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Get our Unsexy Business Idea Database: https://clickhubspot.com/sfci Episode 831: Sam Parr ( https://x.com/theSamParr ) and Shaan Puri ( https://x.com/ShaanVP ) tell the story of a $270M/yr meat purveyor plus Nick Sleep's letter and the framework that will make Elon a trillionaire. — Show Notes: (0:00) The $300M butcher (18:28) Nick Sleep letters (23:20) the idiot index (35:44) breaking your own frame (39:04) how to become a king maker (51:49) Idea: Teen nerd awards (57:23) Sam's List — Links: • https://www.lafrieda.com/ • https://www.omahasteaks.com/ • https://drinklmnt.com/ — Check Out Sam's Stuff: • Hampton (joinhampton.com): My community for founders. Average member does $25m/year. Many of the guests are members. Get after it...apply: http://joinhampton.com/mfm — Check Out Shaan's Stuff: • Shaan's weekly email - https://www.shaanpuri.com • Visit https://www.somewhere.com/mfm to hire worldwide talent like Shaan and get $500 off for being an MFM listener. Hire developers, assistants, marketing pros, sales teams and more for 80% less than US equivalents. • Mercury - Need a bank for your company? Go check out Mercury (mercury.com). Shaan uses it for all of his companies! Mercury is a financial technology company, not an FDIC-insured bank. Banking services provided by Choice Financial Group, Column, N.A., and Evolve Bank & Trust, Members FDIC • I run all my newsletters on Beehiiv and you should too + we're giving away $10k to our favorite newsletter, check it out: beehiiv.com/mfm-challenge My First Million is a HubSpot Original Podcast // Brought to you by HubSpot Media // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano /
Paul Tran started Manscaped with $50,000, a bloody problem nobody was talking about, and a category that didn't exist. The company hit $300 million in revenue in just 36 months, eventually turned down a $1 billion SPAC deal, and has become the #3 men's grooming brand in a category dominated by companies over 100 years old—while staying profitable the entire way. In this interview, the founder and CEO of Manscaped breaks down the exact DTC playbook that got him from 10,000 units sold out in two weeks to nine figures in annual media spend, why he waited until $50–60 million in marketing spend before entering retail, and the counterintuitive brand decisions—including turning down better-performing ads—that built one of the most recognizable men's lifestyle brands in the world. What you'll learn in this interview: • How Paul identified a completely unaddressed category and validated it with just 10,000 units and $5-a-day Facebook ads • Why Manscaped had lower revenue than Paul's other two businesses at launch—and the three signals that told him it had the highest potential • The $18,000 mistake that wiped out a third of the starting budget in one hour—and what it taught him about brand vs. performance media • Why he deliberately waited until $50–60 million in annual media spend before entering retail—and why most brands jump in too early • The brand values decision that cost them short-term revenue: why they turned down better-converting ads that used provocative imagery • How 66% of first-time buyers chose a starter kit—and the bundle-testing framework behind it • Why he walked away from a $1 billion SPAC deal in 2021—and why that decision looks like genius three years later • The post-purchase upsell structure that turns a single transaction into a lifetime customer • Why consumer brands should never adopt the VC "raise and burn" playbook—and how Manscaped scaled to $300M while staying profitable • What Paul would do radically differently if he started today—and why AI changes the entire early-stage playbook If you're building a DTC brand, trying to figure out the right time to go into retail, or looking for the real story behind how a category-defining brand gets built from scratch on a shoestring, this conversation will fundamentally change how you think about timing, positioning, and what profitable scale actually looks like. SAVE 50% ON OMNISEND FOR 3 MONTHS Get 50% off your first 3 months of email and SMS marketing with Omnisend with the code FOUNDR50. Just head to https://your.omnisend.com/foundr to get started. WANT TO GROW YOUR BRAND WITH META ADS? Join the Foundr Operators Waitlist → https://foundr.com/operators HOW WE CAN HELP YOU SCALE YOUR BUSINESS FASTER Learn directly from 7, 8 & 9-figure founders inside Foundr+ Start your $1 trial → https://www.foundr.com/startdollartrial PREFER A CUSTOM ROADMAP AND 1-ON-1 COACHING? → Starting from scratch? Apply here → https://foundr.com/pages/coaching-start-application → Already have a store? Apply here → https://foundr.com/pages/coaching-growth-application CONNECT WITH NATHAN CHAN Instagram → https://www.instagram.com/nathanchan LinkedIn → https://www.linkedin.com/in/nathanhchan/ CONNECT WITH PAUL TRAN Instagram → https://www.instagram.com/paultran/ LinkedIn → https://www.linkedin.com/in/paulhtran/ Website → https://www.manscaped.com/ FOLLOW FOUNDR FOR MORE BUSINESS GROWTH STRATEGIES YouTube → https://bit.ly/2uyvzdt Website → https://www.foundr.com Instagram → https://www.instagram.com/foundr/ Facebook → https://www.facebook.com/foundr Twitter → https://www.twitter.com/foundr LinkedIn → https://www.linkedin.com/company/foundr/ Podcast → https://www.foundr.com/podcast
After 18 years, 42 deals, $300M raised, and 500+ accredited investors, I can tell you exactly what sophisticated LPs want — in this order: 1️⃣ Capital preservation (above all else) 2️⃣ Capital growth (with conservative underwriting) 3️⃣ Communication (proactive, transparent, factual) In this clip from The Vinney & Beau Show, I break down the institutional-grade standards every HNW investor should demand from a sponsor: savvy operators with $1B–$3B track records, 73–83 page institutional packets, sensitivity analysis, and diversified fund structures that protect against asset-class concentration. If your sponsor can't show all three? Walk away.
You're an accredited investor. You've got capital to deploy. But before you wire $100K, $250K, or $500K into a syndication — do you actually know what to look for? In this episode of The Vinney & Beau Show, Beau Eckstein asks Vinney Chopra — 4x Amazon bestselling author with $300M+ raised, 42 deals, 5,000+ units, and 500+ accredited investors — the questions every sophisticated LP wishes they had asked before their first wire transfer. A rare, candid behind-the-curtain conversation about how veteran syndicators actually structure deals, vet operators, manage K-1s, and protect investor capital.
The AI infrastructure race is escalating fast, and the stakes are now measured in billions of dollars, gigawatts of power, and global regulatory pressure. This week on the Tech Field Day News Rundown, Tom Hollingsworth and Alastair Cooke break down Google and Blackstone's massive proposed AI cloud partnership, the environmental controversy surrounding Utah's gigantic Stratos AI data center campus, and reports that Arm Holdings is under FTC investigation over its growing influence in the AI chip market. They also discuss mounting concerns over AI-driven cybersecurity risks, Anthropic's aggressive $300 million Stainless acquisition, Elon Musk's latest courtroom loss against OpenAI and Sam Altman, and how AI hyperscale data centers may be permanently reshaping U.S. electricity prices. From semiconductors and cloud computing to energy infrastructure and enterprise AI, this episode explores the growing battle to control the future of artificial intelligence.This and more on the Tech Field Day News Rundown with Tom Hollingsworth and Alastair Cooke. Time Stamps: 0:00 - Cold Open0:31 - Welcome to the Tech Field Day Rundown 1:19 - Google & Blackstone Launch $25B AI Venture to Challenge NVIDIA and Fix Data Center Shortage5:15 - Utah's Massive AI Data Center Sparks Environmental Backlash Over Heat and Water Use9:06 - Arm Faces FTC Investigation Over AI Chip Ambitions and Qualcomm Dispute13:00 - Tech Coalitions Urge Federal Action on AI-Driven Cybersecurity Threats16:50 - Anthropic Buys Stainless for $300M in Major Blow to OpenAI, Google, and Meta20:46 - Jury Rejects Elon Musk's Lawsuit Against OpenAI24:58 - Electricity Costs Jump 75% as AI Data Centers Trigger Massive Power Price Surge34:19 - The Weeks Ahead 35:39 - Thanks for Watching the Tech Field Day News RundownFollow our hosts Tom Hollingsworth, Alastair Cooke, and Stephen Foskett. Follow Tech Field Day on LinkedIn, on X/Twitter, on Bluesky, and on Mastodon.
For high-net-worth clients, financial planning is often not just about managing their money during their lifetimes, but also a matter of preparing the next generation to inherit family wealth. Which suggests that meeting with clients' children can both offer value for current clients and also begin a relationship with the next generation that could last for decades to come. Liz Miller is the founder of Summit Place Financial Advisors, a $300M firm she built by focusing on deep relationships and multigenerational planning. Listen in to hear how she raises multigenerational conversations with clients and their children and how she converts clients' children from regular contacts to fee-paying clients. Liz also discusses how she finds that ensuring "nothing falls through the cracks" can be more valuable than chasing returns (and the value of checklists to support this task) as well as why she lets her high-net-worth prospects lead off discovery meetings with their most pressing concern rather than starting with her own qualifications and services. For show notes and more visit: https://www.kitces.com/490
“If one more person brings me another pilot, they're getting fired," one C-level supply chain executive told Zero100 — and they're far from alone in their frustration. Most companies are drowning in AI projects that never scale because they're optimizing functions in isolation while ignoring how decisions actually flow across the business. The solution? PowerThreads: end-to-end AI-enabled workflows that connect sensing, deciding, and acting across organizational silos. This week, VPs, Research & Advisory Lauren Acoba, Jenna Fink, and Geraint John break down the five core PowerThreads driving 95% of enterprise value, share a $300M sourcing transformation that required five years of unglamorous foundation-building, and explain why optimizing one bottleneck without understanding the full system often just moves the problem downstream.
Three retail moves worth talking about this week.Amazon is retiring the Rufus name. The shopping chatbot has hit roughly 300M users since its 2024 beta, but Amazon is folding the whole thing into Alexa and calling it "Alexa for shopping." We get into why that branding choice is risky given Alexa's history with kids accidentally ordering things and the privacy lawsuits, and what it signals about an "Alexa for healthcare" or "Alexa for law" coming next.The Watson Weekly Weekend edition is sponsored by Avalara - the agentic AI platform automating global tax and compliance for leading eCommerce brands. For more details: https://avalaratax.watsonweekly.comThen GameStop's Ryan Cohen and his $56 billion unsolicited bid for eBay, half cash, half stock. The eBay board's response was a 216-word letter calling the offer "neither credible nor attractive." We dig into whether Cohen ever expected a yes, or whether the whole thing was theater aimed at GameStop's stock price and the $100 billion valuation target tied to his own bonus plan.Finally, Lululemon. Heidi O'Neill, 30 years at Nike, took the CEO job on April 22. Founder Chip Wilson is already on record saying she's not the transformation the company needs. We talk about why Alo Yoga and Vuori are pulling away on the celebrity side, why the men's line feels over-assorted, and what it means when Amazon, Costco, and Target all stock convincing dupes of your core product.
Spurs Chat: Discussing all Things Tottenham Hotspur: Hosted by Chris Cowlin: The Daily Tottenham/Spurs Podcast Hosted on Acast. See acast.com/privacy for more information.
Spurs Chat: Discussing all Things Tottenham Hotspur: Hosted by Chris Cowlin: The Daily Tottenham/Spurs Podcast Hosted on Acast. See acast.com/privacy for more information.
What does it really take to build a $100M–$300M+ home—and work with some of the wealthiest people in the world? In this episode, Grant Bowen, founder of Peak Projects, breaks down the hidden world behind ultra-luxury residential development. From managing complex projects to acting as a trusted advisor, Grant shares how his firm became one of the leaders in the space. But this conversation goes far beyond real estate. Grant reveals what it's actually like working with ultra-high-net-worth clients—and how the reality is often very different from the stereotypes. He also dives into trust, relationships, and why transactional thinking fails at the highest levels. If you're building a business, raising capital, or serving elite clients, this episode is packed with lessons you won't hear anywhere else. Learn more about your ad choices. Visit megaphone.fm/adchoices
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
What happens when a $6.4 billion PE buyout becomes a cautionary tale for every SaaS operator, investor, and board member? In this episode, Dave "CAC" Kellogg and Ray "Growth" Rike break down Private Credit: what it is, how it works, and why it is showing up everywhere from venture rounds to leveraged buyouts. Then they walk through the Medallia deal step by step to show exactly how the model breaks.What we covered:Private credit 101: from venture debt to leveraged buyoutsPrivate credit is non-bank lending done by funds instead of banks, with a repayment-first mindset rather than a returns mindset. Capital deployment hit nearly $600 billion in 2024, up 78% from 2023, with 22 to 25% of that concentration in SaaS companies. Ray and Dave explain the difference between venture debt (lending to startups post-round) and direct lending (providing the "L" in LBO transactions), and why these structures have moved from niche to standard in software finance.How debt is priced and why it costs what it costsPrivate credit loans are floating-rate instruments priced at SOFR plus 500 to 800 basis points. In the zero-rate era that meant 6 to 9% all-in. Today it means 10 to 13%. Dave explains warrants as the "sweetener" (typically 5 to 15% of the loan amount, translating to under 2% equity ownership) and why the real economic driver is repayment, not upside. Ray frames the contrast with VC math: a lender who loses principal on one deal has no portfolio-level offset.The terms that matter: PIK, bullets, and covenantsPay-in-kind interest defers cash pain today by adding to the principal balance tomorrow. A $100M loan PIK-ing at 10% annually becomes $121M in two years and $133M in three. Bullet loans put the entire principal due at maturity, which for most companies means refinancing or a sale event. Dave's strongest language is reserved for covenants, which he calls the "third rail": liquidity, EBITDA, ARR growth, and coverage ratio thresholds that give lenders the right to call the loan if tripped. He argues these belong on page one of every board dashboard, every time.The Medallia case study: when all the assumptions move against youThoma Bravo acquired Medallia in 2021 for $6.4 billion at 9x revenue, with roughly $1.8 billion of debt backed by Blackstone, Apollo, and KKR. The deal was underwritten on continued growth and margin expansion toward 25% free cash flow. Instead, growth slowed, base rates rose more than 400 basis points, PIK interest compounded the balance from $1.8B to $2.2B, and EBITDA of $200M fell below annual interest expense of $300M. Interest coverage dropped below 1x. Thoma Bravo's $5 billion equity investment went to zero. Lenders took the keys via debt-for-equity conversion.Why these structures can look stable and then break fastThe Medallia deal was not unusual at entry. The problem was that PIK, rising rates, and slowing growth are individually manageable and jointly lethal. By March 2026, Blackstone was marking its first-lien Medallia debt at 60 cents on the dollar. Ray notes that between 2015 and 2025, more than 1,900 software companies were acquired by PE in deals worth over $440 billion, and 20 to 25% of all private credit went to SaaS. The exposure across the sector is large.The lesson Rory O'Driscoll would underlineDave closes with a line from Rory O'Driscoll: as soon as something becomes a formula, the play is probably over. Private credit for SaaS worked reliably for nearly a decade. The combination of higher rates, compressed multiples, and closed IPO and M&A windows revealed that the formula was underwriting a world that no longer existed. Senior debt gets paid first. When the debt is impaired, the equity is gone. The math does not negotiate.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Today we're exploring a topic impacting millions of women—yet one that is often misunderstood, overlooked, or quietly endured: women's health, weight struggles, hormones, and the emotional toll that comes with feeling disconnected from your body. In this episode of Follow Your Joy, I'm joined by entrepreneur and MyStart Health founder, Matt Stern, for a candid conversation about the future of women's healthcare, personalized wellness, and the intuition it takes to build something meaningful in a crowded industry. Matt's story is one of resilience, reinvention, and trusting his gut instincts against the odds. From being homeless at 17 and living in a pool house… to building multiple businesses and helping generate over $300M in growth…his entrepreneurial journey has been anything but conventional. Today, through MyStart Health, Matt and his team are focused on expanding access and affordability to life-changing medications—beginning with GLP-1 weight loss treatments and now strongly expanding into personalized hormone health programs for women over 50. Inside this conversation, we explore: • The growing health concerns impacting women today • Why women's hormones, metabolism, and wellness need more personalized support • Matt's personal story from hardship to entrepreneurial success • How intuition helped him go "all in" on MyStart Health despite industry skepticism • Why impact and personalization matter more than simply scaling a business • The emotional and physical realities women face with weight, aging, and health changes • What true success feels like when your work genuinely changes lives What I appreciated most about this conversation is that it's not just about medications or healthcare trends. It's about listening—to your body, your instincts, your health, and the deeper knowing that you deserve support, vitality, and solutions that honor your life stage. Matt is offering Follow Your Joy listeners a special lifetime savings of 25% off all MyStart Health plans. You can access Matt's savings code details inside the Portal of Joy. Please sign up for access to all of the podcast guest's gifts: https://marladiann.com/podcast/portal/ If you've been navigating changes in your health, energy, hormones, weight, or overall well-being—or simply want a more hopeful conversation around women's health—this episode is for you. Resources + Links: www.mystarthealth.com https://www.instagram.com/mattsternsf www.linkedin.com/company/thesterngroup -Marla's Amazon Store – shop for favorites. Summer specials & savings https://www.amazon.com/shop/marla_diann -If your own midlife (40+) reinvention is asking to be revealed or supported, let's explore it. What does freedom, more creative expression, success look and feel like for you at this stage of your life? We begin our connection with a reinvention conversation. Use my Calendly link to get on the calendar. https://calendly.com/successcoach-marladiann/connection Loved this episode? Don't stop here, unlock the tools, resources, and free downloads mentioned during the episode. Click here to access the portal of joy. https://marladiann.com/podcast/portal/
Swearing on sales calls can boost win rates by up to 8% — that's just one of the counterintuitive insights that helped Gong grow from 11 customers to over $300M in annual revenue.Udi Ledergor joined Gong as employee #13 and marketer #1, eventually becoming CMO and now Chief Evangelist. His data-driven content marketing approach turned proprietary sales call analytics into viral marketing gold that media outlets couldn't resist covering.Udi is the author of "Courageous Marketing" and has over 28 years of marketing experience across multiple successful tech companies. He's pioneered creative growth tactics like securing Super Bowl ads and Wall Street Journal placements for a fraction of their usual cost, all while building one of B2B's most recognizable brands.In this episode, you'll discover why AI-generated marketing ideas should be eliminated rather than used, how to create content so valuable that university professors want to license it, and why the best way to use a small marketing budget is to show up where your audience already congregates instead of trying to build your own party.Here's what you'll learn in this episode:(00:00) Intro(01:00) Why Gong focused on LinkedIn and ignored their website(07:21) Why best practices are the enemy of standing out(13:31) The reciprocity principle: Give value before asking for anything(18:21) How swearing on sales calls became viral marketing gold(25:18) How to make your marketing budget unlimited(33:29) Creating websites for AI vs. humans in the age of answer engines(40:36) Why you need preemptive "marketing experiments" budget(44:18) Punching above your weight(51:23) Using AI to eliminate obvious ideas, not generate them(56:54) The Netflix test: Would people pay for your content?(1:01:31) Finding talent in unlikely placesWe hope you enjoyed this episode of Ahrefs Podcast! As always, be sure to like and subscribe (and tell a friend).Where to find Udi Ledergor:LinkedIn: https://www.linkedin.com/in/udiledergor/X: @ledergorWebsite: https://www.gong.io/Where to find Tim:LinkedIn: https://www.linkedin.com/in/timsoulo/X: @timsouloWebsite: https://www.timsoulo.com/Referenced:Robert Cialdini (Influence): https://www.robertcialdini.com/Chip and Dan Heath (Made to Stick): https://heathbrothers.com/Malcolm Gladwell: https://gladwell.com/Adam Grant (Think Again): https://adamgrant.net/Daniel Pink: https://www.danpink.com/Peter Walker (Carta): https://www.linkedin.com/in/pwalk/Ahrefs: https://ahrefs.com#ContentMarketing #B2BMarketing #GrowthMarketing #AhrefsPodcast
David covers three Monday stories: the NFT mini-revival (Bored Apes doubled in a month), Canton raising $300M at a $2B valuation, and Saylor's AI rap video defending Strategy from “Ponzi” accusations. Enjoy! TIMESTAMPS: (00:00) Intro (01:15) Are NFTs Back? (07:24) Nexo Ad (07:59) Are NFTs Back? (Cont.) (12:06) Canton (16:52) Nexo Ad (17:46) Canton (Cont.) (23:27) Strategy Rap FOLLOW THE SHOW › David — https://x.com/dcanellis › The Breakdown — https://x.com/TheBreakdownBW SPONSORS › NEXO Nexo is the premier digital wealth platform. Receive interest on your crypto, borrow against it without selling, and trade a range of assets. Now available in the U.S with 30 days of exclusive privileges. Get started at http://nexo.com/breakdown Get top market insights and the latest in crypto news. Subscribe to the Blockworks Daily Newsletter: https://blockworks.co/newsletter/ DISCLAIMER As always, remember this podcast is for informational purposes only, and any views expressed by anyone on the show are solely their opinions, not financial advice.
Crypto News: Bitcoin has a strong weekly close and Altcoin such as SUI, ONDO, and Solana are breaking out. US Bitcoin Reserve news is expected this week a long with Clarity Act markup. Canton Network creator targets $300M in capital raise.Brought to you by
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Seven lawsuits blame OpenAI for enabling a mass shooting. Could the same legal theory come for DeFi? Thanks to our sponsor! Coinbase One Get 20% off the first year of your Coinbase One annual plan coinbase.com/unchained Seven families just sued OpenAI in federal court, arguing ChatGPT was a defective product that helped plan a mass shooting. OpenAI's own safety team flagged the risk eight months earlier and did nothing. The legal theory being tested here, that software developers can be held liable for foreseeable misuse of their tools, is the same theory that has been circling DeFi for years. Meanwhile, April ended as the most hacked month in crypto history, with over $600 million stolen in roughly 30 exploits, most of them linked to North Korea and its weapons programs. DeFi United, a $300M relief coalition led by Aave, emerged as the industry's response. KK, Vy, and Jessi unpack what it means when the 'code is law' defense starts to crack, why basic operational security is still not standard practice, and how close the Clarity Act actually is to crossing the finish line. Hosts: Katherine Kirkpatrick Bos, General Counsel at StarkWare. Previously held senior legal roles across DeFi and centralized exchanges. Jessi Brooks, General Counsel at Ribbit Capital TuongVy Le, General Counsel at Veda Learn more about your ad choices. Visit megaphone.fm/adchoices
Join the Growth Letter for weekly strategic perspectives on sustainable business growth - https://www.darrellevans.net/subscribeMost business owners blame their ad creative when their Facebook or Instagram campaigns stop working. After 14 years and over $300M in revenue generated for our clients, I can tell you that's almost never the real problem. There's an invisible gap inside your campaign that silently drains your budget while making it look like social media ads just don't work for your business. The fix takes less than an afternoon once you know where to look. If you've ever run a Meta ad and questioned whether it was actually working, this one's for you.Join us in Las Vegas at Winning The AI Visibility Shift - mindshiftdigital.co/vegas
In this episode of The Steward Chair, Garry Ridge, Culture Coach and Former Chairman and CEO of WD-40 Company, shares their journey of transitioning from a "command and control" leader to a servant leader, exploring how "the will of the people" drives meaningful, long-term success. We discuss the specific algorithm Garry used to grow WD-40's market cap from $300M to $3.5B, the defining moments of cultural safety during global crises, and why a leader's number one responsibility is to be a learner and a teacher. This conversation provides actionable takeaways for leaders committed to stewardship, integrity, and impact. Key Takeaways The Algorithm for Culture: Sustainable success is built through the formula of Values + Behavior x Consistency. Consistency is the "magic word" that prevents cultural toxins from eroding the organization. Profit as Applause: Shift the focus from the bottom line to the people; profit is the natural "applause" that follows when employees feel safe, seen, and empowered to do their best work. Leading Like a "Donkey": Effective stewardship requires the humility and reliability of a donkey—carrying the load and helping others reach their destination rather than seeking the spotlight. Resources Mentioned Visit https://thelearningmoment.net/ Follow The Learning Moment on LinkedIn: https://www.linkedin.com/company/the-learning-moment/ Visit https://www.wd40.com/ Follow WD-40 on LinkedIn: https://www.linkedin.com/company/wd-40-company/ Follow Garry Ridge on LinkedIn https://www.linkedin.com/in/garryridge/ Join the ConversationThe Steward Chair is about equipping and inspiring business leaders to build organizations that stand the test of time. If this episode resonated with you, share your biggest takeaway and tag us on LinkedIn: Chat With Leaders Media https://www.linkedin.com/company/chatwithleaders/ and End of the Line Productions https://www.linkedin.com/company/end-of-the-line-productions/. Elevate your podcast, company meeting, or industry event strategies to better engage stakeholders and drive meaningful growth! Visit ChatWithLeaders.com to learn more about how we can help.See omnystudio.com/listener for privacy information.
Most people dream about making money work for them. Collin Schwartz actually did it—turning $100K in home equity into a $300 million real estate empire. But it wasn't easy. It took grit, discipline, and a refusal to stay trapped in a system designed to keep you stuck.In this episode, we go deep into:The brutal truth about real estate investing (and why most people fail)How Collin leveraged debt, discipline, and the right partnerships to scale fastThe red flags in business deals that almost took Eric down—and how to spot themWhy being comfortable in your own skin is the real key to successThe 4-step process that can change everything in just 30 daysThis one's a masterclass in building wealth, trusting your gut, and playing the long game. If you're ready to break free from the cycle of working for money and start making money work for you, hit play now.Listen to this episode on other podcast platforms:Spotify: https://tinyurl.com/BeAuthenticSpotifyGoogle Podcast: https://tinyurl.com/BeAuthenticGooglePodcastApple Podcast: https://tinyurl.com/BeAuthenticApplePodcastAmazon Music: https://tinyurl.com/BeAuthenticAmazonMusicConnect with "Be Authentic or GTFO" on social media:Facebook: https://www.facebook.com/beauthenticorGTFOInstagram: https://www.instagram.com/beauthenticorGTFOWebsite: https://beauthenticorgtfo.comFollow Podcast Host Eric Oberembt on social media:Facebook: https://www.facebook.com/ericoberembtInstagram: https://www.instagram.com/ericoberembt
The second hour of The Charlie James Show on May 6, 2026, featured interviews with Attorney General candidate State Senator Stephen Goldfinch, who discussed fighting federal overreach and state government efficiency, and District 1 candidate Craig Dean regarding his campaign platform. The program also featured former Representative Adam Morgan criticizing a $300 million South Carolina state budget earmark, alongside analysis of efforts to redraw Congressman Jim Clyburn's district to make it more competitive. Listen to the full episode on Audacy.
Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we're diving into a series of significant updates that highlight the dynamic nature of our industry, driven by cutting-edge science, regulatory evolution, and strategic business innovations. The pharmaceutical landscape is ever-shifting, with recent developments underscoring this fluidity. Pfizer and Arvinas have secured early FDA approval for their breast cancer drug, Veppanu. This move signals the FDA's growing inclination to fast-track promising therapies, particularly in areas with high unmet needs. Interestingly, Pfizer and Arvinas are opting not to commercialize Veppanu independently and are instead seeking a partner. This decision reflects a broader industry trend where biopharmaceutical companies leverage partnerships to maximize the reach and impact of their therapies. Such strategies are becoming increasingly common as companies navigate complex market dynamics. Legal and healthcare domains intersected dramatically when the U.S. Supreme Court temporarily restored online access to the abortion pill mifepristone. This decision underscores the profound influence of legal rulings on healthcare access and distribution channels within the pharmaceutical sphere in the United States. It's a poignant reminder of how regulatory decisions can ripple through healthcare systems, affecting both providers and patients. Meanwhile, Samsung Biologics faces significant financial repercussions—estimated at $102 million—due to an ongoing union strike. This situation reveals vulnerabilities within contract development and manufacturing organizations (CDMOs) regarding labor relations, emphasizing the need for robust operational strategies to maintain production continuity. In a move that underscores its commitment to biologics manufacturing, Amgen announced a $300 million investment in Puerto Rico. This expansion aligns with growing global demand for biologics and biosimilars and highlights Puerto Rico's strategic importance as a pharmaceutical manufacturing hub, thanks to its favorable business environment and skilled workforce. On a different front, Novartis is optimizing its workforce by implementing additional job cuts at its U.S. headquarters. These adjustments are part of a larger multiyear plan aimed at streamlining operations and reallocating resources towards areas with higher growth potential within its portfolio. The potential resurgence of psychedelics is gaining traction, partly fueled by political support from figures like Donald Trump. The regulatory landscape for psychedelics remains complex, with discussions focusing on their therapeutic potential versus societal acceptance and legitimacy concerns. In the competitive weight-loss drug market, Novo Nordisk's Wegovy pill is currently outperforming Eli Lilly's Foundayo in prescription trends. This rivalry highlights the dynamic nature of the GLP-1 receptor agonist segment—a market experiencing rapid growth due to increasing attention on obesity management as a critical public health issue. Deloitte's latest analysis reveals an intriguing shift: obesity drugs have now overtaken oncology as the leading contributor to late-stage pipeline value for the first time in 16 years. This transition underscores a growing recognition of obesity as an essential therapeutic area with significant market potential, driven by advances in drug efficacy and heightened patient demand. Celcuity has reached a pivotal milestone in breast cancer treatment development with its Phase 3 trial success of a pan-PI3K/mTOR inhibitor against Novartis' Piqray. Achieving primary endpoints positions Celcuity favorably for FDA review, further highlighting continuous innovation within oncology research. UCB's acquisition of Candid Therapeutics for $2 billion exemplifies intensified competition Support the show
La principal exportación de EEUU en febrero de 2026 fue el oro: 18.500 millones de dólares en un solo mes. Más que farmacéuticos (9.700M), semiconductores (8.300M) y petróleo (7.900M). ¿Qué está pasando? ¿Saben algo que no sabemos? Hosted on Acast. See acast.com/privacy for more information.
Explosive debate: after one of DeFi's biggest attacks left Aave facing bad debt, DeFi United raised more than $300M to stop the contagion. But did the ecosystem prove its strength - or expose hidden trust assumptions, opaque risk, and the need for a real DeFi backstop?The Defiant's Camila Russo is joined by Dean Eigenmann (Markets Inc.), binji (Ethereum Foundation), and David Phelps (Confetti) to debate whether crypto bailouts are good for crypto, what this means for decentralization, and what DeFi must fix before it can scale to the mainstream.Watch the full discussion and decide for yourself.Big thanks to our sponsor;NEXONexo is a premier digital assets wealth platform that helps clients build, manage, and preserve their wealth through advanced interest-generating products, crypto-backed credit, advanced trading tools, and 24/7 client care. Get started at https://nexo.com/defiant
Good morning from Pharma Daily: the podcast that brings you the most important developments in the pharmaceutical and biotech world. Today, we delve into an array of dynamic changes and strategic maneuvers within the industry, showcasing how these transformations are shaping the future of healthcare and patient treatment. Starting with regulatory innovation, the FDA's new initiative to facilitate real-time review of clinical trial data is a potential game-changer for drug development timelines. With AstraZeneca and Amgen participating in this pilot program, the industry anticipates a more efficient approval process that addresses issues during trials rather than post-completion. This could significantly reduce the time it takes for patients to access cutting-edge therapies, marking a pivotal shift towards more agile regulatory frameworks. Such initiatives reflect a broader move towards streamlining drug approvals without sacrificing safety and efficacy. Turning to strategic investments, AstraZeneca's renewed commitment to a £300 million investment in the UK, following earlier disputes over drug pricing, signals confidence in resolving these issues favorably. This decision underscores AstraZeneca's dedication to fostering innovation within the UK's life sciences sector. Similarly, Teva Pharmaceuticals' $700 million acquisition of Emalex Biosciences positions it to introduce a new therapy for Tourette syndrome, highlighting efforts to address conditions with limited treatment options. In oncology, GSK is aligning its strategy with industry trends by focusing on cancer therapies. Despite withdrawing from a partnership with Mersana Therapeutics and pausing its mRNA bird flu shot program, GSK aims to channel resources into more promising ventures. This reflects a broader industry focus on oncology due to its high unmet needs and market potential, which companies are eager to capitalize on through innovative treatments. Additionally, Kite Pharma is preparing for potential approval of its next-generation CAR-T therapy for multiple myeloma, representing ongoing progress in personalized medicine approaches aimed at complex diseases like cancer. Furthermore, Pfizer's Elrexfio has shown promising results in multiple myeloma trials, potentially expanding treatment options and reinforcing Pfizer's oncology market position. The competitive dynamics in Alzheimer's treatments are also noteworthy as Biogen and Eli Lilly vie for market share with Leqembi and Kisunla, respectively. This competition highlights complexities in chronic disease management where dosing differences may influence patient decisions. The biotech sector remains robust in fundraising activities despite challenges. Noteworthy is Vivacta's $50 million Series A round and Coultreon's $125 million fundraising effort, indicating strong investor confidence in biotech innovations. These financial injections are vital for advancing ambitious projects that promise transformative impacts on patient care. Strategic acquisitions continue to shape the industry landscape. Chiesi Group's $1.9 billion acquisition of KalVista Pharmaceuticals exemplifies this trend, focusing on expanding rare disease offerings—a niche market with significant unmet needs but fewer competitors. Meanwhile, AbbVie's acquisition option for Kestrel Therapeutics underscores its strategic expansion into targeted cancer therapies, particularly through Kestrel's promising oral pan-KRAS inhibitor. In regulatory scrutiny news, AstraZeneca's camizestrant faces intense evaluation ahead of advisory committee meetings. Such scrutiny ensures that only effective treatments reach the market while emphasizing the rigorous standards required during drug development processes. Lastly, technological integration within pharmaceutical operations is becoming increasingly crucial as companies leverage AI to enhance R&D efficiency and accelerate value crSupport the show
Ships and planes carrying parts to the front lines would be vulnerable to attack. Defense startup Firestorm Labs thinks the answer is a drone factory that fits inside a shipping container. Also, BMW i Ventures has launched a new $300 million fund and will invest in early-stage through Series B startups in North America and Europe that are working on agentic AI and physical AI. Learn more about your ad choices. Visit podcastchoices.com/adchoices
People think of Aave and Morpho as competitors. But Morpho only lost $1 million when North Korea drained $300M from a DeFi protocol. The architecture explains why. ======================================================== Thank you to our sponsors! Coinbase One 20% off first year of annual plan + $50 Bitcoin bonus. Offer valid until May 31. coinbase.com/unchained Citrea Bitcoin changed how money works. Satya changes how Bitcoin scales. citrea.xyz/unchained Ether.fi 15% cash back on food and ride apps, 3% on everything else. ether.fi/unchained ======================================================== After North Korea's Lazarus Group drained nearly $300 million from Kelp DAO's bridge, the contagion spread fast, leaving close to $200 million in bad debt on Aave. Morpho, one of the largest lending protocols in DeFi, ended up with about $1 million in exposure. Paul Frambot, co-founder and CEO of Morpho, explains why the protocol's modular, isolated architecture produced a different outcome, and what it reveals about how DeFi lending is supposed to work. He also addresses the ongoing debate over whether DeFi lenders are fairly compensated for risk, the institutional reaction to the hack and what it means for the sector's timeline, the moral complexity of Arbitrum's decision to freeze stolen funds, and why formal verification may be DeFi's last line of defense in an age of increasingly powerful AI. Host: Laura Shin, Host / Unchained Guests: Paul Frambot, Co-founder and CEO of Morpho Labs Learn more about your ad choices. Visit megaphone.fm/adchoices
EP 679: Logan Chierotti Alright, let’s be real — most supplement companies are just slapping a label on a generic capsule and calling it a day. Logan Chierotti didn’t do that. The guy bootstrapped Physician’s Choice from nothing — literally from the ashes of a failed energy mint company and a basement mugshot removal hustle — into the number one probiotic brand in the world, pushing $300 million in annual sales. No VC money. No trust fund. Just relentless grinding, a willingness to look failure in the eye, and the smarts to go all-in on one thing when everyone else was trying to do everything. But here’s what really got me interested in Logan — it wasn’t just the business stuff. It was watching a guy who built something massive still show up for his kids. Still out there on the mountain with them. Still manufacturing adversity in a life that could easily get too comfortable. That’s the tension I think a lot of us deal with, and Logan’s as honest about the struggle as anyone I’ve talked to. We go deep on the entrepreneur journey — the early days of getting sued and losing everything, the hard pivot that changed everything, what it means to go all-in on one category, and why your work ethic might actually be your biggest enemy. We also get into the dad stuff — raising kids who aren’t entitled when you’ve worked hard to give them more than you had, and why nature and hard things might be the most important parenting tools we have. If you’re an entrepreneur, a parent, an outdoorsman, or all three — this one’s for you. Pull it up on the drive or the trail. Timestamp Chapters 0:00 — Intro & Sponsor: OnX Hunt 1:45 — Sponsor: Bridger Watch 3:00 — Welcome + Who Is Logan Chierotti? 4:30 — Balancing entrepreneurship and being present for your kids 7:00 — Physician’s Choice: 30,000 foot overview — $300M bootstrapped probiotic empire 9:00 — The mugshot removal hustle: Logan’s wild first online business 13:00 — Why business plans almost never survive contact with reality 15:30 — How Physician’s Choice was born: probiotics, a failed energy mint company, and a nagging wife 19:00 — Losing everything: the $1M first-year loss and lying on the floor ready to file bankruptcy 22:00 — How you survive: don’t quit, hire smart people, and get your head around the numbers 25:00 — The all-in pivot: cutting every other product to go deep on probiotics 28:30 — How to pick a category: find what’s working and do it better (the Metamucil / Grüns framework) 31:30 — Hardest lessons learned: stress, lawsuits, key employees quitting, and not taking it home 33:30 — Founder vs. CEO: why Logan will never hire an outside CEO again 35:30 — Balancing family, skiing with kids, and life outside the office 37:00 — Raising kids right: manufacturing adversity, camping, hard things, and the bidet incident 38:15 — Final advice: slow down, you have more time than you think 39:00 — Outro Episode Sponsors OnX Hunt If you’re hunting out west and you’re not running OnX, I genuinely don’t know what you’re doing. This isn’t a plug just because they write a check — I use this thing every single day. Land ownership, public/private boundaries, terrain analysis, offline maps, trip planning — it’s the full suite. The confidence it gives you knowing you’re in the right spot, you’re legal, and you can find your way back to the truck? That alone is worth it. Become an Elite Member today and save 20% with code TRO. Download the app or sign up at onxmaps.com Coupon Code: TRO — 20% off Elite Membership Bridger Watch Full disclosure — this one’s mine. I built it, our whole team built it, and we built it because every watch on the market was at best “usable” for hunters. That wasn’t good enough. Bridger Watch is a full-feature smartwatch built for the hunting lifestyle — not just the hunt. It does your training metrics, mapping, texts, all of it — and it does it with an insane battery life that actually holds up in the backcountry. No compromise. No fluff. Just a tool built by hunters, for hunters. Check it out at bridgerwatch.com — use code TRO at checkout. 3 Key Takeaways 1. Going all-in on one thing is a competitive advantage, not a limitation. Logan turned a flatlining supplement company into the world’s number one probiotic brand by doing something counterintuitive: he cut products that were making money to go deeper on one category. Most entrepreneurs spread themselves thin chasing every opportunity. The real unlock is picking the thing you can be truly elite at and pouring everything into it. Saying no to money today to dominate tomorrow is one of the hardest and most important decisions a founder can make. 2. You don’t need to invent something new — you need to do something existing much better. Logan’s framework for picking a business or a category isn’t about creating blue oceans — it’s about finding something that’s clearly working (proven demand, clear leader) and identifying the one thing that can be improved. Metamucil is doing $400M in fiber and their product is outdated junk. That’s not a threat — that’s an invitation. The best businesses often aren’t revolutionary ideas; they’re relentless improvements on proven ones. 3. You have more time than you think — stop the rush and be present. Both Logan and Cody landed on the same insight from different angles: the frantic urgency that drives early entrepreneurs often costs them the things that matter most — time with their kids, their health, their relationships. Logan’s parting advice says it all: your brain will work until you’re 70, there’s always more time to make money, but you can’t get time back. Whether it’s slowing down on the hustle or getting out in the mountains with your kids — presence is the play.
David Peterson is Senior Vice President of Sales at Annuity, a powerhouse in the home improvement industry. With over 15 years of experience, David has scaled multi-brand platforms to nearly 1,000 sales representatives and helped grow Mad City Windows from a regional player to a $300 million+ powerhouse before its acquisition. David is a proven expert in in-home selling, KPI-driven management, and building repeatable sales systems that deliver consistent results. From overcoming early career challenges to leading a billion-dollar company's sales strategy, David shares insights on leadership, sales culture, financing in home improvement, and the power of humility in success. Check Out My Social Media: Tiktok ⟶ https://www.tiktok.com/@officialtommymello Instagram ⟶ https://www.instagram.com/officialtommymello/ Facebook ⟶ https://www.facebook.com/thomasmello/ My other podcast: Home Service Expert ⟶ https://open.spotify.com/show/4WHQ3ldGThHsP1cfzNF33G Live Q&A submission form: https://homeserviceexpert.com/questions
A $300M bridge exploit is forcing the question DeFi has been avoiding: when users lose money, who is actually responsible — the protocol, the infrastructure provider, or both? Thanks to our sponsors! * As Bitcoin's application layer, Citrea gives you access to the first trust-minimized BTC on a fully programmable platform and a native stablecoin for Bitcoin, ctUSD. You can now participate in Bitcoin capital markets with lending, privacy, payments, Bitcoin yield, trading and predictions. You get expanded Bitcoin utility without sacrificing its security. Citrea mainnet is live. Put your BTC to work at citrea.xyz/unchained. * Nexo is the premier digital wealth platform. Receive interest on your crypto, borrow against it without selling, and trade a range of assets. Now available in the U.S with 30 days of exclusive privileges. Get started at http://nexo.com/unchained A $300 million bridge exploit at Kelp DAO has put DeFi's most uncomfortable question back on the table: when users lose money, who is actually responsible? Katherine, Jessi, and Vy dig into the Kelp and Layer Zero finger-pointing and ask whether the industry's core values — permissionlessness, open composability — have become its greatest vulnerability. Then: the Ninth Circuit heard oral arguments on prediction markets last week, and the panel's pointed questions signal the case is headed to the Supreme Court sooner than most expect. Finally: American Express just solved three of agentic commerce's hardest problems — identity, mandate, and accountability — with a product that's live today. The crypto industry, which should be leading this race, is watching from the sidelines. Hosts: Katherine Kirkpatrick Bos, General Counsel at StarkWare. Previously held senior legal roles across DeFi and centralized exchanges. Jessi Brooks, General Counsel at Ribbit Capital TuongVy Le, General Counsel at Veda Learn more about your ad choices. Visit megaphone.fm/adchoices
Markets are hitting new highs, but crypto just took a major hit. Ryan and David break down the $300M KelpDAO exploit and why it exposed deeper flaws across DeFi and Layer 2s, including Arbitrum's controversial decision to freeze funds. They also explore whether AI will drive deflation or inequality, unpack a new bullish ETH thesis, and debate why the biggest risks in crypto may be building beneath the surface. ---
Shawn Porat is the founder and Chief Fortune Officer of OpenFortune — the media platform turning the humble fortune cookie into a high-impact PI marketing channel. With distribution across 30 countries and 300M impressions a month, OpenFortune has delivered viral wins for brands like Capital One, Duolingo, and even investor Gary Vaynerchuk — who calls it “the new Super Bowl ad.” If you think billboards are your only out-of-home option, think again. In this episode, Shawn shares the strategy, the science, and the stories behind turning a slip of paper into a marketing powerhouse. For more resources on how to dominate your market, visit us at Rankings.io. Listen to the full episode with Shawn Porat on Personal Injury Mastermind, powered by Rankings.io, below: Spotify Apple Podcasts Watch the Episodes On YouTube OpenFortune Website | LinkedIn If you like what you hear, hit subscribe. We do this every week. Buy tickets for PIMCON 2026: https://hubs.li/Q04bf9vT0 Subscribe to our newsletter: pimnewsletter.beehiiv.com Get Social! Personal Injury Mastermind (PIM) powered by Rankings.io is on Instagram | YouTube | TikTok
In this episode, we're talking with Neal Conlon, who has raised over $300 million across multiple companies, about the hidden levers that unlock eight-figure valuations. Neal reveals how a protein bar company with almost no revenue secured a $10M valuation and shares frameworks for building a business worth far more than you think. Work with the team on building businesses and exits at https://capitalism.com/partners Learn more at https://capitalism.com Timestamps (0:00) Intro - Your business may be worth more (1:45) Neal's $300M+ fundraising background (4:00) The protein bar company story - from almost (6:00) Key numbers that determine company value (8:00) Moving from founder-dependent to scalable (10:00) Revenue per seat and valuation multiples - new (12:00) Understanding comparable companies and what they've raised or (14:00) Using retention and unit economics to drive growth (16:00) The pitch deck - the first step in (18:00) Market saturation vs market opportunity (20:00) Types of investors — VCs, angel investors, and (22:00) Making it feel real with actual investor money (24:00) The importance of experiencing these concepts to truly (26:00) Building your product roadmap and demonstrating market validation (28:00) Collecting real feedback from potential buyers vs friends (30:00) Moving from $300K profit to $1.5M valuation through (32:00) The first lever — improving the offer and (34:00) Identifying multiple levers — bookkeeping and software platform (36:00) How small changes in processes can dramatically increase (38:00) The healthcare company example - valuing by brand (40:00) Empowering your team and moving yourself out of (42:00) Case study — going from $8M to $50M (44:00) Transitioning from trader to owner mindset - revaluing (46:00) Hiring the right people to replace founder dependencies (48:00) The power of hiring specialists to handle entire departments (50:00) Creating multiple revenue streams and business units (52:00) Portfolio approach — combining multiple businesses under one
The U.S. Department of Agriculture has tapped Palantir for IT modernization and other digital work as part of a $300 million Blanket Purchase Agreement that continues the data analytics and software giant's bonanza of business under the Trump administration. The first task order under the BPA, announced this morning, formalizes USDA's work with Palantir — specifically the agency's National Farm Security Action Plan and its “One Farmer, One File” initiative. Agriculture Secretary Brooke Rollins previewed the launch of a “single, streamlined record that follows the farmer” during a February event in San Antonio, but the new task order makes it official. The Federal Aviation Administration is making progress on its goals tied to the modernization of the systems powering air traffic control and the National Airspace System, according to Administrator Bryan Bedford and Transportation Secretary Sean Duffy. The Department of Transportation officials were two of the speakers that gave updates on the initiative during the agency's Modern Skies Summit at its headquarters in Washington, D.C., on Tuesday. The Daily Scoop Podcast is available every Monday-Friday afternoon. If you want to hear more of the latest from Washington, subscribe to The Daily Scoop Podcast on Apple Podcasts, Soundcloud, Spotify and YouTube.
Send us Fan MailIn this episode of the WTR Small-Cap Spotlight podcast, Tal Jacobson, CEO of Perion Network (NASDAQ: PERI), joins host Tim Gerdeman and WTR's James Kisner to discuss how the repositioned, AI-native Perion is tackling fragmentation in the trillion-dollar digital ad market. Jacobson walks through Perion One — a platform that sits above ad-buying platforms like Google, YouTube, Meta, and TikTok rather than replacing them — and Outmax, the company's AI agent that applies algorithmic-trading methodology to media buying for 52 Fortune 100 companies. Because Perion owns no inventory, it can recommend shifting spend to whichever channel delivers the best outcome — an edge single-channel players can't match. The conversation covers Q4 momentum, growth in CTV, Digital Out-of-Home, and Retail Media, partnerships with Amazon, Walmart Connect, and Mastercard, and a three-year plan targeting 20%+ organic ex-TAC CAGR, 28% EBITDA margins by 2028, and $300M+ in cash.
Market update for April 22, 2026.Check out the Public app for incredible investing tools and to support the show (LINK)Follow us on Instagram (@TheRundownDaily) for bonus content and instant reactions.In today's episode:Kevin Warsh faces tough questions over Fed independenceSpaceX strikes a $60 billion AI deal with coding startup CursorPalantir signs a $300 million USDA deal to help safeguard the food supplyBoeing shares rise after reporting a smaller-than-expected lossVertiv falls despite strong AI data center demandAverage tax refunds are up more than 11% this season
In today's episode, you'll discover: 1. How to challenge the traditional approach to sales presentations by shifting the focus from slides and scripts to performance and presence. 2. Shares how sales professionals can transform routine pitches into engaging, trust-building experiences that move buyers to action. 3. Practical ways to elevate delivery, communicate value with clarity, and use storytelling and presence to stand out in competitive sales conversations. To support these three takeaways, I chose a quote from Robert McKee: "Stories are the creative conversion of life itself into a more powerful, clearer, more meaningful experience." About Carmen Sederino: Carmen Sederino is the founder of Illuminated Story and a performance expert specializing in high-stakes communication. She helps executives, founders, and professionals transform presentations into compelling, story-driven experiences that build trust and inspire action. With over 18 years of managing a $300M portfolio, Carmen blends corporate strategy with theatrical training to develop executive presence. Through her Illuminated Story Method®, she equips leaders to communicate with clarity, confidence, and lasting impact in critical moments. How to Get in Touch with Carmen Sederino: Website: https://illuminatedstory.com/about/ Email: carmen@illuminatedstory.com.au Gift: https://illuminatedstory.com/masterclass/ Stalk me online! Linktr.ee: https://linktr.ee/conniewhitman Communication Style Assessment (CSA)™: https://changingthesalesgame.com/communication-style-assessment/ Subscribe to the Changing the Sales Game Podcast on your favorite podcast streaming service or YouTube. New episodes are posted every week - listen as Connie delves into new sales and business topics or addresses problems you may have in your business.
Netflix beat on revenue and income but dropped 10%+ on weak Q2 guidance as Reed Hastings exits the board. Anthropic launches Claude Design, OpenAI overhauls Codex Desktop with computer control, and DeepSeek seeks its first outside funding at $10B+. Netflix reports Q1 revenue up 16% YoY to $12.25B, vs. $12.2B est., net income up 83% YoY to $5.28B, and forecasts Q2 EPS and revenue below est.; NFLX drops 10%+ (Bloomberg) Anthropic launches Claude Design, a new experimental product that lets users create visuals like prototypes, slides, one-pagers, and more using Claude (TechCrunch) Sources: Dario Amodei is set to meet with WH Chief of Staff Susie Wiles on Friday, a breakthrough in Anthropic's effort to resolve its fight with the Pentagon (Axios) OpenAI updates its Codex desktop app with features like computer control, an in-app browser, image generation, automation memory, plugin support, and more (ZDNet) Sources: DeepSeek is in talks to raise outside capital for the first time, seeking at least $300M at a valuation of at least $10B (The Information) Longreads India produces 1.5M+ CS graduates annually, but AI coding tools are forcing its $315B IT outsourcing industry into an existential reckoning (Bloomberg) Doug Liman's $70M movie Bitcoin: Killing Satoshi uses AI for sets, lighting, and more in post-production, cutting costs from an estimated $300M (The Wrap) Defunct startups are being liquidated for their Slack archives, Jira tickets, and email threads—operational exhaust that AI labs now treat as premium training data (Forbes) Learn more at liquid.trade/techbrew. Disclaimer: ● Initial 3 week subscription and 4 weeks of medication from $79 plus tax and $179 per month plus tax for 12 week subscription thereafter. Final pricing depends on program selection. ● Noom GLP-1Rx Program involves healthy diet, exercise and support. Individual results vary. Meds & personalization based on clinical need. Not reviewed by FDA for safety, efficacy, or quality. No affiliation with Novo Nordisk Inc., the only US source of FDA-approved semaglutide. Not available in all 50 US states ● Based on an analysis of self reported data from 1,254 engaged Noom users. Learn more about your ad choices. Visit megaphone.fm/adchoices
Anthropic dropped Claude Opus 4.7 with better vision, better coding and… better everything. And, along with OpenAI's new Codex, AI is accelerating ever faster. This week on AI For Humans, Anthropic released Claude Opus 4.7, a major step up from Opus 4.6 with better visual reasoning, improved software coding and even makes presentations for cavemen. Benchmarks put Opus 4.7 between 4.6 and the unreleased Mythos preview, and the new default xhigh reasoning level means more token burn but more reliability on hard problems. The same day, OpenAI updated Codex with better computer use, an integrated browser, and a bunch of new tools. Then Jensen Huang's epic Dwarkesh Patel interview broke the internet, with Jensen explaining why NVIDIA keeps selling AI chips to China and dropping the instantly iconic "you're not talking to someone who woke up a loser" line. Plus, Reese Witherspoon is now in on AI, Doug Liman's Killing Satoshi got made for $80M using AI tools (would have cost $300M without them) and we got our first look at AI Val Kilmer. OPUS 4.7 HAS LANDED. CODEX GOT UPGRADED. IT'S ALL HAPPENING Come to our Discord: https://discord.gg/muD2TYgC8f Join our Patreon: https://www.patreon.com/AIForHumansShow AI For Humans Newsletter: https://aiforhumans.beehiiv.com/ Follow us for more on X @AIForHumansShow Join our TikTok @aiforhumansshow To book us for speaking, please visit our website: https://www.aiforhumans.show/ // Show Links // Claude Opus 4.7 Official Blog Post https://www.anthropic.com/news/claude-opus-4-7 Claude Opus 4.7 System Card https://cdn.sanity.io/files/4zrzovbb/website/037f06850df7fbe871e206dad004c3db5fd50340.pdf Opus 4.7 Is Better at Presentations https://x.com/nadzi_mouad/status/2044814009040261336?s=20 A4H for Cavemen by Cavemen https://x.com/gavinpurcell/status/2044822422868865209?s=20 Opus 4.7 Default xhigh Reasoning and Token Burn https://x.com/mattpocockuk/status/2044802839709372798?s=20 Opus 4.7 Has a New Tokenizer and Base Model https://x.com/natolambert/status/2044788470179332533?s=20 OpenAI Codex Update: Codex for Almost Everything https://openai.com/index/codex-for-almost-everything/ Reese Witherspoon Is Now in on AI https://www.hollywoodreporter.com/news/general-news/reese-witherspoon-ai-comments-instagram-reel-book-authors-1236566844/ The Jensen Huang Interview With Dwarkesh Patel https://youtu.be/Hrbq66XqtCo?si=NpEzxTuuXreLiNRs Dwarkesh Pushes Jensen on Selling Chips to China https://x.com/dwarkesh_sp/status/2044483393941848131?s=20 First Look at AI Val Kilmer https://x.com/Variety/status/2044491101990535460?s=20 Killing Satoshi: Doug Liman's $80M AI-Made Movie https://x.com/TheWrap/status/2044414225158635528?s=20
Amazon acquired Globalstar to take on SpaceX… because Starlink's margin is Amazon's opportunity.PopUp Bagels just hit a $300M valuation… because breakfast is eating tummy share.Happy Tax Day… But did you know the IRS is our most profitable agency?Plus, Zuck is cloning himself… Because AI Zuck wants to give you unsolicited fashion advice.$META $SPY $Artists in the Rock & Roll Hall of Fame as both an individual and a member of a group Eric Clapton (Yardbirds, Cream)John Lennon (The Beatles)Paul McCartney (The Beatles)George Harrison (The Beatles)Ringo Starr (The Beatles)Stevie Nicks (Fleetwood Mac)Tina Turner (Ike & Tina Turner)Ozzy Osbourne (Black Sabbath)Rod Stewart (The Faces)Neil Young (Buffalo Springfield)Stephen Stills (Buffalo Springfield)Clyde McPhatter (The Drifters)Jeff Beck (The Yardbirds)Peter Gabriel (Genesis)Curtis Mayfield (The Impressions)Peter Tosh (The Wailers)Phil Collins (Genesis)NEWSLETTER:https://tboypod.com/newsletter OUR 2ND SHOW:Want more business storytelling from us? Check our weekly deepdive show, The Best Idea Yet: The untold origin story of the products you're obsessed with. Listen for free to The Best Idea Yet: https://wondery.com/links/the-best-idea-yet/NEW LISTENERSFill out our 2 minute survey: https://qualtricsxm88y5r986q.qualtrics.com/jfe/form/SV_dp1FDYiJgt6lHy6GET ON THE POD: Submit a shoutout or fact: https://tboypod.com/shoutouts SOCIALS:Instagram: https://www.instagram.com/tboypod TikTok: https://www.tiktok.com/@tboypodYouTube: https://www.youtube.com/@tboypod Linkedin (Nick): https://www.linkedin.com/in/nicolas-martell/Linkedin (Jack): https://www.linkedin.com/in/jack-crivici-kramer/Anything else: https://tboypod.com/ About Us: The daily pop-biz news show making today's top stories your business. Formerly known as Robinhood Snacks, The Best One Yet is hosted by Jack Crivici-Kramer & Nick Martell. Hosted on Acast. See acast.com/privacy for more information.
The Twenty Minute VC: Venture Capital | Startup Funding | The Pitch
Anj Midha is the founder of AMP, and a founding investor in Anthropic. Most recently, Anj was General Partner at Andreessen Horowitz, leading frontier AI investments. He serves on the boards of Mistral, Black Forest Labs, Sesame, LMArena, OpenRouter, Luma AI and Periodic Labs and is an early angel in ElevenLabs among others. Prior to that, Anj was the cofounder/CEO of Ubiquity6 (acquired by Discord) and a partner at Kleiner Perkins. AGENDA: 04:00 Why the "Scaling Laws are Dead" rumor is dangerously wrong 05:30 The 4 bottlenecks stopping us from reaching Super Intelligence 11:30 Where will the actual value accrue in an AI-dominated world? 12:00 Why Europe is building a "Sovereign Stack" to escape US dominance 15:00 Inside the brutal early days of Anthropic and the 21 VCs who said "No" 19:30 Why the most successful AI startups are ditching the "Profit-First" motive 34:30 The 1885 Industrial Revolution: Why we have a "GPU Wastage" bubble 38:00 Is the CCP actually winning the full-stack AI systems race? 43:30 Monopoly Mafias: Will model providers eventually kill the App Layer?
Description:Episode #970====Sign up for the Ron & Don Newsletter to get more information at www.ronanddonradio.com (http://www.ronanddonradio.com/)====To schedule a Ron & Don Sit Down to talk about your Real Estate journey, go towww.ronanddonsitdown.com (http://www.ronanddonsitdown.com/) ====Thanks to everyone that has become an Individual Sponsor of the Ron & Don Show. If you'd like to learn more about how that works:Just click the link and enter your amount athttps://glow.fm/ronanddonradio/RonandDonRadio.com (https://anchor.fm/dashboard/episode/ea5ecu/metadata/RonandDonRadio.com)Episodes are free and drop on Monday's , Wednesday's & Thursday's and a bonus Real Estate Only episode on Fridays.From Seattle's own radio personalities, Ron Upshaw and Don O'Neill.Connect with us on FacebookRon's Facebook Page (https://www.facebook.com/ron.upshaw/)Don's Facebook Page (https://www.facebook.com/theronanddonshow
This week on REKT Vision, Bijan Maleki, filling in for Mando, sits down with Raoul Pal for his highly anticipated update on the business cycle, macroeconomic conditions, crypto price action, AI's breakneck evolution, and lifestyle tips. Binance is the world's leading blockchain ecosystem, trusted by over 300M users in 100+ countries. It offers an unmatched portfolio of digital asset products such as trading, finance, Web3, payments, and more.
April 10, 2026: Your daily rundown of health and wellness news, in under 5 minutes. Today's top stories: Function acquires Getlabs to bring blood draws in-home, expanding beyond Quest partnership after acquiring Ezra to build integrated diagnostics stack US gym membership hits record 81M in 2025 with 26.1% of Americans belonging to facilities, as no-show rates drop from 10% to 4.6% Unilever acquires Grüns for $1.2B after brand scales to $300M revenue in four years shipping 10M daily greens gummies daily across 7K retail locations Today's episode is brought to you by AIIR — a modern communications and experiential agency for health, wellness, fitness, and performance brands. From earned media to events and creator-led campaigns, AIIR helps companies sharpen their story, earn attention, and build trust that compounds. Visit https://aiir.agency to learn more. More from Fitt: Fitt Insider breaks down the convergence of fitness, wellness, and healthcare — and what it means for business, culture, and capital. Subscribe to our newsletter → insider.fitt.co/subscribe Work with our recruiting firm → https://talent.fitt.co/ Follow us on Instagram → https://www.instagram.com/fittinsider/ Follow us on LinkedIn → linkedin.com/company/fittinsider Reach out → insider@fitt.co
Welcome back to The Kristian Harloff Show! Today's episode is loaded with huge updates from across the biggest franchises in movies and TV—box office wins, DC shakeups, Wizarding World dominance, and major uncertainty surrounding Star Trek. We start with Project Hail Mary crossing the $300M mark at the global box office, a massive milestone for the sci-fi hit starring Ryan Gosling. What's driving its success, and how big can it go? On the DC side, new reports reveal that Supergirl test screenings have had mixed reactions, with multiple cuts, three different endings, and a noticeably darker tone than the comics. Kristian breaks down what this could mean for the future of the DCU. Meanwhile, the Harry Potter TV Series continues to dominate headlines as its trailer breaks major records, proving there's still massive demand for the Wizarding World. Is this shaping up to be HBO's next mega-hit? We also dive into the future of Star Trek as Alex Kurtzman provides an update amid the ongoing Paramount-Skydance uncertainty. What's next for the franchise on both film and streaming? And in one of the wildest stories of the day, Andy Weir—the mind behind Project Hail Mary—reveals his rejected Star Trek pitch and doesn't hold back on his thoughts about the current state of the franchise. Topics include: Project Hail Mary box office passes $300M Supergirl test screenings: multiple cuts, endings, and tone concerns Harry Potter TV series trailer breaks records Star Trek's future amid Paramount-Skydance uncertainty Andy Weir's controversial Star Trek comments The latest movie news, TV news, and entertainment analysis If you're a fan of sci-fi, DC, Harry Potter, Star Trek, and the biggest conversations in Hollywood, this is the episode you don't want to miss. Make sure to like, subscribe, and comment below—what's your take on Supergirl's mixed reactions? Is Project Hail Mary the biggest surprise hit of the year? And should Star Trek be heading in a new direction? #KristianHarloffShow #ProjectHailMary #Supergirl #HarryPotter #StarTrek #RyanGosling #DCU #MovieNews #TVNews #BoxOffice
Farokh Sarmad, president of Rug Radio, joins Mando to discuss the Iran war and how the closure of the Strait of Hormuz is impacting global markets, Bitcoin, altcoins, and the broader cycle. Get your weekly fix of crypto chaos every Friday at 11:30AM ET. Hosted by Rekt Co-Founder Mando, each episode features a new guest dissecting the hottest crypto news and spotting emerging opportunities in the space. Binance is the world's leading blockchain ecosystem, trusted by over 300M users in 100+ countries. It offers an unmatched portfolio of digital asset products such as trading, finance, Web3, payments, and more.
Mando, Rekt co-founder, sits down with Gmoney, crypto-native entrepreneur and investor, to break down the crypto market's reaction since the Iran US conflict, with bitcoin hovering around key levels and whether capital is about to rotate back into altcoins.Binance is the world's leading blockchain ecosystem, trusted by over 300M users in 100+ countries. It offers an unmatched portfolio of digital asset products such as trading, finance, Web3, payments, and more.
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