Podcasts about Shiv

In Episode 156 of “The Trusted Advisor,” RSPA CEO Jim Roddy sits down with Shiv Sundar, the Co-Founder of ISV Esper, to discuss his journey leading a company from its startup phase to over $100 million in investments with hundreds of customers, partners, and team members. “The Trusted Advisor,” powered by the Retail Solutions Providers Association (RSPA), is an award-winning content series designed specifically for retail IT VARs and software providers. Our goal is to educate you on the topics of leadership, management, hiring, sales, and other small business best practices. For more insights, visit the RSPA blog at www.GoRSPA.org.  The RSPA is North America's largest community of VARs, software providers, vendors, and distributors in the retail, restaurant, and grocery verticals. The mission of the RSPA is to accelerate the success of its members in the retail technology ecosystem by providing knowledge and connections. The organization offers member-to-member warm introductions, education, legal advice, industry advocacy, and other services to assist members with becoming and remaining successful. RSPA is most well-known for its signature events, RetailNOW and Inspire, which provide face-to-face learning and networking opportunities. Learn more by visiting www.GoRSPA.org.

Kildare singer-songwriter Shiv joins Dermot to talk about returning to Zimbabwe for the first time in 15 years.

Send us a text if you want to be on the Podcast & explain why!Thinking about becoming a personal trainer after 50?SUF has helped over 10,000 people pass NASM CPT. In this episode, we sit down with Shiv, who passed her NASM exam at age 59 using the Show Up Fitness Study Guide and is proving it's never too late to start a second career in fitness.We discuss: • Becoming a personal trainer later in life • Overcoming self-doubt and imposter syndrome • How she passed NASM • What the fitness industry is really like • Advice for aspiring trainers over 50 • Why experience and people skills matter more than age• Hands on learning with SUF CPT in Santa Monica

Guru Hi Brahma Guru Hi Vishnu Bapuji Shiv Swarup : AI Bhajan

Guru Hi Brahma Guru Hi Vishnu Bapuji Shiv Swarup : AI Bhajan

In this episode, Alex Rawlings speaks with Shiv Narayanan, CEO of How to SaaS, about how private equity firms can use marketing and AI as value creation levers. They discuss why many B2B companies underinvest in marketing, how AI is changing buyer behavior, and why brand authority and content are becoming critical for growth.Timestamps00:03 – Introduction to Shiv Narayanan and How to SaaS01:55 – Common marketing mistakes in PE-backed businesses04:15 – Finding the right marketing strategy and spend08:06 – How PE firms should assess CMOs12:49 – AI as a value creation lever14:40 – How AI search is changing buyer behavior16:02 – Why brand authority matters more than ever17:54 – AI agents and the future of marketing teams20:20 – The importance of investing in content22:44 – Why podcasts and video are growing in B2B marketing24:22 – The risk of overusing AI-generated content25:21 – Shiv's books and final adviceKey TakeawaysMany B2B companies rely too heavily on sales and underinvest in marketing.Strong CMOs focus on revenue, forecasting, and enterprise value creation.AI platforms like ChatGPT are reshaping how buyers research solutions.Businesses need strong brands and quality content to remain visible.Podcasts, YouTube, and thought leadership content are becoming essential.Raw Selection partners with Private Equity firms and their portfolio companies to secure exceptional executive talent. We focus on de-risking executive recruitment through meticulous search and selection processes, ensuring top-tier performance and long-term success.

Communication is one of the biggest things that can either build connection or slowly break it down in relationships. In this episode, Shiv and Dave have an honest conversation about what actually makes communication healthy, why so many of us struggle to express what we feel, and how understanding yourself is a huge part of being able to communicate well with someone else. They dive into emotional awareness and the difference between reacting versus truly communicating. This episode is a reminder that good communication is not about saying the perfect thing, it's about learning how to understand yourself enough to show up honestly, clearly, and with connection. *For our Spotify listeners who enjoy watching the podcast, I totally forgot to turn my camera off after the episode and lost the footage, whoops! So sorry, but the episode was too good to not launch without the video! -Shiv   Ready to do the work and go deeper in your own healing? Book a consultation call with us or find Dave's course here: Dave: https://stan.store/davemartinell Shiv: https://calendly.com/siobhanmartinell/30min   ⟡ Instagram:   / https://www.instagram.com/allofyoupodcast/ ⟡ TikTok:   / https://www.tiktok.com/@allofyoupodcast ⟡ Email: Hello@allofyoupodcast.com

Siobhan Priest - or Shiv - appears as @thefiremum on Instagram and I love seeing her posts about her life as a firefighter in the London Fire Brigade.Shiv started her working life as a model and continued in that job when she became a single mum to her daughter, at the age of 24. However she says she was always looking for a helping role in life. Firefighting appealed from an early age but it took many years for her to research it properly and then go for it when her second child, her little boy, was starting school.She loves the routine and stability that her job gives her, and being part of a watch where she has 10 ‘brothers'.Shiv says that every day is different, and putting out a fire is fun, as it's a hands-on job and they are professional problem-solvers.Shiv is proud of being a firefighter and of influencing other women to follow in her footsteps, and it was an absolute joy to meet someone so excited and fulfilled by their job.Extra info:Home Fire Safety Checker:Get tailored advice for your home or for someone you care for. Our tool allows you to carry out a thorough check of your home in only a few minutes. It's simple and practical, giving you specific advice for your family and your home. Visit: https://www.london-fire.gov.uk/home-fire-safety-checker/#ChargeSafe campaignOn average there is a fire from a lithium battery in an e-bike or e-scooter every two days in London. Many of these fires are caused by incompatible chargers, modifications to e-bikes, or faulty or counterfeit products which are purchased online. This includes chargers, lithium batteries and conversion kits for e-bikes. For safety tips, check out our campaign webpage: www.london-fire.gov.uk/chargesafeCadetsLondon Fire Brigade Fire Cadets is a free programme for young people aged 13-17, available across all London boroughs. The course gives young people a chance to learn valuable life skills and build their confidence, whilst gaining an insight into careers in the emergency services. https://www.london-fire.gov.uk/community/young-people/fire-cadets/Outreach/RecruitmentIf you are interested in finding out more about jobs/careers at London Fire Brigade, email: outreach@london-fire.gov.uk to request updates and info on recruitment activities and events. To find out more about Firefighter Siobhan Priest's typical working day, watch her in London Fire Brigade's You Tube film - https://www.youtube.com/watch?v=M0ASQfxHIO8Spinning Plates is presented by Sophie Ellis-Bextor, produced by Claire Jones and post-production by Richard Jones. Hosted on Acast. See acast.com/privacy for more information.

Communication is one of the biggest things that can either build connection or slowly break it down in relationships. In this episode, Shiv and Dave have an honest conversation about what actually makes communication healthy, why so many of us struggle to express what we feel, and how understanding yourself is a huge part of being able to communicate well with someone else. They dive into emotional awareness and the difference between reacting versus truly communicating. This episode is a reminder that good communication is not about saying the perfect thing — it's about learning how to understand yourself enough to show up honestly, clearly, and with connection.   Ready to do the work and go deeper in your own healing? Book a consultation or find Dave's course here: Dave: https://stan.store/davemartinell Shiv: https://calendly.com/siobhanmartinell/30min   ⟡ Instagram:   / https://www.instagram.com/allofyoupodcast/ ⟡ TikTok:   / https://www.tiktok.com/@allofyoupodcast ⟡ Email: Hello@allofyoupodcast.com

We all need someone who just gets it! In this session, Shiv will explore the importance of building godly connections with others in ministry.Shiv is the Children's Ministry Director at City Gates Church with 20 year's experience in kids & youth work and serves on the National Aspire Team.

Ore, Jesse and Shiv wrap up our stream with an audience Q&A.

Ore, Jesse and Shiv wrap up our stream with an audience Q&A.

We all need someone who just gets it! In this session, Shiv will explore the importance of building godly connections with others in ministry.Shiv is the Children's Ministry Director at City Gates Church with 20 year's experience in kids & youth work and serves on the National Aspire Team.

Shiv Rao is the CEO and Co-Founder of Abridge, a leader in generative AI for healthcare. The company reached a $5.3 billion valuation following a $300 million funding round with investors including Jensen Huang, Henry Kravis, USV, Bessemer Venture Partners and Elad Gill. A practicing cardiologist, Shiv has scaled the company to 450 employees and partnered with major health systems like Emory and Yale.  AGENDA: 04:00 — You just have to survive long enough to not die 06:00 — Did USV liking music make them billions of dollars? 13:58 — The three variants of an AI native company 15:00 — How do you know if foundation models are going to kill or help you? 22:15 — Why OpenAI and Anthropic doing consultancies is such an obvious move 41:00 — Biggest lesson from Jensen Huang at Nvidia 41:00 — What Founder Mode truly means 52:00 — What the founder of Duolingo taught me about sacrifice 56:37 — If I started a company again, Elad Gill would be the one investor I go to    

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

On this episode, Amy Kramer, Head of the Go-To-Market Operating Group at Level Equity, shares findings from their 2026 Go-To-Market Insights Report—a benchmark study covering sales efficiency, marketing spend, headcount, compensation and AI adoption across their portfolio companies.Amy and Shiv dig into what the data shows: why SDRs are having to reach out to more prospects to book the same number of meetings, how the best-performing teams are breaking through the noise with smarter targeting and more direct outreach, and why companies are shifting more budget toward brand and top-of-funnel. They also cover the rise of AI visibility as a pipeline driver, how teams are restructuring around rev ops and product marketing, and what the shift in go-to-market rhythm means for how companies hire and retain customers.The information contained in this podcast is not intended to constitute, and should not be construed as, investment advice.

When Shiv's hot take from a previous episode sparked a bigger conversation, we knew we had to go there. In this honest and unfiltered episode, Shiv and Dave dive into the question so many women quietly (or loudly) ask… are there actually good men out there? Together, they unpack the beliefs, wounds, experiences, and expectations that shape how we see men, relationships, and what it really means to be “good.” From challenging assumptions to exploring what personal growth actually looks like on both sides, this conversation is full of perspective shifts, hard truths, and plenty of hot takes along the way.   Ready to do the work and go deeper in your own healing? Book a consultation or find Dave's course here: Dave: https://stan.store/davemartinell Shiv: https://calendly.com/siobhanmartinell/30min   ⟡ Instagram:   / https://www.instagram.com/allofyoupodcast/ ⟡ TikTok:   / https://www.tiktok.com/@allofyoupodcast ⟡ Email: Hello@allofyoupodcast.com

In this episode, we're sharing the story of our breakup, what led to the relationship ending, what we didn't fully understand at the time, and some of the hard truths we both had to face after walking away. We talk about the heartbreak, the confusion, the season of no contact, what made Shiv decide to reach back out, and what gave us the confidence to believe trying again could actually be different. If you're navigating a breakup, questioning your relationship, or wondering if people can truly change, this conversation is for you. We share what had to change, both individually and together, how we rebuilt trust, and how we were able to create a relationship that feels healthier, safer, and more connected than ever before. This is a conversation about heartbreak, healing, second chances, and what it really takes to create lasting love.   Ready to do the work and go deeper in your own healing? Book a consultation or find Dave's course here: Dave: htthttps://stan.store/davemartinell Shiv: https://calendly.com/siobhanmartinell/30min   ⟡ Instagram:   / https://www.instagram.com/allofyoupodcast/ ⟡ TikTok:   / https://www.tiktok.com/@allofyoupodcast ⟡ Email: Hello@allofyoupodcast.com

Gothic Industrial Music Ep194 - EBM - Darkwave - Electro-Industrial 2026https://www.youtube.com/@shadowsradiomixes0:00:00 - The Synthetic Dream Foundation – Eclipse of the Mechanical Seraphim0:04:26 - Rabia Sorda – Indestructible0:08:37 - Noisuf-X – Nervouz Beatz0:13:28 - Agonoize – Slave To The Needle0:18:46 - E-CRAFT – From Above (instrumental)0:25:49 - Die Form – Rain Of Blood (Download Remix)0:30:34 - Necro Facility – Tuxedo (Tentacle Remix)0:34:31 - Totem Obscura – Waldgeist (Endzeit Edit)0:38:22 - E Nomine – Mitternacht [Deutsche Synchronstimme: Robert de Niro]0:42:05 - Shiv-r – Eye of the Needle (Thanosmylonas Remix)0:46:23 - Skinny Puppy – Rodent (Ken 'Hiwatt' Marshal Remix (DDT Mix))0:53:29 - In Strict Confidence – Promised Land

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What does it actually take to build a pitch competition that changes lives and keeps founders in their hometown? In this episode, Molly King, VP of Clients at Economic Impact Catalyst, sits down with Cenzo Caronna and Shivang Thakor of STartUP Northshore to pull back the curtain on the Inspire Startup Slam, one of the Gulf South's largest pitch competitions, running out of a 100-year-old theater in Hammond, Louisiana.Cenzo and Shiv share how they've built a $100K prize ecosystem (combining $50K in non-dilutive grant cash with in-kind services) serving a three-parish rural region north of New Orleans, and why the event they designed for "the person who got dragged there" has become the cornerstone of their startup ecosystem.In this episode:How STartUP Northshore came together across St. Tammany, Tangipahoa, and Washington Parishes, and why trust-building in rural communities requires a boots-on-the-ground, not broadcast, approachThe full founder journey: from the ID Institute 12-week accelerator → Launchpad ($5K competition) → Inspire Startup Slam ($50K)Why they scrapped winner-take-all after year one and what they changedThe six judging criteria, including one most competitions miss: commitment to and potential impact for the North ShoreHow they built a $100K prize pool starting with their own money, a Chevron partnership, and a cold call from Capital OneThe pro tip that saved them $28,000 (hint: book a theater, not a conference room)Why in-kind services like accountants, marketing firms, and coworking space may matter more than the cash prizeLeading vs. lagging indicators: why business formation is a "vanity metric" and what STartUP Northshore actually tracksPractical advice for other program managers: expect the unexpected, do non-scalable things, and text your foundersThis year's winner didn't make the finals the previous year. He kept building with STartUP Northshore's support and came back to win the whole thing.Guests:Cenzo Caronna, Executive Director, STartUP NorthshoreShivang Thakor, Program Manager, STartUP NorthshoreHost: Molly King, VP of Clients, Economic Impact Catalyst

On this episode adapted from a recent webinar, Shiv Narayanan, CEO and Founder of How To SaaS and bestselling author of AI Marketing Blueprint, explains how companies can adapt their go-to-market and thrive in an AI-first world.Companies everywhere are missing their revenue targets, and the old playbook is breaking down. Inbound is getting more expensive, more competitive, and less effective.Why? Because, in an AI-first world, most of a buyer journey is invisible. What once required research, website visits, and sales conversations can now happen in seconds — without ever engaging your business.Companies need to rethink their go-to-market and focus on increasing visibility and authority to reduce the risk of AI disruption. In this episode, you'll get a practical, evergreen framework that outlines the 7 new rules CEOs, investors, and revenue leaders need to focus on to stay competitive.The information contained in this podcast is not intended to constitute, and should not be construed as, investment advice.

There's a pill on Amazon called Fukitol.  It contains nothing. And yet people buy it, swear by it, and give it five stars.  Today, Nir Eyal explains the remarkable science behind why placebos work. --- Listen to the bonus episode: https://nudge.kit.com/40414a1b44 Nir's book Beyond Belief: geni.us/beyondbelief Nir's free belief change guide: nirandfar.com/belief-change Join 11,934 readers of the Nudge Newsletter: https://www.nudgepodcast.com/mailing-list Unlock the Nudge Vaults: https://www.nudgepodcast.com/vaults Connect on LinkedIn: https://www.linkedin.com/in/phill-agnew/  --- Today's sources:  Ariel, G., & Saville, W. (1972). Anabolic steroids: The physiological effects of placebos. Medicine and Science in Sports and Exercise, 4(2), 124–126. Branthwaite, A., & Cooper, P. (1981). Analgesic effects of branding in treatment of headaches. British Medical Journal (Clinical Research Ed.), 282(6276), 1576–1578. Dawkins, L., Shahzad, F. Z., Ahmed, S. S., & Edmonds, C. J. (2011). Expectation of having consumed caffeine can improve performance and mood. Appetite, 57(3), 597–600. Draganich, C., & Erdal, K. (2014). Placebo sleep affects cognitive functioning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40(3), 857–864. Kaptchuk, T. J. (2018). Open-label placebo: Reflections on a research agenda. Perspectives in Biology and Medicine, 61(3), 311–334. Lee, C., Linkenauger, S. A., Bakdash, J. Z., Joy-Gaba, J. A., & Profitt, D. R. (2011). Putting like a pro: The role of positive contagion in golf performance and perception. PLoS One, 6(10), e26016. Plassmann, H., O'Doherty, J., Shiv, B., & Rangel, A. (2008). Marketing actions can modulate neural representations of experienced pleasantness. Proceedings of the National Academy of Sciences, 105(3), 1050–1054. Richter, C. P. (1957). On the phenomenon of sudden death in animals and man. Psychosomatic Medicine, 19(3), 191–198. Rozenkrantz, L., Mayo, A. E., Ilan, T., Hart, Y., Noy, L., & Alon, U. (2017). Placebo can enhance creativity. PLoS One, 12, e0182466. Wager, T. D., Rilling, J. K., Smith, E. E., Sokolik, A., Casey, K. L., Davidson, R. J., et al. (2004). Placebo-induced changes in fMRI in the anticipation and experience of pain. Science, 303(5661), 1162–1167.

Shiv Patel is in the Acement this week. Shiv was born in India but grew up in RI. A fan of comedy from a young age but took awhile before he decided to make his stand up debut. What drew him to the stage? Why did he take a break? What was his Funny Bad Gig? Follow Shiv on Instagram @shivthecomealtorCheck out Ground Control Radio at https://www.groundcontrolradio.net/Check out Ace at ⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.aceaceto.lol/⁠⁠⁠⁠⁠⁠⁠⁠⁠ and Scott at ⁠⁠⁠⁠⁠⁠⁠⁠⁠https://scotthigginscomedy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠Go to ⁠⁠⁠⁠⁠https://www.buymeacoffee.com/behindthefQ⁠⁠⁠⁠⁠ to buy us a coffee or a bourbon. Get your BTF gear at ⁠⁠⁠⁠⁠https://btfpodstore.dashery.com/ 

A CMO Confidential Interview with Shiv Singh, CEO of Savvy Matters, Co-Founder of AI Trailblazers, former CMO of Lending Tree, and author of Marketing with AI for Dummies. Shiv discusses how to operate in an environment where "We have no idea about what is coming," how managing humans and AI agents impacts leadership, and why he believes marketers will need to make fewer, harder decisions. Key topics include: why losing isn't fatal, but forgetting is; the risk of thinking human beings are special; and the concept of "invisible failure." Tune in to hear why you should rewrite your job description and become a "wartime" leader.Topics Covered:- Where Is AI Taking Marketing?- Leadership in a Human + AI Workforce- Fewer, Harder Strategic Bets for CMOs- AI's Impact on Creativity and Marketing Processes- Context Graphs, Memory, and the Future of Marketing OperationsThis episode is sponsored by Typeface - the agentic AI marketing platform that turns one idea into thousands of on-brand assets. Learn more: typeface.ai/cmo Subscribe for weekly episodes featuring world-class marketing leaders, board members, and C-Suite executives.⏱️ Chapters00:00 – Welcome to CMO Confidential01:38 – Meet Shiv Singh02:35 – Where is AI Taking Marketing? The "Jagged Frontier" of AI06:10 – What Does Leadership Look Like Managing Humans + AI Agents09:35 – Making Fewer, Harder Strategic Bets10:50 – Budget Pressure, Boards, and AI Expectations14:43 – Invisible Failure in AI Adoption17:26 – AI and the Future of Creativity21:26 – Governance, Consumer Acceptance, and AI Bias26:05 – The End of the Marketing Brief27:40 – Who Remembers Wins: Context Graphs Explained31:47 – Building a Marketing Context Layer35:30 – What Marketers Should Be Doing Now37:38 – Anthropic vs. Government: AI Ethics & Risk42:38 – Predictions Through 202645:04 – The Changing Definition of a Marketer45:50 – Funniest Story and Practical Advice#AIMarketing #MarketingLeadership #CMOConfidential #ShivSingh #GenerativeAI #FutureOfMarketing #AIStrategy #CreativeAI #MarketingTransformation #EnterpriseAISee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

On this week's Medics' Money podcast, host Cyra (a chartered accountant and doctor) interviews BMA UK Resident Doctor's Committee leaders Shiv (deputy chair, psychiatry ST3) and Arjun (education and training co–deputy chair, BMA director) about ongoing resident doctor strikes. They say negotiations after December strikes were initially constructive but collapsed when the government shifted a proposed pay investment from two years to three, reduced it, and labeled it a “best and final offer,” then refused to negotiate while strikes were called. They criticise the Prime Minister's removal of 1,000 training posts, warning it worsens workforce shortages, creates bottlenecks, and harms patients. They discuss job insecurity, doctor substitution, training capacity concerns, and NHS mismanagement and waste, arguing a credible pay offer and clear plans for training posts and progression are needed to end strikes.00:00 Training Posts Cut Fallout01:04 Podcast Intro and Guests02:16 Why Resident Doctors Strike05:17 Talks Breakdown and Pay08:23 Jobs Bottlenecks and Trust12:59 NHS Decline and Waste16:22 Doctor Substitution Risks23:25 Ending Strikes and Deal Terms26:37 Strike Timing and Consultants30:41 Costs and Devaluation Claims32:40 Tiered Care and Culture34:25 Personal Stories and ClosingWant the latest financial tips for doctors and exclusive invites? Join 71,000 doctors here https://www.medicsmoney.co.uk/join-medics-money/Want a free assessment of your finances? Click here https://medics-hnz5twj1.scoreapp.comWant to improve your finances fast? Then come on our coursehttps://www.medicsmoney.co.uk/medics-money-financial-wellbeing-course/Want to find out more about our other courses?www.medicsmoney.co.uk/coursesFollow us on InstagramFollow us on TwitterDisclaimer:The information provided in this content is for educational and informational purposes only and does not constitute financial advice. You should not rely on this content as a substitute for professional advice tailored to your specific financial situation. The value of your investments can go down as well as up. Past performance is not indicative of future results.

Internal medicine and functional medicine physician Shiv K. Goel discusses the article "Why lifestyle matters more than BPC-157 and semaglutide." Shiv explains how the modern longevity boom has trained patients to seek out quick fixes like peptides and weight loss injections while ignoring foundational habits. He shares a compelling patient story to illustrate why optimizing sleep, circadian rhythms, and stress management is far more powerful than any supplement drawer. Shiv argues that tracking endless biomarkers with wearables without understanding a patient's personal story only creates anxiety. By prioritizing deep listening and addressing the root causes of metabolic dysfunction, clinicians can offer real solutions rather than just another health care transaction. Discover how aligning your daily routines with your biology is the ultimate biohack for a longer, healthier life. Partner with me on the KevinMD platform. With over three million monthly readers and half a million social media followers, I give you direct access to the doctors and patients who matter most. Whether you need a sponsored article, email campaign, video interview, or a spot right here on the podcast, I offer the trusted space your brand deserves to be heard. Let's work together to tell your story. PARTNER WITH KEVINMD → https://kevinmd.com/influencer SUBSCRIBE TO THE PODCAST → https://www.kevinmd.com/podcast RECOMMENDED BY KEVINMD → https://www.kevinmd.com/recommended

What'd you like? Send us a text.FIRST:Jim gets spammed with politically incorrect jokes. Naked old men at the gym. Prison myths. And we find out Jim used to call his penis Captain B.B. THEN: In honor of St. Paddy's Day, Tim, Jim, and Walt discuss the old classic Irish movie "The Quiet Man." And Tim professes his love for Ireland, not that he's ever been there, because of how it probably smells (like stale beer).

Internal medicine and functional medicine physician Shiv K. Goel discusses his article "Agentic AI in medicine: the danger of automating the doctor." Shiv analyzes the new ARPA-H "ADVOCATE" program which aims to deploy autonomous AI agents for heart disease care within three years. The conversation highlights the critical difference between processing data and understanding a patient, noting that AI cannot read the fear in a voice or the silence between words. Shiv warns of the "liability black hole" that arises when algorithms make high-stakes decisions and argues that technology must serve the healer rather than replace the human connection. Discover why the next three years will determine whether code redefines the sacred responsibility of medicine. Partner with me on the KevinMD platform. With over three million monthly readers and half a million social media followers, I give you direct access to the doctors and patients who matter most. Whether you need a sponsored article, email campaign, video interview, or a spot right here on the podcast, I offer the trusted space your brand deserves to be heard. Let's work together to tell your story. PARTNER WITH KEVINMD → https://kevinmd.com/influencer SUBSCRIBE TO THE PODCAST → https://www.kevinmd.com/podcast RECOMMENDED BY KEVINMD → https://www.kevinmd.com/recommended

Lawrence Zarian welcomes actress Justine Lupe to his podcast and they joke about prisms, John Hamm comparisons, and first meeting at The Kelly Clarkson Show. Lawrence asks Justine what she sees in the mirror, and she says she most identifies through her relationships—as a daughter, mother, sister, wife, and friend—crediting her close, codependent upbringing for prioritizing intimacy and safety over career volatility. They discuss her early creative spark (playing the Wicked Witch in The Wizard of Oz), her New York years, and her work on Succession (choosing Shiv on a desert island) and Nobody Wants This, including Morgan's vulnerability, fearlessness, and dynamic with Sasha. Justine talks about transparency around body image and social media, advises focusing on soulfulness over aesthetics, and defines relationship essentials as mutual respect, vulnerability, and optimism. She ends by stating she is beautiful because she is soulful, kind, thoughtful, and deeply witnesses the people she loves.Timestamp Menu: 00:17 Prism Compliments00:58 John Hamm Mixup02:10 Kelly Clarkson Encounter05:33 Singing Suddenly Seymour07:06 Finding The Right Fit08:47 Mirror Question Identity11:21 Family Roots Of Closeness15:19 Midwest And New York Years16:58 Early Spark For Acting20:00 Parents And Healing23:10 Succession Breakout26:32 Desert Island Roy Pick28:57 Nobody Wants This Praise29:43 Fearlessness From Dad30:41 Embracing Goofy Humor32:56 Fearless Creative Risks33:35 Trusting the Film Crew35:56 Desert Island Character Pick36:48 Why Morgan Resonates39:25 Morgan and Sasha Chemistry41:33 Body Image and Transparency43:56 Advice for Social Media Pressure53:23 Season Two and Relationship Keys58:21 Beauty Definition and Farewell

Internal medicine physician Shiv K. Goel discusses his article "Claude for Healthcare vs. administrative burden: a physician's review." Shiv contrasts the two hours he spent fighting a prior authorization with the promise of Anthropic's new "Claude for Healthcare," an AI system designed to handle claims and verify coverage in minutes. The conversation explores the race between Anthropic and OpenAI to dominate medical AI and the potential for these tools to liberate physicians from paperwork. Shiv warns, however, that without physician input, these efficiencies could simply be used to increase patient quotas rather than improve care. Discover whether the AI administrator is the solution to burnout or a new threat to the profession. Partner with me on the KevinMD platform. With over three million monthly readers and half a million social media followers, I give you direct access to the doctors and patients who matter most. Whether you need a sponsored article, email campaign, video interview, or a spot right here on the podcast, I offer the trusted space your brand deserves to be heard. Let's work together to tell your story. PARTNER WITH KEVINMD → https://kevinmd.com/influencer SUBSCRIBE TO THE PODCAST → https://www.kevinmd.com/podcast RECOMMENDED BY KEVINMD → https://www.kevinmd.com/recommended

Guru Hi Shiv Hain : AI Bhajan

Guru Hi Shiv Hain : AI Bhajan

Atma Shiv Ki Upasana : Pujya Sant Shri Asharamji Bapu Satsang

Atma Shiv Ki Upasana : Pujya Sant Shri Asharamji Bapu Satsang

Our limiting beliefs and insecurities don't come out of nowhere—they're learned, often in moments where we were trying to protect ourselves. In this episode, Dave and Shiv explore how these patterns form and why they can feel so hard to break. Dave shares how his beliefs around working out kept him from going to the gym for years, while Shiv opens up about how past trauma led her to suppress her feelings and avoid vulnerability. We talk about how these thoughts once helped us feel safe, but can quietly limit our relationships, growth, and self-trust as adults. This conversation is about understanding your patterns with compassion—and learning how to move beyond them without shame.   Ready to do the work and go deeper in your own healing? Book a consultation call with us: Dave: https://calendly.com/dlmartinell/30min Shiv: https://calendly.com/siobhanmartinell/30min   ⟡ Instagram:   / https://www.instagram.com/allofyoupodcast/ ⟡ TikTok:   / https://www.tiktok.com/@allofyoupodcast ⟡ Email: Hello@allofyoupodcast.com

In this podcast episode, Shiv is in conversation with Bhargav Bhikkaji, a tech leader, entrepreneur, and founder-CTO of MajorDomo Inc with deep expertise in cloud and Gen-AI driven enterprise software. In this episode of Software People Stories, Bhargav shares his inspiring journey from his early interest in physics and mathematics to becoming a DevOps pioneer. Initially focused on engineering and computer networking, Bhargav's career path led him through various roles at HCL Cisco and Dell before transitioning to an entrepreneurial venture. He describes the challenges of moving back to India, starting a consulting business, and eventually founding a company that focuses on DevOps and AI deployment solutions. Bhargav emphasizes the importance of understanding customer needs, continuous learning, and staying grounded through personal practices like long-distance running. 00:00 Introduction and Welcome00:31 Early Interests and Education03:13 First Steps in Networking04:21 Career Growth and Innovations05:13 Transition to DevOps and Kubernetes07:23 Starting a Consulting Business10:27 Challenges and Strategies in Networking18:08 Patents and Open Source Debate26:36 Navigating the Transition from Technical Expert to Entrepreneur27:42 Understanding Customer Needs in Product Development29:45 Balancing Immediate Customer Demands with Long-Term Solutions32:10 Managing Multiple Roles in a Startup33:49 The Importance of Customer Conversations and Product Market Fit34:36 Challenges in Developing Developer Tools37:42 Articulating the Value Proposition of Major Domo40:41 Handling Rollbacks and Ensuring Transaction Integrity46:52 Keeping Pace with Rapid Technological Changes49:00 Staying Grounded Amidst the Chaos50:57 Conclusion and Final ThoughtsThe timestamps are approximate and do not include the time for the intro. Add about 90 seconds to locate the section.Bhargav Bhikkaji is a tech leader, entrepreneur, and founder-CTO of MajorDomo Inc with deep expertise in cloud and Gen-AI driven enterprise software. As the Founder, Bhargav leads the development of platform solutions that simplify and automate complex cloud-native and Gen-AI operations. He is passionate about bridging the gap between cutting-edge technologies and real-world outcomes, sharing insights on GenAI, RAG, LLMOps, enterprise AI, and governance through talks, articles, and video series. Bhargav's work blends engineering excellence with strategic thinking, helping teams adopt scalable, secure, and cost-effective cloud and AI solutions. Outside work, Bhargav enjoys endurance running and often reflects on resilience and growth—bringing the same mindset to tech and leadership that he applies in life's challengesOne can reach out to Bhargav Bhikkaji at https://www.linkedin.com/in/bhargav-bhikkaji and http://x.com/bbhikkaji

Checkout ASUS ExpertBook P Series: ⁠⁠https://www.flipkart.com/bbd-eb-intrigue-at-store⁠⁠Guest Suggestion Form: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://forms.gle/bnaeY3FpoFU9ZjA47⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Disclaimer: This video is intended solely for educational purposes and opinions shared by the guest are his personal views. We do not intent to defame or harm any person/ brand/ product/ country/ profession mentioned in the video. Our goal is to provide information to help audience make informed choices. The media used in this video are solely for informational purposes and belongs to their respective owners.Order 'Build, Don't Talk' (in English) here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://amzn.eu/d/eCfijRu⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Order 'Build Don't Talk' (in Hindi) here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://amzn.eu/d/4wZISO0⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow Our Whatsapp Channel: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.whatsapp.com/channel/0029VaokF5x0bIdi3Qn9ef2J⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe To Our Other YouTube Channels:-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@rajshamaniclips⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@RajShamani.Shorts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠(00:00) - Intro(03:58) - The role of a board of advisors(09:14) - A company that flourished with the help of its board of directors(11:55) - Why big companies are slower than small companies(20:38) - How feedback makes you better(27:53) - What small companies underestimate about big companies(34:43) - Billion-dollar CEOs and their biggest fear(38:18) - How large companies kill innovative ideas(45:19) - Why India doesn't have enough capital backing(49:48) - BlackBerry & Nokia: why they vanished(52:53) - Why large companies employ consultants(55:10) - How young founders can beat giants(57:05) - Three business niches(1:00:39) - Dominating one business(1:06:33) - Unilever: one thing that changed the market and one that failed(1:12:15) - How to build trust in a low-trust society(1:14:41) - Differences between Indian, American, and Chinese customers(1:18:46) - Convincing low-income consumers to buy phones(1:21:41) - Which sector sees maximum growth as a country develops(1:26:24) - OutroIn today's episode, we have Shiv Shivakumar, ex-CEO of Nokia (Emerging Markets) and PepsiCo India, to cover how startups build massive brands and why big companies struggle to keep up. We also talk about understanding customers across India, the US, and China, and how trust, authenticity, and relevance are critical to scaling a business in a low-trust society. From building trust with new audiences to creating brands that last, Shiv shares practical insights for founders and leaders who want to win big.Subscribe for more such conversations!Follow Shiv Shivakumar On:LinkedIn: https://www.linkedin.com/in/shivshivakumar/X: https://x.com/ShivShivakumar⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠About Raj ShamaniRaj Shamani is an Entrepreneur at heart that explains his expertise in Business Content Creation & Public Speaking. He has delivered 200+ speeches in 26+ countries. Besides that, Raj is also an Angel Investor interested in crazy minds who are creating a sensation in the Fintech, FMCG, & passion economy space.To Know More,Follow Raj Shamani On ⤵︎Instagram @RajShamani ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/rajshamani/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Twitter @RajShamani ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/rajshamani⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook @ShamaniRaj ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.facebook.com/shamaniraj⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn - Raj Shamani ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/rajshamani/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠About Figuring OutFiguring Out Podcast is a Candid Conversations University where Raj Shamani brings raw conversations with the Top 1% in India.

In this episode, Dave and Shiv share why making peace with your past is foundational to healing, emotional safety, and healthy relationships. They explore how unresolved experiences quietly shape the way we show up, love, and protect ourselves. Shiv vulnerably shares some of the hardest parts of her own past to make peace with, and what that process has actually looked like in real life. This conversation is an invitation to soften toward your story, release what no longer needs to define you, and begin relating from a place that feels more grounded, honest, and free.   Ready to do the work and go deeper in your own healing? Book a consultation call with us: Dave: https://calendly.com/dlmartinell/30min Shiv: https://calendly.com/siobhanmartinell/30min   ⟡ Instagram:   / https://www.instagram.com/allofyoupodcast/ ⟡ TikTok:   / https://www.tiktok.com/@allofyoupodcast ⟡ Email: Hello@allofyoupodcast.com

Internal medicine and functional medicine physician Shiv K. Goel discusses his article "Mind-body connection in chronic disease: Why traditional medicine falls short." Shiv explores the limitations of conventional medical models that focus solely on acute intervention while ignoring the emotional roots of chronic conditions. He shares the compelling story of Linda whose physical ailments were deeply connected to decades of suppressed grief and trauma. The conversation delves into the science of psychoneuroimmunology and how unprocessed emotions get stored in the body's tissues eventually manifesting as disease. Shiv explains why true healing involves not just medication but also meditation and somatic therapy to rewrite the body's internal narrative. Listen to discover how changing your consciousness can fundamentally alter your biological reality. Partner with me on the KevinMD platform. With over three million monthly readers and half a million social media followers, I give you direct access to the doctors and patients who matter most. Whether you need a sponsored article, email campaign, video interview, or a spot right here on the podcast, I offer the trusted space your brand deserves to be heard. Let's work together to tell your story. PARTNER WITH KEVINMD → https://kevinmd.com/influencer SUBSCRIBE TO THE PODCAST → https://www.kevinmd.com/podcast RECOMMENDED BY KEVINMD → https://www.kevinmd.com/recommended

Holding space is one of the most powerful and misunderstood relationship skills. In this episode, Shiv and Dave break down what emotional validation really is, why it's what every partner wants when they are upset, and what makes it so hard to offer in the moment. They explore the blocks to holding space, from personalizing and ego stories to emotional immaturity and share real examples from their own relationship around home, family, and navigating hard conversations without defensiveness. You'll also learn practical tools for doing this well, like checking for capacity, getting clear on your own feelings first, and learning how to ask to be seen instead of fixed. If you want deeper connection, more emotional safety, and fewer reactive cycles in your relationship, this episode is for you.    Ready to do the work and go deeper in your own healing? Book a consultation call with us: Dave: https://calendly.com/dlmartinell/30min Shiv: https://calendly.com/siobhanmartinell/30min   ⟡ Instagram:   / https://www.instagram.com/allofyoupodcast/ ⟡ TikTok:   / https://www.tiktok.com/@allofyoupodcast ⟡ Email: Hello@allofyoupodcast.com

Family estrangement is one of the most painful and complex relationship experiences a person can walk through, and this episode approaches it with honesty, compassion, and deep care. Dave shares his personal journey with family estrangement, what it has cost him, what it has taught him, and how he's been able to create healthier relationships on the other side. Shiv and Dave explore estrangement from the perspectives of the parent, adult child, and sibling, and the emotional dynamics that often shape these ruptures. The conversation unpacks why someone may choose distance, the grief that follows for everyone involved, and the critical importance of validating the hurt of the person who comes to you — because that validation can be the difference between preserving a relationship or losing it. A grounded, tender episode about boundaries, repair, and the courage it takes to do family differently.   Ready to do the work and go deeper in your own healing? Book a consultation call with us: Dave: https://calendly.com/dlmartinell/30min Shiv: https://calendly.com/siobhanmartinell/30min   ⟡ Instagram:   / https://www.instagram.com/allofyoupodcast/ ⟡ TikTok:   / https://www.tiktok.com/@allofyoupodcast ⟡ Email: Hello@allofyoupodcast.com

The holidays are often framed as the happiest time of the year… but for many, they can also be the hardest. In this episode, Dave and Shiv talk honestly about why the holiday season can feel so heavy, from unmet expectations and family dynamics to grief, loneliness, and the invisible mental load—especially for women. Shiv shares why she used to dread the holidays, and together they explore what it's like to navigate this season when you're already on emotional thin ice. A real, compassionate conversation for anyone trying to hold space for what this time of year actually brings.   Ready to do the work and go deeper in your own healing? Book a consultation call with us: Dave: https://calendly.com/dlmartinell/30min Shiv: https://calendly.com/siobhanmartinell/30min   ⟡ Instagram:   / https://www.instagram.com/allofyoupodcast/ ⟡ TikTok:   / https://www.tiktok.com/@allofyoupodcast ⟡ Email: Hello@allofyoupodcast.com

In this episode, we unpack the complicated relationship between trauma and growth—and why going through something hard isn't what makes you stronger. We talk about the difference between experiencing trauma and actually healing from it, and Shiv opens up about how the saying “what doesn't kill you makes you stronger” she was told in the wake of extreme loss did more harm than good. We also dig into the way our upbringing shapes us, especially the belief that harshness builds resilience. (Spoiler: yelling doesn't make kids stronger—it creates insecure attachment.) By looking at our childhood patterns with honesty and compassion, we can understand ourselves more clearly and choose a healthier path forward. It's a tender, grounding conversation about growth without glorifying the pain that came before it.   Ready to do the work and go deeper in your own healing? Book a consultation call with us: Dave: https://calendly.com/dlmartinell/30min Shiv: https://calendly.com/siobhanmartinell/30min   ⟡ Instagram:   / https://www.instagram.com/allofyoupodcast/ ⟡ TikTok:   / https://www.tiktok.com/@allofyoupodcast ⟡ Email: Hello@allofyoupodcast.com

Even though Shaun Morash and Nikki Gist have crossed paths before, Gio believes Morash when he says he didn't see the Daboll tweet. Gio thinks both were working off the same source ‘shivving' Brian Daboll — but one caller is not happy with Gio for backing his friend!

#160 Being single can come with real challenges — from loneliness to questions of self-worth to the tension of waiting on God's timing. In this week's episode, Kim and Charlotte sit down with Shivani, one of their seasoned single co-workers, to talk honestly about what makes singleness hard and how she's learned to navigate through those things.Shiv opens up about: • Loneliness — and what it looks like to come to God honestly instead of avoiding Him • Self-Worth — how to stop settling for “scraps” of attention and anchor your identity in God's delight • Waiting — how to trust God's timing, live fully now, and embrace the life He's given youIf you've ever wrestled with unmet desires or wondered if God sees you, this episode will remind you that He does — and that He's writing a beautiful story in your life, even in the waiting.Listen in for honest stories, practical encouragement, and a reminder that you don't have to wait for marriage to live a full, faith-filled life. Resources mentioned in this episode:None Like Him: 10 Ways God is Different Than Us by Jen Wilkin The Attributes of God by A.W. TozerSome of the verses on Shiv's scripture ring:Zephaniah 3:17Galatians 4:7 Isaiah 49:16  Isaiah 43:4  Ephesians 1:4Ephesians 2:10 For more information about the podcast and the Loving Your Husband Before You Even Have One book, please visit ⁠www.kimvollendorf.com⁠ and www.amazon.com.To learn more about StuMo, visit https://www.stumo.org/For video clips from this episode, please visit our instagram page ⁠@sixonesis⁠Thanks for listening!

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