Podcasts about glean

Arvind Jain is the Founder & CEO of Glean, the enterprise AI leader valued at $7.2 billion after raising more than $770 million from investors including Kleiner Perkins, DST Global, and more. Before Glean, Arvind co-founded Rubrik, helping build it into one of the world's leading cloud infrastructure companies before its successful IPO. Prior to that, he spent over a decade at Google as a Distinguished Engineer, working across Search, Maps, and YouTube. AGENDA: 00:00 – The Shocking Truth About Frontier AI: 90% Is Already a Commodity 02:04 – Can OpenAI & Anthropic Own Enterprise AI? The Battle for the Workplace Begins 10:18 – Will OpenAI and Anthropic Win the App Layer 18:03 – Microsoft Is the Real Enemy… Not OpenAI? 20:53 – "Where's the ROI?" Why Enterprises Are Starting to Question the AI Hype 26:00 – Will AI Replace Your Job? Harry & Arvind's Heated Clash Over the Future of Work 33:43 – The Billion-Dollar Mistake Every AI Company Is Making on Token Spend 39:20 – The AI Land Grab Is On: Why Founders Must Move Now or Lose Forever 42:20 – China vs America: Who Really Wins the AI Race? 47:20 – Rapid Fire: The Future of Computer Science, Hiring, Fundraising & AI's Biggest Winners  

Has the AI funding boom peaked?Many investors expected higher interest rates, rising infrastructure costs, and questions around AI monetization to slow venture capital activity.Instead, the opposite is happening.New AI unicorns continue to emerge, with billions of dollars flowing into AI infrastructure, enterprise software, robotics, and cybersecurity.In this episode, we break down where venture capital is placing its biggest bets—and what that means for the next phase of the AI economy.⭐ Sponsored by Podcast10x - Podcasting agency for VCs - https://podcast10x.comKey topics we explore:– Why AI funding remains incredibly strong despite macro uncertainty– The startups attracting the largest funding rounds across AI infrastructure, enterprise AI, robotics, and cybersecurity– Why investors are backing companies like Thinking Machines Lab, Glean, Harvey, Figure AI, and Skild AI– How venture capital is shifting from foundation models to the broader AI ecosystem– What today's private market funding says about tomorrow's public market opportunities– Why infrastructure, automation, and enterprise software could become the next major AI winnersThe bigger question:Are we witnessing another venture capital bubble, or are investors funding the next generation of category-defining AI companies?For investors, following where the smartest venture capital is flowing can provide an early signal of where long-term value is being created.LINKSPrashant Choubey - ⁠https://www.linkedin.com/in/choubeysahab⁠Subscribe to VC10X newsletter - ⁠https://vc10x.beehiiv.com⁠Subscribe on YouTube - ⁠https://youtube.com/@VC10X⁠Subscribe on Apple Podcasts - ⁠https://podcasts.apple.com/us/podcast/vc10x-investing-venture-capital-asset-management-private/id1632806986⁠Subscribe on Spotify - ⁠https://open.spotify.com/show/7F7KEhXNhTx1bKTBFgzv3k?si=WgQ4ozMiQJ-6nowj6wBgqQ⁠VC10X website - ⁠https://vc10x.com⁠For sponsorship queries reach out to prashantchoubey3@gmail.comThis channel is for asset managers, allocators, and investors who want analysis that holds up—not headlines dressed as insight.Subscribe for weekly data-driven breakdowns of the forces reshaping capital markets.#AI #ArtificialIntelligence #VentureCapital #Startups #EnterpriseAI #Robotics #Cybersecurity #Infrastructure #Investing #VC10X

Is AI really making us more productive, or are we spending more time managing it than benefiting from it? Should employees pay the price when leadership gets strategy wrong? And is Growth Mindset really the performance booster it's often claimed to be? This week on Truth, Lies & Work, Leanne introduces a brand new Word of the Week: Botsitting – the hidden work of feeding AI context, checking its answers and correcting its mistakes. Drawing on new research from Glean's Work AI Index, we explore why AI may be creating a new form of invisible labour that's leaving employees exhausted rather than empowered. We also unpack Microsoft's decision to cut around 3,200 Xbox jobs after CEO Asha Sharma admitted the business had "lost focus", asking where accountability should lie when strategy fails. Plus, new UK research suggests many senior finance professionals are choosing better leadership, career opportunities and work-life balance over higher salaries, challenging long-held assumptions about what motivates top talent. In Truth or Lie, Leanne is joined by Professor Rob Briner, one of the UK's leading experts in evidence-based management, to separate the science from the hype around Growth Mindset. Together they explore what the research actually says, why fashionable workplace ideas often outpace the evidence, and why organisations should focus less on finding the next big trend and more on understanding the problems they're trying to solve. Finally, in Workplace Surgery, we answer your questions about rebuilding team energy, supporting employees who don't want promotion, and whether exit interviews really reveal why people leave. Resources & Links Lincolnshire's unusual place names:https://www.lincolnshirelive.co.uk/news/local-news/strange-place-names-lincolnshire-list-438323 Rebecca Hinds' LinkedIn post:https://www.linkedin.com/posts/rebecca-hinds_we-recently-published-our-first-work-ai-index-share-7478531898693066752-jv9u/ Glean Work AI Index:https://www.glean.com/work-ai-institute/reports/work-ai-index HR Director:https://www.thehrdirector.com/business-news/remuneration/finance-candidates-no-longer-choosing-roles-salary-alone-report-finds/ Full Croft&Co Finance Professionals Report: www.croftandco.com/report Fortune:https://fortune.com/2026/07/06/exclusive-xbox-ceo-asha-sharma-job-cuts-studios-axed-layoffs/ More from Rob Briner Rob Briner on LinkedIn: https://www.linkedin.com/in/rob-briner/ Rob Briner's Is That Really a Thing LinkedIn posts (including one on Growth Mindset): https://tinyurl.com/2rzzzus3 Corporate Research Forum's Work Psychology Network: https://tinyurl.com/4s4pmz9e Work Psychology Network report on behaviour change in organizations: https://tinyurl.com/3p3rwerb Connect with Truth, Lies & Work – LinkedIn: https://www.linkedin.com/company/truthlieswork  – Al Elliott: https://www.linkedin.com/in/thisisalelliott  – Leanne Elliott: https://www.linkedin.com/in/meetleanne  – Email: hello@truthliesandwork.com  – Book a call: https://savvycal.com/meetleanne/chat

In this exploration of Ruth chapter 2, we discover a beautiful portrait of what it means to live as disciples in the fields of our Redeemer. Ruth, a foreign woman who has lost everything, demonstrates the kind of humility that opens the door to receiving God's abundant grace. Rather than demanding her rights under the law that allowed the poor to glean, she humbly asks for permission, works diligently, and responds with overwhelming gratitude when shown kindness. Her posture teaches us that humility isn't weakness—it's the position from which we can truly receive from God. We see how Boaz, representing Christ, notices this humble foreigner and extends extraordinary favor, commanding his workers to intentionally drop grain for her to find. This isn't just a love story between two people; it's a living picture of how God draws us to Himself not through harsh demands, but through His overwhelming kindness and chesed—His never-ending, covenant love. The message challenges us to examine our own hearts: Are we gleaning only in the fields of Christ, or are we distracted by the empty promises of the world? And perhaps most convicting, are we taking what we've received from the Lord's table and sharing it with those in our sphere of influence, just as Ruth brought her abundance back to Naomi? This passage reminds us that we are all foreigners in need of a Redeemer, and that same Redeemer invites us to feast at His table, promising safety, provision, and ultimately, to make us His bride.ChaptersChapter 1: Ruth's Humble Request to Glean0:00 - 7:41We are introduced to Ruth's desperate situation and her humble request to glean in the fields, which leads her providentially to Boaz's field, where she demonstrates remarkable humility and work ethic.Chapter 2: Boaz's Extraordinary Kindness and Protection7:41 - 21:30Boaz notices Ruth and extends remarkable favor, inviting her to stay in his fields, providing protection, food, and intentionally dropping extra grain for her to gather, displaying the loving-kindness of God.Chapter 3: The Kinsman Redeemer Revealed21:30 - 36:20Ruth returns with an abundant harvest for Naomi, who recognizes that Boaz is their kinsman-redeemer, sparking hope and pointing to Christ as our ultimate Redeemer who purchases us back from sin and death.Chapter 4: Living as Disciples Who Share What We've Gleaned36:20 - 44:52We are called to remember that we, too, were slaves redeemed by God, and like Ruth, we should share the abundance we've received from the Lord's fields with those in our sphere of influence through humble, kind witness.

Will governments, budgets, and AI economics—not model intelligence—determine who wins the AI race?This week, the biggest AI stories weren't just about new models. They revealed three major shifts that every business leader should be paying attention to. Governments are becoming active gatekeepers of frontier AI. Companies are moving away from "use the smartest model for everything" toward cost-optimized AI strategies. And specialized AI solutions are rapidly gaining ground over one-size-fits-all frontier models.If you're leading AI adoption inside your organization, these changes will influence your technology choices, budgets, competitive strategy, and long-term planning.In this episode, Isar connects the dots between regulation, economics, and innovation to explain where AI is heading—and what it means for businesses over the next 12–24 months.In this session, you'll discover:Why the U.S. government's approach to frontier AI appears to have shifted dramatically.What the Anthropic and OpenAI model restrictions could mean for future AI releases.Why China's AI ecosystem may benefit from tighter U.S. controls.How regulatory decisions could affect OpenAI, Anthropic, and their upcoming IPO ambitions.Whether independent AI safety auditors could become the future of AI regulation.Why Ford rehired hundreds of engineers after discovering AI couldn't fully replace experienced talent.What OpenAI's latest model revealed about AI reliability and model "cheating."Why enterprises are abandoning expensive frontier models for dramatically cheaper alternatives.How companies like Lindy, Uber, Amazon, and Glean are changing their AI spending strategies.Why orchestration layers and specialized AI models may become more valuable than simply building larger foundation models.The week's most important rapid-fire AI product launches, funding announcements, and industry developments.About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

The Ringer's own Danny Heifetz and Sean Fennessey get together to examine the New York football vibes after the Knicks won their NBA championship. Sean then breaks down his level of Jets optimism heading into next season, while Danny wonders if John Harbaugh will be able help Jaxson Dart and his beloved Giants level up in 2026.(00:00) The Knicks' championship and New York football(01:46) Have the Knicks given hope to Jets and Giants fans?(06:24) NY Jets optimism(26:23) Jaxson Dart, John Harbaugh, and the NY Giants(48:35) New York football team predictions for ‘26(53:12) Sean on watching the Knicks win a championship with his daughterThe Ringer is committed to responsible gaming. Please visit www.rg-help.com to learn more about the resources and helplines available.Host: Danny HeifetzGuest: Sean FennesseyProducer: Chris SuttonVideo Editor: Stefano SanchezProduction Supervision: Conor Nevins and Arjuna Ramgopowell Learn more about your ad choices. Visit podcastchoices.com/adchoices

Master the new Microsoft Marketplace ecosystem. Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ Discover the tectonic shifts happening within the Microsoft ecosystem as Cyril Belikoff and Jon Yoo dive deep into the unification of the Microsoft Marketplace and the explosive rise of AI-driven commerce. This comprehensive discussion explores how the marketplace is transitioning from an incubation island to the mainland of Microsoft’s go-to-market strategy, allowing partners to tap into massive Azure consumption commitments. Learn how product-led growth, AI agents, and optimized digital flows are replacing traditional sales motions, making cloud marketplaces the default engine for scaling revenue in 2026 and beyond. https://youtu.be/cAeSIEXbnNo Key Takeaways Microsoft unified its various marketplaces into a single digital flywheel for customers to discover, try, and buy applications. Applications, such as Copilot certified agents, are contextually surfaced directly within Microsoft products to meet users in their flow of work. Customers are making massive Azure commitments, and purchasing full software stacks through the marketplace retires those commitments entirely. Cloud marketplaces have evolved from a secondary channel into the default go-to-market engine with triple-digit revenue growth. Partners must shift from deal-led transactions to product-led growth by optimizing their digital marketplace listings for AI and search engines. The future of software procurement will increasingly involve AI agents acting on behalf of organizations to seamlessly integrate multiple smaller applications. 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: Microsoft Marketplace, Azure commitments, AI agents, Frontier transformation, M365 Copilot, Foundry, digital flywheel, co-selling, product-led growth, ecosystem shift, SaaS distribution, REO, resale enabled offer, listing optimization, search engine optimization, agentic commerce, cloud go-to-market, revenue recognition, multi-party private offers, Hyperscalers Transcript: Cyril Belikoff and Jon Yoo Audio Podcast [00:00:00] Cyril Belikoff: Why do you have to outsource this or create this vi? Just edit the video right there yourself. Like why do you have to just go do the, as a marketer you want to create a beautiful piece of content, just go and create it. ’cause it can create it for you. Now [00:00:13] Jon Yoo: you can feel it happening. The ecosystem is shifting beneath us, the way Hyperscalers are partnering. [00:00:19] Jon Yoo: How AI is remaking the channel and what it means to win in 2026. [00:00:25] Vince Menzione: Welcome to The Ultimate Partner Podcast. I’m Vince Manzione, your host. [00:00:30] Jon Yoo: We just wrapped up two days in Bellevue with some of the sharpest partner leaders in the business, and what we heard wasn’t incremental, it was tectonic. In this series, we’re going deeper into these conversations, the insights, the frameworks, the real movement we’re seeing in this market right now, because being in the room changes everything and we’re bringing that room to you. [00:00:55] Vince Menzione: And I am thrilled actually for this next one. Uh, I’ve had this opportunity to spend a little bit of time with this gentleman before, and welcoming him back is a pleasure and an honor. Cyril Beov, the vice president. I’m gonna botch up your title ’cause I always say marketplaces, but it’s much more than that. [00:01:14] Vince Menzione: So come on up, zero. And we’re gonna have a conversation and Cyril is amongst other things at Microsoft. Good to see you, sir. Yeah, [00:01:24] Cyril Belikoff: you too, [00:01:25] Vince Menzione: uh, is responsible for the, the Microsoft marketplace. [00:01:29] Cyril Belikoff: Are these your notes here? [00:01:30] Vince Menzione: These are, um, what is that? Yeah, these is gonna be our, our questions, so we, yeah, I, I need help sometimes so prompting, but, uh, so great to have you. [00:01:38] Vince Menzione: So just for purposes of title and context, ’cause your role is much bigger than just marketplace. Yeah. And we’re, we’re gonna sit down and John, is this me? Yes. And then John’s gonna join us. [00:01:47] Cyril Belikoff: Okay. Great. [00:01:48] Joe, [00:01:48] Vince Menzione: well, we’re gonna get started and start having a conversation. And we’ve, we’ve done some of these things before. [00:01:53] Vince Menzione: I’ve, I’ve had, you had me on your stage Yes. In your event at Alyssa Taylor’s event. [00:01:57] Cyril Belikoff: Yes. [00:01:58] Vince Menzione: And then, uh, we’ve, we’ve done some nice things together on stage, both at, at our event in Redmond last year. Yes. Then at our big, uh, ignite breakfast back [00:02:07] Cyril Belikoff: and forth, we had Vince come to our wider org and sort of, uh, I got to do the reverse. [00:02:13] Cyril Belikoff: And so interview him in front of a bunch of, uh, 500 marketers on what do we have to think about for partners. And so he gave us sort of the what’s going on in the partner ecosystem, how to think about it as we think about it, our marketing. [00:02:26] Vince Menzione: And I tried to be candid and represent this group. [00:02:28] Cyril Belikoff: Yeah, that’s great. [00:02:29] Yeah. [00:02:30] Vince Menzione: Was wonderful. Thank you for doing that. [00:02:31] Cyril Belikoff: Yeah. [00:02:32] Vince Menzione: You, you work, you work in an amazing organization. Um, I’ve known Alyssa for many years as well, and you’re an incredible leader. And I just, I wanna frame this maybe with a conversation about, ’cause we’re gonna talk about what’s changed, but, but I think it’s still important for everybody in the room to understand what you did and what your team orchestrated around market. [00:02:52] Cyril Belikoff: Yeah, [00:02:52] Vince Menzione: because it was fragmented, it was in different organizations, it felt very dis disorganized, I guess. Yeah. For lack of a better word. [00:02:59] Cyril Belikoff: Yeah. Thank you. Um, essentially we took it from incubation Island to the mainland Microsoft. I love it. Uh, GTM [00:03:07] Vince Menzione: Yeah. [00:03:07] Cyril Belikoff: Is the simplest way to think about it. Um, and, uh, come September last year now, uh, we unified the, the, the, the many marketplaces. [00:03:19] Cyril Belikoff: Uh, whether it was an Azure marketplace or AppSource and others, and we created the new Microsoft marketplace. Yeah. So one single place for customers to come, discover, try, buy, and for partners, software companies and other NSIs to put their wares up. And, uh, it was the first step in a vision for us to create this digital flywheel for us to bring our customers and our partners together in a more, you know, automated way. [00:03:47] Vince Menzione: Which it, it sounds crazy when you think about it, right? Microsoft has always been like the partnership leader, the leader in the technology, and to have fragmented marketplaces before, right? Yeah. And so what clarity to bring that all together. [00:04:00] Cyril Belikoff: Yeah. It was a big, it was a big step for us and I think what we realized is that customers and partners were saying, Hey, uh, it’s all one place. [00:04:07] Cyril Belikoff: If I’m looking for a SaaS application or an agent or, and plug into teams or whatever it is. I just want to get it all in one spot. Uh, and, uh, and then we need you to connect us to your channel. [00:04:21] Vince Menzione: Yes. [00:04:21] Cyril Belikoff: Uh, and your partners and, uh, can you build out, you know, partner capabilities for that Connects channel to software companies, to, to customers in a digital flywheel way. [00:04:30] Cyril Belikoff: Um, one of the things we actually also announced at that time was this concept of what we call. The marketplace framework. So it’s not just the fact that we have this digital experience or web experience, but that, um, applications that go into the marketplace, depending on the type of applications they get contextually surfaced within Microsoft products. [00:04:53] Vince Menzione: Okay. [00:04:53] Cyril Belikoff: And so if you’re, [00:04:54] Vince Menzione: explain that for this Yeah. For me and for this crap. [00:04:57] Cyril Belikoff: Yeah. So if you are a, um, if you’re a co-pilot certified agent. M 365 copilot agent, you’ll be in the marketplace, but you’ll also be automatically surfaced inside the M 365 copilot, um, product. [00:05:11] Vince Menzione: Nice. [00:05:12] Cyril Belikoff: Same for, uh, large language models in foundry, add-ins in teams, those sort of things. [00:05:18] Cyril Belikoff: ’cause it’s one thing to be where people go to discover Tribu, but users also go into these stores, whether it’s a developer user or an end user. They go in the flow of their work and they want to move quickly. Yes. And so, uh, so they have access. We think that’s very attractive. And the feedback was, Hey, that’s quite differentiated. [00:05:35] Cyril Belikoff: ’cause we have, uh, hundreds of millions of customers in these products every day. [00:05:39] Vince Menzione: Yes. [00:05:39] Cyril Belikoff: And so giving our customers access, our partners access to it and improving their customer experience, um, has worked out well. [00:05:47] Vince Menzione: And something else you did too, because at one point, you know, we were talking about co-selling and single-threaded. [00:05:53] Vince Menzione: And Microsoft has this incredible ecosystem and channel. [00:05:57] Cyril Belikoff: Yes. [00:05:58] Vince Menzione: And it, it was totally disconnected from the whole marketplace strategy, right? Yes. Yeah. [00:06:03] Cyril Belikoff: Yeah. So we, uh, um, we launched, I think in, uh, sorry. At that time in September, we launched five of our largest distributors who were also federating the Microsoft marketplace into their marketplaces. [00:06:16] Cyril Belikoff: And then in the November timeframe at Microsoft Ignite, we launched resale enabled offer [00:06:23] Vince Menzione: RO, [00:06:23] Cyril Belikoff: which is REO, which is a new capability that is proven extremely popular, that connects the software company and the reseller, you know, um, to go and do more deals at scale faster. So [00:06:36] Vince Menzione: we have, we have four of the Es in the room, [00:06:38] Cyril Belikoff: right? [00:06:38] Vince Menzione: Some of the four at the top. Five or six. And then also one of your largest resellers software, one is here as well. [00:06:45] Cyril Belikoff: Yeah. Great. [00:06:46] Vince Menzione: Yeah. [00:06:47] Cyril Belikoff: Great. So it’s, uh, we’ve been busy. [00:06:48] Vince Menzione: Yeah. [00:06:49] Cyril Belikoff: Yeah. Like I said, um, uh, much more work to do. But we are moving from like this incubation project to mainland get it integrated into our core customer, go to market, uh, so that our, uh, software companies and partners have access to those customers. [00:07:03] Cyril Belikoff: And then integration, uh, with the channel. So [00:07:06] Vince Menzione: it’s been a lot happening these last 12 months. Uh. Then Frontier, let’s talk about Frontier. That’s another piece of this. [00:07:14] Cyril Belikoff: Yeah. I’m sure Steven touched on, uh, frontier Transformational or becoming Frontier or Frontier Firm. So probably not helpful to me rehash that. [00:07:23] Cyril Belikoff: I think in general. If you go to a Microsoft discussion or session and you don’t hear about frontier or Frontier transformation, please like, raise your hand and give feedback because that is, um, [00:07:34] Vince Menzione: I kept away from the word frontier with Steve. We were talking about it, but we weren’t using the term frontier. [00:07:38] Vince Menzione: Yeah. ’cause I feel like it gets overused. It’s, [00:07:40] Cyril Belikoff: yeah, it does. It’s, it’s sort of, um, what we try to do with it is articulate it in a way that it’s not just about AI for AI’s sake. [00:07:48] Vince Menzione: Yeah. [00:07:48] Cyril Belikoff: But it’s AI based on what the customer outcome is trying to, what the customer is trying to achieve on their outcome. Um, and so as part of that, of course, you know, agents, AI applications is a big part of what’s driving customers’ capability of, uh, to become frontier. [00:08:05] Cyril Belikoff: And, uh, the marketplace is part of that. ’cause they can either custom build that. Uh, or they can, you know, buy off the shelf, right? Uh, or, and actually in Combin they do mostly they do both, right? And so, um, in order to accelerate their ability to become more frontier, we have these, uh, partners that build, uh, third party solutions through a marketplace. [00:08:27] Cyril Belikoff: And then, uh, uh, those same partners or others that build bespoke solutions around that. So, um, a lot of momentum around, uh, AI apps and agencies, as you can imagine. Um, we have, I think, 5,000 plus AI apps and agents. [00:08:43] Vince Menzione: I was gonna ask you what you’re focused on now, but you, I think you’re tying into this now already. [00:08:47] Cyril Belikoff: Yeah. Um, so of course that’s important. [00:08:50] Vince Menzione: Yeah. [00:08:50] Cyril Belikoff: But really for us it’s about doubling down on driving customer, customer demand. How do we merchandise the right things that customers are looking for? How do we, uh, accelerate any of our flows? Like we will spend hours just looking at like the flow of one scenario. [00:09:07] Cyril Belikoff: Where is it getting stuck? How do we improve it? Um, and then how do we connect it to the channel? And, and what, what more things can we do like EO or multi-party private offers and the like, and we have. Probably an announcement a month in the next three or four months. [00:09:24] Vince Menzione: Oh, come on. Let’s, [00:09:24] Cyril Belikoff: that will, [00:09:25] Vince Menzione: I know, I know it’s early. [00:09:26] Vince Menzione: I know it’s early, but you, you’ve got some, I know you’ve got some things. Think [00:09:29] Cyril Belikoff: about the customer experience. Think about the channel integration and dream about [00:09:33] Vince Menzione: what maybe more of a global scale with some of the offerings, maybe, maybe. Um, so, you know, I, so I, the earnings, we talked about the earnings with Steven, but I thought that there was a very compelling number around the commitment number. [00:09:46] Vince Menzione: Yes. You and you run your Azure as part of your remit. We didn’t go through your entire remit. [00:09:50] Cyril Belikoff: Yep. [00:09:51] Vince Menzione: It’s not just marketplace. You also, you also own the Azure number. [00:09:53] Cyril Belikoff: Yep. [00:09:54] Vince Menzione: Let’s talk about that. [00:09:55] Cyril Belikoff: Yeah. Um. You know, it’s, it’s exciting times for customers. They want to do things not only with us, but with everyone in the room. [00:10:04] Cyril Belikoff: Um, and they are making very large commitments, huge commitments over the next two to three years to spend on Azure. Um, and so it’s now our joint jobs to go and help them identify the right business outcome and go and, you know, consume that commitment. It is just a commitment. It’s not actual. Consumption. [00:10:27] Cyril Belikoff: Yes. And so it’s all of our jobs to take advantage of that. The, the customers are saying, Hey, we have line of sight to the types of things we want to go do over the next two to three years. Uh, probably not everything is, you know, i’s dotted and t’s across, but they have line of sight to most of it. Um, and how do we go help them drive that? [00:10:46] Cyril Belikoff: Um, and so from an Azure perspective, obviously that’s very encouraging for us. It, it allows us to. Invest in more data centers and more capacity that we are doing as fast as we can. Um, and then, um, and then of course on marketplace, the marketplace can retire. [00:11:04] Vince Menzione: That’s [00:11:04] Cyril Belikoff: that Azure commitment. That’s, [00:11:05] Vince Menzione: I wanted to make sure [00:11:06] Cyril Belikoff: people understand that the, in fact, not only the Azure component from the marketplace, but the full software stack from the partner, uh, the software company retires the Azure commitment. [00:11:17] Cyril Belikoff: So if it’s. Uh, I’ll make it up if it’s, uh, a hundred bucks and it’s 50 50, it’s not 50 50, but, um, I won’t disclose any percentages, but let’s say it’s 50 50, it’s much more for the software company, by the way. Um, if it’s 50 50, it’s not just like the $50 for Azure that gets retired. It’s the entire a hundred dollars that gets retired on the customer commitment, commitment, which is great for the software company. [00:11:39] Cyril Belikoff: It’s also great for the customer that they can, you know. Bring that through, uh, to, uh, to their Azure commitment. And we do the same thing with our sellers. So the marketplace sales retire our sellers compensation. So we’re like checking every box so that there’s no friction in the system. So that, so the partner, the customer, our sellers, they’re all juiced to go and. [00:12:03] Cyril Belikoff: Deals with marketplace. [00:12:04] Vince Menzione: I, I hope everybody un understand. I mean, I understand this. I hope everybody else understand this too. ’cause I, I was, watch, you know, I, I, I watched LinkedIn and I see people post things and somebody made a comment about how difficult it was to use Microsoft Portal. And I was thinking to myself, do you realize that there’s, I’ll say 150 billion, but it’s probably a bigger number than that. [00:12:22] Vince Menzione: That’s a total addressable market available to you if you’re a Microsoft partner. You can access these customer commitments. [00:12:30] Cyril Belikoff: Oh yes. [00:12:30] Vince Menzione: If you bring your product on Mark over 400 now [00:12:32] Cyril Belikoff: Yeah. [00:12:33] Vince Menzione: You bring your product into the marketplace, you have access and the customer can retire that commitment. [00:12:38] Cyril Belikoff: Yeah. [00:12:39] Vince Menzione: Without having to justify a new cost justification. [00:12:41] Cyril Belikoff: Yeah. They don’t have to go to procurement. They don’t have to. Yeah. [00:12:44] Vince Menzione: Yeah. I mean, it’s a huge opportunity. [00:12:46] Cyril Belikoff: Yeah. [00:12:46] Vince Menzione: So, um, so you’ve been focused on quite a bit. We wanted to invite John on stage. [00:12:53] Cyril Belikoff: Great. [00:12:53] Vince Menzione: John, you from Sugar is here. Where’s John? Is John in the house? Where’s John? [00:12:57] Cyril Belikoff: There he is. [00:12:58] Vince Menzione: Who’s also an expert in Marketplace. [00:13:00] Vince Menzione: A great friend of Ultimate Partner. Great. Hey, how’s it going? He’s a great partner of Ultimate Partner. Great to see you, sir. All the way from San Fran. Oh, actually we’re on your side of the coast, so, uh, it’s long. He looks [00:13:12] Jon Yoo: cooler than us [00:13:12] Vince Menzione: though. He, he always looks cool. I said that about time. [00:13:15] Jon Yoo: You know, I gotta be comfortable. [00:13:16] Jon Yoo: I gotta be comfortable. [00:13:18] Vince Menzione: So John, good to, good to have you. You’re thanks for having us. You guys are like, every time I see a post from you, you’re moving into a new office space ’cause you’ve outgrown your office space. [00:13:26] Jon Yoo: Yeah, we’re, we’re really excited about the new office. We have hvac, which is a, a big, big, uh, it’s a big thing. [00:13:31] Jon Yoo: Improvements, high ceilings, you know, the whole works h help [00:13:36] Cyril Belikoff: sometimes. Yeah, [00:13:37] Jon Yoo: yeah, yeah, yeah. We, we have our, uh, office opening party if anyone’s an SF on Ally first. [00:13:41] Vince Menzione: Nice. Nice. Yeah. May, may, May 21st. May 21st. That’s right. Well, so, so you’ve been, you’ve had like a front row seat to all of this. I would love to get your perspective on what you’re seeing across partners and what’s changed over the last 12 months. [00:13:55] Jon Yoo: Yeah, so, uh, for those that don’t know, um, sugar is a, uh, a revenue platform for cloud marketplaces and co-selling. Um, so we partner very closely with Microsoft as well as either hyperscalers and other marketplaces like Snowflake, Alibaba, et cetera. And what we, what, what I’m seeing is a couple things. One marketplace is becoming a default to go to market engine. [00:14:17] Jon Yoo: So, you know, I think a lot of people see the stats about how the, the GMV, so, you know, the, the throughput through these marketplaces have been doubling. We’re seeing that in our data as well. So we’re seeing triple digit revenue growth from marketplaces. We’re seeing companies who, you know, maybe the earlier end of marketplaces were infra platform layer of software that used to really adopt it. [00:14:37] Jon Yoo: Now you’re seeing. You know, explosion in a business application layer of companies as well. And so that’s super exciting. I’d say the second piece is channel players are getting more and more involved. Um, I think, you know, I’m the Silicon Valley SaaS bubble, uh, or the AI bubble, so to speak. And I didn’t know as much about the channel world and even these big AI companies. [00:14:59] Jon Yoo: I mean, you’re seeing unprecedented, unprecedented demand, uh, for these, you know, LMS or these AI biz apps. And despite that, they are really working with a partner ecosystem because you’re realizing that most of the world do not really know how to adopt ai. Yeah. And they’re really leaning on expertise. [00:15:18] Jon Yoo: And these AI companies, AI native companies, are looking to channel partners who have these. You know, relationships with their end buyers on how to deliver change managements, how to deliver enablement, not just a system integration site. [00:15:32] Vince Menzione: And they’re also looking to the platform or platforms in the case, Microsoft here also. [00:15:36] Vince Menzione: Right. Which, because like what, where am I gonna do just go out and buy philanthropic or Claude or whatever? I need that to be integrated into my enterprise as well. Right, exactly. [00:15:46] Jon Yoo: So we’re definitely seeing like more adoption of Microsoft Foundry, for example, as people think about security and governance and whatnot. [00:15:52] Vince Menzione: Yeah. Very cool. Any comments on, on? [00:15:55] Cyril Belikoff: Yeah, that makes absolute sense. It’s, uh, um, you know, lots of layers to AI from data. The a, the AI layer itself, the application layer, uh, and innovations happening at all of those pieces of the stack. Um, and so when a software company is trying to, you know, uh, modernize their thought process or build new. [00:16:19] Cyril Belikoff: They ha they have to think about all of those components. Foundry obviously is the AI layer and it provides them with capability to be agile and move quickly and do compliance and, and snap into an organization’s, um, architecture. But it’s the same applies to data, like how it’s fine to have AI but doesn’t, doesn’t do anything without data. [00:16:40] Vince Menzione: Right. [00:16:40] Cyril Belikoff: And so then how do they get data in the cloud? Um, uh, [00:16:44] Vince Menzione: and then how do you security [00:16:45] Cyril Belikoff: govern and govern, right? And how you secure govern, you know, all those sort of things. So obviously we have first party experiences, but they have partners with, um, their own solutions. They’re built on top of, [00:16:53] Vince Menzione: yeah. [00:16:53] Cyril Belikoff: Those Microsoft layers. And, uh, you know, like John says, lots of momentum. [00:16:58] Vince Menzione: So let’s talk about the maturity curve. Like walk us through it. Where do you see partners in this room sitting today? And what does it take to move for them to move to the next stage? Like, what would be your guidance for, for this group? [00:17:11] Jon Yoo: So, you know, we, we work across the entire spectrum of companies. You know, we work with the, the largest enterprises who’ve done billions through these marketplaces like Snowflake, workday, to leading AI companies like Glean or OpenAI, uh, to earlier stage startups who are completely new to marketplaces and really look for, for guidance around what do I do in my first 90 days? [00:17:33] Jon Yoo: How do I get the attention of Microsoft sellers, or how do I. Optimize my marketplace operations so that we can be discovered, uh, really easily on Microsoft Marketplace and others. And the, the way that I think about it is companies first come on, because it is buyer driven oftentimes. So, I mean, that’s just the truth of the nature of you have these big enterprises that want to, you know, burn down their Mac agreements, for example, and that’s how they get started. [00:17:59] Jon Yoo: And or a, a as like this whole space maturing, you’re seeing. A new CRO come in and they’ve done this at X, Y, Z companies and they want to bring that playbook over. But then as they start to do a couple deals through these marketplaces, they think, and this is start of the the flywheel, right? Hey, what do I need for co-selling? [00:18:19] Jon Yoo: And there are systems, you know, integrations and playbooks that need to be done well. Once you do have co-selling figured out, how do I now know which opportunities to co-sell? So we have things like intense signals to be able to. Help them, you know, help overlay a cloud, go to market lens over your existing pipeline. [00:18:36] Jon Yoo: And then now it becomes less of a partnership initiative and actually elevates up to the CRO initiative. And across each of those, um, layers, uh, you have different automation needs because once you actually start to get the flywheel going, it becomes. Holy crap. Now I’m doing 10, 20, 30, 50% of my revenue through these market, you know, through, through marketplace. [00:18:58] Jon Yoo: And it’s creating different data pipelines of work to be done. And now I have to figure out my finance angle of how do I do revenue recognition through these indirect channels. And so that, that, that is kind of the maturity covers. I think about it when you try to retrofit like, uh, all the automation up front, it doesn’t go as well. [00:19:16] Jon Yoo: You have to do a crawl, walk, run approach so that you can also build and bring the rest of the organization with you. So if I look at ’em to the, you know, there’s some familiar faces, some folks that probably are wondering some new faces [00:19:28] Vince Menzione: as well. [00:19:29] Jon Yoo: Yeah. What, what Microsoft marketplace or what marketplace even is. [00:19:33] Jon Yoo: I’d probably say people are in that transformation bucket of, Hey, I’ve done a couple of deals, we’re in this early stages of co-selling. Now I, now I gotta figure out how to supercharge it because this is kind of the future, you know, we can talk more about agenta commerce and whatnot, but, um, I think a lot of people are figuring it out than looking for. [00:19:51] Jon Yoo: For guidance here, [00:19:53] Vince Menzione: zero. [00:19:55] Cyril Belikoff: Yeah. You know, I would say, um, I’ll get what I call tactical yet strategic. [00:20:01] Vince Menzione: Okay. [00:20:01] Cyril Belikoff: Which just think about product-led growth. Yeah. Just think about, uh, similar to the consumer world, if you wanna sell something, you need search engine optimization. If you are thinking about these marketplaces and Microsoft marketplace being one, how are you optimizing your listing so that when a user goes into like the search bar, it’s optimized to bring back the results that make sense to you? [00:20:29] Cyril Belikoff: I think historically we’ve had scenarios where some partners have used the marketplace primarily as like a transaction thing. They’ve done the deal, but then they’re transacted on the marketplace ’cause they wanna retire the the Azure commitment. And that is changing to be sort of product led and marketplace led, uh, versus deal led. [00:20:49] Cyril Belikoff: Uh, but you cannot have a generic one line sentence in your listing. You will not be surfaced unless the customer literally knows your name and will search for you. You’ll not be surfaced if they search for a particular category healthcare app that does something right. And if that’s your thing, you should have the right keywords, you have the right images, have the right videos, and we believed in it so much. [00:21:14] Cyril Belikoff: We actually built a listing optimization AI tool that will auto look at your listing. And based on our best practices, and we know what our search engine is doing, we will make recommendations to you on how to improve. Listing. And so it’s not like a read a document as a best practice. It actually will be an ai, uh, customized tool for your particular listing. [00:21:37] Cyril Belikoff: So lean into those types of things, you know, to, as John says, says, think about the digital flow. This over time will become much more of your, of your business. So make sure you’re thinking about, you know, if someone’s on the marketplace and they decide to trial something for you, how, how are you following up? [00:21:56] Cyril Belikoff: Like, how do you take the next steps? Um, maybe you, maybe you working with a reseller and you don’t have your own sellers, but how do you wiring that into your resellers so your resellers are following up? Or if you have your own sellers, you know, your own sellers are doing it. So you have to sort of digitize your thinking on sales and marketing in this new market, commercial marketplace world, versus in the same way we would’ve done in like the consumer marketplace on Amazon or, you know, um, as you search something on Google. [00:22:26] Cyril Belikoff: Maybe bing. Um, so, um, yeah, yeah. This [00:22:29] Vince Menzione: size. B Come on, come on. [00:22:31] Cyril Belikoff: I [00:22:31] Vince Menzione: had bing. [00:22:32] Cyril Belikoff: Um, [00:22:33] Jon Yoo: I do wanna double click on that, which is when, when we, you know, I talk about like, hey, a lot of it’s buyer driven. A lot of people think about private offers, but then the marketplace offers. But the reality actually, when we look at the data is that there’s a lot more self-service [00:22:46] Vince Menzione: correct [00:22:47] Jon Yoo: offers than there are what they call private offers. [00:22:50] Jon Yoo: So where there’s deal led and that, that, that, that shift. It’s super exciting to see where now these marketplaces are becoming where buyers or users go to discover new products. Let alone, you know, in the future where let’s say there’s an AI agent that has some reward seeking function, and in order to do its job, it needs to go purchase a tool marketplace might be the channel where this happens, which is all the reason why that your listing does need to be optimized. [00:23:16] Jon Yoo: So that one, the agent knows exactly what your tool does and it can match against. Reward. And then second, you have to win the a EO race, right? Yeah. Of, of how you show up in these AI engines. So just wanted to double click on how important that is. Yeah. [00:23:31] Cyril Belikoff: Makes, makes total sense. And we’re, we’re seeing that shift from private office to having a public listed price and a, a public offer as well. [00:23:39] Cyril Belikoff: And so we’re, we’re ourselves investing in our own marketing demand generation. Just in the last three to four months because we’ve seen that taking off and as soon as we saw the signal, we’re like, oh, we should pour fuel on that fire. Because if, if we see organically customers don’t do it, we should, you know, we should go after that and help them understand that we’re here. [00:23:58] Cyril Belikoff: And, uh, if it’s working for some that are doing it by themselves, it’ll work for others and we’re seeing really, really good results. Um, so yeah, get your listing optimized. Think about your digital flows. As John says, the flows of the future are probably agentic wise, maybe not even a human coming to the Microsoft marketplace. [00:24:17] Cyril Belikoff: So you gotta be thinking, not now, it’s okay. You have time. Um, but in the future, you know, six months from now, that quite easily is a scenario. So you really have to start thinking about those, uh, those, uh, digital flows. [00:24:29] Vince Menzione: Yeah. It’s so insightful. Well, it’s what you call product led growth, basically. Yes. And agent led growth. [00:24:35] Vince Menzione: Yes. Right. In some respects. So this is where like we need to think about that. The future model where the agent comes in and actually does the purchasing. We’re not there yet, are we? [00:24:45] Jon Yoo: No. With some products, you know, um, especially, especially if it’s like developer tools. We’re starting to see some of that, but, uh, no one’s gonna buy a cybersecurity solution. [00:24:55] Jon Yoo: Um, that’s seven figures or an agent’s not gonna do that. Right. But the eng the, the search functions are a little bit changing. [00:25:03] Vince Menzione: Yeah. So a lot of ai, you know, we had Steven Boyle on earlier, we were talking about ai, AI natives, you know, that’s Jason Grey’s organization does a lot of work in that area. How do these organizations need to think and act and how do they leverage the cloud marketplaces? [00:25:17] Vince Menzione: Or how do le how do marketplaces fit into the equation? ’cause most of them are coming from a different like paradigm or mindset. [00:25:24] Jon Yoo: I mean, uh, it’s like the number one question, you know, I’ll give a little personal story of like, I get a lot of parking tickets, uh, and I don’t know how to pay off my late fees. [00:25:34] Jon Yoo: So we set up a little open claw that will actually go and ask me questions, and it actually pays off my parking tickets on my behalf. Um, so, so, so, you know, on the other hand, like there, there’s layers to it, right? It could be like layer one, I ask ai, Hey, how do I pay off my parking tickets? Layer two could be. [00:25:52] Jon Yoo: Hey, you know, what’s the best way for me to structure my cadence to pay off the parking tickets? And then layer three is like, Hey, AI, proactively check if I have parking tickets and go pay it on my behalf. Here’s my credit card information, you know, stored in a secure vault. So, uh, what, what I mean by a native as a company bring that analogy is, uh, bringing it at its core. [00:26:12] Jon Yoo: So like a little for, for sugar. We’ve spent the past six months optimizing around our entire, like company brain where we are storing all the context. To a singular, singular place that is queryable, that there’s no coordination tax between teams. Our entire product development process actually is automated where we have multiple agents debating one another, PRDs to code generation, code review, uh, you know, security, qa, et cetera, et cetera. [00:26:40] Jon Yoo: And then there’s humans in the loop across the process. So I would actually think about when we, when we fit that into, into marketplaces, it’s not just thinking about who’s going to send the offers or who’s gonna do the co-sell, or who’s gonna X, Y, Z, but how do you bring all that together so that your sales reps and your partnership person and Microsoft all has to share context in a given deal and it’s done automatically without, you know, back office folks having to update what partner led or partner influence means and reporting things in a, in an old way. [00:27:11] Jon Yoo: So it’s almost re-imagining. Entire workflow, given everyone should have open context of what’s going on in a given deal. So let’s say maybe a hand wavy way of answering that question. Yeah. But it does mean that we should be re-imagining, uh, what the job to be done is, uh, very fundamentally [00:27:28] Vince Menzione: cy, how are you thinking about it since you’re building the, the, the toolkit at Microsoft? [00:27:32] Cyril Belikoff: Yeah. Um, yeah. Well, John says absolutely is right, particularly around marketplace and how that sort of flow and ecosystem will work. Um, in addition to that, it’s. Not only about how marketplace or about developers ’cause the developer, uh, scenario or persona was the first globally to see the value of AI and agents in the flow of their work. [00:27:55] Cyril Belikoff: It was the first to like, oh, one developer can now do much more. I can be empowered, I can build agents to work on my behalf. I can, uh, I can be an architect instead of a hands-on coder. Right. It’s changed the profile of what developers do exactly what. Uh, John mentioned what he’s doing himself. That’s just one profile of a user. [00:28:17] Cyril Belikoff: Th there are many other profiles. A sales person, a marketer, a uh, CFO team, hr. Each of these are literally going through the same transformation. They’re one beat behind developers because developers, you know, was really tech enabled and tech stack driven, and the, and the opportunity was, was obvious quite quickly. [00:28:39] Cyril Belikoff: These are coming really quickly. This is not like the internet adoption cycle that took many multiple years. This is like, we’re talking months. So, and even inside Microsoft, we have our own, what we call, uh, frontier Marketing Internal Initiative to re, um, rewire marketers and how they go about their daily job and stop doing it this way and do it this way. [00:29:04] Cyril Belikoff: Just pick up M 365 copilot with. Coworking Claude, uh, embedded and just go do the work. Why do you have to outsource this or create this? Just edit the video right there yourself. Like, why do you have to just go do the, as a marketer you want to create a beautiful piece of content, just go and create it. [00:29:21] Cyril Belikoff: ’cause it can create it for you now. [00:29:22] Vince Menzione: Yeah, [00:29:23] Cyril Belikoff: three months ago, literally three months ago, it couldn’t do that. And so what is it gonna be in two months for a marketer or a salesperson or finance person? I mean, just co-pilot in Excel. What that is doing to the financial business is in, like if you, any financial person will tell you they live and breathe through in Excel, like it’s just, it’s their equivalent of, it runs [00:29:44] Vince Menzione: most businesses [00:29:44] Cyril Belikoff: their dev tool, right? [00:29:45] Cyril Belikoff: It’s their thing. It’s really [00:29:46] Vince Menzione: is. [00:29:47] Cyril Belikoff: So this is happening over and over again. Again, if you can package those things up and package a package, that piece of IP on a marketplace is, is not only gonna be a big system, it could be a very, very small piece of. Functionality in a flow for a marketer or a salesperson that is so valuable that can be solved many times on a marketplace. [00:30:08] Cyril Belikoff: And in many cases, everyone’s a software company at this point. Everybody can go and package something up. And so I would be stunned if we don’t see cross pollination from system integrators to channel partners, all just publishing on marketplaces based on, oh, I’ve done this thing four times. I cannot do it 400 times. [00:30:26] Cyril Belikoff: Let me just package it up and put it on a marketplace. So. Yeah, I’m sure you know, John alluded to that. It’s, there’s a lot of exciting times. [00:30:33] Vince Menzione: No, the future [00:30:33] Jon Yoo: is [00:30:33] Vince Menzione: great. What do you [00:30:34] Jon Yoo: totally, I mean, it’s, it’s a case of like, how do, what does being marketplace native also mean? [00:30:38] Cyril Belikoff: Yes. [00:30:38] Jon Yoo: You know, and not, I feel like I’ve seen so many people just use it as a, Hey, we’re opportunistically there and if we get some leads out of it, great. [00:30:45] Jon Yoo: Or if there’s some deals, great, but they’re not really leaning in and being marketplace native the way, you know, and right now there’s, this might be uncomfortable, but there’s no excuses. You know, like creating a partner marketing content with your brand guidelines. It should not take so many time. Or it’s a 10 minute exercise. [00:31:02] Jon Yoo: Exactly. Exactly. Three [00:31:03] Cyril Belikoff: prompts. [00:31:05] Vince Menzione: It used to be that ops used to get involved because, oh, we, we know that they have a commitment. Let’s go run it through the marketplace. Right now, what you both have been suggesting here is it becomes a discoverable process. It becomes part of your normal go to market strategy, and you’re, you’re driving pr, product led growth and SEO and all the things you need to do running a a corporation and modern corporation today. [00:31:26] Cyril Belikoff: Yeah, [00:31:26] Vince Menzione: exactly. So, what’s the future like? Where, where do we go in 12 months with this? What do you think? What do you predict? We, we get out a Ouija board or a crystal ball here. What, what, what do we think? [00:31:38] Cyril Belikoff: I think more broadly in the industry, you’re gonna, some of the scenarios that John alluded to, agent to agent interactions, uh, many agents acting on behalf of humans and on behalf of organizations, uh, and doing. [00:31:55] Cyril Belikoff: Simple things and quite complex things. Uh, and those need to be, uh, managed carefully, uh, with, uh, the right, uh, engagements and built carefully. And in, in many cases, customers will look to partners that are quote unquote certified on, uh, uh, HyperCloud platform. Uh, as a way to get going quickly, but also get the, the quality that they need. [00:32:21] Cyril Belikoff: Um, and I think instead of buying this monolithic, massive application, I think we will see lots more of smaller applications being built that have cleaner open, uh, you know, MC, you know, MCP type agent interfaces that can just work together, uh, without, you know. More complicated integration work, [00:32:42] Vince Menzione: cobbled together your own solutions as opposed to big [00:32:45] Cyril Belikoff: monolithic [00:32:45] Vince Menzione: applications. [00:32:46] Cyril Belikoff: Yeah, there’ll be a much more agile, [00:32:48] Vince Menzione: yeah. [00:32:48] Cyril Belikoff: Uh, personalization. I mean, a lot of people saying SaaS is dead. It’s not quite dead. But I think that SaaS will get significantly more agile, more custom and more personal, uh, for, uh, for customers. [00:33:03] Jon Yoo: I, I would agree with that. Um, I mean, I’m biased, but I think marketplace are gonna be even more relevant than ever. [00:33:08] Jon Yoo: I mean, it’s already highly relevant, but, uh, as you, you know, I think in the past the, the narrative was, well, there’s a new generation of buyers and they’re millennials. They, they wanted, you know, uh, I always talk about consumer experience to B2B sales, you know, and, and now it’s like agents that’s that on steroids. [00:33:24] Jon Yoo: Um, but what I, you know, beyond that, I think, uh, there, there’s a lot of talk of like SAS apocalypse or software companies getting. Destroyed by, you know, the, the, the AI labs or SaaS is dead or whatever. I think SaaS is gonna, I mean, there’s gonna be way more software companies because the cost to build is a lot easier. [00:33:46] Jon Yoo: That means that competition will be. Even more fierce than ever. And you have all these AI native companies that are coming out with a quicker time to market, quicker time to value, and they have some recursive loops that makes the product even that much better. Um, so what that means for everyone is like, one, you gotta go back to the, the core differentiations. [00:34:06] Jon Yoo: Or like in the past, maybe the, the time to build was the differentiation, but today it’s, or you know, there’s some other, obviously the core elements, but then there’s. Distribution. Yeah. So how do you partner with Microsoft, for example? How do you have your own self-improving like distribution model? That makes sense. [00:34:22] Jon Yoo: Marketplace being a huge component of that. Two is obviously there, there’s a piece of like network and data. That’s what we think about of hey, what makes our product better as more, more people use it. Um, because yeah, competition is crazy fierce and it finally goes into times deployment. That’s why you see these companies like. [00:34:41] Jon Yoo: Open AI anthropic that are competing for their enterprise, uh, pie. And instead of doing it themselves, they’re, I mean, I think OpenAI just did a joint venture of like $4.1 billion into the deployment company. Anthropics doing the same, they’re surrounding themselves with the ecosystem and channel is going to be more relevant than than ever, as long as you know how to enable AI services and know how to deliver on this technology to the, to the broader world. [00:35:07] Jon Yoo: And so. Channel awesome. Marketplace, awesome. You know, competition’s gonna be fierce. Success is not going anywhere. [00:35:16] Vince Menzione: Good conversation, gentlemen. [00:35:18] Jon Yoo: Awesome. [00:35:18] Vince Menzione: I’ve been told we’re over time. I wanted to open it up to questions. Um, but I do feel like we, yeah, I’ll get, I’ll get yelled at. But this was incredible. Um, some great, I mean, the, the pa it, it’s terrific to see. [00:35:36] Vince Menzione: How far we’ve come in, so shorter period of time, and it’s only gonna continue to get better. I think the one question I’ll have is like, what, what would hold any of these companies back at this point? It feels like it’s such a compelling reason we need to move forward. Is there, is there anything, like why would, why would we hold back? [00:35:54] Cyril Belikoff: Um, you know, some of the discussions we have, um, is about how to balance today’s world with tomorrow’s world a little bit. [00:36:01] Vince Menzione: Yeah. [00:36:02] Cyril Belikoff: Today’s business model with tomorrow’s business model, today’s financial results with tomorrow’s financial results. Um, and there are very different approaches to all of this. [00:36:13] Cyril Belikoff: Those AI natives, they’re like, there is no yesterday. There’s only tomorrow. Um, there those companies that realize they’re being threatened by AI natives and so they have to move quickly. Um, and, uh. Figure out a, a, a business model and then someone else who wants to do a bit of both and bridge into it. [00:36:32] Vince Menzione: Yeah. [00:36:32] Cyril Belikoff: Um, and just within that frame there are different ways to tackle it, whether it’s create two teams, one’s the future team, one’s the current team, and, you know, may the best team win, um, makes sense with the customer and that, uh, or, uh, give the, give the customer the choice and have the teams going together. [00:36:49] Cyril Belikoff: So there are lots of different approaches. [00:36:51] Vince Menzione: Right. So great to have you. Did you have some, did you have a comment to make on that? [00:36:55] Jon Yoo: Uh, no. Just, uh, unwillingness to lean in and learn something new. Yeah. [00:36:59] Vince Menzione: Yeah. [00:37:00] Jon Yoo: I’m, I’m, I’m much more in the burn all boats buckets. Yeah, [00:37:03] Vince Menzione: I know. Me too. [00:37:03] Jon Yoo: Of, uh, no, no old team and new team. [00:37:05] Jon Yoo: Just new team and you know, that’s just push forward. [00:37:07] Vince Menzione: Well, great to have two amazing leaders on stage with [00:37:10] Cyril Belikoff: us. Thanks [00:37:10] Vince Menzione: so [00:37:10] Cyril Belikoff: much. [00:37:10] Vince Menzione: So thank you [00:37:11] Jon Yoo: so much. Yeah, thank [00:37:11] Vince Menzione: you [00:37:15] Jon Yoo: so much. [00:37:16] Vince Menzione: Don’t forget. Ultimate Partner Live is coming soon, October 26th through October 28th in Reston, Virginia. I hope to see you there. [00:37:28] I.

In this episode:

Rebecca Hinds is the bestselling author of Your Best Meeting Ever and a leading expert on work transformation and the future of work. Rebecca earned a B.S., M.S., and Ph.D. from Stanford University. In 2022, she founded the Work Innovation Lab at Asana, a first-of-its-kind think tank that conducts actionable research to help leaders and organizations navigate the growing challenges and changes of work. In 2025, she founded the Work AI Institute at Glean, where she leads cutting-edge research on how AI is reshaping work. Rebecca's award-winning research and insights are consistently featured in places like Harvard Business Review, The New York Times, The Wall Street Journal, Forbes, Inc., and Time. Rebecca is a co-instructor for the CNBC Make It course, How to Use AI to Be More Successful at Work, and a columnist at Inc. and Reworked.Link to claim CME credit: https://www.surveymonkey.com/r/3DXCFW3CME credit is available for up to 3 years after the stated release dateContact CEOD@bmhcc.org if you have any questions about claiming credit.

We're using AI more than ever. And yet, according to research from Glean's Work AI Institute, only 10% of Australians say AI is significantly improving organisational performance. The truth is that most organisations have done what Dom Price calls the "Woodstock theory" of AI adoption: build it and they will come. Throw the tools out there, hope people figure it out, and wait for the productivity miracle that never quite arrives. In this episode, I sit down with Dom Price, former Atlassian work futurist and one of the sharpest thinkers I know on how organisations actually work. Dom joins me off the back of new Australian research showing the enormous gap between AI adoption and AI impact, and we dig into exactly why that gap exists and what to do about it. If you care about building genuine AI capability in your team rather than just looking busy with AI, this conversation will make you rethink where to start. Dom and I discuss: The "Woodstock theory" of AI adoption, and why most organisations are getting almost nothing back for their investment Botsitting and botshitting: two new terms that capture exactly what's going wrong with how we use AI at work The minus one, zero, plus one framework for figuring out where AI actually belongs in your organisation Why managers are becoming the unexpected bottleneck in an AI-enabled workplace Dom's board of directors inside Claude, and how he uses it to catch his own blind spots The question Dom asks every leadership team that almost no one can answer Key quotes "If you have inefficient and ineffective processes and people systems, and you layer in AI, you are doing stupid things faster." "Most of the businesses I work with in the ASX, their human operating system's Windows 95. So you might have Claude 5.9. You're using Ferrari-style horsepower in your technology, but the way your humans and teams work and meet and make decisions... all those things are Windows 95." Connect with Dom Price on Instagram, LinkedIn and his website. If this conversation sparked something, you'll also love my recent chat with Professor Scott Anthony on how AI has changed the way he approaches problem-solving and his day-to-day workflows. Listen here. My latest book The Energy Game is out on July 7, 2026. You can order a copy here: https://amzn.to/48ID29M Connect with me on the socials: Linkedin (https://www.linkedin.com/in/amanthaimber) Instagram (https://www.instagram.com/amanthai) If you are looking for more tips to improve the way you work and live, I write a weekly newsletter where I share practical and simple to apply tips to improve your life. You can sign up for that at https://amantha.substack.com/ Visit https://www.amantha.com/podcast for full show notes from all episodes. Get in touch at amantha@inventium.com.au Credits: Host: Amantha Imber Sound Engineer: The Podcast Butler See omnystudio.com/listener for privacy information.

Most organizations treat meetings as the default answer to everything, but that's costing you more than you think. Rebecca Hinds, Head of the Work AI Institute at Glean, researcher, and author of YOUR BEST MEETING EVER, brings a product design mindset to the most expensive form of collaboration in your org. She shares how to spot meeting dysfunction, use AI to audit your calendar, and make intentional changes that actually stick. In this episode: • Why meetings have become the 'junk drawer' of organizational communication, and how visibility bias keeps the habit alive. • How to use return on time investment (ROTI) scoring, meeting minimalism, and shared language to redesign your meeting culture. • The role AI and data play in building the business case for calendar reform, especially with a skeptical C-suite. Timestamps [00:01:10] Why Rebecca went all-in on meeting research and the psychology of visibility bias. [00:02:19] The meeting junk drawer: why meetings become the default for everything. [00:04:39] Treating meetings like a product, including the concept of meeting debt. [00:06:26] Return on time investment (ROTI): a data-driven way to rate your meetings. [00:08:16] How leadership buy-in determines how boldly you can reform your calendar. [00:08:56] Using AI to build meeting calculators and get C-suite buy-in. [00:10:52] Making the business case by anchoring on what the most powerful person cares about. [00:13:54] Building psychological safety so people feel empowered to flag bad meetings. [00:16:36] Shared language for meeting dysfunction, including Meeting Doomsday and meeting minimalism. [00:21:05] The one thing every leader can do this week: intentional design across four meeting dimensions. Guest Bio Rebecca Hinds is the author of YOUR BEST MEETING EVER, a leading expert on organizational behavior and the future of work, founder of the Work Innovation Lab at Asana and the Work AI Institute at Glean. She holds a BS, MS, and PhD from Stanford University. Her research is consistently featured in top-tier publications like Harvard Business Review, The New York Times, The Wall Street Journal, Forbes, Wired, and more. She is a trusted advisor to companies navigating the challenges of modern work, from meeting overload and hybrid dysfunction to the messy realities of AI adoption and organizational change. Brought to You by Paylocity Paylocity is the fastest growing unified platform for HR, Finance, and IT. Paylocity brings your people, processes, and data together in one place so HR leaders can spend less time managing systems and more time doing the work that actually moves their organizations forward. Learn more at paylocity.com Keywords: meetings, meeting culture, organizational behavior, future of work, meeting debt, return on time investment, psychological safety, AI, calendar reform, Meeting Doomsday, meeting minimalism, collaboration, HR leadership, Rebecca Hinds, HR Mixtape

Some conversations stay with you for days. This one did. Last week at Team '26 in Anaheim, I spoke to my favourite Tamar Yehoshua, Chief Product and AI Officer at Atlassian. A week later, I'm still thinking about three things she said.Here's the thing about Tamar. I always learn something new every time we talk. She's one of those rare leaders who can zoom from a product detail to a 5-year vision in the same breath without missing a beat.What makes her perspective so useful: Tamar has shipped product at Google Search, led product at Slack through their tenfold growth and IPO, and ran product and technology at Glean. Three different eras of how knowledge workers find what they need at work. And now she's leading Atlassian's AI strategy at the moment the entire category is being redefined.Team '26 was her first Team event as CPO and AI Officer. You could feel the weight of that moment in the room.Here's what we got into:- Day one through her eyes. What it actually felt like to walk on stage as the new CPO and announce the biggest set of AI launches in Atlassian's history.- The connective thread. Atlassian covered massive ground in the keynote. AI for developers, service teams, product teams, agents in Jira. I asked Tamar how she wants people to think about Atlassian's AI strategy as one story instead of five. Her answer reframed the whole keynote for me.- How customers are actually using Rovo. Not the marketing version. The real version. What's working, what's surprising, where the patterns are forming.- The shifts that matter. Tamar has lived through search becoming the default interface, then SaaS becoming the default workplace, then chat-based collaboration becoming the default for distributed teams. I asked what excites her most about this moment. Her answer wasn't what I expected.- The next 5 years. How teams will actually work differently. Not predictions. Patterns she's already seeing inside Atlassian's own teams.The throughline across everything she shared: context is the moat. Models will keep getting better and cheaper. What separates the winners is what your AI knows about how your company actually works.Big thank you to Tamar for the time and the candor, and for being so generous with her thinking every time we connect. And to the Atlassian team for hosting me at Team '26.#data #ai #atlassian #team26 #theravitshow

Enterprise AI buying has moved quickly, but durable adoption still depends on context, security, workflow fit, and measurable business impact. Daniel Simon, Enterprise Account Executive at Glean, joins John Kaplan and John McMahon to discuss what it takes to sell AI in complex enterprise environments, why multi-threading matters more when buyers are evaluating broad organizational change, and how strong sellers build trust by tying use cases to productivity, governance, and ROI instead of relying on product excitement alone. Daniel Simon is an Enterprise Account Executive at Glean, where he works with large enterprises on AI adoption, knowledge discovery, and productivity across complex organizations. He brings experience selling enterprise technology into multi-stakeholder buying environments. Connect with Dan: LinkedIn Resources mentioned: The Qualified Sales Leader by by John McMahon  The Go-Giver by by Bob Burg  Key takeaways from this episode:  00:00 – Introduction 02:40 – What it really takes to move from product fluency to business impact in enterprise sales. 06:35 – Why many sellers mistake a strong champion for a qualified enterprise deal. 08:47 – A look inside how AI can expose qualification gaps without replacing sales fundamentals. 18:12 – What leaders often overlook about context as the real differentiator in enterprise AI. 30:51 – Why face-to-face engagement quietly creates leverage in a crowded AI market. 42:58 – Dan Simon's perspective on why consumption-based pricing raises the bar for customer success. 57:12 – Why AI will amplify strong sales discipline and expose weak execution. Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

The core structural shift highlighted in this episode is the commoditization of AI model platforms and concurrent consolidation at the vendor and platform layer, forcing Managed Service Providers (MSPs) to move their value proposition above reselling models to orchestrating, governing, and verifying AI outputs. The discussion references the rising concentration and valuation of platforms such as NinjaOne—a founder-led, profitable RMM platform with a $12.3 billion valuation and 70% year-over-year growth—and Pax8 building business toolkits that draw more operational functions onto their rails. At the same time, major AI developers like OpenAI are entering the channel more directly by launching partner programs aimed at MSPs and consultants. The most consequential development is the confirmed shift from reselling AI models to managing their outputs and risks. Glean surveyed 6,000 digital workers and found that while AI delivers approximately 11 hours of weekly time savings, nearly 6.4 hours are reclaimed by “bot sitting”—the human intervention required to supply context, verify, and correct AI outputs. This hidden labor raises a risk scenario: two-thirds of workers admit to releasing unchecked AI outputs, and Ivanti found that only 42% of IT environments actually have a named owner for each AI agent, despite 85% claiming so—a 43-point gap in accountability. Asana and Deloitte further reinforce the issue, reporting frequent cost overruns and unmanaged autonomous AI deployments among enterprise and SMB environments. Supporting developments underscore this governance and accountability gap. TechCrunch cited that ChatGPT's AI market share has dropped below 50% as the field becomes more interchangeable and less differentiated by underlying model. Vendors such as Anthropic and OpenAI, recognizing model commoditization, are seeking revenue through high-volume partner channels, blurring the lines between vendor and channel competitor. According to Asana, more than 80% of UK IT leaders encountered unplanned AI costs, and over half reported business harm from autonomous AI actions, shifting operational and liability risks squarely onto MSPs and IT service providers. Operationally, these trends compel MSPs to take explicit ownership of the orchestration and governance layer, rather than relying on tool reselling. The transcript advises mapping every AI-driven decision or output that reaches client endpoints and identifying who verifies these outputs before customer exposure. Failing to address these governance blanks does not avoid work but shifts it to unbilled, post-incident cleanup, often with financial, legal, or compliance consequences. Effective MSPs will need to price, document, and regularly review their verification, orchestration, and risk assumption, positioning these as standalone, billable services to manage risk and maintain margin as AI platforms commoditize and vendor dependencies rise. 00:00 Bigger Platforms, Unwatched AI 03:44 The Vendor Walks Into the Channel 05:56 Govern It or Absorb It 08:52 Why Do We Care?  Supported by:  ScalePad  Sign up for the SMB Online Conference: www.smbonlineconference.com

Rebecca Hinds, author of "Your Best Meeting Ever" and Head of the Work AI Institute at Glean, breaks down the surprising findings from the new Work AI Index 2026 report surveying 6,000 workers. While 87% now use AI and report saving 13 hours per week, only 13% say their organization is performing significantly better—a paradox explained by two new concepts: "botsitting" (the hidden labor of making AI useful) and "botshitting" (delivering AI-generated work you can't defend). They discuss practical solutions including better-integrated AI systems, smarter AI detection policies, and aligning work to meaningful missions. LINKS: Rebecca Hinds Personal Website Glean Work AI Institute Your Best Meeting Ever Book Glean Enterprise AI Platform Stanford Future of Work Glean Enterprise Graph Pangram Labs AI Detection OpenAI ChatGPT Product Page Anthropic Claude Product Page Google Gemini Product Page Microsoft 365 Copilot Page Glean AI Transformation 100 Rebecca Hinds LinkedIn Profile Sponsor: Claude: Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr CHAPTERS: (00:00) About the Episode (03:22) Grounding AI adoption (06:46) Methodology and Glean (12:25) Productivity paradox emerges (Part 1) (20:31) Sponsor: Claude (22:22) Productivity paradox emerges (Part 2) (25:36) Bot sitting burden (34:00) Hidden time savings (39:56) Meaning versus automation (47:14) Enterprise graph potential (53:13) Detecting bot slop (01:00:32) Retention and incentives (01:07:54) Transformation and mission (01:20:06) AI teammate model (01:26:01) Future organizational design (01:32:31) Research and meetings (01:41:43) Episode Outro (01:45:07) Outro PRODUCED BY: https://aipodcast.ing

Primary election day in different parts of the country on Tuesday. What is the take away? Blois Olson and Vineeta Sawkar discuss on The Morning Take.

South Korean chip startup Xcena is betting that AI's real bottleneck is not compute, but memory. Also, the enterprise AI search startup tripled its annual revenue even as tech giants entered the category. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Rebecca Hinds discusses the simple shifts that turn meetings from time-wasters into value-generators.— YOU'LL LEARN — 1) Why most meetings don't feel like “real work”2) Why every organization needs a “meeting doomsday”3) The easy agenda fixes that save so much timeSubscribe or visit AwesomeAtYourJob.com/ep1156 for clickable versions of the links below. — ABOUT REBECCA — Rebecca Hinds is a leading expert on organizational behavior and the future of work. She holds a BS, MS, and PhD from Stanford University. Rebecca founded the Work Innovation Lab at Asana and the Work AI Institute at Glean, first-of-their-kind corporate think tanks dedicated to conducting cutting-edge research on the future of work.She is a trusted advisor to companies navigating the challenges of modern work—from meeting overload and hybrid dysfunction to the messy realities of AI adoption and organizational change.• Book: Your Best Meeting Ever: 7 Principles for Designing Meetings That Get Things Done• LinkedIn: Rebecca Hinds• Website: RebeccaHinds.com— RESOURCES MENTIONED IN THE SHOW — • Tool: Glean• Book: Give and Take: Why Helping Others Drives Our Success by Adam Grant• Book: Scaling Up Excellence: Getting to More Without Settling for Less by Robert Sutton and Huggy Rao• Book: The Friction Project: How Smart Leaders Make the Right Things Easier and the Wrong Things Harder by Robert Sutton and Huggy Rao• Past episode: 492: Making Meetings Work with J. Elise Keith• Past episode: 684: Achieving More by Tapping into the Science of Less with Leidy Klotz— THANK YOU SPONSORS! — • Scribe. Book a personalized enterprise demo with scribe.how/awesome• Narwhal. Treat your home to spotless, fresh floors with us.narwhal.com/pete.• Monarch.com. Get 50% off your first year on with the code AWESOME.• Shopify. Sign up for your $1/month trial at Shopify.com/awesomepodSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Are your meetings actually working? Or has your calendar just become a system nobody knows how to switch off? In this episode, David Green is joined by Rebecca Hinds, Stanford-trained organisational researcher, Head of the Work AI Institute at Glean, and author of Your Best Meeting Ever: 7 Principles for Designing Meetings That Get Things Done. In this conversation, David and Rebecca discuss: Why organisations should treat meetings as a product, and what that actually means in practice The concept of meeting debt, and why calendars accumulate bloat in the same way codebases accumulate technical debt What a 48-hour calendar cleanse involves, and what typically happens when organisations rebuild their calendars from scratch The patterns that show up most consistently when mapping how work actually moves between teams How AI is being used to improve meetings, and the ways it can make dysfunctional meeting culture worse What the conversation looks like in the room when CHROs start rethinking collaboration for the AI era This episode is sponsored by TechWolf. The world of work is being rewritten faster than HR systems can keep up. Skills age in months. Roles get redesigned quarter by quarter. CHROs have quietly become AI transformation leads, and the data they need to lead it doesn't exist in any HR system. That's why the world's most forward-looking enterprises such as HSBC, AMD, T-Mobile, GSK, ServiceNow, Pfizer, have built on TechWolf. As the data layer for the AI era of work, TechWolf gives enterprises the skills, they need to move faster and lead with confidence. Skills Intelligence, Work Intelligence, and Market Intelligence, in one layer. Visit techwolf.ai. Resources: Your Best Meeting Ever: 7 Principles for Designing Meetings That Get Things Done Hosted on Acast. See acast.com/privacy for more information.

Join Yogi Goel, Co-founder, CEO, and CFO of Maxima, for an unvarnished conversation on breaking the legacy architecture of corporate finance. After a 20-year career spanning auditing at EY, tech IPOs at Citi and Barclays, and scaling Rubrik from $5M to $900M in ARR, Yogi was firmly on the venture-backed CFO track. Instead, he realized that despite decades of enterprise software, accounting teams were still trapped in a monthly cycle of manual data wrangling and spreadsheet anguish. In this episode, we explore how Maxima secured $41M in funding from Kleiner Perkins and Redpoint, why the "semi-annual close" debate misses the mark, and why the future of finance relies on AI acting as a horizontal system of work layered directly over existing ERPs.

Step into the story of Ruth, a journey from emptiness to redemption. Discover how God's faithful love meets us in loss, works through the ordinary, and leads us toward hope.Thanks for listening to the Christ Church Mequon Podcast. Find your next step and let us know how we can be praying for you at ChristChurchMequon.LIFE/Podcast. Hit that subscribe button and, until next week, God bless.

Rebecca Hinds, author of "Your Best Meeting Ever" on the principles for designing effective meetings. The founder of Work Innovation Lab at Asana and the Work AI Institute at Glean, emphasizes the importance of applying product design principles to meetings, including her 4D system - Discussion, Development, Decision or Debate to reduce "meeting debt" and determine if a meeting is necessary at all. Meetings, she argues, should be limited to discussions requiring complex, emotional or one-way decisions, which leads to real insights into organizational design in the workplace. The best organizations, she says are where leaders lead with curiosity, find the bridge builders, the super connectors and the difference makers, who themselves play with the technology and ask not what they can do with it, but what it can do for them.

Special discounts up for AIE Melbourne (LS discount) and AIE World's Fair (group discounts up to 25% - CFPs still open for Autoresearch and Vertical AI) Cya there!Abridge did not start as an “GPT wrapper”. It was founded in 2018, years before the Cambrian explosion of AI application layer companies. OpenAI launched ChatGPT publicly on November 30, 2022 and by then, Abridge had already spent years doing the unglamorous work of building trust for one of the highest context, most important workflows in healthcare: the conversation between a patient and a clinician.Abridge's original wedge was clinical documentation. Listen to the visit, generate the note, reduce the clerical burden, and let clinicians spend more time with patients instead of the EHR. By focusing on how doctors actually document, how health systems actually buy, how EHR integration actually works, how clinicians verify outputs, and how missing context during a visit turns into downstream friction across billing, prior authorization, quality, and follow-up, the adoption of LLMs became a force multiplier on a workflow already optimized for sensitive context gathering.The company has scaled fast: Abridge says it is projected to support 80M+ patient-clinician conversations this year across 250 large and complex U.S. health systems, with support for 28+ languages and 50+ specialties. It raised $300M at a $5.3B valuation in June 2025, after a $250M round earlier that year.Today, Janie Lee and Chaitanya “Chai” Asawa of Abridge join us for another crossover pod with Redpoint's Jacob Effron (who is on the board of Abridge) to dive into how Abridge is building the clinical intelligence layer for healthcare starting with ambient documentation, then expanding into clinical decision support, prior authorization, payer/provider/pharma workflows, and eventually real-time agents that act before, during, and after the patient conversation. We go inside the product, data, infra, evals, workflow, privacy, and org design choices behind bringing AI into one of the highest-stakes enterprise environments from 100M+ medical conversations and specialty-specific evals to real-time alerts, EHR integration, de-identification, clinician-scientist teams, and why healthcare may solve some of the hardest AI problems first.We discuss:* Why Abridge started with clinical documentation, “pajama time,” and saving clinicians 10–20 hours a week* The transition from ambient scribe to clinical intelligence layer: save time, save money, and save lives* Why conversations between patients and clinicians may be the most important workflow in healthcare (patient visit summary feature)* Chai's “healthcare-coded Glean” framing: context is king, but healthcare raises the stakes on safety, evals, and rollout* Why Abridge wants AI to feel like “air conditioning”: always in the background, but only interrupting when it truly matters* The prior authorization example: turning a denied MRI weeks later into real-time guidance while the patient is still in the room* Why payer policies, EHR data, medical literature, and hospital-specific guidelines make the problem hard, and also create the moat* How Abridge thinks about ambient form factors: mobile, desktop, in-room devices, nursing workflows, multimodality, and future AR* The multi-sided healthcare customer: CMIOs, CFOs, CIOs, clinicians, patients, payers, and pharma* The hardest AI problem at Abridge: high-quality, low-latency, low-cost real-time support in a high-stakes clinical setting* When Abridge uses frontier models vs proprietary models, and why its unique data from medical conversations matters* Why “every agent is a coding agent underneath,” and how the EHR can be thought of as a filesystem for healthcare agents* How Abridge approaches personalization across individual doctors, specialties, and health systems* Why “AI slop” is AI without context, and how edits, memories, and clinician preferences create a data flywheel* Abridge's eval stack: LFDs, LLM judges, in-house clinicians, third-party evaluators, specialty-specific evals, and progressive rollout* HIPAA, PHI, de-identification, one-way anonymization, customer contracts, and learning from healthcare data safely* What changes when you operate at 100M+ conversations: reliability, cost, post-training, model routing, and infrastructure optimization* Why the same clinical conversation can serve doctors, patients, payers, pharma, and future clinical-trial workflows* How Abridge works with EHRs, and why deep interoperability is table stakes for clinician adoption* Why healthcare AI has regulatory tailwinds, why 80/20 does not work here, and why high-stakes domains may drive AI forward* Why Abridge embeds “clinician scientists” into product and eval teams* What Chai learned from Glean about search, quality, and durable AI infrastructure* Why the future of AI infra may look like context layers, event-driven systems, Kafka, Temporal, sockets, CRDTs, and tools built for humans* Why Janie changed her mind on “PRDs are dead,” and why crisp written clarity matters more in complex AI products* How Abridge uses Claude Code, Cursor, and coding agents internallyAbridge:* Website: https://www.abridge.com/* X: https://x.com/AbridgeHQJanie Lee:* LinkedIn: https://www.linkedin.com/in/janiejleeChaitanya “Chai” Asawa:* LinkedIn: https://www.linkedin.com/in/casawaTimestamps00:00:00 Introduction and what Abridge does00:02:05 From ambient documentation to clinical intelligence00:04:04 Clinical decision support and context as king00:06:57 Alert fatigue, proactive intelligence, and prior authorization00:12:36 Ambient AI form factors and healthcare customers00:16:59 The hardest AI problems in healthcare00:18:26 Frontier models, proprietary data, and model strategy00:21:07 The EHR as a filesystem for agents00:24:03 Personalization, memory, and clinician preferences00:30:40 Evals, LLM judges, and progressive rollout00:36:47 HIPAA, de-identification, and privacy00:39:21 100M conversations and operating at scale00:44:10 EHR integration and the clinical intelligence layer00:46:39 Healthcare regulation, latency, and high-stakes AI00:50:11 Clinician scientists and long-tail quality00:53:04 Lessons from Glean and durable AI infrastructure00:57:03 The future of agentic healthcare workflows00:57:34 PRDs, product clarity, and building serious AI products01:03:11 AI coding tools at Abridge01:04:06 OutroTranscriptIntroduction: Abridge, Clinical Intelligence, and the Latent Space x Unsupervised Learning CrossoverSwyx [00:00:00]: Okay. This is a special crossover Latent Space Unsupervised Learning pod.Jacob [00:00:07]: Very excited to do this.Jacob [00:00:08]: At this point, we get together once a year.Swyx [00:00:10]: Once a yearJacob [00:00:11]: And this is a fun occasion to get to do it on.Swyx [00:00:13]: I really wanted to talk to Abridge but I felt very underqualified because healthcare is not something we cover very intensely. It just so happens that Redpoint's our big investors and supporters of Abridge.Jacob [00:00:27]: Anytime you want to have a portfolio company on your podcastJacob [00:00:29]: Please, by all means.Swyx [00:00:31]: So we'll introduce our guests. Chai and Janie, welcome to the pod.Janie [00:00:34]: Thanks for having us.Chai [00:00:35]: Thank you.Janie [00:00:35]: We're excited to be here.Chai [00:00:36]: Thank you.Swyx [00:00:36]: So for listeners, what do you guys do, just to situate you guys in the company?Janie [00:00:42]: Abridge is a clinical intelligence layer for health systems. We really started with documentation and building for clinicians and as we think about reducing the burden that clinicians have, they're spending 10 to 20 hours a week on documentation. There's a massive doctor shortage in the country. We also think that conversations between patients and clinicians are probably the most important workflow in healthcare. It's where care is given and received but if you think about the 20% of our GDP that goes towards healthcare, almost everything is a derivative of that conversation, whether it's the claim, the payment, the actual diagnosis given, the treatment. And we've started with a conversation to reduce the burden for doctors on documentation but we're really excited about the path ahead as we become this broader clinical intelligence layer.Chai [00:01:34]: I'm Chai. I work on clinical decision support at Abridge.Swyx [00:01:37]: Yes.Chai [00:01:37]: And so as Janie said, we're uniquely situated where we started off with the clinical note. What I'm really excited about and where we're expanding towards is what are all the things you can do before the conversation, during the conversation and after the conversation if you did have access to all the context about patients, payer guidelines, medical literature and put that together and to serve, how healthcare could look fundamentally different.Swyx [00:02:01]: And that's the context engine that you guys have?Chai [00:02:04]: Yes.Swyx [00:02:04]: Is that what it's called? Okay.Swyx [00:02:05]: So historically, as I understand it, the company started in 2018. A lot of people would be familiar with the AI voice notes form factor that doctors would be “Well, do you consent to being recorded?” It replaces handwriting and what have you. But it sounds like more recently there's been a big transition in the company. Tell me about the broader transition.From Documentation to Clinical Intelligence: Save Time, Save Money, Save LivesJanie [00:02:26]: So from a transition perspective, we really think about our journey as The first act was: how do we help save time? And that's where a lot of that original product was.Swyx [00:02:37]: By the way, one of those interesting statsSwyx [00:02:39]: On your landing page was, doctors spend time after hours.Janie [00:02:43]: They call it pajama time.Swyx [00:02:44]: Why is that pajama time?Janie [00:02:46]: Doctors after work in their pajamasSwyx [00:02:48]: In their pajamas. OhJanie [00:02:49]: At home are just writing and catching up on their notes every day.Janie [00:02:53]: Some of our favorite customer love stories, we have a Slack channel called Love Stories. We have clinicians telling us, “Abridge has helped us, from retiring early or we're now finally able toJanie [00:03:06]: go home and eat dinner with our kids for the first time.”Chai [00:03:08]: Save the marriage in some cases.Swyx [00:03:10]: One of the quotes was “We're not divorcing anymore.”Swyx [00:03:12]: I'm asking, “Why?”Swyx [00:03:14]: Because they're working too much.Janie [00:03:16]: But, in terms of where we're going and where we're expanding, we really think about our second and third acts around how do we help health systems save and make more money. Health systems are operating with record-low operating margins. It's getting harder and harder to serve patients and they have regulatory, some tailwinds but also a lot of headwinds coming their way and AI is ripe for helping on the saving and make-more-money piece. And then ultimately, how do we help save lives? The fact that our software and our product is open millions of times a week before, during and after a patient walks in the room, gives us massive opportunity with products like clinical decision support, which Chai is building but so many others to improve patient outcomes and probably one of the most important workflows and problems to be going after right now.From Glean to Healthcare: Context Is KingJacob [00:04:04]: One thing that's interesting, Chai, is you came over to Abridge from Glean and clinical decision support, which for our listeners is, in the context of a visit, helping a doctor figure out the right type of care. It's really a search problem in many ways, going through lots of different data sources. Very analogous to your previous role as one of the earliest engineers over at Glean. I'm sure a lot of our listeners are curious what's similar about the problems that you're going after now and what feels different, now that you're in healthcare.Chai [00:04:33]: Very similar. Taking a step back, with every wave, there's a lot of very similar patterns that happen across different products. A lot of social networking products look the same. A lot of credit-based products look the same. And we're seeing that very similar in the agent era with many companies, of course, in Redpoint's portfolio and so forth. And the key insight between both companies is that you have amazing models but context is king. Context is what puts them to work. So I see it in a lot of ways, a lot of similarities in this is a healthcare-coded version of Glean but the differences are really interesting. A couple things that come to mind. First and foremost, the rigor of the setting we're in. The downside risk is extremely high here in healthcare. It can be fatal in some cases. You prescribe something that the patient is allergic to for example. Whereas at Glean, it's “Oh, you got the question wrong.” It wasn't the end of the world in most cases. And so what does that mean? That shapes our evaluation strategy, both offline evaluation, progressive rollout and there's a lot more we could go into there. Second thing that comes to mind is, vertical versus horizontal. In both cases, there's a large variance but when Glean is, it's a much more horizontal company, there's a variance of personas, companies that you're working with. We also have a variance of personas, different types of specialties, different hospital systems. But the variance is a little more narrow. So from a product perspective, you're able to focus far more, especially when you have a maturing technology and you're building new products that never existed before. It lets you go after them much more easily and especially in healthcare where so many problems were solved with labor and process, that it's extremely ripe for AI to keep helping augment and enable. And the final thing that's really interesting, Abridge specifically compared to many other companies in the AI area, is the modality we started with where we're ambient and we're always listening in the background. And many more AI products will go that way but it's how we started. And that's the greatest form of AI we can create, AI that's seamless. You're not looking at your screen. It's always there. It's always helping you out and being proactive. The Jarvis vision that, every hackathon I went to over the past decade, there was always a Jarvis competitor. But Abridge very much started from the opportunity and continues to go that way.Ambient AI and Alert Fatigue: When Should the Product Interrupt?Jacob [00:06:57]: One thing that is super interesting then from a product perspective is you have this always-on seamless in the background and then you have to decide when you break the wall almost and say, “Hey, clinician, you might not have thought about X,” or whatever it is that you want to do. And in healthcare traditionally there's been this idea of alert fatigue and a million pop-ups and then a doctor just ignores all of them. It's probably a pattern that a lot of builders are thinking through now. How do you think about the right way to intervene or to pop up in a doctor visit?Janie [00:07:26]: It's such a good question. Alerts are notorious in healthcare specifically. Over 90% of alerts are ignored. The first and most important thing is context is everything, as Chai alluded to and I also think about how do we go from being reactive alerting to really proactive intelligence at the point at which it matters most. One thing we like to say is we want our product to feel like air conditioning. It should be in the background just making things better and if there is something that has great clinical risk and we're acutely aware that intervening now and not later is incredibly important, we should decide to act. But if you think about proactive versus reactive, instead of alerting a clinician during a visit when they're with their patient having a pretty serious and sensitive conversation, how do we prep a clinician before they walk into the room with that patient? And so historically, clinicians might have to manually go through charts with a patient that they've had over the course of months or years and they'll try to suss out what are the things they should be doing. You can imagine a world with Abridge. We'll summarize all of the most recent context for you, tell you based on the reason for a visit the patient is coming in for the types of things you should be discussing. And so you're going into that conversation prepped rather than walking in cold to that patient visit and then having this product interrupt you five or 10 times throughout the visit. And there might be times where it's really important to interrupt. We have a product called Prior Authorization and so this is when you may go into a doctor's office with knee pain. They'll prescribe you an MRI and so many of us have had this experience before, where in four weeks you'll get a call saying, “Hey, Sean, that MRI that you were prescribed wasn't approved and why don't you come back in? We'll figure it out.” In a world with Abridge, we might choose to quietly but still alert a doctor in that visit. And alert is probably not even the word we would want to use. Before a patient leaves, we would want to tell the doctor, “Hey, Doctor, before Sean leaves, you should ask him, has he had physical therapy and has his pain lasted for more than six weeks? Because the Aetna plan that he's on in California requires six things. We've already confirmed four of them have been met ‘cause we have all the context. But these two last criteria, if you can address with Sean before he leaves the room, we could guarantee that your MRI is approved before you leave.” And so when you think about clinical usefulness, impact to the patient, there are instances in which if we can catch a doctor while the patient is still in the room, as we think about save time, save money, save lives, we get to check all of those boxes. But when doctors have 15 minutes between visits, we have to be really thoughtful about when it matters.Prior Authorization: Reducing Latency in CareChai [00:10:23]: There's this interesting product opportunity AI has is reducing latency in the world. For example, prior authorization is an example of where care gets delayed and so great AI can reduce that. And the problem with alerts before partially is a technical problem: the quality of your alerts really matters. They're going to get ignored if you get alerts that... Similarly in engineering, where they're noisy alerts that you can't act on. But if you can make really high-quality alerts with both the context, as Janie said, and really high-quality models, then you can create a whole other game.Janie [00:10:53]: And I really like that experience because it starts to tease apart, what makes this so hard and unique. One, to make that prior authorization example possible, think about all the data that you need to have. You need to integrate with the electronic health record to know all of the patient context. Do we have access to your previous labs, previous imaging? And then to match you and to know that you're on Aetna, we have to collect all of the different payer policies and they vary by state. Some of these payer policies live on websites. Some of them live in unstructured 50-page PDF files.Jacob [00:11:31]: I thought this episode wasJacob [00:11:31]: To make sure we didn't scare people from healthcare.Janie [00:11:34]: But when you think about the things that make it hard, it also gives you the moat.Janie [00:11:39]: And then the second is the AI and the model quality we need to be able to hang our hat on. And so the bar, similarly when I worked at Opendoor, I worked on pricing models. Every outlier wiped out the margins of 30 and so similarly here in healthcare, the bar for accuracy is so high. And then I'd say the last is workflow is everything. If insurance companies deploy AI, it typically happens too late and this is when you have the notorious comical examples of AI just fighting each other when it's too late. But if we can pull forward the use of both the AI but also the ability to solve problems when the patient's in the room, you can start to collapse what typically takes weeks or months after your visit, ideally down to minutes or real-time. And it's where healthcare is both very difficult but also extremely rewarding if you can crack it.Product Form Factors: Mobile, Desktop, In-Room Devices, and ARSwyx [00:12:36]: Just to get some baseline on the form factors, because I've seen some videos on your website and stuff. You guys talk a lot about ambient AI. Is it primarily on the phone? Is there any other form factor that people get Abridge in? Is there an Abridge room setup where it's always on? I don't know.Jacob [00:12:55]: An Abridge podcast studio.Janie [00:12:58]: Primary form factor is mobile and desktop. UsuallyJanie [00:13:00]: Clinicians are walking in and out of rooms with mobile but at the end of the day, when they're closing out their notes or wanting to prep for the day ahead, they might use desktop. We have been having a lot of really interesting partnership conversations with a lot of these in-room device companies as you think about the power of multimodality and even more data, as you think about all of what is not captured today. It is fascinating to think about, especially even as we go into building and scaling our nursing product. It's one where nurses constantly, as they're walking in to check in on a patient for two minutes or maybe even 30 seconds,Janie [00:13:43]: Starting an Abridge experience is probably going to take longer than the visit. And so what can we do with in-room devices that are always on starts to raise really interesting and fun product questions.Swyx [00:13:54]: I was thinking, the way in tech companies we have all these Google MeetSwyx [00:13:58]: And other things, we might as well set up entire rooms with just Abridge tech.Chai [00:14:02]: Very much. AR glasses and related form factors are also relevant: how do we bring the information to the clinician in real-time without a screen, while still letting them focus on the patient?Swyx [00:14:18]: Do you think they want that? I'm skeptical of AR, but I'm curious what you've tried.Chai [00:14:26]: Admittedly, it's not a near-term product roadmapChai [00:14:29]: By any means. I'm being far-fetched.Jacob [00:14:31]: There's some sick AR stuff for surgeries.Swyx [00:14:33]: Really?Jacob [00:14:33]: When people are trying to visualize, you're about to make an incision but you want to see, what the cut might look or what the body might look like inside and they can layer in imaging.Swyx [00:14:43]: That's cool.Chai [00:14:45]: At some point in the future.Janie [00:14:46]: But there are a lot of our largest customers and at the largest health systems integrating already and so even as we think about building into it, unlocks a lot of product capabilities.Swyx [00:14:57]: And just to establish the terminology. Sorry, and I know I'm asking basic questions somewhat for myself but also for the audience who might beHealth Systems, Buyers, Clinicians, Patients, and PayersSwyx [00:15:05]: Less integrated. When you say health systems, it's like the Johns Hopkins, the Kaiser Permanentes.Janie [00:15:09]: Mayos, the Kaisers of the world.Swyx [00:15:10]: These are your customers, right? And the outcome that you deliver for them is happier doctors, reduced cost of processing, reduced mistakes. It's weird in a sense that I feel like there's also, a secondary customer, the customer of the customer and I don't know if you — do you think about it that way?Janie [00:15:28]: The other interesting and complex part of building product is we have our buyers, who are the chief medical information officersJanie [00:15:39]: The chief financial officers, the CIOs of these large health systems. Our users today are clinicians but if you think about who downstream is impacted, it's patients. And so as we build, with every product in mind, we think about who we're building for, who the secondary user is and what does that mean either in terms of experience, security compliance, ROI that we have to make tangible. And so like you said, time savings is one of them. But for CFOs, they care a lot more than just time savings. We have to show for every dollar you put into Abridge, because you have more compliant documentation or because you have fewer queries coming from your billing team, we save or add real dollars to your bottom line or top line, are things that we're constantly thinking about because of the dynamic across all three sets of users.Chai [00:16:32]: There's a whole other axis too with the payers and pharmaChai [00:16:35]: as well. Connecting all these three big stakeholders in healthcare isSwyx [00:16:39]: Do the payers ever see your data? Sorry, the payers meaning the insurers, right?Chai [00:16:44]: Yes.Swyx [00:16:44]: They also see Abridge data?Chai [00:16:47]: NoSwyx [00:16:47]: Like the direct integration to you guysChai [00:16:48]: They wouldn't see the raw Abridge data but when you're working together on something like prior authorization, whatever information they need, we'd communicate to them.Jacob [00:16:59]: That's cool. I would love to dig into the AI side. You still have a lot of problems on the AI side. And so maybe to start at the highest level, what's one of the hardest problems you have to solve in AI at Abridge today?The Hardest AI Problems: Quality, Latency, and CostChai [00:17:11]: To make things simple, let's take, building off the prior auth example. So one thing Janie talked about is okay, this data is all over the place and there's this combinatorial explosion of procedures, payer policies and even sometimes different health systems. There can be some cross-product of all of these different considerations you have to take into account. But what's really hard about this problem is doing it real-time in the conversation. So, in any AI product, usually the three KPIs you care about are quality, latency and cost. Now, what we're saying is we want you to do this real-time in the conversation, guiding the clinician. How do we do it in a way that does not break the bank? But we're using — But we also need very intelligent models because you're working with this cross-product of data and this, all this context layer as well. So you need high intelligence and high-quality because you don't want the alert fatigue but you also need to be fast and cost-effective. And so that's where a lot of clever engineering goes. It's okay, without getting into all the details here, can you model these policies in some intermediate representation or other things that you can do that can make this problem tractable? And of course, the Pareto frontier is always changing but we are also trying to do this now.Model Strategy: Third-Party Models, Proprietary Data, and Medical ConversationsJacob [00:18:26]: What implications has that had for what you take off-the-shelf and say, “ what? We don't need to be world-class at X. We'll just take this from the model providers or from some infrastructure player,” and what you're “No, this is where we spend most of our time focused on”?Chai [00:18:38]: This is, the fun challenge in AI?Jacob [00:18:42]: It changes every three months? SoChai [00:18:42]: Of course, with the shifting landscape, we try to be extremely thoughtful on predicting the trends of where third-party models are going and where we can uniquely go. And, sometimes when you talk about AI models, we're the models are just going to get infinitely better. But I don't think... It may be in the grandness of time you could say that but, within every month, every quarter, there's specific ways they're getting better. They're training on a lot more, coding data to be better coding agents, for example. And soChai [00:19:14]: We have to think about where are the things that won't — unique data that we're uniquely training on or to step back a little, where is a proprietary model bringing advantage to us is if it can give higher quality or lower cost and latency for similar quality, very similar to many other companies. And when we can do that is when we have proprietary data. So, for example, we have on the order of eighty million or hundreds of millions now getting close to of medical conversations.Jacob [00:19:44]: It's insane.Chai [00:19:45]: This is a unique data set. And this data set, it's very interesting because this data set is effectively a large part of the trace between the patient and the provider. That's where the quote-unquote debugging happens in healthcare. We have these traces at scale, as in as, our CEOs even called it, an exhaust that comes out of our product. And so when you have these traces, that's how you can train better agents on certain use cases, whether it's your transcription diarization use cases or so on or like note generation models and we can do that much cheaper and faster. But we're always also working with these third-party model providers. We closely collaborate with them and that's how we predict where the trends are going. The thing that I think about a lot is that, I know that the model providers are going to train much more on agentic workflows and so forth, so that's great, so that you have a better agentic harness. But the other thing that's interesting is that the model providers, because a large class of the consumer model providers is healthcare queries, that they might, optimize to train a lot of healthcare data to encode the knowledge in its weights. And this is just a great thing for us as well, where the off-the-shelf models can keep bett-getting better at general healthcare information, such that what our strategy is, we have a constellation of models, we can use something for this, that and, we only care about, at the end of the day, the best product experience.EHR as File System: Agentic Workflows and Real-Time InterfacesJacob [00:21:07]: And, you have, overall capabilities improving. I'm curious, as these models get better, is there something you look at and you're “, three months ago, we really couldn't do that but God, the the latest models really allow us to do it”?Chai [00:21:19]: So here's something interesting that I've, been toying with. So all models are... This wasn't super obvious a year ago but now it's become clear and clear that almost every agent is a coding agent underneath the hood? So you give it whatever file system, it can write its own code and so forth. So when you think about within healthcare and the use case that we have, you can think of the EHR effectively like a file system. It's just — it's a storage of all this information. It's a lot of information there that cannot fit into the context window, at least of today's models and you want to use that context effectively for all these product use cases we're talking about. And so if you have better agents that can, manipulate data, read that data, treat it as a file system as we see they're going and we know model companies are investing this way, then that very directly benefits us.Swyx [00:22:09]: Yeah. Okay, cool. Again, just establishing basic things. But we're going back to the model stuff. I'm really interested in double-clicking more on the real-time, element, which is pretty important for both of you. Is it — Is real-time just batches of every one minute, every five minutes? Is that how we do it? Or is there some more native, genuinely real-time in the sense that OpenAI has a real-time API or Gemini has a real-time API?Chai [00:22:35]: Yeah. Yeah. So today it is more on the on the batch basis but there's interestingChai [00:22:41]: Prototypes that we have that we're still not fully, full time, voice in text out or in that sense. But, can you trigger your models, your agents or agentic workflows, depending on the right times in the conversation?Chai [00:22:58]: And so you can imagine, different techniques to bring this latency down and, you want to bring the feedback loop down as much as you can. And so a lot of clever engineering there without fully... Maybe one day we'll do full voice in and text out, train a model to do something like that.Swyx [00:23:15]: You do — People don't want voice in voice out?Chai [00:23:18]: Now we aren't creating experiences that are, during the conversation, inter — It's almost likeSwyx [00:23:25]: Might be too disruptiveChai [00:23:26]: Too disruptive until, who knows, maybe eventually you could have full voice agents once we — the quality and we improve the comfort of the technology. But right now gra — that change is much more gradual and it's more text focus, text out.Janie [00:23:42]: And so much of currently what our product is trying to do is allow a clinician to focus on their patient and maybe at some point but right now patients, clinicians don't want a third voice, at least in a literal voice in that room. And so how do we be there with all the contacts and information ready at hand when there's the right moment?Personalization: Individual Doctors, Specialties, and Health SystemsJacob [00:24:03]: Jenny, one thing I'm curious about is how you think about, personalization in the product. I imagine, every doctor is a special snowflake in their own way, has their own way they like to do things. There are probably a bunch of different approaches you could take to doing that, both within the model layer itself but then also just with clever prompting or engineering. How do youJacob [00:24:20]: Deliver on that?Janie [00:24:21]: It's such a good question. Personalization is massive for us. We think about personalization at three levels. The first is at the individual, the second is at the specialty level and then the third is at the health system or the organization level. To your point, there are a lot of individual preferences. You-When a note is produced, it almost is a reflection that is so deeply personal of a doctor's work and how they give care. And so do they have preferences on things like style? They might want bullets versus paragraphs, really concise versus comprehensive. They also might have phrases that they really like to use or the templates that they want every note to be structured. And, we see it in our feedback all the time. We want two spaces in between sentences or I refuse to use this tool. And so that's something that we've had to build in. And the tricky part is how do you make sure that stylistic preferences don't interrupt accuracy and quality and that's something that we've really had to refine and hone over time. Second is at the specialty level. A cardiologist note or workflow is going to look very different from a dermatologist workflow.Jacob [00:25:32]: I assume cardiology notes are the highest stakes for you guys, given your CEO is a cardiologist.Jacob [00:25:36]: It's “Oh my God, make sure we get this one.”Janie [00:25:37]: Shiv, our CEO, is still a practicing cardiologist. He rounds once a month. And so, first call when we want just quick and easy user feedback too.Janie [00:25:46]: But, specialties require a lot of personalization, both in terms of what does the product look and so we make sure that as new users onboard, we catch that and the product proportionally reflects that. But also on the back end, evals at the specialty level, they are hard-earned to calibrate and get. What does a really great dermatology note look like? What makes it complete? What makes it compliant and billable is very different than a primary care doctor. And so it's not just about what does the product experience look but on the back end tuning and really deepening our understanding for the specialists. What does great output look like? And that's, a problem that we need to calibrate internally, externally, online, offline but, takes lots of cycles but is necessary in a high-stakes environment. And then at the health system level, for products like clinical decision support, you have health systems who've spent years or decades refining their best practices and they want to know, “Hey, we love your clinical decision support product but how do we embed our own hospital guidelines into them to inform clinicians before, during or after a visit what brest — best practices should look like?” And as you think about, deepening moats as well, when health systems, trust us with that data, allow us to productize it and directly into the clinical workflow, makes us a really great partner to health systems who want to build something that truly meets their needs, their practicing guidelines.AI Slop, Memory, and Product Data FlywheelsChai [00:27:23]: And I want to add onto that. The for the clinical documentation problem, it's very similar to AI writing that doesn't feel like your own and then we call that slop. But the way I describe one framing of slop is like AI without context. But we have all that context and both the clinicians, can have it and can guide it. And so part of the other interesting exhaust for us is, memory is, one of these new systems recordsChai [00:27:49]: Almost.Janie [00:27:50]: And we also have all the edits people make on our product and when you think about a data flywheel and how we get better over time becomes really powerful as a mechanism to just going deeper in personalization.Jacob [00:28:04]: It's interesting. I love this idea of working with systems on the guidelines they built up over a long time. I feel like so many of the best AI app companies today are... The question is: How do you take the expertise that a law firm or a bank has built up over many years and then add that as context and also a special sauce over, a an AI tool? And so seems like y'all are really doing that very effectively.Janie [00:28:24]: We're now starting to have our customers ask, “What are other customers doing?”Janie [00:28:28]: “And how are they doing it?”Janie [00:28:30]: And as we think about having visibility across such a large set of care being delivered right now, a really interesting place we could also partner.Swyx [00:28:40]: I'm just curious. I — This may be a nothing question but, how different are health system guidelines from each other? Don't they all converge to the same thing? And if not, where do they differ?Chai [00:28:52]: At a really high level, they're going to talk about very similar things but the difference is probably in some more of the details. “Oh, you should refer to specialists only when XYZ conditions are met,” or so forth and maybe different organizations have different practices and guidelines around that. But high level, talking about similar things but the details are what, of course, that shapes the context and the decisions you make.Swyx [00:29:15]: And this all goes into the context engine and it might affect the notes but maybe not.Chai [00:29:21]: The — For these local pathways, we're definitely thinking about it a little more for our clinical decision support product.Chai [00:29:26]: So yeah.Swyx [00:29:27]: Which is your stuff, yeah.Swyx [00:29:28]: And then the memory which you raised, let's just tell us more about that. What have you tried in memory? What's the structure of the memory? What works? What doesn't work?Chai [00:29:38]: There's, of course, many different ways you could do memory, where it's okay, can you bake it into the model weights or can you do it in some external store? For us, what's interesting is, of course, when you think the models are rapidly changing, whether it's in-house or third-party, baking into the model weights, sometimes you worry that it could be a little throwaway. And so, how do you... You need to find a way that you decompose the problem, the preferences from the underlying models and so forth. The thing we're right now most both that's easiest to start with and we're excited about is having, a separate store for memory, where you have, for example, a memory sub-agent that's, working in the background, figuring out what are the important parts of the clinician's actions that we want to remember for the long term. And then you can also imagine, other things where in the — you have background jobs that are running that are collating these, memories similar to Sleep, of course and what other pattern, patterns products do as well. Learning over all these action, all the action data we have, again, note edits, the conversations they did and the actual transcripts.Evals: LFD, LLM Judges, and Clinical SafetyJacob [00:30:40]: What about evals? How in the world do you... It is such a complex product surface area. We would love to hear you riff on that and also how has that evolved? I'm sure you've gotten better at it, so any learnings along the way.Janie [00:30:50]: From an evals perspective, we, from day one when we build any new product or feature, we think about, what does good look like? And there are table stakes things like clinical safety but then you start to get deeper into what does good quality look like. And when you go into something like our core product, there's stuff like style and completeness and there's things like does this note become something that can be billable, which is very high stakes for a health system. We have a number of ways in which we get confidence for this. We have, internal in-house clinicians who do what we call an LFD process to give us our very first pass at is this or isn't this a good enough output, look at the effing data.Jacob [00:31:41]: LFD?Chai [00:31:42]: That's why I was smiling. I was “Is Janie going to mention what it stands for?”Jacob [00:31:46]: I was not... There's like a million acronyms.Jacob [00:31:48]: How am I supposed to know that I don't? So “Oh yeah, of course, an LFD.”Swyx [00:31:51]: I've never heard of LFDs.Chai [00:31:53]: It's a bridge for sure.Janie [00:31:55]: I got through three days and then I had to ask someone.Janie [00:31:58]: I thought it was just me that didn't knowJanie [00:32:01]: It's our internal process.Swyx [00:32:02]: But look at the data as a meme in ML, ‘cause you tend to not look at it. You just want to look at number go up.Chai [00:32:06]: Exactly.Swyx [00:32:07]: But yes.Janie [00:32:08]: But so, we make sure we look at the data and then as we think about all of the components of good output, we, one, create LLM judges across all of these and we make sure with annotated data and either internal or external evaluators, we feel like these judges are calibrated. And then depending on the stakes, we also work with in-house and third-party evaluators across all of these before we ship any big change. And the goal is, in terms of evolution, how do you go from this process taking months, down to weeks, down to days? Some of it is, a true science and ML problem. A lot of it's also just, hard operational work. Have you planned ahead in terms of what you need? Have you really optimized the capacity that you need across all of the different specialties you need? Have you gotten a really good sense of which third parties are great to work with for what use cases? This takes a lot of domain, expertise and, lots of mistakes and errors in figuring that out. And so as much of it is an ML problem, so much of it has also been operational gains that are hugely important, where domain-specific expertise is everything.Specialty-Level Evaluation and Progressive RolloutsJacob [00:33:23]: But it's funny, ‘cause I feel like people talk about healthcare like it's one giant market and the reality isJacob [00:33:26]: It's, dozens and dozens of sub-markets. And so it feels like in your evals you have to build that up across the board, probably.Swyx [00:33:34]: And is specialization the primary cardinality at... That's the word that comes to mind.Janie [00:33:40]: Sometimes, depending on the product or the use case. And so if we're making a note improvement or feature for a particular specialty, definitely but we have products that are for nurses. We have products that, are really aimed at making the document or the output a lot more billable. And so we'll want to work with coding teams and not necessary clinicians. And so likeJacob [00:34:05]: Coding meaning healthcare coding.Janie [00:34:06]: Yes. Yes.Jacob [00:34:07]: NotChai [00:34:07]: Yes. I see you.Swyx [00:34:07]: Other kinds.Janie [00:34:09]: But is this output proportional to the work that was delivered? Is there sufficient documentation to justify the amount that a health system may end up charging? And so, specialty sometimes but also domain, very different across all of the different products that we're working for. And building out that network is, not easy and is where a lot of our operational investments have gone into.Chai [00:34:35]: And I view a lot of analogies to self-driving cars here, where, part of it is we really want progressive rollout of features to test in the real world is this useful? Is this going to work? One big difference compared to past lives is before I'd build a product, maybe I'd alpha it and then I'd like GA it the next week, ‘cause I'm “Go, move fast, ship,” and whatnot. But the mentality is like you... I want to make contact with the reality as quick as possible but I want a progressive rollout. Because as much as I get as large of an offline eval set, I want the distribution of that to match real-life distribution. And over time, by rolling out early, similar to Waymo has a tagline, “The world's most experienced driver,” another thing that can, at least linearly increase for us is, both the size of our evaluation offline and online, that and it all feeds back.Janie [00:35:25]: Something that's been earned over time, speaking of evolution, is just the trust we've gotten with customers. Historically, a lot of these health systems, when they bring on new vendors, their release cycles are quarters, sometimes twice a year. We've gotten our customers onto monthly release cycles, which is pretty fast for health systems but what is more exciting over the last, call it, few quarters, has been, a subset of our customers have said, “We want to innovate with you. We trust you,” and we have a pretty, decent chunk of our customers who say, “We'll develop with you outside of these monthly release cycles. We have a higher tolerance. We know that the stakes are very high but we want to be the first ones using these products, giving you feedback.” And so for a pretty substantial set of our customers, we've been able to convince them to be able to ship, in this gradual way before GA. Something we talk about a lot internally is, trust is earned in drops, earned in buckets and so we still can't do what I used to do when I worked at Loom. We had 30 million users. I'd just be, rolling out experiments left and. The bar is still quite high for iterative rollout but because of the trust we've earned, we're able to learn at pretty high volume very quickly.Privacy, HIPAA, and De-IdentificationSwyx [00:36:45]: Your scale is still pretty huge.Swyx [00:36:47]: One thing I want to... We were going to go into scale? In a sec. One thing I wanted to call up, follow up on evals, which, again, just coming from a generalist engineer point of view, just thinking through what would people be scared of in doing this, the privacy and HIPAAJacob [00:37:00]: Elements of this. I have zero experience in that. What do you have to do? What is surprisingly not that bad?Chai [00:37:06]: So one thing that's really important here from a compliance perspective is very much that any of the data we use needs to be de-identified, any real-world data we use as a basis of online eval sets we're learning from. And so you have to — And there's, very clear, government guidelines, what counts as PHI. And so we've even have built models that can take, for example, a clinical transcript and remove all the key PHI indicators and so you have a scrubbed/de-identified version. And then once you... And so one thing that's important is first you've got to get confidence in that model in the first place? And prove that out. Because, now you have, multiple probabilistic systems on top of each other.Chai [00:37:46]: But once you have that, then you can train on it use it for evaluation and so forth, provided one of the cool things also that you can do from a business side is the right data contracting as well with your partners.Jacob [00:37:57]: Is the anonymization one way? Once it's done, you cannot undo it? Or is there someoneChai [00:38:01]: YesJacob [00:38:02]: Who holds the master key that can... Yeah, okay. So it's one way.Chai [00:38:05]: It's one way. Yeah.Jacob [00:38:06]: That's how it works. I just wanted to... Because, there's a lot of this, learning from feedback and everything that, you would want to debug more but you can't because you just physically don't allow yourself to.Janie [00:38:17]: Some of it's also written in our customer contracts in terms of who can or can't access PHI data, how long do we retain it,Jacob [00:38:27]: Very goodJanie [00:38:27]: Before it gets de-identified. And so we have a pretty high bar for who can access that PHI data, just to make sure that we always respect our customer data and privacy. But that's something that we partner with our customers on too, to make sure that as we want full, as close to precision as possible in that qualityJanie [00:38:48]: We can still use it.Jacob [00:38:50]: But it'll be fascinating to see how that space evolves? Because you think about, I used to work at a company that, did a lot of healthcare data in the cancer space and if you asked, the average cancer patient, “Hey, do you want people, do you want other patients to be able to learn-”Chai [00:39:03]: Take it.Jacob [00:39:03]: “... Learn from your experience?”Chai [00:39:04]: Take it all.Jacob [00:39:05]: They're “Please.”Jacob [00:39:06]: “I'd love, nothing more than for other people to be able to learn fromJacob [00:39:10]: The experience that I had.” And so in the past it was a lot harder to do that learning. But with this technology, that might really be practical and so it'll be fascinating to see how that continues to evolve.Chai [00:39:21]: There's so much in our data set of 100 million conversations.Chai [00:39:26]: You can imagine things like insights that you can give to the clinician. How could you, oh, how could you have reacted to this? In coaching or insights around, which treatments are effective or, like... Because you have this, again, this data source that was never captured before but that's, where, intuition or experience is created from, going back to this idea that the conversation is the agent of truth.Operating at Scale: Reliability, Cost, and Token EfficiencyJacob [00:39:46]: Back to the 100 million conversations, I feel like you have this insane scale that maybe only a few other AI app companies have and everyone else dreams of. So not everyone has had to confront this yet but maybe just talk about some of the challenges of operating at that scale and what, our listeners have to look forward to if they ever get to this level of scale.Chai [00:40:05]: At large and larger in scale, so of course there's a general, infrastructure reliability. When you... In any given startup, you're building the plane while it's flying. So there's some notion of that. But what gets interesting on the AI and ML side for sure is this, as you get at more and more scale, so one, you have the data to first and foremost do this. But, you start thinking about costs or infrastructure in a whole different way at scale versus, a prototype.Chai [00:40:34]: You can use the most expensive model, you can burn as many tokens as you want but when you're doing 100 million conversationsJacob [00:40:41]: Token max on leaderboards are less upsetting than that context.Chai [00:40:45]: . When you're doing that and so that comes for we have the data and we also have the team that's able to post-train based on this and you can optimize for efficiency, especially in areas where you believe that maybe a lot of the quality headroom is less so and you don't expect the other off-the-shelf models to go that way, such that you want to do, efficiency maximization, in terms of compute and tokens.Jacob [00:41:08]: I feel like you guys live in the future in some way where most use cases today are really just in use case discovery mode, where it's “God, I really hope I can find something that can get to scale,” and so you're always going to use the most powerful model. And then the few things that do get to this level of scale, you start to do those optimizations.Chai [00:41:22]: It's a natural trajectory where it's like zero-to-one, we're not talking about any of these optimizations.Chai [00:41:26]: But when maybe we're in the one-to-100 or so forth, then we're in optimization mode and, what works out really well is you've got all this data from zero-to-one that lets you do this.What Comes Next: The Conversation as the Shared Healthcare PlatformJacob [00:41:36]: That's fascinating. I feel like one thing that's so interesting about the Abridge footprint is that you're in the doctor-patient visit in real-time. I always like to say, there's like probably 50 years' worth of product you could build on top of that. What gets each of you, I don't know, what are you most excited about building, either in the short term or medium term or even, long down the line?Janie [00:41:53]: Something that I get really excited about is that the same conversation can serve so many stakeholders. If you think about the conversation, a doctor needs to know what is the documentation, how do I make sure that this fully represent the care I gave? A patient needs to know, “What the heck just happened? This was really overwhelming. What are my next steps?” A payer needs to know, was this the proper and appropriate care given? A pharma company might want to know why isn't this drug being properly used or is there a good candidate for this clinical trial that I'm about to run? And where I get excited is that our product and our platform and our infrastructure can be the same product across all of those things and start to what's today, separate, very expensive, complex systems that serve each one of these stakeholders in very different ways, start to collapse all of that into a singular platform that enables not just more efficiency across the board but also better outcomes for everyone. And, all of us experience healthcare in probably very painful ways and knowing that there is a world in which we can simplify a lot is really exciting to me and it all starts with the conversation.Chai [00:43:15]: It's interesting. Of it very similar to going back to the KPIs that any AI product cares about. How do you increase quality of care? How do you reduce latency to care? And how do you reduce costs? Which is a huge, in healthcareJacob [00:43:28]: They call it the triple aim in healthcare.Chai [00:43:30]: But very similar to building AI products and the thing that really excites me is when we talk about that latency piece, we talked about one example earlier of prior authorization, can you reduce the latency to care? But you can imagine so much more. Oh, as soon as the lab value gets updated, do you have like a background agent that, kicks off and uses all the context to be “Oh, hey, the patient should do this next,” for example. And of flagging that to the clinician who's always in the loop but reducing that latency, to care. And then you can imagine this is much further down the road but it's like even connecting that to the direct patient and the consumer. And so how can you, how can you build a bridge to all of these things?EHR Partnerships and the Clinical Intelligence LayerJacob [00:44:10]: Very cool. The connections piece is just an ever-growing thing. And one of the key partners is the EHR and I wonder what that relationship is like. Will they, look at this as, something that is valuable enough that they want to own someday?Janie [00:44:29]: Our partnerships with the EHR is, we know that we have to be extremely close partners with all the EHRs who we partner with. Being able to not only pull and push all of the data into the right places is, not only table stakes, if we can't do that, health systems don't want to use us. The second and the reality of today is clinicians spend a lot of their days in the EHR. So much of what allowed us to win in the largest health systems was pretty direct and, very close partnerships with some of the largest electronic health records that allowed us to pull and push data with APIs that weren't ready out of the box. And clinicians want to save clicks. Anytime we introduce a new product that, adds two clicks for them in their day, they're “We're not going to use it.”Janie [00:45:21]: They have 15-minute back-to-back appointments with their patients. They're spending, hours during pajama time doing documentation. Every second and every minute counts and so we really think about being deeply integrated into the EHR as also table stakes to getting real usage and adoption. And anything that we build or introduce, we really talk about earn the right internally a lot, which is we have to provide so much value or save so much time that people will use us. But those are the two things that are close to us, is we know that the product won't be used unless it is deeply interoperable.Chai [00:46:01]: And strategically, to your point, it's like what does EHR want to own versus us? EHRs are really focused on the clinical workflows and so forth but some of the things that we're talking about here, I do these traditionally are outside of the domain where it's oh, connecting pairs and providers together with provider policies or the clinical trial matching, as Janie brought up. And so these are, entirely — we position ourselves as building this entirely new intelligence, clinical intelligence layer across, again, providers, pharma and, payers.Chai [00:46:33]: And so that's a it's a whole different ballgame that we try to playChai [00:46:36]: In combination with them.Jacob [00:46:37]: But it's like a different layer of scope.Healthcare AI Regulation, Technical Depth, and What Changed Their MindsJacob [00:46:39]: I'm curious, you are both relatively newcomers to healthcare. People have these, there's lots of futuristic healthcare AI takes of “Oh, everything will look different.”, now that you've been in healthcare for a bit, you live at the edge of AI, what have you, changed your mind on around this, as you think about what healthcare looks like in ten, 20 years? Any updates to your mental model from the time being close to the problems?Chai [00:47:02]: One thing that IChai [00:47:04]: Was hesitant about before and it's a common thing when I'm trying to recruit engineers that people ask me around, is definitely oh, healthcare, heavily regulated space. And it is, rightfully so. You want to keep, the patients at the end of the day safe. But one of the interesting things that, is a that surprised me how much it is coming to the company is there's a lot of really favorable regulatory tailwinds as well. Where you think about, government really wants interoperability between all these systems that we talked about and so agents can access this information. The government just in January, the FDA released updated guidance on clinical decision support, what I work on in such a way that they used to have guidance from like 2022 that required you to have, mention all these options and do all these other things but it's a very forward and forward-looking way. And so for me, what's been really cool to work on is this, there's this very special moment both in AI in general, we all know that but there's a special moment also regulatory in healthcare as well.Janie [00:48:05]: One thing I would call out is for the very reasons things are higher stakes or, potentially considered more difficult in healthcare, it's where some of the hardest AI problems will get solved first, just because the bar is so high. When I first joined, I was “Oh, this is where we'll be on the tail end of where, all of the AI innovation will be able to be applied.” But when you think about, zero error evals or multi-step workflows that have really low tolerance, a lot of the innovation will happen here just because we have to or else we can't ship.Jacob [00:48:42]: ‘Cause like in other domains, you'd much rather just solve the 80%-is-good-enough problems firstJanie [00:48:46]: 80/20 doesn't work hereChai [00:48:48]: And building off that, traditionally, there was a bit of stigma that, oh, healthcare companies are not that interesting from a technical perspective or I've seen that or faced that myself. But these are really hard and fun problems from a pure technical perspective beyond just the impact. How do you bring the latency of this thing down and make it really high-quality?Reducing Latency: Clinical Workflows, Agents, and Implementation RealityJacob [00:49:07]: How do you bring the latency of things down?Chai [00:49:10]: Yeah. Yeah. Yeah. So okay, let's answer the latency question. And maybe hopefully not too redundant with some of the things I've said earlier but some part of it is with any latency, you have to like what is, what is really your bottleneck. In a lot of workflows, it's sometimes it's the model itself. And so that's where like our data flywheel, our post-training team and so forth come in so that can you make the models far more efficient. So that's one aspect of latency. But there's whole other aspects of latency where it's okay, on top of that, if you use a constellation of different models, can you use — can you first use like a — it's like thinking fast and slow. Can you use a cheap, fast model that triages and hands it off to a larger model where you get more intelligence and so forth and so all theseChai [00:49:56]: Clever tricks to make it work.Chai [00:49:58]: And by the way, we are totally — we also realize that the parameter frontier is changing and so these tricks will — may not get us to where we want to be in five years but we need to if we want to build a useful product right now.Jacob [00:50:11]: Should we go to the quick-fire or you want to ask more about Abridge? We can stuff everything that's not Abridge into the quick-fireSwyx [00:50:16]: I don't mind. I was — I feel like Janie was on the topic of more long tail stuff, which isSwyx [00:50:21]: Not the eighty/twenty thing and that really matters. And I'll —, if you have any tips or cool stories or just general approaches that have worked for you that's interesting to dig into.Janie [00:50:32]: One of them is even just how we staff our teams looks different than a traditional software engineering team, I'd say.Swyx [00:50:40]: Let's go.Clinician Scientists, Edge Cases, and Evals at ScaleJanie [00:50:41]: We have a bunch of folks with different roles who are clinicians and so we have this role called the clinician scientist and I heard one of our leaders refer to them as mutants recently. But they are people who've had clinical backgrounds, so MDs typically, who are also deeply technical, somewhere, on the spectrum of like a full stack engineer all the way to like extremely scrappy prompter. But having each of these people embedded within our teams instantly raises the bar for everything that we build because not only are they determining, is this product clinically useful but they're deeply embedded in our whole evals process. And so when we talk about LFDs, when we talk about what is our actual evaluation criteria, you don't want Chai or me creating what those are because we don't have clinical background. But is probably unique to Abridge but has been game changing. And when you think about where the puck is going, you have people build with clinical backgrounds who are technical and where AI tools are going, they just becomeJanie [00:51:53]: More and more, critical and like the killers of the team. And so that's one. And then the second is just the scale at which we do evals to catch that long tail up front before anything ever gets into production is something that we've pretty much like really started to fine-tune, both from a scale but when do we know we need to get several hundred versus several thousand offline responses, what helps us make that quick decision and make this less of an art and as much of a science as possible. But that's also been something we've had to tune over time.Swyx [00:52:27]: And you have partners who opted in to give you those evals.Janie [00:52:31]: So we work either internally or with third-party for offline evals and then we have customers who also agree to give us, whether it's like thumbs up, thumbs down to like choose this or that, a lot of data to get us to what is as close to fully confident as possible.Swyx [00:52:51]: The term that comes to mind isSwyx [00:52:53]: Like active learning on things where you're weak. I feel like it's a lost artSwyx [00:52:58]: Is a lot of the polish that comes into doing something like this.Janie [00:53:02]: Really.Chai [00:53:03]: Hundred percent.Lessons from Glean: Technical Foundations and AI App InfrastructureJacob [00:53:04]: Maybe, on a totally unrelated note, Chai, you had a very, storied run at Glean b

Atlassian connected its AI agents to a richer layer of company knowledge (documents, projects, goals, people) and measured a 44% improvement in answer accuracy using 48% fewer resources. Same models. Different information. Brian Armstrong restructured Coinbase the same week: 14% headcount cut, five management layers maximum. When AI can surface what previously required institutional memory and senior tenure, the organizational layers built around that knowledge become harder to justify.The visible shift gets covered in tech headlines. What gets lost in the announcement energy: none of this works if the company hasn't decided what it wants AI to do.The more widespread barrier is upstream of governance. Most executives approving AI budgets are working through the aftermath of pilots that underdelivered, first-generation deployments that didn't survive contact with their actual data, and early model results that left skepticism the current tools have since substantially outrun. That trust deficit — organizations evaluating new AI investment based on experiences two generations old — is where enterprise AI projects most commonly stall. Shadow AI governance and deployment intent are real risks, but they're downstream of that harder problem. There is no closing the capability gap inside an organization that is quietly waiting for the next deployment to fail too.John Willis co-wrote The DevOps Handbook because software teams were shipping code fast without feedback loops or governance. He sees the same pattern repeating with AI — and he spent five decades documenting what happens when the gap between vendor promises and operational reality gets this wide.* Why shadow AI is more dangerous than an outright ban* Why throughput without governance means instability at scale* Why governance creates flow instead of stopping it* Why most teams have ML evaluation tools when they need audit trails* Why even a five-person startup needs digitally signed records of agent decisions* What AI winters teach us about where we actually are nowListen: Spotify | Apple PodcastsRikki Singh leads product innovation at Twilio — what the company calls its biggest launch in 17 years. Before Twilio she was at McKinsey, where she co-authored the definitive research on what makes a great PM. The Qualtrics 2026 CX Trends Report found nearly 1 in 5 consumers who used AI customer service saw zero benefit. That number is the benchmark she is working against.* Why most AI CX is still FAQ automation with better packaging* Why the LLM wrapper creates false confidence — the model generates strings, it is not thinking* Vitamins vs painkillers: how to parse what customers don't say out loud* How to protect long-horizon bets inside a public company* Why the brand owns the accountability when AI gets a high-stakes interaction wrongListen: Spotify | Apple Podcasts

1. Both Men and Women Are Created in the Image of God. 2. What Is the Structure of Genesis? 3. What Can we Glean from the First Toledoth? 4. The Divine Breath Is what Separates Humans from the Other Animals. 5. God Breathed His Divine Breath into a Creature Only Once. 6. At Creation Man and Woman Were Equal but with Different Responsibilities. 7. Several Points Can Be Made from Genesis 2:24-25. 7.1. Marriage Divorces Other Relationships to Establish a New One. 7.2. The Act of Marriage Creates a Bond that Unites Two Persons into One. 8. The Fall Changed Everything. 9. The Curses Are Still with us.

Patricia Montesi didn't start her career in payments, she started it in car rental. After nine years at Alamo and National Rent-A-Car, she was recruited into fintech with zero industry experience. That outsider perspective became her edge, and she never let go of it. Today, she's the CEO and co-founder of Qolo, a payments infrastructure platform that combines card issuing, money movement, and a bank-grade ledger on a single API-first stack.What We CoveredHow nine years in car rental shaped Patricia's outsider approach to paymentsGetting recruited into Wild Card Systems with no payments background, and why that fresh lens became an advantageThe fragmentation problem at the heart of payments infrastructure and why point products create hidden complexityQolo's three-product suite: Quantum Ledger, Qascade money movement, and Qinetic card issuingWhy Qolo isn't quite a side core, it overlays and integrates with existing bank cores rather than running in parallelRail agnosticism and why Qolo still supports checks in 2026The dual go-to-market: commercial banks and B2B fintechs, same platform, different vernacularHow the Synapse collapse changed the ledger conversation for banks and fintechs alikeWinning KeyBank in a competitive RFP against much larger players, and launching virtual account management in nine monthsHow banks are using Qolo to protect commercial deposits from modern non-bank competitorsAI inside Qolo: from Glean to Claude, and their internal "Turning Hours into Minutes" program130% year-over-year growth and 142% net revenue retentionKey TakeawaysThe moat problem: Patricia set out to build a company where customers stay because of the value delivered, not because switching is too painful. That philosophy shaped every product decision at Qolo.Ledger first: Most point-product fintechs have basic ledgers that only support one rail. Qolo's bank-grade dual-entry forward-posting ledger underpins every rail, making reconciliation and real-time money visibility a solved problem rather than a vendor management challenge.Synapse's legacy: The debacle forced banks and fintechs alike to ask harder questions about who actually owns the ledger and where money sits at any given moment. Qolo had been making that argument for years before the market was ready to hear it.Bank as distribution: KeyBank and Huntington aren't just clients — they're strategic investors using Qolo to defend their commercial deposit base against modern non-bank alternatives.About Patricia MontesiPatricia Montesi is CEO and co-founder of Qolo, a payments infrastructure company she built from the ground up after more than 20 years in the industry. She started her career at Alamo and National Rent-A-Car before being recruited into fintech with zero payments background — an outsider perspective she has held onto ever since. At Qolo, she and her team built the ledger, money movement, and card issuing stack as first-party infrastructure, without relying on third-party processors underneath.Connect with Fintech One-on-One:Tweet me @PeterRentonConnect with me on LinkedInFind previous Fintech One-on-One episodes

Season 10, Episode 11: Your Best Meeting Ever with Rebecca Hinds, PhD In this episode, we welcome Rebecca Hinds, PhD, to discuss her new book, Your Best Meeting Ever, and how teams can rethink the role of meetings in today's evolving workplace. Drawing on her background in organizational behavior and research at Asana and Glean, Rebecca shares how meetings can be treated as products—designed with intention, structure, and purpose. The conversation explores practical strategies for busy, multidisciplinary oncology teams, including how to embrace meeting minimalism, create stronger starts, and ensure every participant is an active stakeholder rather than a passive attendee. Rebecca also introduces the concept of calm technology and discusses how AI can support more effective, focused collaboration across healthcare teams. Learn more about Rebecca and her work at: https://www.rebeccahinds.com/ This episode offers actionable insights to help oncology professionals streamline communication, improve team engagement, and make meetings more meaningful in delivering patient-centered care.  

Send us Fan MailWe talk constantly about the future of work — AI agents, automation, leaner teams, productivity gains.But what if the real drag on performance isn't technology — it's coordination?Unproductive and unnecessary meetings cost companies up to $1.4 trillion every year. Seventy-one percent of senior leaders say meetings are inefficient. The average knowledge worker now spends around 11 hours a week in meetings. And nearly half admit to faking excuses to avoid them.This isn't a scheduling issue.It's a systems issue.Dr. Rebecca Hinds — founder of the Work Innovation Lab at Asana, the Work AI Institute at Glean, and author of YOUR BEST MEETING EVER: 7 Principles for Designing Meetings That Get Things Done — argues that meetings are organizational “junk drawers.” Instead of asking whether a meeting is necessary, companies simply default to adding another recurring invite.Her solution is radical in its simplicity: treat meetings like products.Define the user. Clarify the outcome. Design the experience. Measure performance. Iterate.In this episode, we zoom out beyond tactics and ask deeper questions:Why are humans so inefficient at coordinating with one another? What do broken meetings reveal about incentives, trust, and accountability? Does AI meaningfully solve meeting dysfunction — or simply automate it? And in a world pushing toward automation, what is the human role in collaboration?If coordination is broken, no productivity tool can save us.And if meetings are the canary in the coal mine, we should probably pay attention.

Ben Rice is at the heart of the Brooklyn music scene, making records at his studio, Degraw Sound in Gowanus, since 2012. He's since worked with legends like Valerie June, The National, Joan Osborne, The Candles, and Northern Soul greats The Flirtations, earning him an Americana Producer of the Year nom along the way.What I love about Ben is that nothing about him is in a rush. This is a very chill podcast episode. I literally felt me blood pressure fall as we recorded this. That's part of the magic here!He was mentored by Eddie Kramer. He runs his sessions through a Trident console that used to belong to James Iha. He's been part of the indie rock revival, and he's working every day on new music, quietly making some of the best-sounding records in the city.We talk about the long arc from basement sessions to a room of his own, what it actually takes to build a studio that lasts, and why "calm and thorough" is underrated as a production philosophy.For 30% off your first year of DistroKid to share your music with the world click ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠DistroKid.com/vip/lovemusicmore⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

(00:00) Mark Dondero, Andrew Callahan & Tyler Milliken - LIVE from the Fenway Cask N Flagon on Patriots' Day - are in for Zolak & Bertrand. Jordan Schultz floats out the news that Kayshon Boutte is being shopped around. The guys elaborate on the Patriots' WR room.(11:40) Is there anything the Celtics can do to impress you in the first round versus the 76ers? The fellas discuss.(21:57) The guys react to the Red Sox-Tigers game live during Patriots' Day. Should the Red Sox shop around Jarren Duran?(31:42) Today's TakeawaysSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI vendors overuse "agentic" without explaining real business value. Chris O'Neill, CEO of GrowthLoop, brings decades of scaling experience from Google Canada ($500M to $2B) and launching Glean to $7.2B valuation. He shares how to bypass lengthy proof-of-concept cycles by moving customers directly into production within 24 hours. O'Neill discusses building composable CDPs that automate marketing cycles and create compounding growth engines through intelligent data activation.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI vendors overuse "agentic" without explaining real business value. Chris O'Neill, CEO of GrowthLoop, brings decades of scaling experience from Google Canada ($500M to $2B) and launching Glean to $7.2B valuation. He shares how to bypass lengthy proof-of-concept cycles by moving customers directly into production within 24 hours. O'Neill discusses building composable CDPs that automate marketing cycles and create compounding growth engines through intelligent data activation.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Most marketers claim to be data-driven but lack the infrastructure to act on insights in real-time. Chris O'Neill, CEO of GrowthLoop, brings experience scaling Google Canada from $500M to $2B and launching Glean to a $7.2B valuation. He explains how agentic AI learns from customer data to automate marketing cycles across channels and discusses rapid deployment strategies that bypass traditional six-week proof-of-concept timelines. O'Neill also shares how composable CDPs create compounding growth engines that iterate based on real-time performance insights.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Most marketers claim to be data-driven but lack the infrastructure to act on insights in real-time. Chris O'Neill, CEO of GrowthLoop, brings experience scaling Google Canada from $500M to $2B and launching Glean to a $7.2B valuation. He explains how agentic AI learns from customer data to automate marketing cycles across channels and discusses rapid deployment strategies that bypass traditional six-week proof-of-concept timelines. O'Neill also shares how composable CDPs create compounding growth engines that iterate based on real-time performance insights.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Marketing teams struggle with AI workflow implementation at scale. Chris O'Neill, CEO of GrowthLoop, brings experience scaling Google Canada from $500M to $2B and launching Glean to a $7.2B valuation. He demonstrates using Claude for automated investor updates and building custom applications that convert newsletters into podcast feeds through transcription and RSS automation. The discussion covers agentic AI systems that learn from data patterns and activate across marketing channels with real-time performance optimization.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Marketing teams struggle with AI workflow implementation at scale. Chris O'Neill, CEO of GrowthLoop, brings experience scaling Google Canada from $500M to $2B and launching Glean to a $7.2B valuation. He demonstrates using Claude for automated investor updates and building custom applications that convert newsletters into podcast feeds through transcription and RSS automation. The discussion covers agentic AI systems that learn from data patterns and activate across marketing channels with real-time performance optimization.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Most leaders know meetings are broken. What's harder to admit is that we keep reinforcing the system that makes them that way.Calendars are full. Teams are exhausted. And yet, meetings continue to multiply. Not because they work, but because they signal work.In this episode, Rebecca Hinds challenges the idea that meetings are just a scheduling problem. She reframes them as a deeper organizational issue rooted in visibility, status, and outdated ways of measuring value.You'll hear why meetings often aren't the root problem, but the most visible symptom. Why hybrid work and AI haven't fixed collaboration, and in many cases have made it worse. And what it actually looks like to design meetings and workflows with intention, not habit.For HR leaders, this conversation is a wake-up call. Not just to reduce meetings, but to rethink how work itself is defined, measured, and experienced.Because if we don't change it deliberately, AI will simply help us do more of what isn't working.About our guest Rebecca Hinds is a leading expert in organizational behavior who works with companies navigating the challenges of modern work. She founded the Work Innovation Lab at Asana and the Work AI Institute at Glean, where she bridges the gap between academic research and real organizational practice. Her work has been featured in Harvard Business Review, The New York Times, The Wall Street Journal, and more. She is also an instructor for CNBC's Make It Masterclass: How to Use AI to Be More Productive and Successful at Work, and the author of Your Best Meeting Ever.Key Topics & Timestamps[~12:50] Check-In: Rebecca's best meeting ever [~19:40] Why meetings got worse after the pandemic[~22:10] Visibility bias: why we equate busyness with value[~24:45] The WWII sabotage manual [~27:00] The $1.4 trillion problem [~30:10] Meetings as your most expensive, overlooked product [~35:45] "Process is a proxy" [~36:00] Where AI helps and where it hurts meetings[~38:30] The brainstorming debate: to AI or not to AI? [~43:40] One tip for HR leaders: where to start[~45:05] It's okay to cancel Resources & Links

What happens when your AI agents start making decisions faster than your security team can even see them? In this episode, I sit down with Sunil Agrawal, Chief Information Security Officer at Glean, to unpack a shift already underway in enterprises. With predictions that 40 percent of enterprise applications will include autonomous AI agents by the end of 2026, we are moving from human-led workflows to machine-to-machine interactions at a scale most organizations are not fully prepared for. Sunil brings a rare perspective, blending more than 25 years of cybersecurity experience with an inventor's mindset shaped by over 40 patents. What stood out to me in our conversation is how quickly the traditional security model is becoming outdated. As he explained, "autonomous agents break those assumptions because they operate across tools, varying permissions and data sources with alarming speed and autonomy." This creates what he calls the "autonomy gap," in which the CIO's drive for speed collides with the CISO's need for visibility and control. We explore how that tension is playing out in real organizations today, and why so many are already falling behind. Nearly half of businesses still lack the AI-specific controls needed to prevent untraceable incidents, and the risks are not always what you might expect. Sunil argues that the first major rogue-agent incident is unlikely to be a malicious attack. Instead, it will come from confusion: a well-intentioned system taking the wrong action in the wrong context, with consequences that ripple across the business. The conversation then turns practical. Sunil breaks down his AWARE framework, a structured way to introduce real-time guardrails that evaluate intent, context, and risk before an agent takes action. Rather than relying on static policies, this approach focuses on continuous runtime enforcement, where systems are constantly assessed based on behavior rather than assumptions.   What I found particularly valuable is how this moves beyond theory into something leaders can act on today. From starting with tightly scoped use cases to investing in full observability, this episode offers a clear roadmap for balancing innovation with accountability. As Sunil put it, organizations that succeed will not be the ones that move fastest, but the ones that prove trust at scale.   So how do you embrace the productivity gains of autonomous AI without opening the door to invisible risk, and are your current security models ready for a world where the "user" is no longer human? Useful Links Connect with Sunil Agrawal on LinkedIn Learn more about Glean Follow Glean on LinkedIn Visit the Tech Talks Network Sponsor NordLayer Browser

AI threatens traditional customer data platforms with automated marketing cycles. Chris O'Neill, CEO of GrowthLoop, brings experience scaling Google Canada to $2B and launching Glean to a $7.2B valuation. He discusses how agentic AI learns from data patterns to activate campaigns across channels automatically. The conversation covers building composable CDPs that iterate based on real-time performance insights and circumventing traditional proof-of-concept timelines to deploy marketing automation within 24 hours.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI threatens traditional customer data platforms with automated marketing cycles. Chris O'Neill, CEO of GrowthLoop, brings experience scaling Google Canada to $2B and launching Glean to a $7.2B valuation. He discusses how agentic AI learns from data patterns to activate campaigns across channels automatically. The conversation covers building composable CDPs that iterate based on real-time performance insights and circumventing traditional proof-of-concept timelines to deploy marketing automation within 24 hours.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI forces marketing teams to pivot faster than ever before. Chris O'Neill, CEO of GrowthLoop, brings experience scaling Google Canada from $500M to $2B and launching Glean to a $7.2B valuation. He explains how agentic AI learns from customer data to automate marketing cycles across channels. The discussion covers building compounding growth engines that iterate based on real-time performance insights.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI forces marketing teams to pivot faster than ever before. Chris O'Neill, CEO of GrowthLoop, brings experience scaling Google Canada from $500M to $2B and launching Glean to a $7.2B valuation. He explains how agentic AI learns from customer data to automate marketing cycles across channels. The discussion covers building compounding growth engines that iterate based on real-time performance insights.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Free guide to climb the AI Skill Ladder (7 agent tools + prompts): https://clickhubspot.com/mgtv Ep. 415 What if you could turn AI into your second brain? Kipp, Kieran, and guest Kevin Hutson (Futurepedia) dive into the levels of AI maturity and how marketers can go from AI novices to master workflow builders. Learn more on the step-by-step journey to AI fluency, the power of building reusable AI skills, and how to leverage tools like Manus to automate complex marketing workflows and outperform the competition. Mentions Kevin Hutson https://www.youtube.com/@futurepedia_io Futurepedia https://www.futurepedia.io/ Manus https://manus.im/ Glean https://www.glean.com/ Get our guide to build your own Custom GPT: https://clickhubspot.com/customgpt We're creating our next round of content and want to ensure it tackles the challenges you're facing at work or in your business. To understand your biggest challenges we've put together a survey and we'd love to hear from you! https://bit.ly/matg-research Resource [Free] Steal our favorite AI Prompts featured on the show! Grab them here: https://clickhubspot.com/aip We're on Social Media! Follow us for everyday marketing wisdom straight to your feed YouTube: ​​https://www.youtube.com/channel/UCGtXqPiNV8YC0GMUzY-EUFg  Twitter: https://twitter.com/matgpod  TikTok: https://www.tiktok.com/@matgpod  Join our community https://landing.connect.com/matg Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934   If you love this show, please leave us a 5-Star Review https://link.chtbl.com/h9_sjBKH and share your favorite episodes with friends. We really appreciate your support. Host Links: Kipp Bodnar, https://twitter.com/kippbodnar   Kieran Flanagan, https://twitter.com/searchbrat  ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by Hubspot Media // Produced by Darren Clarke.

This is Part 2 of our live at the CHRO Association's annual CHRO Summit in Orlando, Florida.I was honored to be invited to the CHRO Association's annual CHRO Summit that brought together more than 300 CHROs and senior HR leaders for strategic conversations truly shaping the future of work. With 25 presenters and panelists on the agenda, I was able to sit down and interview seven of those amazing speakers and bring their insights directly to you across these two episodes (EP 186 & EP 187).In this episode, Part 2 - we're going to hear from three more incredible leaders:Darrell Ford, Vice Chair of the CHRO Association and Executive Vice President and CHRO at UPSRebecca Hinds, PhD, Head of the Work AI Institute and Thought Leadership at Glean, and author of Your Best Meeting EverKevin Cox, Former CHRO at General Electric and President of LKC Advisory LLCConnecting with CHRO Summit presenters: Connect with Darrell Ford on LinkedIn Connect with Rebecca Hinds on LinkedInConnect with Kevin Cox on LinkedInLearn more about CHRO AssociationEpisode Sponsor: Next-Gen HR Accelerator - Learn more about this best-in-class leadership development program for next-gen HR leadersHR Leader's Blueprint - 18 pages of real-world advice from 100+ HR thought leaders. Simple, actionable, and proven strategies to advance your career.Succession Planning Playbook: In this focused 1-page resource, I cut through the noise to give you the vital elements that define what “great” succession planning looks like.

What if you treated every meeting you lead as a product you were responsible for designing? In this conversation, Kevin sits down with Rebecca Hinds to explore why meetings—arguably the most important product in any organization—are often created with less intention than the products and services we sell. Rebecca shares why meetings become organizational "junk drawers" and explains how applying a product-design lens can transform them from time drains into high-leverage leadership tools. Drawing on the seven principles from her book, she challenges leaders to rethink meeting volume, confront "meeting debt," clarify communication systems, and use meaningful measures like return on time investment to evaluate effectiveness. Rebecca's Story: Rebecca Hinds is the author of Your Best Meeting Ever: 7 Principles for Designing Meetings that Get Things Done. She is a leading expert on organizational behavior and the future of work and holds a BS, MS, and PhD from Stanford University. Rebecca founded the Work Innovation Lab at Asana and the Work AI Institute at Glean, first-of-their-kind corporate think tanks dedicated to conducting cutting-edge research on the future of work. Her research is consistently featured in top-tier publications and has appeared in places like Harvard Business Review, The New York Times, The Wall Street Journal, Forbes, Fast Company, Wired, TIME, CNBC, Bloomberg, Axios, the Washington Post, and more. Rebecca has been invited to speak on major stages all across the world, including Dreamforce, SXSW, INBOUND, Ai4, Cloudfest, and the Gartner Digital Workplace Summit. She regularly appears on podcasts, webinars, and online education programs, including appearances on Adam Grant's Worklife podcast, Deloitte's Capital H podcast, and as an instructor for CNBC's Make It Masterclass, "How to Use AI to be More Productive and Successful at Work." https://www.rebeccahinds.com/ https://www.linkedin.com/in/rebecca-hinds/ Looking to Develop Stronger Leaders? Want help developing the leaders in your organization? Reach out to explore how the Kevin Eikenberry Group can support your team at info@kevineikenberry.com  Book Recommendations Your Best Meeting Ever: 7 Principles for Designing Meetings That Get Things Done by Rebecca Hinds When the Dove Appears by Steve Barley Like this? Making Meetings Matter with Elise Keith Managing the Modern (Hybrid) Meeting with Karin Reed Reducing Meeting Fatigue (in 20 minutes or less) with Jennifer Moss Leave a Review If you liked this conversation, we'd be thrilled if you'd let others know by leaving a review on Apple Podcasts. Here's a quick guide for posting a review. Review on Apple: https://remarkablepodcast.com/itunes    Join Our Community If you want to view our live podcast episodes, hear about new releases, or chat with others who enjoy this podcast join one of our communities below. Join the Facebook Group Join the LinkedIn Group   Podcast Better! Sign up with Libsyn and get up to 2 months free! Use promo code: RLP  

Pradeep Mannakkara (CIO) and Ben Mayrides (CISO) of Cvent explain how they govern AI agents at scale across their 5,500-person organization, which now has over 6,000 agents in production. In this fireside chat recorded at a Glean event in NYC, they walk through the AWARE framework developed by Glean's Work AI Institute with Databricks and Palo Alto Networks, and describe the practical tradeoffs of moving fast while managing risk. The conversation covers agent identity, observability, cultural adoption, CIO/CISO dynamics, and what enterprise-grade AI governance looks like in practice.You'll discover:✅ Why traditional IAM and observability controls fail in agentic architectures where agents reason, delegate, and act autonomously✅ How Cvent deliberately encouraged 6,000 agent creations to build AI fluency before layering in moderation and metrics✅ The AWARE framework's five pillars: identity, context, guardrails, risk scoring, and ecosystem observability✅ Why "risk is too high" is never the final answer, only "risk is too high for now"✅ How Cvent filters AI demand through ROI gates before projects reach security review✅ Why replacing gut-feel security objections with shared criteria moves the CISO from gatekeeper to business partner✅ The sandbox-first approach that separates experimentation from production deployment✅ Why SOC 2 control criteria for AI agents are likely within 18 to 24 months⏱️ TIMESTAMPS0:00 Introduction and the AWARE framework0:34 Core challenges of agent governance2:43 What agents do for us and to us4:36 Applying the AWARE framework in practice7:09 Choosing platforms with built-in controls9:25 Making governance a cultural shift11:51 Earning trust through deliberate risk decisions13:49 Replacing gut reactions with shared criteria15:20 Managing the CIO/CISO tension18:54 Shared language for hard tradeoffs22:01 Go/no-go decisions are never one and done24:48 Advice for putting AWARE into practice26:38 Scaling to 6,000 agents

Jessica Fain is a product leader at Webflow and former Chief of Staff to the CPO at Slack, where she worked alongside April Underwood and many past podcast guests including Stewart Butterfield, Annie Pearl, Tamar Yehoshua, and Noah Weiss. She's spent her career learning how executives actually make decisions—and why most people completely misunderstand the process.We discuss:1. Why great ideas often don't get buy-in2. Why executive calendars are “like strobe lights” and why the first 30 seconds of a meeting matter so much3. Why executives are usually optimizing for a global maximum while you are often optimizing locally4. The best question Jessica uses when a leader says something that seems wrong: “That's so interesting. What led you to believe that?”5. Why you should go in to learn, not to convince6. Why showing only one option is a mistake7. Why AI will make influence more important, not less—Brought to you by:Omni—AI analytics your customers can trustLovable—Build apps by simply chatting with AIVanta—Automate compliance, manage risk, and accelerate trust with AI—Episode transcript: https://www.lennysnewsletter.com/p/the-art-of-influence-jessica-fain—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Jessica Fain:• LinkedIn: https://www.linkedin.com/in/jessica-fain-79b8989—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Jessica Fain(03:53) Why influence is the highest-leverage skill in product(04:47) Why great ideas fail without executive buy-in(06:00) How executives actually think(09:05) The fundamentals: context-setting, communication, and empathy(10:22) Stop pitching for approval—start co-creating with execs(12:59) Influence vs. politics (and why people get it wrong)(15:44) How to disagree with execs without losing trust(17:20) Going in to learn, not to convince(19:08) How to present ideas(26:05) The Minto-style approach and tailoring your communication to each exec(28:22) Why Jessica doesn't like the question “What's top of mind for you?”(30:24) Understanding incentives to unlock buy-in(32:10) Aligning product work with company strategy(35:10) Quick summary(37:31) Disarming the executive(40:49) Speed matters: why fast follow-up builds momentum(43:32) How to run high-impact meetings (the 60-second rule)(47:00) Why influencing execs is part of your job(49:15) Asking for more resources and thinking in 10x bets(52:23) What to do when your idea gets rejected(54:18) Clarifying information(56:50) How to build trust and make ideas stick(58:30) Shrinking big ideas into experiments(01:02:27) Common mistakes people make when influencing leaders(01:06:00) How to grow into your next role(01:09:32) How AI is changing influence and product work(01:17:55) Using AI to simulate exec feedback and improve pitches(01:21:15) Protecting our brains from overwhelm(01:22:44) Lightning round and final thoughts—Referenced:• Box: https://www.box.com• Slack: https://slack.com• Brightwheel: https://mybrightwheel.com• Webflow: https://webflow.com• April Underwood on LinkedIn: https://www.linkedin.com/in/aprilunderwood• Lessons in product leadership and AI strategy from Glean, Google, Amazon, and Slack | Tamar Yehoshua (Product at Glean, ex-Google and Slack): https://www.lennysnewsletter.com/p/you-dont-need-to-be-a-well-run-company-to-win-tamar-yehoshua• Atlassian: https://www.atlassian.com• Behind the scenes of Calendly's rapid growth | Annie Pearl (CPO): https://www.lennysnewsletter.com/p/behind-the-scenes-of-calendlys-rapid• Calendly: https://calendly.com• Glassdoor: https://www.glassdoor.co.in/index.htm• The 10 traits of great PMs, how AI will impact your product, and Slack's product development process | Noah Weiss (Slack, Foursquare, Google): https://www.lennysnewsletter.com/p/the-10-traits-of-great-pms-how-ai• Ethan Eismann on X: https://x.com/eeismann• Slack founder: Mental models for building products people love ft. Stewart Butterfield: https://www.lennysnewsletter.com/p/slack-founder-stewart-butterfield• Ilan Frank on LinkedIn: https://www.linkedin.com/in/ilanfrank• Checkr: https://checkr.com• Ali Rayl on LinkedIn: https://www.linkedin.com/in/alirayl• Rachel Wolan on LinkedIn: https://www.linkedin.com/in/rachelwolan• How Webflow's CPO built an AI chief of staff to manage her calendar, prep for meetings, and drive AI adoption | Rachel Wolan: https://www.lennysnewsletter.com/p/how-webflows-cpo-built-an-ai-chief• Barbara Minto's website: https://www.barbaraminto.com• How Slack invests in big little details through Customer Love Sprints: https://slack.design/articles/sweating-the-small-stuff• Building product at Stripe: craft, metrics, and customer obsession | Jeff Weinstein (Product lead): https://www.lennysnewsletter.com/p/building-product-at-stripe-jeff-weinstein• The Enneagram Institute: https://www.enneagraminstitute.com/type-descriptions• The Pitt on Prime Video: https://www.amazon.com/The-Pitt-Season-1/dp/B0DNRR8QWD• Towel warmer: https://www.amazon.com/FLYHIT-Large-Towel-Warmer-Bathroom/dp/B0CB5K34L2• Casa: https://getcasa.com• Jimi Hendrix: https://en.wikipedia.org/wiki/Jimi_Hendrix• Greek Theatre: https://en.wikipedia.org/wiki/Greek_Theatre_(Los_Angeles)—Recommended books:• Pachinko: https://www.amazon.com/Pachinko-National-Book-Award-Finalist/dp/1455563927• Homegoing: https://www.amazon.com/Homegoing-Yaa-Gyasi/dp/1101971061• A History of Burning: https://www.amazon.com/History-Burning-Janika-Oza/dp/1538724243• The Overstory: https://www.amazon.com/Overstory-Novel-Richard-Powers/dp/039335668X—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Rebecca Hinds: Your Best Meeting Ever Rebecca Hinds is a leading expert on organizational behavior and the future of work. She founded and led the Work Innovation Lab at Asana and the Work AI Institute at Glean, where she partners with leading experts to help organizations transform their work with AI. She is the author of Your Best Meeting Ever: 7 Principles for Designing Meetings That Get Things Done (Amazon, Bookshop)*. Considering the amount of time we all spend in meetings, it's odd that most organizations do so little to measure meeting results. If that's sounding familiar, this conversation between Rebecca and me will show you exactly how to get started. Key Points Metrics that only measure the costs of meetings (dollars and time) can be useful, but rarely capture the full picture. Use Return on Time Invested (ROTI) anonymously to survey attendees to determine if a meeting was a good use of time. Also ask, “What would it take for you to improve your rating by one point?” Survey sparingly to avoid survey fatigue. Bringing in a survey 10% of the time is a benchmark to start from. If the amount of time in meetings vastly exceeds 10 hours a week, there's likely an opportunity to scale back or redefine the work before or after meetings to use time better. Equal speaking time in meetings is a key indicator of team performance. Be transparent with employees about any technology you use to capture data. Punctuality and attendance rate are indicators of how valued meetings are for people. Resources Mentioned Your Best Meeting Ever: 7 Principles for Designing Meetings That Get Things Done by Rebecca Hinds (Amazon, Bookshop)* Interview Notes Download my interview notes in PDF format (free membership required). Related Episodes How to Lead Meetings That Get Results, with Mamie Kanfer Stewart (episode 358) Moving Towards Meetings of Significance, with Seth Godin (episode 632) How to Lead Engaging Meetings, with Jess Britt (episode 721) Discover More Activate your free membership for full access to the entire library of interviews since 2011, searchable by topic. To accelerate your learning, uncover more inside Coaching for Leaders Plus.

Episode 764: Neal and Toby recap the latest from the World Economic Forum as it heads into its last day, ending with Elon Musk making his debut after publicly criticizing the conference. Then, ‘Sinners' shatters the record for most Oscar nominations. Plus, the hit show ‘Heated Rivalry' has jolted interest from newcomers into hockey. Meanwhile, Japanese toilet maker Toto has its best performance thanks to an AI upgrade. Finally, a roundup of the biggest headlines from the day.  Get your tickets for the Morning Brew Variety Show! https://tinyurl.com/MBvariety  Explore Indeed's full findings at https://www.indeed.com/2026hiringtrends Learn more about Lightspeed at https://www.lsvp.com Subscribe to Morning Brew Daily for more of the news you need to start your day. Share the show with a friend, and leave us a review on your favorite podcast app. Listen to Morning Brew Daily Here:⁠ ⁠⁠https://www.swap.fm/l/mbd-note⁠⁠⁠  Watch Morning Brew Daily Here:⁠ ⁠⁠https://www.youtube.com/@MorningBrewDailyShow⁠ This special episode is produced in partnership with Lightspeed Venture Partners. Lightspeed holds the largest early-stage AI portfolio in the world both number of companies and capital deployed, investing in 165 AI companies and deploying over $5.5 billion in AI investments. Lightspeed's invested in some of the most valuable AI companies globally, including Anthropic, Mistral AI, Glean, Reflection AI and more. Learn more about Lightspeed's recent investments in Skild AI here, and stay tuned for more exciting AI coverage on the show this week: https://www.skild.ai/blogs/series-c Learn more about your ad choices. Visit megaphone.fm/adchoices

Episode 763: Neal and Toby dive into the markets' reaction to Trump walking back his threats of European tariffs over Greenland during his address at the World Economic Forum in Davos. Then, Ryanair's spat with Elon Musk over Starlink has actually been good for Ryanair. Also, Amazon is building its largest physical retail store as it flirts with the big box. Meanwhile, Neal shares his favorite numbers (from Davos) on chimney sweeping, the Golden Gate bridge, and how to market time.  Grab your desktop calendar with games now! https://shop.morningbrew.com/products/2026-daily-games-desk-calendar  Explore Indeed's full findings at https://www.indeed.com/2026hiringtrends Learn more about Lightspeed at https://www.lsvp.com Subscribe to Morning Brew Daily for more of the news you need to start your day. Share the show with a friend, and leave us a review on your favorite podcast app. Listen to Morning Brew Daily Here:⁠ ⁠⁠https://www.swap.fm/l/mbd-note⁠⁠⁠  Watch Morning Brew Daily Here:⁠ ⁠⁠https://www.youtube.com/@MorningBrewDailyShow⁠ This special episode is produced in partnership with Lightspeed Venture Partners. Lightspeed holds the largest early-stage AI portfolio in the world both number of companies and capital deployed, investing in 165 AI companies and deploying over $5.5 billion in AI investments. Lightspeed's invested in some of the most valuable AI companies globally, including Anthropic, Mistral AI, Glean, Reflection AI and more. Learn more about Lightspeed's recent investments in Skild AI here, and stay tuned for more exciting AI coverage on the show this week: https://www.skild.ai/blogs/series-c Learn more about your ad choices. Visit megaphone.fm/adchoices