Podcasts about glean

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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.

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

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

About This Episode  Meeting culture is one of the most overlooked yet costly dysfunctions in modern organizations. In this episode of The Future of Work® Podcast, Daniel Lamadrid was joined by Rebecca Hinds, PhD—organizational behavior expert and author of Your Best Meeting Ever—to speak about why meetings persist despite being widely disliked, and how leaders can transform them into powerful tools for progress.  Drawing on research from Stanford, Worklytics, and her experience founding the Work Innovation Lab at Asana and the Work AI Institute at Glean, Rebecca explains how visibility bias, meeting debt, and hybrid dysfunction are driving calendar overload and burnout. She introduces practical frameworks like the 4D Test (Decide, Debate, Discuss, Develop), Meeting Doomsday, and Return on Time Investment (ROTI) to help leaders and teams reset their collaboration systems.  As AI alters how we work, Rebecca challenges organizations to use technology intentionally—freeing meetings for the deeply human work of creativity, trust-building, and decision-making. This episode is a masterclass in designing meeting culture that truly advances business outcomes in the future of work. 

“Recently I (Rachel) had the honor of speaking at the National Widows Conference where more than 550 expectant (and some reluctant) women showed up to experience what so many women in our community have experienced first hand. The room was tender and strong all at once. We took time to name what we're tired of carrying and made the decision to lay it down.My message, Glean. Stay. Plant., came from Genesis 26, and I didn't preach it from theory — I've lived the famine. I know the fear, the scrambling, the ache of wondering how you'll make it. But I also know this: God's economy still works, and the law of harvest is more trustworthy than the noise of scarcity. If you can stay. If you can plant. If you can abide in what He's already given you — there is provision there. And there is life in abundance.Learn more about There is More: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thereismorecollective.com/Check Out Our Resources, including the Father's House Study, Go to Girls, and the Spiritual Warfare Workshop: https://thereismorecollective.com/resourcesGet 10% discount on Father's House Study with code: FH10Follow There is More Podcast on Instagram: @thereismorepodcastPartner With Us: ⁠⁠https://neveralonewidows.kindful.com/?campaign=1284937⁠

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.

In this episode, I'm joined by Rebecca Hinds — organizational behavior expert and founder of the Work AI Institute at Glean — for a practical conversation about why meetings deteriorate over time and how to redesign them. Rebecca argues that bad meetings aren't a people problem — they're a systems problem. Without intentional design, meetings default to ego, status signaling, conflict avoidance, and performative participation. Over time, low-value meetings become normalized instead of fixed. Drawing on her research at Stanford University and her leadership of the Work Innovation Lab at Asana, she shares frameworks from her new book, Your Best Meeting Ever, including: The four legitimate purposes of a meeting: decide, discuss, debate, or develop The CEO test for when synchronous time is truly required How to codify shared meeting standards Why leaders must explicitly give permission to leave low-value meetings We also explore leadership, motivation, and the myth that kindness and high standards are opposites. Rebecca explains why effective leaders diagnose what drives each individual — encouragement for some, direct challenge for others — and design environments that support both performance and belonging. Finally, we talk about AI and the future of work. Tools amplify existing culture: strong systems improve, broken systems break faster. Organizations that redesign how work happens — not just what tools they use — will have the advantage. If you want to run better meetings, lead with more clarity, and rethink how collaboration actually happens, this episode is for you. You can find Your Best Meeting Ever at major bookstores and learn more at rebeccahinds.com.  00:00 Start 00:27 Why Meetings Get Worse Over Time Robin references Good Omens and the character Crowley, who designs the M25 freeway to intentionally create frustration and misery. They use this metaphor to illustrate how systems can be designed in ways that amplify dysfunction, whether intentionally or accidentally. The idea is that once dysfunctional systems become normalized, people stop questioning them. They also discuss Cory Doctorow's concept of enshittification, where platforms and systems gradually decline as organizational priorities override user experience. Rebecca connects this pattern directly to meetings, arguing that without intentional design, meetings default to chaos and energy drain. Over time, poorly designed meetings become accepted as inevitable rather than treated as solvable design problems. Rebecca references the Simple Sabotage Field Manual created by the Office of Strategic Services during World War II. The manual advised citizens in occupied territories on how to subtly undermine organizations from within. Many of the suggested tactics involved meetings, including encouraging long speeches, focusing on irrelevant details, and sending decisions to unnecessary committees. The irony is that these sabotage techniques closely resemble common behaviors in modern corporate meetings. Rebecca argues that if meetings were designed from scratch today, without legacy habits and inherited norms, they would likely look radically different. She explains that meetings persist in their dysfunctional form because they amplify deeply human tendencies like ego, status signaling, and conflict avoidance. Rebecca traces her interest in teamwork back to her experience as a competitive swimmer in Toronto. Although swimming appears to be an individual sport, she explains that success is heavily dependent on team structure and shared preparation. Being recruited to swim at Stanford exposed her to an elite, team-first environment that reshaped how she thought about performance. She became fascinated by how a group can become greater than the sum of its parts when the right cultural conditions are present. This experience sparked her long-term curiosity about why organizations struggle to replicate the kind of cohesion often seen in sports. At Stanford, Coach Lee Mauer emphasized that emotional wellbeing and performance were deeply connected. The team included world record holders and Olympians, and the performance standards were extremely high. Despite the intensity, the culture prioritized connection and belonging. Rituals like informal story time around the hot tub helped teammates build relationships beyond performance metrics. Rebecca internalized the lesson that elite performance and strong culture are not opposing forces. She saw firsthand that intensity and warmth can coexist, and that psychological safety can actually reinforce high standards rather than weaken them. Later in her career at Asana, Rebecca encountered the company value of rejecting false trade-offs. This reinforced a lesson she had first learned in swimming, which is that many perceived either-or tensions are not actually unavoidable. She argues that organizations often assume they must choose between performance and happiness, or between kindness and accountability. In her experience, these are false binaries that can be resolved through better design and clearer expectations. She emphasizes that motivated and engaged employees tend to produce higher quality work, making culture a strategic advantage rather than a distraction. Kindness versus ruthlessness in leadership Robin raises the contrast between harsh, fear-based leadership styles and more relational, positive leadership approaches. Both styles have produced winning teams, which raises the question of whether success comes because of the leadership style or despite it. Rebecca argues that resilience and accountability are essential, regardless of tone. She stresses that kindness alone is not sufficient for high performance, but neither is harshness inherently superior. Effective leadership requires understanding what motivates each individual, since some people thrive on encouragement while others crave direct challenge. Rebecca personally identifies with wanting to be pushed and appreciates clarity when her work falls short of expectations. She concludes that the most effective leaders diagnose motivation carefully and design environments that maximize both growth and performance. 08:51 Building the Book-Launch Team: Mentors, Agents, and Choosing the Right Publisher Robin asks Rebecca about the size and structure of the team she assembled to execute the launch successfully. He is especially curious about what the team actually looked like in practice and how coordinated the effort needed to be. He also asks about the meeting cadence and work cadence required to bring a book launch to life at that level. The framing highlights that writing the book is only one phase, while launching it is an entirely different operational challenge. Rebecca explains that the process felt much more organic than it might appear from the outside. She admits that at the beginning, she underestimated the full scope of what a book launch entails. Her original motivation was simple: she believed she had a valuable perspective, wanted to help people, and loved writing. As she progressed deeper into the publishing process, she realized that writing the manuscript was only one piece of a much larger system. The operational and promotional dimensions gradually revealed themselves as a second job layered on top of authorship. Robin emphasizes that writing a book and publishing a book are fundamentally different jobs. Rebecca agrees and acknowledges that the publishing side requires a completely different skill set and infrastructure. The conversation underscores that authorship is creative work, while publishing and launching require strategy, coordination, and business acumen. Rebecca credits her Stanford mentor, Bob Sutton, as a life changing influence throughout the process. He guided her step by step, including decisions around selecting a publisher and choosing an agent. She initially did not plan to work with an agent, but through guidance and reflection, she shifted her perspective. His mentorship helped her ask better questions and approach the process more strategically rather than reactively. Rebecca reflects on an important mindset shift in her career. Earlier in life, she was comfortable being the big fish in a small pond. Over time, she came to believe that she performs better when surrounded by people who are smarter and more experienced than she is. She describes her superpower as working extremely hard and having confidence in that effort. Because of that, she prefers environments where others elevate her thinking and push her further. This philosophy became central to how she built her book launch team. As Rebecca learned more about the moving pieces required for a successful campaign, she became more intentional about who she wanted involved. She sought the best not in terms of prestige alone, but in terms of belief and commitment. She wanted people who would go to bat for her and advocate for the book with genuine enthusiasm. She noticed that some organizations that looked impressive on paper were not necessarily the right fit for her specific campaign. This led her to have extensive conversations with potential editors and publicists before making decisions. Rebecca developed a personal benchmark for evaluating partners. She paid attention to whether they were willing to apply the book's ideas within their own organizations. For her, that signaled authentic belief rather than surface level marketing support. When Simon and Schuster demonstrated early interest in implementing the book's learnings internally, it stood out as meaningful alignment. That commitment suggested they cared about the substance of the work, not just the promotional campaign. As the process unfolded, Rebecca realized that part of her job was learning what questions to ask. Each conversation with potential partners refined her understanding of what she needed. She became more deliberate about building the right bench of people around her. The team was not assembled all at once, but rather shaped through iterative learning and discernment. The launch ultimately reflected both her evolving standards and her commitment to surrounding herself with people who elevated the work. 12:12 Asking Better Questions & Going Asynchronous Robin highlights the tension between the voice of the book and the posture of a first time author entering a major publishing house. He notes that Best Meeting Ever encourages people to assert authority in meetings by asking about agendas, ownership, and structure. At the same time, Rebecca was entering conversations with an established publisher as a new author seeking partnership. The question becomes how to balance clarity and conviction with humility and openness. Robin frames it as showing up with operational authority while still saying you publish books and I want to work with you. Rebecca calls the question insightful and explains that tactically she relied heavily on asking questions. She describes herself as intentionally curious and even nosy because she did not yet know what she did not know. Rather than pretending to have answers, she used inquiry as a way to build authority through understanding. She asked questions asynchronously almost daily, emailing her agent and editor with anything that came to mind. This allowed her to learn the system while also signaling engagement and seriousness. Rebecca explains that most of the heavy lifting happened outside of meetings. By asking questions over email, she clarified information before stepping into synchronous time. Meetings were then reserved for ambiguity, decision making, and issues that required real time collaboration. As a result, the campaign involved very few meetings overall. She had a biweekly meeting with her core team and roughly monthly conversations with her editor. The rest of the coordination happened asynchronously, which aligned with her philosophy about effective meeting design. Rebecca jokes that one hidden benefit of writing a book on meetings is that everyone shows up more prepared and on time. She also felt internal pressure to model the behaviors she was advocating. The campaign therefore became a real world test of her ideas. She emphasizes that she is glad the launch was not meeting heavy and that it reflected the principles in the book. Robin shares a story about their initial connection through David Shackleford. During a short introductory call, he casually offered to spend time discussing book marketing strategies. Rebecca followed up, scheduled time, and took extensive notes during their conversation. After thanking him, she did not continue unnecessary follow up or prolonged discussion. Instead, she quietly implemented many of the practical strategies discussed. Robin later observed bulk sales, bundled speaking engagements, and structured purchase incentives that reflected disciplined execution. Robin emphasizes that generating ideas is relatively easy compared to implementing them. He connects this to Seth Godin's praise that the book is for people willing to do the work. The real difficulty lies not in brainstorming strategies but in consistently executing them. He describes watching Rebecca implement the plan as evidence that she practices what she preaches. Her hard work and disciplined follow through reinforced his confidence in the book before even reading it. Rebecca responds with gratitude and acknowledges that she took his advice seriously. She affirms that several actions she implemented were directly inspired by their conversation. At the same time, the tone remains grounded and collaborative rather than performative. The exchange illustrates her pattern of seeking input, synthesizing it, and then executing independently. Robin transitions toward the theme of self knowledge and its role in leadership and meetings. He connects Rebecca's disciplined execution to her awareness of her own strengths. The earlier theme resurfaces that she sees hard work and follow through as her superpower. The implication is that effective meetings and effective leadership both begin with understanding how you operate best. 17:48 Self-Knowledge at Work Robin shares that he knows he is motivated by carrots rather than sticks. He explains that praise energizes him and improves his performance more than criticism ever could. As a performer and athlete, he appreciates detailed notes and feedback, but encouragement is what unlocks his best work. He contrasts that with experiences like old school ballet training, where harsh discipline did not bring out his strengths. His point is that understanding how you are wired takes experience and reflection. Rebecca agrees that self knowledge is essential and ties it directly to motivation. She argues that the better you understand yourself, the more clearly you can articulate what drives you. Many people, especially early in their careers, do not pause to examine what truly motivates them. She notes that motivation is often intangible and not primarily monetary. For some people it is praise, for others criticism, learning, mastery, collaboration, or autonomy. She also emphasizes that motivation changes over time and shifts depending on organizational context. One of Rebecca's biggest lessons as a manager and contributor is the importance of codifying self knowledge. Writing down what motivates you and how you work best makes it easier to communicate those needs to others. She believes this explicitness is especially critical during times of change. When work is evolving quickly, assumptions about motivation can lead to disengagement. Making preferences visible reduces friction and prevents misalignment. Rebecca references a recent presentation she gave on the dangers of automating the soul of work. She and her mentor Bob Sutton have discussed how organizations risk stripping meaning from roles if they automate without discernment. She points to research showing that many AI startups are automating tasks people would prefer to keep human. The warning is that just because something can be automated does not mean it should be. Without understanding what makes work meaningful for employees, leaders can unintentionally remove the very elements that motivate people. Rebecca believes managers should create explicit user manuals for their team members. These documents outline how individuals prefer to communicate, what motivates them, and what their career aspirations are. She sees this as a practical leadership tool rather than a symbolic exercise. Referring back to these documents helps leaders guide their teams through uncertainty and change. When asked directly, she confirms that she has implemented this practice in previous roles and intends to do so again. When asked about the future of AI, Rebecca avoids making long term predictions. She observes that the most confident forecasters are often those with something to sell. Her shorter term view is that AI amplifies whatever already exists inside an organization. Strong workflows and cultures may improve, while broken systems may become more efficiently broken. She sees organizations over investing in technology while under investing in people and change management. As a result, productivity gains are appearing at the individual level but not consistently at the team or organizational level. Rebecca acknowledges that there is a possible future where AI creates abundance and healthier work life balance. However, she does not believe current evidence strongly supports that outcome in the near term. She does see promising examples of organizations using AI to amplify collaboration and cross functional work. These examples remain rare but signal that a more human centered future is possible. She is cautiously hopeful but not convinced that the most optimistic scenario will unfold automatically. Robin notes that time horizons for prediction have shortened dramatically. Rebecca agrees and says that six months feels like a reasonable forecasting window in the current environment. She observes that the best leaders are setting thresholds for experimentation and failure. Pilots and proofs of concept should fail at a meaningful rate if organizations are truly exploring. Shorter feedback loops allow organizations to learn quickly rather than over commit to fragile long term assumptions. Robin shares a formative story from growing up in his father's small engineering firm, where he was exposed early to office systems and processes. Later, studying in a Quaker community in Costa Rica, he experienced full consensus decision making. He recalls sitting through extended debates, including one about single versus double ply toilet paper. As a fourteen year old who would rather have been climbing trees in the rainforest, the meeting felt painfully misaligned with his energy. That experience contributed to his lifelong desire to make work and collaboration feel less draining and more intentional. The story reinforces the broader theme that poorly designed meetings can disconnect people from purpose and engagement. 28:31 Leadership vs. Tribal Instincts Rebecca explains that much of dysfunctional meeting behavior is rooted in tribal human instincts. People feel loyalty to the group and show up to meetings simply to signal belonging, even when the meeting is not meaningful. This instinct to attend regardless of value reinforces bloated calendars and performative participation. She argues that effective meeting design must actively counteract these deeply human tendencies. Without intentional structure, meetings default to social signaling rather than productive collaboration. Rebecca emphasizes that leadership plays a critical role in changing meeting culture Leaders must explicitly give employees permission to leave meetings when they are not contributing. They must also normalize asynchronous work as a legitimate and often superior alternative. Without that top down permission, employees will continue attending out of fear or habit. Meeting reform requires visible endorsement from those with authority. Power dynamics and pushing back without positional authority Robin reflects on the power of writing a book on meetings while still operating within a hierarchy. He asks how individuals without formal authority can challenge broken systems. Rebecca responds that there is no universal solution because outcomes depend heavily on psychological safety. In organizations with high trust, there is often broad recognition that meetings are ineffective and a desire to fix them. In lower trust environments, change must be approached more strategically and indirectly. Rebecca advises employees to lead with curiosity rather than confrontation. Instead of calling out a bad meeting, one might ask whether their presence is truly necessary. Framing the question around contribution rather than judgment reduces defensiveness. This approach lowers the emotional temperature and keeps the conversation constructive. Curiosity shifts the tone from personal critique to shared problem solving. In psychologically unsafe environments, Rebecca suggests shifting enforcement to systems rather than individuals. Automated rules such as canceling meetings without agendas or without sufficient confirmations can reduce personal friction. When technology enforces standards, it feels less like a personal attack. Codified rules provide employees with shared language and objective criteria. This reduces the perception that opting out is a rejection of the person rather than a rejection of the structure. Rebecca argues that every organization should have a clear and shared definition of what deserves to be a meeting. If five employees are asked what qualifies as a meeting, they should give the same answer. Without explicit criteria, decisions default to habit and hierarchy. Clear rules give employees confidence to push back constructively. Shared standards transform meeting participation from a personal negotiation into a procedural one. Rebecca outlines a two part test to determine whether a meeting should exist. First, the meeting must serve one of four purposes which are to decide, discuss, debate, or develop people. If it does not satisfy one of those four categories, it likely should not be a meeting. Even if it passes that test, it must also satisfy one of the CEO criteria. C refers to complexity and whether the issue contains enough ambiguity to require synchronous dialogue. E refers to emotional intensity and whether reading emotions or managing reactions is important. O refers to one way door decisions, meaning choices that are difficult or costly to reverse. Many organizational decisions are reversible and therefore do not justify synchronous time. Robin asks how small teams without advanced tech stacks can automate meeting discipline. Rebecca explains that many safeguards can be implemented with existing tools such as Google Calendar or simple scripts. Basic rules like requiring an agenda or minimum confirmations can be enforced through standard workflows. Not all solutions require advanced AI tools. The key is introducing friction intentionally to prevent low value meetings from forming. Rebecca notes that more advanced AI tools can measure engagement, multitasking, or participation. Some platforms now provide indicators of attention or involvement during meetings. While these tools are promising, they are not required to implement foundational meeting discipline. She cautions against over investing in shiny tools without first clarifying principles. Metrics are useful when they reinforce intentional design rather than replace it. Rebecca highlights a subtle risk of automation, particularly in scheduling. Tools can be optimized for the sender while increasing friction for recipients. Leaders should consider the system level impact rather than only individual efficiency. Productivity gains at the individual level can create hidden coordination costs for the team. Meeting automation should be evaluated through a collective lens. Rebecca distinguishes between intrusive AI bots that join meetings and simple transcription tools. She is cautious about bots that visibly attend meetings and distract participants. However, she supports consensual transcription when it enhances asynchronous follow up. Effective transcription can reduce cognitive load and free participants to engage more deeply. Used thoughtfully, these tools can strengthen collaboration rather than dilute it. 41:35 Maker vs. Manager: Balancing a Day Job with a Book Launch Robin shares an example from a webinar where attendees were asked for feedback via a short Bitly link before the session closed. He contrasts this with the ineffectiveness of "smiley face/frowny face" buttons in hotel bathrooms—easy to ignore and lacking context. The key is embedding feedback into the process in a way that's natural, timely, and comfortable for participants. Feedback mechanisms should be integrated, low-friction, and provide enough context for meaningful responses. Rebecca recommends a method inspired by Elise Keith called Roti—rating meetings on a zero-to-five scale based on whether they were worth attendees' time. She suggests asking this for roughly 10% of meetings to gather actionable insight. Follow-up question: "What could the organizer do to increase the rating by one point?" This approach removes bias, focuses on attendee experience, and identifies meetings that need restructuring. Splits in ratings reveal misaligned agendas or attendee lists and guide optimization. Robin imagines automating feedback requests via email or tools like Superhuman for convenience. Rebecca agrees and adds that simple forms (Google Forms, paper, or other methods) are effective, especially when anonymous. The goal is simplicity and consistency—given how costly meetings are, there's no excuse to skip feedback. Robin references Paul Graham's essay on maker vs. manager schedules and asks about Rebecca's approach to balancing writing, team coordination, and book marketing. Rebecca shares that 95% of her effort on the book launch was "making"—writing and outreach—thanks to a strong team handling management. She devoted time to writing, scrappy outreach, and building relationships, emphasizing giving without expecting reciprocation. The main coordination challenge was balancing her book work with her full-time job at Asana, requiring careful prioritization. Rebecca created a strict writing schedule inspired by her swimming discipline: early mornings, evenings, and weekends dedicated to writing. She prioritized her book and full-time work while maintaining family commitments. Discipline and clear prioritization were essential to manage competing but synergistic priorities. Robin asks about written vs. spoken communication, referencing Amazon's six-page memos and Zandr Media's phone-friendly quick syncs. Rebecca emphasizes that the answer depends on context but a strong written communication culture is essential in all organizations. Written communication supports clarity, asynchronous work, and complements verbal communication. It's especially important for distributed teams or virtual work. With AI, clear documentation allows better insights, reduces unnecessary content generation, and reinforces disciplined communication. 48:29 AI and the Craft of Writing Rebecca highlights that employees have varying learning preferences—introverted vs. extroverted, verbal vs. written. Effective communication systems should support both verbal and written channels to accommodate these differences. Rebecca's philosophy: writing is a deeply human craft. AI was not used for drafting or creative writing. AI supported research, coordination, tracking trends, and other auxiliary tasks—areas where efficiency is key. Human-led drafting, revising, and word choice remained central to the book. Robin praises Rebecca's use of language, noting it feels human and vivid—something AI cannot replicate in nuance or delight. Rebecca emphasizes that crafting every word, experimenting with phrasing, and tinkering with language is uniquely human. This joy and precision in writing is not replicable by AI and is part of what makes written communication stand out. Rebecca hopes human creativity in writing and oral communication remains valued despite AI advances. Strong written communication is increasingly differentiating for executive communicators and storytellers in organizations. AI can polish or mass-produce text, but human insight, nuance, and storytelling remain essential and career-relevant. Robin emphasizes the importance of reading, writing, and physical activities (like swimming) to reclaim attention from screens. These practices support deep human thinking and creativity, which are harder to replace with AI. Rebecca uses standard tools strategically: email (chunked and batched), Google Docs, Asana, Doodle, and Zoom. Writing is enhanced by switching platforms, fonts, colors, and physical locations—stimulating creativity and perspective. Physical context (plane, café, city) is strongly linked to breakthroughs and memory during writing. Emphasis is on how tools are enacted rather than which tools are used—behavior and discipline matter more than tech. Rebecca primarily recommends business books with personal relevance: Adam Grant's Give and Take – for relational insights beyond work. Bob Sutton's books – for broader lessons on organizational and personal effectiveness. Robert Cialdini's Influence – for understanding human behavior in both professional and personal contexts. Her selections highlight that business literature often offers universal lessons applicable beyond work. 59:48 Where to Find Rebecca The book is available at all major bookstores. Website: rebeccahinds.com LinkedIn: Rebecca Hinds  

Today's guest, Diane Lawbaugh, shares about her newest book, Don't Let Misunderstanding Win. Glean great nuggets of teaching from Diane's best-selling book about how to better navigate relationships through improved communication and understanding.   

Too many meetings. Too little impact. In this episode of Inspirational Leadership, Kristen Harcourt is joined by Rebecca Hinds, organizational behavior expert and author of Your Best Meeting Ever, to unpack why meetings feel broken — and how leaders can fix them. Rebecca shares a practical framework for deciding which meetings should exist, how to design meetings that actually drive decisions and alignment, and why collaboration — not busyness — is the real driver of performance. This conversation is a must-listen for leaders, managers, and professionals who want fewer meetings, better collaboration, and more meaningful work. In this episode, you'll learn: Why most meetings fail before they even start The 4D + CEO Test to decide if a meeting is necessary When meetings should be async instead How collaboration culture impacts performance Why one-on-one meetings matter more than ever Practical ways to reclaim your calendar About the guest: Rebecca Hinds is a leading expert on organizational behavior and the future of work. She holds a PhD from Stanford, founded Asana's Work Innovation Lab, and leads the Work AI Institute at Glean.

Glean wisdom and perspective as Pastor Eric Curtis and Wife Allison lead the Marriage Q&A Panel, posing challenging marriage-related questions to 3 couples: Pastor Gary Jane and Wife Cindy (married 43 years), Elder Matthew Bellingham and Wife April (married 21 years), and Deacon Sam Scrabeck and Wife Romana (married 6 years).

Glean wisdom and perspective as Pastor Eric Curtis and Wife Allison lead the Marriage Q&A Panel, posing challenging marriage-related questions to 3 couples: Pastor Gary Jane and Wife Cindy (married 43 years), Elder Matthew Bellingham and Wife April (married 21 years), and Deacon Sam Scrabeck and Wife Romana (married 6 years).

Send us a textAbout This EpisodeThis episode rethinks meetings from the ground up with organizational behavior expert Dr. Rebecca Hinds. Instead of accepting packed calendars as productive, the conversation reframes meetings as products that should be intentionally designed to create decisions, healthy debate, development, and real progress. Using product design principles, you'll learn how to cut meeting overload, move status updates to async tools, and use simple structures and signals to measure whether a meeting is truly worth the time. The result is a bold new way to collaborate: fewer, shorter, sharper meetings that improve focus, decision quality, and human connection at work. About Rebecca HindsRebecca Hinds is the author of Your Best Meeting Ever. She 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. Additional ResourcesWebsite: rebeccahinds.comLinkedIn: @RebeccaHindsSupport the show-------- Stay Connected www.leighburgess.com Watch the episodes on YouTube Follow Leigh on Instagram: @theleighaburgess Follow Leigh on LinkedIn: @LeighBurgess Sign up for Leigh's bold newsletter

How to design meetings with purpose so they actually move work forward.Meetings are a necessary part of work. But for many people, they're also a major source of frustration. According to Rebecca Hinds, meetings don't have to feel like a drain—better meetings start when we stop treating them as a default and start designing them with intention.Hinds is the author of Your Best Meeting Ever: Seven Principles for Designing Meetings That Get Things Done, and a future-of-work expert who founded the Work Innovation Lab at Asana and the Work AI Institute at Glean. She argues that the problem isn't meetings themselves, but the sheer number of poorly designed ones, and by being more thoughtful about what actually deserves synchronous time, teams can redesign how they communicate in the workplace “Meetings are the most important product in our entire organization, and yet they're also the least optimized,” she says. “The first step is recognizing we need to be much more intentional about how we're designing meetings.”In this episode of Think Fast, Talk Smart, Hinds and host Matt Abrahams discuss why meetings so often go wrong—and what it takes to make them work. Whether you're leading a team, trying to protect focus time, or simply hoping to spend less of your week in calendar invites, Hinds offers practical frameworks for designing meetings with purpose so they become a tool people actually value.To listen to the extended Deep Thinks version of this episode, please visit FasterSmarter.io/premium.Episode Reference Links:Rebecca HindsRebecca's Book: Your Best Meeting EverEp.124 Making Meetings Meaningful Pt. 1: How to Structure and Organize More Effective Gatherings Ep.125 Making Meetings Meaningful Pt. 2: Key Ingredients for Effective Meetings Connect:Premium Signup >>>> Think Fast Talk Smart PremiumEmail Questions & Feedback >>> hello@fastersmarter.ioEpisode Transcripts >>> Think Fast Talk Smart WebsiteNewsletter Signup + English Language Learning >>> FasterSmarter.ioThink Fast Talk Smart >>> LinkedIn, Instagram, YouTubeMatt Abrahams >>> LinkedInChapters:(00:00) - Introduction (01:42) - Why Meetings Feel Broken (02:57) - The Default-To-Meeting Problem (03:50) - Treat Meetings Like A Product (05:10) - Meeting Doomsday Reset (06:40) - The 4-DCEO Test (08:43) - Designing Better Meetings (10:05) - Creating a Meeting Agenda (12:58) - Context And Meeting Fatigue (14:06) - Memo-First Meetings (16:11) - The Final Three Questions (21:02) - Conclusion ********Thank you to our sponsors.  These partnerships support the ongoing production of the podcast, allowing us to bring it to you at no cost.This episode is sponsored by Strawberry.me. Get 50% off your first coaching session today at Strawberry.me/tftsJoin our Think Fast Talk Smart Learning Community and become the communicator you want to be.

IN EPISODE 260:We have too many meetings and not enough clarity. In Episode 260, Rebecca Hinds is here to make meetings more intentional and effective. Using Rebecca's powerful meetings model, you'll learn what to cut, what to keep, and how to right-size your internal communications. From the rise of "digital twins" to measuring meeting ROI, this episode is packed with no-nonsense tips that will make your next meeting time well spent. ABOUT REBECCA HINDS:Rebecca Hinds 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-edgeresearch on the future of work. Her research is consistently featured in top-tier publications and has appeared all across the popular press. She is the author of Your Best Meeting Ever: 7 Principles for Designing Meetings That Get Things Done.

There is a great opportunity to lead more effective and engaging team meetings. Jason is joined by author and organizational behavior specialist, Rebecca Hinds, for a profound conversation about elevating meeting culture. Jason is joined by leading expert on organizational behavior, Rebecca Hinds, PhD, for a tactical conversation on how to transform meetings from a reactive default into your most valuable organizational product. Please rate and review the podcast to help amplify these messages to others! Summary: In an era of chronic calendar bloat, how do high-performing teams regain their focus and drive results? In this episode of The Thermostat, Jason V. Barger sits down with Rebecca Hinds, PhD—founder of the Work Innovation Lab at Asana and the Work AI Institute at Glean—to discuss the "epidemic" of unproductive meetings. Rebecca challenges leaders to stop "spending" time and start "investing" it by treating every meeting as a carefully designed product intended to build culture and drive decision-making. Moving beyond typical time-management advice, Jason and Rebecca explore the psychology of the "meeting suck reflex" and the social pressures that keep dysfunctional meetings on the calendar. They introduce actionable frameworks like the "4D CEO Test" to determine if a meeting deserves to exist and the "Meeting Doomsday" strategy for resetting organizational habits. From the science of equal airtime to the strategic use of AI and analytics, this episode provides a blueprint for executives to optimize collaboration. Essential listening for C-suite leaders, managers, and anyone navigating the future of work, this conversation offers a fresh perspective on intentionality, corporate culture, and the art of the "Best Meeting Ever". Episode Notes & Timestamps: Intro: Jason introduces the core concept: meetings are the most important, yet least optimized, product in any organization. Meet Rebecca Hinds: An introduction to Rebecca's background at Stanford, Asana, and Glean, and how her career as a competitive swimmer shaped her view of high-performing teams. Meetings as a Product: Rebecca explains why we must apply product development principles—like user-centric design—to our internal communication. The "Meeting Doomsday" Reset: A deep look at the radical strategy of deleting all recurring meetings to rebuild a more intentional and productive calendar. The Jolt of Intentionality: Why changing a meeting from 30 minutes to 27 minutes can shift a team's mindset from the status quo to active engagement. Minimalist Design: Rebecca outlines four dimensions for leaner meetings: length, attendee list (the "stakeholders vs. spectators" rule), agenda items, and frequency. Measuring Effectiveness: How to use return on time investment (ROTI) and AI analytics to track speaking balance and multitasking. The 4D CEO Test: A two-part filter to determine if a meeting is necessary: Does it Decide, Debate, Discuss, or Develop? Is it Complex, Emotional, or a "One-Way Door"? The Future of Work: Jason and Rebecca discuss the importance of intentionality and "fresh starts" when designing corporate culture for 2026. Key Takeaways for Leaders: User-Centric Meetings: Design meetings for the attendees' needs, not just for the organizer's convenience or for those who talk the most. The Power of the Reset: Periodically "cleanse" your communication stack to eliminate outdated social contracts and unproductive habits. Strategic Communication: Use synchronous meetings for complex, high-stakes, or emotionally intense topics; use digital tools for everything else. Listen to the full episode and access show notes at: https://jasonvbarger.com/podcast/best-meeting-ever-rebecca-hinds/ Bio: Jason Barger is a husband, father, speaker, and author who is passionate about business leadership and corporate culture. He believes that corporate culture is the "thermostat" of an organization and that it can be used to drive performance, innovation, and engagement. The show features interviews with business leaders from a variety of industries, as well as solo episodes where Barger shares his own insights and advice. Subscribe to our channel: https://www.youtube.com/@JasonVBarger Make Your 2026 Effective!  Book Jason with your team at https://www.jasonvbarger.com Like or Follow Jason

Rebecca Hinds, Ph.D., is one of the clearest voices I've seen on organizational behavior and the future of work, and this conversation is going to help a lot of leaders. Her brand-new book, Your Best Meeting Ever: 7 Principles for Designing Meetings That Get Things Done, is a research-backed blueprint for fixing the meetings that are draining your calendar, your energy, and your team's momentum. Rebecca earned her B.S., M.S., and Ph.D. from Stanford University, where her research focused on how emerging technologies, including collaboration tools and AI, are reshaping the way we work. From 2022 to 2025, she founded and led the Work Innovation Lab at Asana, exploring practical, research-driven solutions to modern workplace challenges. In 2025, she launched the Work AI Institute at Glean, partnering with leading experts to help organizations translate AI into better collaboration and real execution. If you have ever left a meeting thinking, “That could've been an email,” or “We just lost an hour and gained nothing,” this episode is for you. Rebecca challenges outdated playbooks and gives you a better way to meet, lead, and get things done. Plus, grab your FREE Launch Your Dare Planning System at idareyoupod.com—the worksheets based on Dr. Benjamin Hardy's Future Self framework. Connect with Rebecca: Website: www.rebeccahinds.com

Rebecca Hinds is the Head of the Work AI Institute at Glean. In this episode of The Edge of Work, Rebecca joins Al Dea to unpack why meetings have become one of the biggest barriers to effective collaboration and how leaders can redesign them to actually get work done. Drawing from her research at Stanford, her experience leading innovation labs, and insights from her book Your Best Meeting Ever, Rebecca explains why meetings are often a symptom of broken collaboration systems. The conversation explores meeting overload, calendar “doomsdays,” asynchronous work, and the growing role of AI in meetings and leadership. Rebecca also shares lessons from the Work AI Institute on how organizations can navigate AI transformation with more intention and evidence-based leadership.LinksLinkedIn: https://www.linkedin.com/in/rebecca-hinds/Website: https://www.rebeccahinds.com/Book: https://www.amazon.com/Your-Best-Meeting-Ever-Principles/dp/166806748X

Rebecca Hinds is a leading expert on organizational behavior and the future of work. She earned her BS, MS, and PhD from Stanford University, and founded the Work Innovation Lab at Asana as well as the Work AI Institute at Glean, first-of-their-kind corporate think tanks dedicated to cutting-edge research on the future of work. Her research is consistently featured in top-tier publications and has appeared in Harvard Business Review, The New York Times, The Wall Street Journal, Forbes, Fast Company, Wired, TIME, CNBC, Bloomberg, and the Washington Post, among others. And most recently, Rebecca is the author of the book, Your Best Meeting Ever. In this episode we discuss the following: At a time when our calendars are packed with meetings, Rebecca reminds us that meetings shouldn't just happen—they should be designed. Her "Meeting Doomsday" experiment was interesting: a simple 48-hour calendar purge saved employees an average of 11 hours per month by forcing them to rebuild their schedules with intentionality. A few simple strategies can go a long way: treat our meetings like a product. Fight our instinct to add, and instead use the "Rule of Halves" to cut the duration and/or attendees by 50%. Measure our "Return on Time Investment" (ROTI) with simple post-meeting pulse checks. If we want to overcome organizational inertia and Parkinson's Law—where work expands to fill the time allotted—we have to stop using meetings as a knee-jerk default and start seeing them as our most expensive, yet least optimized, business asset. And then design them carefully.

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

Episode 762: Neal and Toby chat about the revived sentiments of “sell America” amid Trump's beef with European countries, threatening tariffs over his pursuit for Greenland. Then, the biggest names in business are in Davos and are already making headliner statements. Also, Netflix reported earnings that just squeaked by expectations, citing the toughest competition for viewers in recent years. Meanwhile, liquor sales are waning and some major alcohol companies are sitting with a glut of spirits. Finally, a wrap up of the biggest headlines from the day.  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

Episode 761: Neal and Toby discuss the trade war brewing over in Europe as Trump indicates he wants Greenland because he was snubbed for the Nobel Peace Prize. Then, OpenAI thinks it can revolutionize the advertising space by playing ads within ChatGPT. Also, a new ‘mini-sphere' is landing in DC. Meanwhile, Toby dives into the trend of over-stated plots in Netflix content because they're thinking everyone is watching while on their phones. They're…not wrong? Finally, a wrap of headlines as we're recording from Davos!  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

When are meetings the best way to coordinate and make decisions and when do they make things worse?? How do you use the two-pizza rule to hold effective meetings and what happens when you start including too many people in a process?Rebecca Hinds is the head of the Work AI Institute at Glean and the author of Your Best Meeting Ever: 7 Principles for Designing Meetings That Get Things Done, a book outlining the way to address one of the ways productivity is lost in organizations.Greg and Rebecca discuss the importance of intentionality in information flow within organizations, the common pitfalls of meeting culture, and practical strategies to improve meeting efficiency. Rebecca emphasizes the use of data and AI to measure meeting effectiveness and reduce 'meeting bloat', while sharing insights from her experiences at Asana and her studies on organizational collaboration. They also explore the evolving collaboration between HR and IT departments in the era of AI and the necessity for both tech and HR professionals to exchange and enhance their skills.*unSILOed Podcast is produced by University FM.*Episode Quotes:How ‘visibIlity bias' fuels endless meetings[07:28] We know that humans have a bias to associate presence with productivity. And so what I find to be often the case is people start to associate more meetings with more importance and status within the organization, and so when you're stuck and not sure how to make progress or you're worried about productivity, a meeting becomes a knee-jerk solution to solve that. You might not accomplish anything meaningful in the meeting, but at least you've sat together and shown that some progress or perceived progress was made. And so I think at the core of this, is this pervasive productivity theater that goes on in organizations, this visibility bias where we associate meetings with importance within the organization. There are a host of other problems, but at the core, I think that's the fundamental problem that we're dealing with.The pressure ingrained in our calendars and meeting cultures[09:37]  As soon as someone extends a meeting invite. They're establishing this social contract where you feel like you have to reciprocate. Even when we think about terminology around, it's a meeting invite. You either accept or you reject. You start to feel like you're not just rejecting the meeting, but rejecting the person. And it's taken very personally. AI tools can help reveal participation imbalances in meetings[22:59] If you're seeing that leaders are consuming 70%, 80% of the airtime, that's an opportunity to course correct and improve your meeting effectiveness. And often when it comes from an AI tool or an objective analytic tool, it's much more effectively received than a less powerful person trying to voice that takeaway in the meeting and try to veer influence that way.Are we socially conditioned to hate meetings?[28:48] Humans have what I call a meeting suck reflex, right? For a multitude of different reasons.When we hear the word "meeting," we have this negative, visceral reaction. So much so that you know when you're asked to evaluate your meetings in public versus private, you tend to rate your meetings much more negatively when you're around people in public as compared to privately, because we think that we should hate meetings. We've been socially conditioned to feel such, and there's few things that bond coworkers more quickly than bonding over a bad meeting that could have been a five-line email, right? And so to avoid that, assessing whether a meeting was worth your time helps to level set. Everyone has an intuitive sense of whether a meeting was worth their time. Is there something more productive they could have done with that time or not? And so that tends to be a good gauge for you as an organizer.Show Links:Recommended Resources:Asana, Inc.Parkinson's lawSteven RogelbergLaw of TrivialityAmazon's Two-Pizza TeamsROTIRobert I. SuttonGuest Profile:RebeccaHinds.comThe Work AI Institute at GleanLinkedIn ProfileSocial Profile on X for GleanGuest Work:Your Best Meeting Ever: 7 Principles for Designing Meetings That Get Things Done Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Glean has grown into a $7.2B company by giving employees AI assistants and agents that extend their capabilities.CEO Arvind Jain is back on Grit alongside Joubin Mirzadegan. Here's what stood out:“My mindset by default is that if you build something last year, that it's got to be obsolete. There has to be a new way to do that thing better today. If not, then it's just lack of imagination.”“I have no doubts that AI capabilities are just going to increase more and more over the next few years. But even more important is this concept of how much are we even leveraging what AI can do today? I would say that we've not even used 1% of current capabilities of these models”“If you're trying to be everything to everyone, then you just cannot compete with somebody who's focused on a smaller problem and going deep into that.”You can also listen to Arvind's earlier episode here: https://www.youtube.com/watch?v=iIH0Qp6d6bg&list=PLRiWZFltuYPF8A6UGm74K2q29UwU-Kk9k&index=96Guest: Arvind Jain, founder and CEO, Glean​Connect with Arvind JainX: https://x.com/jainarvindLinkedIn: https://www.linkedin.com/in/jain-arvind/Connect with JoubinX: https://x.com/JoubinmirLinkedIn: https://www.linkedin.com/in/joubin-mirzadegan-66186854/Email: grit@kleinerperkins.comFollow on LinkedIn:https://www.linkedin.com/company/kpgritFollow on X:https://x.com/KPGrit​Learn more about Kleiner Perkins:https://www.kleinerperkins.com/