multi-node training, and Modal's capacity pool across 17 cloud providers. Akshat also explains why RL rollouts can require 100,000 sandboxes, why production agents need hard guardrails, why observability may matter more than reading code, and why AI has made infrastructure exciting again.We discuss:* Why Kubernetes wasn't built for bursty AI workloads* How Modal started as a better runtime before becoming an AI cloud* Why Modal added GPUs before ChatGPT* The shift from developer experience to agent experience* Why observability matters when agents are writing the code* Elastic inference for custom models across audio, video, robotics, and comp bio* GPU snapshotting, cold starts, and why inference workloads are so bursty* Why RL rollouts can require 100,000 sandboxes* DeFlash, speculative decoding, and frontier-level inference performance* Auto Endpoints and making optimized inference easier to deploy* What Modal adds beyond vLLM, SGLang, and raw GPU rental* Modal's 17-cloud capacity pool and supercloud strategy* Networked sandboxes, sidecars, private IPv6, and RDMA* Serverless multi-node training for post-training and research workloads* Auto-research, model-guided sweeps, and agents launching GPU experiments* Compute strategy, capacity planning, and batch tiers* Why production agents need specialized sandboxes and hard guardrails* Modal's take on managed agents, CI, Gitpod/Ona, Python, TypeScript, and Modal BenchAkshat Bubna* LinkedIn: https://www.linkedin.com/in/akshat-bubna-188885103* X: https://x.com/akshat_bModal* Website: https://modal.comTimestamps00:00:00 Introduction00:00:39 Modal's origin and why Kubernetes wasn't enough00:04:32 Developer Experience → Agent Experience00:06:21 Modal's AI cloud primitives00:09:14 Sandboxes, agent loops, and proto-Cognition00:12:12 Elastic inference, GPU snapshotting, and 100,000 sandboxes00:15:24 DeFlash, speculative decoding, and Auto Endpoints00:19:59 Production-grade inference beyond raw GPUs00:22:00 Background agents, Ramp Inspect, and the agent lifecycle00:24:08 Modal's 17-cloud supercloud strategy00:26:40 Networked sandboxes, private IPv6, and RDMA00:32:48 Multi-node training, post-training, and auto research00:37:36 Compute strategy, capacity planning, and batch tiers00:40:55 Open models, real-time AI, and production agent infra00:43:06 Hard guardrails, managed agents, and specialized sandboxes00:46:06 Why AI made infrastructure exciting again00:48:30 Model APIs, differentiated products, and agentic video00:51:50 CI, coding-agent infra, SDKs, and Modal Bench00:57:28 Closing ThoughtsTranscriptIntroduction: Modal, Series C, and the Art PartySwyx [00:00:00]: We're here with Akshat, CTO of Modal, together with Vibhu. Congrats on your Series C.Akshat [00:00:10]: Thank you.Swyx [00:00:11]: Your party yesterday was amazing.Akshat [00:00:15]: Yeah.Swyx [00:00:15]: From all the photos and all the swag.Akshat [00:00:17]: We had a bunch of art installations, which was fun, seeing, like, our products on pedestals next to, like, Rodin.Swyx [00:00:25]: Very nice. Very nice. When you started, it was not the GPU inference company. Maybe it was in your mind. Take us back to the origin story.Modal's Origin: A New Runtime Beyond KubernetesAkshat [00:00:39]: I first met Eric, who's the CEO, through an investor. Back then Eric was already thinking about building, a new runtime, and he got there thinking through why are workflow orchestration products so hard to use. It's because you have to run them on Kubernetes. Kubernetes is hard to manage. It's not built for burstiness and, custom images,Swyx [00:01:03]: YeahAkshat [00:01:03]: It has a terrible developer experience.Swyx [00:01:05]: And I'll, I'll interjectAkshat [00:01:06]: YeahSwyx [00:01:07]: For listeners, who are new, we interviewed Eric two years ago, and there's a bit more of the story there from Spotify and all those things.Swyx [00:01:14]: And I came across Eric through Data Council because he did that talk on the serverless container stack that you guys did, which was like, that was my first like, “Okay, I need to take Modal very seriously” moment.Akshat [00:01:26]: Yeah.Swyx [00:01:26]: But it was still very unclear, like, do I need all this for just my data pipelines?Akshat [00:01:33]: Yeah. initially what we were thinking about was if we build a better runtime, it's a very useful primitive in itself. It's There's a lot of things that, get solved by serverless functions, like you can do, ETL stuff, you can do job queues, you can do all this, like, bursty processing, which it turns out every company had needs for. but then we also were thinking about this as like, this is a primitive that we can build a whole collection of products on, which are very verticalized. So perhaps data engineering would've been the first one, but we were thinking about inference. Back then it was more classical inference, like computer vision stuff and running XGBoosts and whatnot. But we added GPUs to the product a year before ChatGPT came out.From Serverless Containers to GPU WorkloadsSwyx [00:02:19]: Nice.Akshat [00:02:19]: We just didn't think it would be that big of a deal.Swyx [00:02:22]: Yeah, just like add A100.Vibhu [00:02:23]: Was there any, like, early key problem that really sparked off why you built it?Akshat [00:02:28]: Yeah. Primarily it's just, none of the tooling that was out there was built for, one, a really great developer experience, and also there's a general trend of, a lot of the workloads that we were seeing were very. I wish there was a better word for it, but compute-heavy. Like, they need, one, like, need a lot more resources, so you need to burst up and down a lot, versus like Kubernetes designed for, like, slow scaling and, more for, like, web server use cases. And also there's just a lot more specialization in, like, what kinds of environments these workloads run in. Like, we had sometimes they need accelerators, sometimes they need different kinds of images, and this is just like a consistent thing that we saw across a lot of companies. That would be the next step.Software-Defined Infrastructure and Decorator-Based DXSwyx [00:03:13]: Yeah. Yeah. Be nice. I don't know how much this factored into the early story, but I wrote a post when I was at Temporal about infrastructure, software-defined infrastructure or something like that.Akshat [00:03:22]: Yeah, the self-provisioningSwyx [00:03:23]: Self-provisioning.Akshat [00:03:24]: Yeah.Swyx [00:03:24]: Yeah. I can't even remember my own post.Swyx [00:03:26]: And then you put me on the landing page.Akshat [00:03:28]: Yeah. We really like, the term and so we stole it.Swyx [00:03:32]: Because you had the insight that everything can just be in decorators co-located with the code, right?Akshat [00:03:37]: Yeah.Swyx [00:03:37]: Was that a big part of the originalAkshat [00:03:39]: YesSwyx [00:03:39]: Story or it was just like a DX layer?Akshat [00:03:41]: That was, really important because we really didn't want people to spend, so much time, writing YAML, and it seemed like you could really condense the surface area of what you're doing, put it in code so you can operate on it just like you operate on other code, and like build stuff that's more expressive and dynamic. and so yeah, that was always a very important part.Swyx [00:04:04]: Then the pushback is this is a DSL.Akshat [00:04:07]: Yeah.Swyx [00:04:07]: It's you're closed source. I am locked into Modal.Akshat [00:04:11]: Yeah. We never really got pushback for that because the nice thing about Modal is you can bring whatever code you have, and sure, the DSL is at the configuration layer for, what hardware you're using, how you're scaling things up, but you still own the code.Akshat [00:04:27]: And that's, that's been an important, part of our story, even as we do inference now.Swyx [00:04:32]: Yeah.Vibhu [00:04:32]: How much of do you think still stays the same today? Like if you were to build something today, DevX very important, but I feel like, a lot of this has been changed with just hook it up to an agent, have Claude Code, have Codex implement a tool. there's very agent native primitives that are different than if I'm doing this myself, right?Developer Experience → Agent ExperienceAkshat [00:04:54]: We've changed our SDK team to think about agent experience instead of, developer experience and we think that the same benefits that apply for DX also apply for AX, which is why would you have an agent read through hundreds of Kubernetes files and like write YAML that's not even typed when it can make a couple of changes in a decorator and it gets this self-provisioning runtime of, being able to see its changes live in action? yeah, it just seems from the customers we talk to, they find Modal is much faster for agents to use versus operating on a different substrate.Swyx [00:05:34]: Yeah, because like you, again, you co-locate the infrastructure requirements to the code that runs it.Akshat [00:05:38]: Yeah.Swyx [00:05:38]: Well, the negative thesis now is that nobody's looking at their code anymore, so there's no point.Akshat [00:05:44]: Yeah, people aren't looking at code. one thing we still see is really important is observability.Swyx [00:05:51]: Yeah.Akshat [00:05:51]: Like how good is your dashboard? And of course, like we have, we push a lot of it to the CLI so the agents can do their own investigation, but you still need humans to go interpret what's going on and, make judgment calls and whatnot. and that's I feel like, Maybe more important now than looking at the code itself.Swyx [00:06:11]: Yes, because like, you can try to treat the code as a black box and then use, see the observable action that comes out of it, and then just prompt a change.What Modal Is For: AI Cloud PrimitivesAkshat [00:06:21]: Yeah.Swyx [00:06:22]: So I think it takes a bit of restraint to not specialize, to say, “I want to ship a new primitive,” and then just be general purpose.Swyx [00:06:31]: People ask you, “What are you for?” You're like, “ I don't know. We can do this, we can do that.”Vibhu [00:06:36]: Well, I'd be curious to see, like, okay, if we were to ask you, like, what is Modal for even at a high level? There's a lot you guys do, sandboxes, GPUs, everything. How do you answer?Akshat [00:06:46]: Modal is a cloud platform that's built for, where we've built the primitives from scratch for AI applications. and right now it covers, inference, training, batch processing, and sandbox workloads.Akshat [00:07:00]: But we're building a lot moreSwyx [00:07:02]: I noticed you didn't say web server, so there is still a role for, like, the always-on large-scale Kubernetes type things.Akshat [00:07:09]: Yeah, absolutely. We're, we're not trying to compete with the renders of the world, because yeah, we think the differentiator for us is the, are the workloads that need specialized compute, need to scale up and down a lot. yeah, they're, they're, they're just shaped differently.Working Alongside Frontier StartupsVibhu [00:07:26]: I think you're building a lot of it alongside the startups, right? They're innovating quite a bit, even in your, like, latest blog post. Like, even in the series C, the customers that you mention here, the cognitions, technical ones, ramps and whatnot, they're, they're innovating with you, right? And that's not something AWS is doing directly with.Akshat [00:07:45]: Yeah, absolutely. I think, this is again classic. We're a small team. We can move really fast. our engineers are working with our customers and figuring it out. Yeah.Swyx [00:07:54]: So my first week at Cognition, I walked in, there was someone wearing a Modal shirt. I was like, “What are you doing here?” They're like, “Yeah, I just. I am embedded inside of Cog.”Akshat [00:08:05]: Yeah, I think that was Peyton. We sent him overSwyx [00:08:07]: Yeah.Akshat [00:08:07]: Because, the latency of communication was too high otherwise.Swyx [00:08:12]: Yeah, distributed node, you have to - you have to place one and collocate.Vibhu [00:08:16]: Yeah.Swyx [00:08:16]: So I had a, I had direct personal experience, right? So I worked on smol developer three years ago. it was inspired by Claude 1. I think you onboarded me at some point, like, just before, and I was like, “Oh, like, I need some bursty compute. Like, I was just gonna try using Modal.” And it was a, it was a pretty pleasant experience. apparently, I showed up in the board meeting, like the analytics.smol developer, Sandboxes, and Proto-CognitionAkshat [00:08:39]: Yeah, you blew up on Hacker News and,Swyx [00:08:41]: YeahAkshat [00:08:41]: We got a big traffic spike. I. I think the way you used smol developer was Modal functions for running stuff, which was. Like, the, that was a good use case. but then, yeah.Swyx [00:08:53]: Yeah. That - So to me, that was proto-cognition.Akshat [00:08:55]: Right.Swyx [00:08:56]: If only I had, like, stuck to it.Swyx [00:08:58]: Like, that was like, if - did you say draw the tech treeAkshat [00:09:00]: AbsolutelySwyx [00:09:00]: You're just like, “Yeah, like, probably this will happen.”Akshat [00:09:02]: Yeah. Like, he was so close. You were just rebuilding upon usSwyx [00:09:04]: I just didn't realize.Akshat [00:09:05]: But the funny story there is at the same time, we were talking to a bunch of customers who needed something like sandboxing.Swyx [00:09:14]: Yeah.Akshat [00:09:14]: This is like twenty-three.Swyx [00:09:15]: Yeah.Akshat [00:09:16]: So we builtSwyx [00:09:17]: You introduced a new API right after that.Akshat [00:09:18]: Yeah.Swyx [00:09:19]: Yes.Akshat [00:09:19]: Like, we built sandboxes in May of twenty-three before anyone was even knew this was gonna be a thing. And the first example we published was, we took smol developerSwyx [00:09:28]: Smol developerAkshat [00:09:28]: And put it in a loop, so the agent can iterate on itself.Swyx [00:09:33]: Loops are hot these days.Vibhu [00:09:34]: It's the looper.Akshat [00:09:34]: Yeah.Vibhu [00:09:35]: Loops in. When was this, twenty-three?Akshat [00:09:38]: Yeah.Vibhu [00:09:39]: A small check.Akshat [00:09:39]: Yeah.Swyx [00:09:39]: It's like twenty-three. so the. the, those for listeners, like, the problem was the models are not built for any of this, right?Swyx [00:09:46]: Like, you're just trying to like. They're not post-training to understand, like, looping and, like, self-correction and tool calling was there, but, like, also not that great.Akshat [00:09:55]: Yeah.Akshat [00:09:55]: I don't remember if you used tool calling in this one, but yeah, the models would just diverge after like ten iterations and not produce anything meaningful.Swyx [00:10:03]: Yeah. But like, then. So okay, like now talking to myself three years ago, the answerVibhu [00:10:08]: Of course they will get betterSwyx [00:10:09]: Collect all the failures, build benchmark, and then collect all the, examples, build the RL environmentAkshat [00:10:15]: RightSwyx [00:10:15]: Sell it for like ten billion dollars to Meta.Swyx [00:10:17]: And then also train a model and then sell that for sixty billion dollars to Elon. And this isAkshat [00:10:23]: Yeah, of courseSwyx [00:10:23]: The funny machine. Like, it's like, it's about the hardware.Akshat [00:10:28]: It's hard to have that inherent conviction that the stuff will get that much better.Swyx [00:10:33]: In retrospect, it's so f*****g obvious.Akshat [00:10:36]: Fair enough.Swyx [00:10:37]: Like, what else were we doing back then? I don't know. anyway. Yeah. So this. That was the start of your sandboxing journey, right? I feel like it didn't blow up until, like, last year.Akshat [00:10:49]: Yeah.Swyx [00:10:50]: So there was like a couple years of quietness.Akshat [00:10:52]: Exactly, yeah. We wereVibhu [00:10:53]: I think very underrated product value. Like, my experience with Modal, Charles, before he had joined Modal, met this guy at a hackathon, and he really insisted we wanted to run some small model, not hosted anywhere, and he's like, “ there's this cool company, Modal. They'll like spin up a GPU sandbox, we can throw it on there. They'll take a Hugging Face link.” And like there's so much value just right there, right? Like instant hosting, spin it up, spin it down. It'll stay cold, but we run the demo a few days later, it'll come back up and like all this stuff in retrospect, like it's still what we needed like today.Akshat [00:11:27]: Yeah, it's still needed today. workload shapes have changed a lot as, we run stuff for people with really massive production scale and, there it's it's not about scaling from zero to one, but it's how do we scale really elastically, from like thousand to fifteen hundred GPUs very quickly in a given region. It's the same shape problem.Elastic Inference, GPU Autoscaling, and Custom ModelsVibhu [00:11:50]: Okay. So you look at, say, Cursor Composer, right?Akshat [00:11:53]: Yeah.Vibhu [00:11:53]: They had a. “We'll do RL on a model every couple hours.” you guys have a whole version of RL inference gym and whatnot.Vibhu [00:12:01]: When you look at workloads like that, you're doing train runs where you need to scale up, scale down every hour thousands of GPUs, right? That's the example for we do need it, right?Akshat [00:12:12]: Yeah. Well, so I'll, I'll take a step back and, maybe talk about like how people use Modal today. because our biggest use case is, elastic inference. And the thing we first found product market fit, with was inference for custom models. So we stayed away from the LLM space, and we were serving companies like Suno for audio, Runway for video, robotics, comp bio companies that train their own model elsewhere. But Modal is the best black box that for deployment, scaling to however many GPUs you need as your traffic pattern changes. And we saw all of them like have a very unpredict- predict- predictable, traffic pattern. it's like diurnal. It's Some days, like the company will do a launch and, they'll need like, way more. And it's not just one model that they deploy. They-- all these companies deploy, lots of different models in different regions, and so the autoscaling problem becomes even harder because then you have to scale within a certain region, and those cycles are offset. So different times you scale up in different regions.Akshat [00:13:20]: So that's like our sortVibhu [00:13:22]: And thatAkshat [00:13:22]: YeahVibhu [00:13:22]: That in and of itself is a huge category. There's a bunch of inference providers which, provide this fireworks, does this as a service together, whatnot, Base10. that's carved into its own niche for language models, at least right now.Akshat [00:13:36]: Yeah. the thing that we have specialized in is the autoscaling aspect.Vibhu [00:13:41]: Yeah.Akshat [00:13:41]: Because we found that it's not universally true that everyone else can autoscale, and we've gone deeper into it on the tech side by, we've incorporated GPU snapshotting into the product so we can take the GPU state, like your torch.compile model, snapshot it, and the next cold start is way faster. And so going back to your question, it's That's why you need a lot of burstiness for inference. But then people also do a lot of demand training, like for RL stuff, your rollouts are bursty, as you said. People also do a lot of batch jobs. So we'll see, a lot of companies, before they have a training run, they'll need thousands of GPUs to run encoding or something like that. And I think those things are much more bursty than. I agree that agents are not that bursty. sandboxes are, except when you're doing RL. RL is justRL, Batch Jobs, and 100,000 SandboxesVibhu [00:14:28]: Or commerceAkshat [00:14:28]: Insanely bursty.Vibhu [00:14:29]: Yeah.Akshat [00:14:30]: Yeah. Like when you're doing, rollouts, you sometimes need a hundred thousand sandboxes in your sandboxes.Vibhu [00:14:37]: Yeah. I'm curious if you've seen early sparks of continual learning. There are some people, like our friends, ngram, recently announced thisAkshat [00:14:45]: YeahVibhu [00:14:45]: They're, they're trying to do training. That also seems like a different workload, right? If you're doing training twenty-four/seven per se, there's a very weird dynamic of how you're using GPUs between people and whatnot, but seems like something you guys would work for.Akshat [00:15:00]: As you said, we're, we're fortunate to work with a number of, customers at the frontier and grab some of our customers. and they are taking the primitives we have, and trying to use them in very interesting ways, like continual learning. It's possible as the stuff gets better, some of that will be part of, our offering as well if, more people need it. but we're, we're just waiting to seeVibhu [00:15:23]: YeahAkshat [00:15:23]: How it shakes out.Vibhu [00:15:24]: Is there a primitive that you added after sandboxing that was the next step in the story?LLM Inference, DeFlash, and Speculative DecodingAkshat [00:15:32]: I guess we've been going much deeper into LLM inferenceVibhu [00:15:35]: YeahAkshat [00:15:35]: Because we realized that some of the advantages we have with like autoscaling, again, especially in different regions and whatnot, are, not present elsewhere. and the place where we had a gap was we weren't, working on the model layer itself. Like we were a black box. And, we realized that, we can get to frontier-level model performance, with, by having great people who work on this. And, we've been open sourcing a lot of our work, in terms of, Recently, we, shared our work on DeFlash, which is a block-based, speculator, and we've open sourced, all of it. So, you can - By using open source DeFlash, you can get the same performance as you would with one of the proprietary providers. And the next thing we're thinking about hereVibhu [00:16:23]: I thought this wasAkshat [00:16:24]: YeahVibhu [00:16:24]: An interesting blog post as well, right? Like, I think in here you make a claim that. Not a claim, just that how effective speculative deco-decoding really just get to.Akshat [00:16:33]: Yeah.Vibhu [00:16:33]: Anything you wanna point out from this around, what people should know?Akshat [00:16:39]: Yeah, absolutely. the high-level summary is, it would help to describe what speculative decoding is.Vibhu [00:16:44]: Yes.Akshat [00:16:44]: I will, yes.Vibhu [00:16:45]: I think, likeAkshat [00:16:46]: YeahVibhu [00:16:46]: So we've covered like Eagle and all thisAkshat [00:16:47]: YeahVibhu [00:16:47]: Like Hydra and all those things, but it was like two years ago.Akshat [00:16:51]: Yeah.Vibhu [00:16:51]: I think it doesn't hurt, right?Akshat [00:16:52]: Yeah. Speculative decoding is you have a smaller model, called a draft model, predict tokens ahead of the bigger model, and then you have the bigger model, verify all of this, all the tokens are predicted. And the reason it's faster is if you're predicting, one token at once, you're bound by memory bandwidth. But if you can batch the verification of, the draft model, then you're much more efficient using compute, and it's faster, and as long as your draft model is producing a lot of tokens that can get accepted, which is called the accept length, you can get a speed up that's, multiple times of, the original model speed. and well, that's what we highlight here. It's Like people talk a lot about we made these kernels faster and whatnot, but improving kernel will only give you like few percentage points of improvement, and, increasing accept length, literally is a multiplicative decreaseVibhu [00:17:47]: Like two to four X.Akshat [00:17:48]: Yeah, exactly.Vibhu [00:17:48]: Without much head-on performance.Akshat [00:17:50]: Yeah. I think it may - you are running a second model, right? So it may be something more expensive in the compute,Vibhu [00:17:57]: I meant quality performanceAkshat [00:17:58]: Probably not by muchVibhu [00:17:58]: But yeah. I thinkAkshat [00:17:59]: So there's no drop in quality performanceVibhu [00:18:01]: YeahAkshat [00:18:01]: Because you're always. You're never accepting a token that the big modelVibhu [00:18:04]: It's strictly betterAkshat [00:18:05]: YeahVibhu [00:18:05]: Or it's same.Akshat [00:18:06]: Exactly.Vibhu [00:18:07]: Right. Yeah.Akshat [00:18:08]: And so we've been working a bunch on DeFlash, which is a block-based speculator. so it's instead of predicting, one token at a time, it's predicting a block. And we've been open sourcing our work with it. The next thing for us here is for helping people train speculators and custom models. it's it's something that traditionally is very forward-deployed engineering driven, support deployed, engineer driven, like you work with customers and help them do that. And our vision for. This is why we launched Auto Endpoints, is we want to make frontier-level performance available to everyone. And so, we mentioned this in the announcement, we teased it. The next thing we're, we're launching is, as you run an auto endpoint, we shadow trafficAuto Endpoints and Frontier-Level PerformanceVibhu [00:18:54]: Do you want to explain what auto endpoints are?Akshat [00:18:57]: Yeah.Vibhu [00:18:57]: I lovely, yeah.Akshat [00:18:58]: Yeah. So, this is, I guess, going back to your Modal is you touch the code, but, sometimes people don't wanna touch the code, and they wanna get started with an endpoint that works and has all the great performance and, scalability that Modal has. So we've made that easier with, a way to create an endpoint from our UI, from the CLI, that has all of our optimizations that we talked about, like the DeFlash stuff already baked in, and there's full transparency. So we give you the code, you can go run it yourself, and if you want, you can eject out into the full Modal experience, which we see as people get sophisticated, they do wanna tweak the models, they wanna, fine-tune stuff. You can still do all of that. It's it's not a black box. And yeah, the next thing, as we teased later in the post, is how do we give you value even beyond this in terms of having your draft models evolve as your data distribution evolves, again, without having to talk to a person and, yeah.Vibhu [00:19:59]: I guess just to understand it directly, you have the GPUs, you have an endpoint that's compatible, you serve open model. If someone was to do this themselves, what's the delta that you guys provide? So you do a lot of open source great work on effective inference. how does it compare to, say, I take the same model, 5.2 FP8, take shelf inference engine, vLLM, SGLang, get compute of similar capacity, similar cost. What's the delta that plugging into something this, like this offers outside of the benefit of, scaling?Production Inference Beyond Raw GPUsAkshat [00:20:34]: It's interesting because we've taken the approach of open sourcing our contributions and upstreaming them. we work closely with the SGLang team. We want the improvements that our team, comes up with to be, there in open source for others to use, even outside of Modal. The benefit to us is we have a team that has significant expertise in terms of if you do have something that is not there, our team can help you get that performance, first. the other thing is with these endpoints, we are way more elastic, as you said, than, anyone else, and you have true scaling to zero. you have true, burstiness, and in practice, that matters a lot more to people than just finding, the GPU and, running Modal code on something.Vibhu [00:21:20]: Yeah. And I will say it's not that straightforward to just. like what I said is easier said than done, right?Akshat [00:21:26]: Yeah.Vibhu [00:21:27]: It's I think still for the average person, still hard to just gut check using different. There's, there's quite a bit of combinations you can make there. the trade-offs aren't really known at face value.Akshat [00:21:40]: Yeah. it's it's not just that. I think it's it's that running production-grade inference is a hard infer problem.Vibhu [00:21:49]: YeahAkshat [00:21:49]: Even if you subtract out the autoscalingVibhu [00:21:50]: YeahAkshat [00:21:51]: Is controlling things like tail latency and, making sure every, request is delivered at least once and whatnot.The Model and Agent LifecycleVibhu [00:22:00]: There's a lot of innovation that you can do here. I think, it's very interesting that you're starting to encroach on, like as you become a full cloud, you're starting to encroach on other people's turf.Vibhu [00:22:09]: What will you not do?Akshat [00:22:13]: Well, we wanna follow our users and, make sure they get like a platform that has everything that works well together. so right now we're focused on the model lifecycle and the agent, lifecycle. so both like going from data prep to training to inference, and then also if I want to deploy a background agent, let's say, sandbox, do persistent storage, a whole bunch of other stuff.Vibhu [00:22:38]: We talked to Cole, who did, OpenInspect. Yeah.Akshat [00:22:42]: Yeah.Vibhu [00:22:42]: And RealInspect also is on Modal.Akshat [00:22:44]: Yeah. So Ramp Inspect was a great example of a background agent that was really successful because they, were able to use some of the primitives like snapshotting and fast scaling to just have something that feels really reactive and works well.Ramp Inspect and Background AgentsVibhu [00:23:02]: Yeah. That's the new CTO of, Ramp right there.Akshat [00:23:05]: Yeah, Rahul.Vibhu [00:23:08]: It was really fun. yeah, okay, I think, all very bullish. Like, one of my reflections was also I did not originally. So when I met you guysThe Inference Inflection: CPU, GPU, and Co-LocationVibhu [00:23:19]: You weren't that much in the GPU game, and now you're all about, inference. And one of the points that I hinged on for Jensen's keynote at GTC this year was, what we're calling like the inference inflection, right? That let's say in AI workloads or machine learning workloads, it used to be like, let's call it eight to one GPU to CPU, and now it's more like one to one, which is like a interesting. Like, - because of how much agents are blocked or call out to this, to CPU heavy stuff the actual, like, limiting factor, like, swings back and forth from GPU to CPU a lot more than it used to be all GPU and then occasional CPU.Akshat [00:24:01]: Yeah.Vibhu [00:24:02]: GPU, CPU. And now it's like just constantly, and you just have to locate everything.Seventeen Clouds and the Supercloud StrategyAkshat [00:24:08]: Yeah. And that's one of the things that, again, we see as, something appealing about Modal, which is we've built this capacity pool that spans, 17 cloud providers, so we're, we're very good at Running on various kinds of cloud capacity across the worldSwyx [00:24:24]: You don't have your own data centers?Akshat [00:24:25]: We don't have our own data centers. We just run across a lot of neo cloudsSwyx [00:24:29]: Yeah. AreAkshat [00:24:30]: Metal providers.Swyx [00:24:30]: Yeah. Question mark.Swyx [00:24:31]: Yeah. You're, you're running the math, and you're like, “What's the cutover point where you're like.”Akshat [00:24:36]: Yeah, it's a good question. part of it is we see our differentiator in the software layer, and, being capital light and focusing on the software helps us move really fast. so far it's worked out well because there are so many other people building data centers that we're able to work effectively with them, and again, focus on what makes us, special.Swyx [00:24:55]: Yeah.Swyx [00:24:56]: 17 gets you into, like, the local providers sometimes. LikeAkshat [00:25:00]: The,Swyx [00:25:01]: Which was the most interesting one?Akshat [00:25:02]: There are a lot more neo clouds than you expect, and they all have various degrees of, various levels of reliability. And, that's why it's something we've invested a lot of time in, is building our own reliability layer on top. so if the GPU falls off the bus or something happens, we user workloads are not affected, and that lets us use a lot more capacity than,Swyx [00:25:30]: YeahAkshat [00:25:30]: You as a user would be able to.Swyx [00:25:32]: It's a useful thing to have because like now everyone knows, like, what layer you are and, like, you optimize for being the super cloud of all clouds.Akshat [00:25:41]: Yeah. That's, that's, that's the idea. and so I guess when you mentioned colocation, that's, that's another interesting thing where, one thing we've seen is people come to us when they want, very specifically located, CPUs or GPUs, like they wantSwyx [00:25:57]: Oh, they pin it in likeAkshat [00:25:58]: YeahSwyx [00:25:58]: EU?Akshat [00:25:59]: Exactly. Or EU, US.Swyx [00:26:01]: Right. Data resiliencyAkshat [00:26:02]: AustraliaSwyx [00:26:02]: Locality thing or performance or what?Akshat [00:26:04]: It's either data locality or latency, yeah.Swyx [00:26:07]: Yeah.Akshat [00:26:07]: Like, you want your. They're running sandboxes and model. They want them to be right next to aSwyx [00:26:10]: Yeah, it's easy thenAkshat [00:26:11]: YeahSwyx [00:26:12]: To. That is important in all those things. and so, like, you've accidentally, I don't know if it's accident, but, like, you've built the perfect primitive for agents to express themselves. And then, like, it's almost very funny how every extra development just involves more file system, just involves more CPU.Akshat [00:26:30]: Yeah.Swyx [00:26:31]: Just like the things that you already have. I don't know much about, if there's any, like, networking usages that are interesting, but you've also done some good work on networking.Networking, Sidecars, Private IPv6, and SandboxesAkshat [00:26:40]: Yeah, that's exactly right. Like, we're just taking compute storage and networking and building stuff on that layer, for, again, the stuff people need.Swyx [00:26:49]: YeahAkshat [00:26:50]: We see a few interesting networking things coming up. one is people want networked sandboxes. so we haveSwyx [00:26:57]: For like a Docker cluster type thing.Akshat [00:26:59]: Yeah.Swyx [00:26:59]: Sorry, Docker Swarm. Oh, f**k. What is it called?Akshat [00:27:02]: Compose.Swyx [00:27:03]: Compose type thing.Akshat [00:27:04]: Yeah. So if you want Docker Compose, our sandboxes now support, this thing called sidecars. So you can. A sandbox is a pod of containers, and you can run multiple containers in, a sandbox. also useful because, going back to networking, people want a lot of control over, outbound networking from a sandbox.Swyx [00:27:23]: Yeah.Akshat [00:27:23]: Like, they might wanna run a middle proxy for, like, maybe logging stuff for RL or, controlling how egress can happen to a domain, injecting credentials. and yeah. So we've, we've had to build a lot of that stuff ourselves.Swyx [00:27:38]: Yeah.Akshat [00:27:39]: But then also sometimes people want, sandboxes spanning multiple nodes to talk to each other, which is an emerging thing we're seeing. We have support for that for a different reason, and yeah, we'll see if that becomes stable.Swyx [00:27:52]: Like, just an open socket. It's a. This is directly like mTLS.Akshat [00:27:56]: We do support that, which is you can, expose a tunnel inside a sandbox.Swyx [00:28:01]: Yeah.Akshat [00:28:01]: And then you can either expose it to public internet or it can be, you can add like a HTTP, auth layer above it. But we have this thing called I6PN, which we haven't talked about, which is this, like, overlay network using IPv6 addresses. so if Modal containers, within the same workspace, when this is enabled, can address each other using this private IPv6 address, and no one else can.Akshat [00:28:28]: So it's like private networking, for containers. We built it because we needed it as a primitive for our distributed training product. so we have this other feature, which is you can add a decorator to a function, and you get a cluster of GPUs. and they have RDMA networking. so you can run a distributed training job, that's truly serverless. and we did the overlay network for that. But then we've seen that people are using it for other reasons, and, I'm intrigued to yeah, what would people do with it.Swyx [00:28:59]: Build primitives and let people figure it out, right?Akshat [00:29:01]: Yeah, exactly.Swyx [00:29:02]: You put out a pretty interestingAkshat [00:29:03]: They're like, they read the docs webpage. Let me use thatSwyx [00:29:06]: YeahAkshat [00:29:06]: Something they never intended to work. This is literally not even in our docs page. People somehow found it, and they're using it.RDMA, Memory Movement, and Distributed TrainingSwyx [00:29:12]: Huh.Swyx [00:29:14]: The way you portrayed it with, like, RDMA versus TCP, like, very well laid out, but just the transfer speed change at scale for RL, like yeah, you have it, you have it built in. I'm sure someone found it. It's found it to be a lot more efficient before you made a thing out of it, right?Akshat [00:29:32]: Yeah. And not to split hairs, I guess the overlay network is the TCP overlay network.Akshat [00:29:39]: The reason we have that is you need that to do the key exchange for RDMA before you set up the RDMA network on top of that. but then people found the TCP part.Swyx [00:29:48]: Can I tell you, this is like a big aha moment for me becauseAkshat [00:29:51]: YeahSwyx [00:29:51]: So I review 2,200 submissions for the World's Fair.Akshat [00:29:56]: Yeah.Swyx [00:29:57]: And then I got this from John OsterhoutAkshat [00:29:58]: HuhSwyx [00:29:59]: Who I don't know if. Do John Osterhout by name?Akshat [00:30:01]: The name sounds familiar.Swyx [00:30:02]: He published a. He's a well-known professor, published a lot of interesting software design books, and this is the talk he chose to submit, is on RDMA at Inference. And I'm like, you wouldn't think that this guy, who is like operating systems guy, would care about RDMA.Akshat [00:30:20]: I, it makes sense to me because I,Swyx [00:30:24]: This is the cloud, right? YeahAkshat [00:30:25]: Like, the way you move around your KV cache and how efficiently you can do it, how efficiently you move, your weights from your training GPUs to your inference GPUs in RL is there's a lot of degrees of freedom, and it is a systems problemSwyx [00:30:41]: YeahAkshat [00:30:41]: Moving memory aroundSwyx [00:30:42]: YeahAkshat [00:30:43]: Scheduling.Swyx [00:30:44]: This shows you how primitive my understanding of networking stuff is.Swyx [00:30:46]: Is this like the domain of WireGuard as well?Akshat [00:30:50]: Not quite.Swyx [00:30:51]: It's adjacent?Swyx [00:30:53]: Explain everything.Akshat [00:30:54]: Sure.Swyx [00:30:56]: How do we move memory around GPUs?Akshat [00:30:58]: Well, so sorry. Yeah, that is memory. Sorry, I was talking more, and maybe I was talking like five minutes back, about the private IPv6, addressing that you've set up.Swyx [00:31:09]: Yeah.Akshat [00:31:09]: Is it like it's a VPN?Swyx [00:31:10]: Yeah, it is like a VPN, and yeah, WireGuard is, yeah, you're right. It is,Akshat [00:31:16]: Right. Yeah, you already moved on to new topicsSwyx [00:31:17]: A similarAkshat [00:31:18]: OkaySwyx [00:31:19]: In the same space, WireGuard is, encrypted and this is,Akshat [00:31:23]: And you don't need encryption.Swyx [00:31:23]: Yeah.Akshat [00:31:24]: Yeah.Swyx [00:31:24]: This is not encrypted. that's the main difference. This is TCP and we have eBPF programs that will reject or allow the TCP connection based on whether you're allowed to do it.Akshat [00:31:35]: Used to involve a full sidecar, but now you have eBPF in the Linux kernel.Swyx [00:31:39]: Yeah.Akshat [00:31:40]: Yeah. I don't know if this is a natural follow-on to the topic of like my skepticism on distributed training is that while, like, people spend a lot of money on, like, cables to hook up GPUs, and even that is not, like, fast enough, and that's the bottleneck, is your networking fast enough?Swyx [00:31:59]: Yeah. So I guess you're talking about fully distributed training like, Dialog or something which is like cross data centerAkshat [00:32:06]: That would be, yes.Swyx [00:32:07]: That's the extreme.Akshat [00:32:08]: Yeah.Swyx [00:32:08]: You're in the middle, and then other people would have like the Mellanox cables up in, like, their actual data center.Akshat [00:32:14]: When you run multi-node training on Modal, RDMA, I think Mellanox, is, or InfiniBand is like a, is all seen as RDMA. but it's a way to bypass the TCP networking stack and, transfer, stuff much faster, between one node, to the other. And we have I think like 3 terabit per second, internal networkingSwyx [00:32:40]: OkayAkshat [00:32:40]: Which is the standard that's needed.Swyx [00:32:42]: Okay. So I misunderstood whatAkshat [00:32:43]: 50Swyx [00:32:43]: What part of the stack you wereAkshat [00:32:44]: 50 gigs overSwyx [00:32:45]: YeahAkshat [00:32:45]: If you wentSwyx [00:32:45]: YeahAkshat [00:32:46]: RDMA.Swyx [00:32:46]: Okay.Swyx [00:32:48]: Yeah. I, very impressive work.Multi-Node Training, Post-Training, and Auto ResearchSwyx [00:32:52]: So effectively you're extending like the model philosophy to the training cluster, like, yeah.Akshat [00:32:59]: Yeah. And we're, we're not going for like large scale training runs. the thing that we've built multi-node training for is, we see a lot of, smaller scale post-training. like, people are post-training like medium sized fund models, so they can, get higher quality on inference. this is a perfect fit, for something like that.Swyx [00:33:21]: Yeah. That is my impression of how a lot of these labs explore branches in post-training and then eventually merge whatever they find in.Akshat [00:33:31]: Yeah. The other use case we've seen for multi-node training is even if you have a big cluster, your researchers are still doing small runsSwyx [00:33:38]: YesAkshat [00:33:39]: Having elasticity thereSwyx [00:33:40]: Right, sureAkshat [00:33:40]: Matters a lot more.Swyx [00:33:41]: Yeah. the, like, this is like the current limiting factor for auto research, which is like you need to give your model some GPUs in order for it to completely run.Akshat [00:33:51]: We have a blog post on auto resource and model is,Swyx [00:33:55]: YeahAkshat [00:33:56]: Yeah, like, turns out to be pretty good substrate for that.Swyx [00:33:59]: So my impression is auto research means many things, likeAkshat [00:34:01]: YeahSwyx [00:34:01]: Anything that Andrej coins. Right now it's still science fair, right? Like not like, I don't know how many people are doing this.Akshat [00:34:08]: We're having a golf.Swyx [00:34:08]: Yeah.Akshat [00:34:09]: I thought the same thing.Swyx [00:34:11]: Yeah, you would know.Akshat [00:34:12]: We, like, our internal both training and inference teams use this the general shape of this quite a bit. like we have this one internal repo called auto inference, which essentially we've automated our own forward-deployed engineering efforts using, this harness, which is, the agent will just spin up a sweep of different things. It'll even run like, NVIDIA inside profiler and it'll like tweak configs and it'll arrive the right thing. it'll change your GPUs both from H200 to B200, and works really well.Swyx [00:34:47]: Nice.Akshat [00:34:47]: So yeah.Swyx [00:34:48]: By the way, I enjoy that your forward-deployed engineering is so technical that you have to do these things.Swyx [00:34:52]: It's very different from forward-deployed engineering from other people.Akshat [00:34:54]: Yeah. For our forward-deployed engineering team is, essentially they're like applied inference researchers or applied training researchers.Swyx [00:35:02]: Someone told me like they have to be able to build, but they also have to be able to sell. do they have to sell or are they like they're good, they're just like post-sale type of thing?Akshat [00:35:09]: It does, being able to talk to a customer and engage effectively with themSwyx [00:35:13]: YeahAkshat [00:35:13]: Matters a lot.Swyx [00:35:14]: They want the same thing.Akshat [00:35:15]: Yeah.Swyx [00:35:15]: ?Akshat [00:35:15]: But it's it's not really a sales, thing. We pair them with-- We have solution architects as well that are more on the sales side.Swyx [00:35:23]: Okay. Let's spend a bit more time on auto research. This is a big focus for for this year. Where does this go? like, have people explored enough? Like, there's all these beautiful charts of like improve and then level off a bit and then you find the next thing. Is this one abstraction up from normal training? Is that how we think about it, or do you think about it differently? Like model level training versus high, like driven hyperparameter search.Auto Inference and Modal BenchAkshat [00:35:51]: Yeah, like,Swyx [00:35:51]: Someone, some people call it like neural architecture search or whatever, right? Like.Akshat [00:35:54]: Yeah, - So the stuff I've seen people do with it is nowhere on the architecture level. It's pretty much tweaking parameters, but it's it's a hyperparameter sweep that's guided by some model intuition, so it's like much more efficient than, whatever other, sweep you would have.Swyx [00:36:12]: Yeah, it's just, it's just a question of where you want to spend your compute?Akshat [00:36:16]: Right.Swyx [00:36:16]: ‘Cause yeah, you can just throw infinite amounts of money on this and somehow you'll bang out Shakespeare?Akshat [00:36:22]: Yeah, infinite monkey.Swyx [00:36:24]: Yeah, so like the very good for model. and I think it's also very important that agents can spin up other agents, can spin up their infrastructure. Like very good for you. how good is our LLMs at generating model code? Like the benefit of existing LLMs is that you are in the data.Akshat [00:36:42]: Yeah. They're, they're surprisingly good. I think like pre Cloud 4 they were not, and then now they're able to shot, stuff out of the box. But we're playing around with releasing like a Modal Bench for like the harderSwyx [00:36:55]: YeahAkshat [00:36:55]: Things, that the LLMs cannot do yet and maybeSwyx [00:36:59]: What's an example of that?Akshat [00:37:01]: I think the things that- Sometimes agents struggle with, without right guidance and a skill is, how to, use the rest of our observability. Like how to. Something is failing, like how do you look at the logs and then update the right thing? It's reasoning about that. But they're able to shot, likeSwyx [00:37:23]: Yeah. You can just add a skill to it?Compute Strategy and Capacity PlanningAkshat [00:37:26]: Yeah. So we have a Modal skill now that. Which is why we built this Modal Bench. It's to find things like that, so we can address them in our tool.Swyx [00:37:35]: Tune a skill. Yeah.Akshat [00:37:36]: Yeah.Swyx [00:37:36]: No. it's it's good. are you facing any shortages? like we talk a lot about GPU shortages, but also CPU, also memory.Swyx [00:37:44]: Yeah.Akshat [00:37:45]: We have had a lot of growth, which means that, there's - we've had to be much better aboutSwyx [00:37:53]: PlanningAkshat [00:37:54]: Proactive capacity planning.Swyx [00:37:55]: Yeah.Akshat [00:37:55]: So we have,Swyx [00:37:57]: Which by the way, like it's like a MBA's like dreamAkshat [00:38:00]: YesSwyx [00:38:00]: Is like just planning this stuff. I think last time you and I talked about something maybe about this.Akshat [00:38:03]: Yeah. we have a really competent team of people that we call, The role is called compute strategy. so yeah, if anyone listening here or wants to work on thatSwyx [00:38:13]: Compute strategy?Akshat [00:38:13]: Yeah.Swyx [00:38:14]: I think,Akshat [00:38:14]: I feel like,Swyx [00:38:15]: I think the normies call it FP&A or something.Akshat [00:38:18]: Well, it's more It's it's not FP&A. It's it's There's a lot of interesting financial questions of like what is the blend between one year and three-year reservations? how do we forecast our own capacity? how do we. especially since our capacity is very fungible across different GPU types and different regions, like you have to model a lot of it. and you also have to have an opinion on how the supply chain is gonna evolve, and then you have to like, take bets,Swyx [00:38:49]: YeahAkshat [00:38:49]: Based on that.Swyx [00:38:50]: Tokenomics.Akshat [00:38:50]: Yeah.Swyx [00:38:51]: This is like probably a not a real point, but, I was trying to think about like what other industries. I was trying to think about like, we cannot be first to like these kinds of problems.Akshat [00:38:59]: Yeah.Swyx [00:39:00]: And what other industries have had this? And I was like, airlines with fuel and like they have to hedge their fuel and like, I think for a long time Southwest because they made like a hero fuel bet, they like were like super low cost becauseAkshat [00:39:12]: OhSwyx [00:39:12]: Compared to everyone else.Akshat [00:39:14]: Yeah. I hadn't thought about that.Vibhu [00:39:16]: We're at a fun time too?Akshat [00:39:18]: Yeah. It's. A lot of the compute business in general, for us is also about being very good about capacity management. That is how you have great unit, economics. but also over time it's how you can unlock more value for customers. Like, one of the things we're building now is like a way for customers to get, If they don't care about latency, like get much cheaper pricing and they'll get results back in like next 24 hours or something, like a batch tier essentially.Batch Tiers and Latency-Insensitive WorkloadsSwyx [00:39:47]: Yeah.Akshat [00:39:47]: And those are levers we have because we control the whole stack and scheduling and whatnot to give people a sufficientSwyx [00:39:53]: Yeah. I feel like they're not as popular. Like those, like the Frontier Labs have all those APIs. They're not as popular as they should be.Akshat [00:40:00]: The demand that we see for something like that is not for LLMs. although sometimes people wanna run evals andSwyx [00:40:08]: OkayAkshat [00:40:08]: Synthetic data prep and there it makes sense.Swyx [00:40:10]: Okay.Akshat [00:40:11]: But it's from a lot of LLM companies, like people who are doing computational bio, like they have to run really big batch jobs and they don't care about when they get it back.Swyx [00:40:22]: Yeah. And like they have a reasonable. It's it's also like a cousin to the stopping problem of like, will this finish in time?Akshat [00:40:30]: Yeah. You can bound it.Swyx [00:40:33]: Yeah.Akshat [00:40:33]: Like you can give peopleSwyx [00:40:34]: YeahAkshat [00:40:34]: SLAs on it.Swyx [00:40:35]: Yeah. I think what's, what's interesting is like the next phase of model.Swyx [00:40:38]: Like what, do people expect from you, now that you're established and you're like well-known compute player among all these leading companies. You had an inference launch week, and we talked a little bit about the launches. like what else? Like what else should people know?What Modal Builds NextAkshat [00:40:55]: We are building primitives that make our users' lives much easier. So, I think for example, with LLM inference, thousands more companies are gonna post-train their own models and, deploy open source models for inference. so we're thinking a lot about what is the best product shape for that. And, that involves everything from our training gym to, then, endpoints that get frontier-level performance. again, but I haven't talked to anyone. It looks somewhat different on other verticals. Like, we're also seeing a lot of real-time, audio-video stuff in there, which is why like, we're working on things like regional routing, with fallbacks. So you can get GPUs that are as close to users as possible. so you get like low latency for video streaming and whatnot. And then on the agent side, it's,Akshat [00:41:52]: We're still working very closely with our customers because stuff is changing so fast in terms of what they need. And, I think beyond sandboxes and persistent file systems, there's a lot of other things people will need from this agent stack as they build production agents. So yeah, we're thinking about those other things that fit in there.Swyx [00:42:13]: I want to ask what the other things are.Akshat [00:42:15]: Yeah. I probably should share right now.Swyx [00:42:17]: I think-- I think, okay, so, I do think a lot about the principal components of cloud, and you do talk about compute storage networking.Akshat [00:42:25]: Yeah.Swyx [00:42:25]: Because so far for me, it's fine. so far for the. the first couple generations of cloud, it's fine. What's different, qualitatively different about agents that you need some new permission level? Like a lot of people, okay, and I'll just kinda spew tokens at you until it like hopefully sparks something.Akshat [00:42:43]: Yeah.Swyx [00:42:44]: Like the new level now is whatever Claude Code does, which is dangerously scope permissions or like allow list by command or like whatever, right? And sometimes they're like, “Well, okay, we have like this adaptive thinking mode where like, just trust me, bro. I will make the calls for you.” Is that it? like mediated permissions.Hard Guardrails vs. LLM-Mediated PermissionsVibhu [00:43:03]: Now you're looping it with a goal and letting it roll.Akshat [00:43:06]: Yeah, I'm, I'm skeptical of LLM media permission for stuff that is at the sandbox level because you do want hard boundaries.Swyx [00:43:16]: Yeah.Akshat [00:43:16]: Otherwise, someone can exfiltrate stuff.Swyx [00:43:20]: But likeAkshat [00:43:20]: YeahSwyx [00:43:20]: Maybe that's old school thinking. Maybe we're the dinosaurs.Swyx [00:43:23]: Maybe the AI OS or the LLM OS is really the kernel is a goddamn LLM.Swyx [00:43:30]: Like it makes you feel uncomfortable.Akshat [00:43:31]: Yeah, I'm, I'm toldSwyx [00:43:32]: But that's what trusting the LLM is. Like imagine a spherical cow perfect LLM.Akshat [00:43:36]: Right.Swyx [00:43:37]: That it.Akshat [00:43:39]: Maybe.Swyx [00:43:41]: I wanna test the boundaries, right?Akshat [00:43:42]: Yeah.Swyx [00:43:42]: Like, and I don't believe that, but I wanna see where I'm wrong ‘cause that's, that's the consensus.Akshat [00:43:49]: Yeah. I think you always need hard guardrails when you want, And you can pair those with softer guardrails, right? And that's gonna be a lot of mediated.Managed Agents and Specialized SandboxesSwyx [00:44:00]: There. I'll also get you a end with a couple of your commentary on like the ecosystem outside of Modal. Manage agents. Everyone has one. Gemini, OpenAI, Claude, very useful for you, but also like it is their way of starting to edge into your space.Akshat [00:44:17]: Yeah.Swyx [00:44:17]: What's going on?Akshat [00:44:19]: Yeah, we're, very excited to partner with Anthropic and some of the other foundation labs, will not name who we're also working with. the way we see it is the manage agent thing is a great place to start if you're starting out building an agent and, But then when you get to, building something more production grade, like you're a company that's like Ramp that's building their own, Ramp also runs their accounting agent on us, so their external-facing agent. You need a lot more control over, your compute primitive on things like, what sort - how do you persist different files that the agent has access to, and how do you snapshot and restore? How do you control the networking? maybe you want GPUs. When you get to that point, you kinda want, a specialized sandbox provider, that gives you those things, and that's the role that we are trying to play.Swyx [00:45:15]: YeahAkshat [00:45:16]: We don't really have an opinion on the harness, whether it runs - it's a cloud-managed agent, and you hook it up to Model Sandbox, or you run the harness in Model Sandbox. We'll see where people converge with that.Swyx [00:45:26]: Yeah. Do you any opinions on like the meta harnesses, or just another layer on top of these things?Akshat [00:45:31]: You mean like the OpenPipeSwyx [00:45:33]: OpenPipe is one. I think Vercel had one, which I can't remember the name of right now. Fredshot had one. and then, to me, most recently was Data Databricks that had Omnigen. All these are meta harness. Like it's kinda pseudo agent cloud type things.Akshat [00:45:50]: I personally have not played around with them.Swyx [00:45:53]: Yeah.Akshat [00:45:53]: Build agents with them.Swyx [00:45:54]: Everything's bullish Modal, as long as it consumes more infra.Akshat [00:45:57]: That's why we're focusing on the infra layer. It's somewhere where our, relative competence is and, also it's a hard problem to solve.Swyx [00:46:06]: Yeah. I will say like just generally reflecting on that, I don't know if - if there's other topics on Modal, but like just generally reflecting as an infra person, not as intense as you, but in that field, this has like been the most exciting time in infra. Like it was boring for a while, and you couldn't really get people excited about data infrastructure. Like Eric would get on Data Console, everyone just watched the video and like say, “Look at how many sandboxes I can spin up,” and no one gave a crap.Why Infrastructure Became Exciting AgainAkshat [00:46:39]: Yeah.Swyx [00:46:40]: And like now everyone gives a crap.Akshat [00:46:42]: That's true. It is a very exciting time, and I think a lot of that's driven by just the amount of scale all of this stuff needs.Swyx [00:46:50]: I think the, like a lot of your initiatives or a lot of your like product directions make sense in retrospect, which is like the best kind, but I wouldn't necessarily have thought about it myself, which.Akshat [00:47:00]: We need the predictions.Swyx [00:47:02]: I think there's a lot that you just don't even see, right? Like you have the batch, you have the voice, you have the multimodal, but what else?Akshat [00:47:10]: What else is coming up for usSwyx [00:47:11]: Yeah. Where do you see things going?Akshat [00:47:13]: Yeah. I, in generalBiotech, Robotics, and Non-LLM AI WorkloadsAkshat [00:47:15]: It's it's clear that there's there's a huge shift happening. I think one thing that's not as obvious to people because LLM inference gets talked about so much and is also we work a lot of companies that are, doing things like drug discovery and computational bio, like the Chai Discoveries of the world. Big things are probably gonna happen there. we work a lot of robotics companies that are putting robots in like active deployments and getting good results out of them.Swyx [00:47:45]: Is there Air Gap Modal? Is there a version that is like prem air gapped whatever?Akshat [00:47:50]: No. We,Swyx [00:47:51]: You should cloud only.Akshat [00:47:51]: Yeah.Swyx [00:47:52]: Yeah. Okay. But yeah, so what you're saying is like because you're focused on primitives and they're good primitives, you find use cases in all these kinds of things.Akshat [00:48:01]: Yeah.Swyx [00:48:01]: Probably diversifies you a little bit away from LMS all the time.Akshat [00:48:05]: Yeah, absolutely. We're, we'- our goal isn't to only serve the LLM inference market.Swyx [00:48:10]: There are a lot just on the website, the audio,Akshat [00:48:12]: Yeah. We said both onSwyx [00:48:14]: Computational bio images. Yeah, there's a lot here. There's QTA TTS, customizing. Oh, Chatterbox. there was customizing Whisper.Akshat [00:48:24]: Okay. Yeah.Swyx [00:48:25]: This screen reminds me of a fallen competitor, which Replicate.Model APIs vs. Differentiated AI ProductsSwyx [00:48:31]: What's your postmortem on what happened?Akshat [00:48:34]: This is one thing we've stayed away from is providing an API for models because I think providing model APIs is some of it ends up serving like a really hobbyist market, which is much less sticky.Swyx [00:48:50]: Yeah.Akshat [00:48:50]: And we've always wanted to build for companies that are building products and need more flexibility that's not just an API.Swyx [00:48:57]: Which you can build an API for a model and this is clearly what it is. But you - but what you're saying, you can wrap it into a more fully functioning back end that you run.Akshat [00:49:06]: Yeah. So all of our examples, it's not that spin up this model, here's an API token, use it. They're all code.Swyx [00:49:13]: Okay.Akshat [00:49:13]: And so the point is that this is just an example.Swyx [00:49:16]: Starter code.Akshat [00:49:17]: Yeah. But you can tweak it however you want.Swyx [00:49:20]: Yeah.Akshat [00:49:21]: And if you're like a company building a product, like, computational bio whatnot, yeah.Swyx [00:49:26]: I guess I'm trying to tease out for listenersAkshat [00:49:28]: YeahSwyx [00:49:28]: When does it stop becoming, oh, you're just an API call and you're just a wrapper on API to becoming what you call a product, right?Swyx [00:49:36]: Like, what is that layer? Like what-- Like, more lines of code, but like beyond that, what is the substance that people add that qualifies it to be something more?Akshat [00:49:46]: I think there's a little bit of like a selection effect of like a lot of the companies who do wanna get deeper into that level are probably building something that's more differentiated. And, I think, an example is like - with LLM inference, originally we, worked with companies that were building their own post-training frameworks or they were, - Ramp early in the day was training their own tokenizer and like swapping out the tokenizer in Llama and whatnot. I'm not saying that's, that successful, in that case. But a better example is like, let's say Suno. because Suno, does not use Modal for training.Swyx [00:50:26]: Mikey on the pod. Yeah.Akshat [00:50:27]: But they use Modal for all their inference and that's because they have like a custom-- They have completely custom model architecture and that means that they have to be at the code level and tweak things that are not, just an API.Swyx [00:50:41]: It's interesting as well, like we had, Ethan, most recently on the xAI Groq team make a prediction that like the next tier in video gen is not a better video model, it's a better model or agent that orchestrates video models.Video Agents and Production WorkflowsAkshat [00:50:56]: Oh, interesting.Vibhu [00:50:56]: Language model backbone that can use toolsAkshat [00:50:58]: RightVibhu [00:50:59]: And write code.Akshat [00:51:00]: Like, yes, I can make my second video or my second video from Groq, but I want my minute video.Akshat [00:51:06]: And I'm not going there through normal video gen.Swyx [00:51:10]: Yeah, that's interesting. I - So we have GPU sandboxes and recently have seen a few companies doing agents that do video manipulation or,Akshat [00:51:22]: Yeah. Give it FFmpeg and just do it.Swyx [00:51:23]: Run FFmpeg. But likeAkshat [00:51:25]: That's not enough.Swyx [00:51:25]: Yeah.Akshat [00:51:26]: You need to give it Adobe.Swyx [00:51:27]: Yeah, I hadn't put it together with like it would be a video production thing. in my mind these things were going more towards editingAkshat [00:51:36]: Yeah.Vibhu [00:51:36]: Well, shout out Mantis.Akshat [00:51:37]: I think about this a lot.Swyx [00:51:38]: .Akshat [00:51:41]: Yeah. Sorry.Vibhu [00:51:41]: Luma. Luma Agent is a version of this for video production, but it's a off.Swyx [00:51:46]: I was gonna get your quick takes, on some other stuff that happensGitpod/Ona, CI, and Runtime SandboxesSwyx [00:51:50]: In recent news and just-just see if you have anything interesting. Gitpod, very li

CERT/CC warns of an unpatched Tenda router backdoor. Adobe races to patch an actively exploited ColdFusion flaw. Canada pulls back the curtain on offensive cyber operations. Anthropic quietly removes hidden tracking from Claude Code. Chinese AI gains momentum as U.S. providers sweeten the deal. U.S. cloud firms challenge South Korea's new security rules. Microsoft's device telemetry helps unmask an alleged Scattered Spider hacker. And Spanish police arrest an alleged pro-Russia hacktivist.Orla Daly, CIO at Skillsoft, discusses if AI is already bypassing its own guardrails and why most organizations aren't ready. The stochastic parrot is back, and it's tired of being misquoted. Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Today we are joined by Orla Daly, CIO at Skillsoft, discusses if AI is already bypassing its own guardrails and why most organizations aren't ready. Selected Reading Hidden Tenda Router Backdoor Grants Admin Access, No Patch Available (Security Affairs) Hackers Exploit Maximum Severity Adobe ColdFusion Flaw (Infosecurity Magazine) Canadian spy agency says it hacked drug traffickers, extremists, and a ransomware gang last year (TechCrunch) Secret Claude tracker shocks users after Anthropic's anti-surveillance stance (Ars Technica) Chinese AI models are gaining ground with U.S. companies as OpenAI, Anthropic costs surge (CNBC) AI Giants Are Handing Out Tons of Free Computing Power to Grab Startup Share (Wall Street Journal) U.S. Big Tech raises concerns over Seoul's proposed cloud security rules (Korea JoongAng Daily) Microsoft device telemetry key to unmasking alleged Scattered Spider hacker (iTnews) Spain collars alleged pro-Russia hacktivist after FBI tip-off (The Register) What Emily Bender Really Meant by "Stochastic Parrots" (IEEE Spectrum) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Episode 489 of the Lens Shark Photography Podcast In This Episode If you subscribe to the Lens Shark Photography Podcast, please take a moment to rate and review us to help make it easier for others to discover the show. Sponsors: - Build Your Legacy with Fujifilm. Latest savings at FujfilmCameraSavings.com - Shop with the legends at RobertsCamera.com, and unload your gear with UsedPhotoPro.com - Benro's special edition America 250 MiniMax at BenroUSA.com - Godox's Summer Savings! - More mostly 20% OFF codes at LensShark.com/deals. Stories: Leica's new 44 megapixel SL3-P. (#) A new twist on and old lens from 1987. (#) Tamron's excellent 17-70mm f/2.8 comes to 2 more mounts. (#) Adobe makes a key acquisition. (#) Fujifilm opens it's GFX Challenge Grant Program 2026. (#)   Connect With Us Thank you for listening to the Lens Shark Photography Podcast! Connect with me, Sharky James on Twitter, Instagram Vero, and Facebook (all @LensShark).

In this episode, Jared & Stephen discuss Adobe's latest acquisition of Topaz Labs, how people shouldn't be scared of noise and "digital grain," Sony's next rumored cameras that are apparently coming soon including the FX5 and RX10 V & more! Text us with any thoughts and questions regarding this episode at 313-710-9729. This is RAWtalk Episode 203! Subscribe and listen to The Daily Fro on Spotify and Apple Podcasts

In this episode of The GaryVee Audio Experience, I sit down with Laura Desmond, CMO of Adobe, at Cannes 2026 for a Marketing for the Now conversation. We get into why experience maxing — IRL, in real life — is mattering more than ever, why VaynerX bet the farm on becoming an experiential agency, and why AI tools will get commoditized at scale exactly the way big data did. I share the Photoshop history lesson every fearful creative needs to hear, why the human matters more in an AI world, not less, and the jungle-gym career advice every young marketer should be writing down.You'll learn about:• Why Experience Maxing (IRL) Is Mattering More Than Ever• Why the Human Variable Matters More, Not Less• The Humility to Take a Step Backwards

Jarrod Lopiccolo - Co-Founder, CEO - He transforms brands with creative digital performance marketing. He has built a global agency serving Adobe, Google, and Disney—earning accolades from Inc. and Ad Age. A dynamic speaker with 100+ engagements and 40+ podcast appearances, he delivers insights leaders can apply instantly. Jarrod's stories captivate CMOs, founders, and teams looking to drive revenue, performance, and innovation.Connect with Jarrod here: https://www.linkedin.com/in/jarrodlopiccolo/https://www.facebook.com/noblestudios/https://www.instagram.com/noblestudios/https://noblestudios.com/Don't forget to register for our free LinkedIn Content Creation Workshop here: https://www.thetimetogrow.com/LinkedInContentRoadmap

Why Ask Credentials If There Are Secret Codes? https://isc.sans.edu/diary/Why%20Ask%20Credentials%20If%20There%20Are%20Secret%20Codes%3F/33118 Adobe Patches and Updated Patch Release Policy https://helpx.adobe.com/security/Home.html https://blog.adobe.com/security/protecting-customers-faster-how-adobe-is-responding-to-ai-accelerated-vulnerability-discovery Google Chrome Update (link had issues loading while recording) https://chromereleases.googleblog.com/2026/06/stable-channel-update-for-desktop_0175352312.html Apple Hide My Email Vulnerability https://www.404media.co/apple-hide-my-email-vulnerability-reveals-peoples-real-email-addresses/ My Upcoming Classes https://www.sans.org/profiles/dr-johannes-ullrich

The US restores exports of Anthropic's most advanced AI models. Adobe and Citrix rush out critical patches. RustDuck emerges as a fast-evolving DDoS threat. The Gentlemen raise the stakes with a new EDR-killing exploit. Rocket lab bets big on Iridium. Researchers unveil browser-only ransomware. New Zealand faces questions about its cyber readiness. Iran's long-running cyber espionage campaign is back in the spotlight. Our guest is Donald Codling, CISO and senior advisor to REGO on cybersecurity and data privacy matters, to discuss the importance of tying security by design to psychological safety and digital trust. VIP backstage access, courtesy of Claude. Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Today we are joined by Donald Codling, CISO and senior advisor to REGO on cybersecurity and data privacy matters, to discuss the importance of tying security by design to psychological safety and digital trust. Selected Reading Fable and Mythos: Anthropic says US lifts export ban on its advanced AI tools (BBC) Adobe patches seven max severity ColdFusion, Campaign flaws (Bleeping Computer) RustDuck: The Botnet That's Still Small but Engineering Like It Plans to Grow (SecurityAffairs) Citrix Patches NetScaler Vulnerabilities, Including New ‘HTTP/2 Bomb' Attack (SecurityWeek) Not very gentlemanly: Analyzing a zero-day exploit used by The Gentlemen ransomware to disable targets' EDRs (Expel) Rocket Lab to Acquire Iridium in Historic Deal, Creating A Fully Vertically Integrated Space Powerhouse Primed for Growth (Globe Newswire) Ransomware that runs inside your browser tab, where antivirus cannot see it (Suriq) Three major cybehttps://suriq.io/blog/browser-only-ransomware-file-system-accessrattacks have raised alarms about New Zealand's security (RNZ) Arrest of Iranian Hacker Spotlights Iran's Movement into Economic Espionage and IP Theft (Zero Day) Claude Helped a Hacker Find a Way to Issue Tickets to Almost Every US Music Festival (WIRED) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

In today's episode, Stig Brodersen is joined by Tobias Carlisle and Hari Ramachandra for a new round of stock pitches. Hari makes the case for Meta as a leading AI-powered advertising platform. Tobias breaks down Booking Holdings and whether its travel moat can withstand the rise of AI assistants. Stig analyzes Adobe, exploring the durability of its creative software ecosystem amid rapid technological change. IN THIS EPISODE YOU'LL LEARN: (00:00:00) Intro(00:02:31) Why Hari is bullish on Meta (Ticker: META), highlighting its advertising dominance, network effects, and long-term monetization potential.(00:03:38) The bear case for Meta, including massive AI infrastructure spending, uncertain returns on capital, and execution risk around AI monetization.(00:14:27) Why Tobias is bullish on Booking Holdings (Ticker: BKNG), emphasizing its capital-light business model and robust travel ecosystem.(00:18:46) The bear case for Booking Holdings, including AI-driven loss of customer mindshare, and potential pressure on its role in the travel booking value chain.(00:27:43) Why Stig is bullish on Adobe, focusing on its switching costs and subscription-based revenue model (Ticker: NASDAQ: ADBE).(00:37:21) The bear case for Adobe, including AI-generated content and the increasing competition from tools like Canva and LLMs. Disclaimer: Slight discrepancies in the timestamps may occur due to podcast platform differences. BOOKS AND RESOURCES Join the exclusive ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Mastermind Community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Stig Brodersen's Portfolio and Track Record.  Our valuation model of Adobe. Our valuation model of Meta. Our valuation model of Booking Holding.com. Check out the Mastermind Discussion Q1, 2026 | Video. Check out the Mastermind Discussion Q4, 2025 | Video. Check out the Mastermind Discussion Q3, 2025 | Video. Check out the Mastermind Discussion Q2, 2025 | Video. Check out the Mastermind Discussion Q1, 2025 | Video. Tobias Carlisle's podcast, The Acquirers Podcast. Tobias' ETF, ⁠ZIG⁠. Tobias' ETF, ⁠Deep⁠. Tweet to ⁠Tobias Carlisle⁠. Hari's ⁠Blog⁠. Tweet to ⁠Hari⁠. Related ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠books⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ mentioned in the podcast. Ad-free episodes on our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Premium Feed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. NEW TO THE SHOW? Get smarter about valuing businesses through ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Investor's Podcast Starter Packs⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Follow our official social media accounts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Try our tool for picking stock winners and managing our portfolios: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Finance⁠⁠⁠⁠⁠⁠⁠⁠⁠. Enjoy exclusive perks from our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠favorite Apps and Services⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn how to better start, manage, and grow your business with the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠best business podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. SPONSORS Support our free podcast by supporting our sponsors: Plus500 Netsuite Vanta Shopify References to any third-party products, services, or advertisers do not constitute endorsements, and The Investor's Podcast Network is not responsible for any claims made by them Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm