Podcasts about Pareto

Send us Fan MailAndrew Palmer is a long-time editor and columnist at The Economist, where he writes the widely read Bartleby column on work and life. He also hosts Boss Class, one of The Economist's most popular podcasts, whose most recent season explored generative AI in the workplace, a topic Andrew approached not just as a journalist, but as a self-described unsophisticated user determined to get smarter by doing.In this episode, Andrew draws on his reporting and interviews with leaders across industries to offer an outside-in view of where AI adoption actually stands, and why the gap between the hype and the reality is not a sign of failure, but of how complex change really is.In this conversation, we discuss:Why AI adoption faces three distinct barriers (behavioral, technical, and organizational) and why solving one without the others leaves productivity gains stranded.Why structural reskilling frameworks (like Denmark's flexicurity model and Singapore's voucher-based lifelong learning system) offer a more credible response to AI disruption than waiting for policy to catch up.Why Johnson & Johnson's "let a thousand flowers bloom" approach to AI experimentation produced a Pareto effect (15% of projects generating 85% of value) and what they changed as a result.How the AI productivity boom is real at the individual level but not yet showing up in aggregate data, and why Andrew believes that gap is a question of time, not technology.Why enlightened corporate leadership requires transparency about potential job disruption and a commitment to adjacent career planning rather than performative optimism.What work in 2036 might look like, and why Andrew's most unsettling prediction has nothing to do with jobs, and everything to do with privacy.Explore this conversation:00:00 Introduction to AI and the Future of Work episode 39101:14 AI fun fact: AI legislative speed versus technological advancement03:51 Meet Andrew Palmer The Economist Bartleby Column Boss Class06:14 Digital Doppelganger and AI Personality Traits07:57 AI Adoption Barriers Behavioral Technical and Organizational11:01 AI Impact at Work Startups vs Large Organizations14:15 Leadership Humility and AI Uncertainty in the Workplace17:41 AI Experimentation at Scale Lessons from Johnson and Johnson24:26 AI vs SaaS Productivity Data and the Speed of Adoption27:35 Balancing AI Automation with Human Meaning at Work31:26 AI Policy Reskilling and Lifelong Learning for the Future36:03 Work in 2036 AI Monitoring Privacy and Constant Surveillance38:47 Who Really Controls AI and What That Means for Workers44:08 Connect with Andrew Palmer and Boss Class The EconomistResources:Subscribe to the AI & The Future of Work NewsletterConnect with Andrew on LinkedInAI fun fact articleOn How Arvind Jain Is Shaping the Future of Enterprise Search Another episode mentioned in the interview: How we can take back control from Big Tech with Tom Wheeler, former FCC Chairman, CEO, VC, and author of Techlash. 

The repo market is $16 trillion globally and most people have never heard of it — until the plumbing breaks. Craig Burchell of FalconX and Matteo Pandolfi of Pareto explain how it works and why bringing it on-chain is the next big unlock for DeFi. --- Heads up! If you haven't yet, be sure to subscribe to Bits + Bips, since the show will migrate there in a few weeks. Follow us on ⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠Unchained⁠⁠⁠⁠⁠ and wherever you get your podcasts. ---- The repo market is $16 trillion globally and it is, as Craig Burchell puts it, the oil that makes everything go. It is also almost entirely absent from on-chain finance — and that gap is creating real problems for RWA liquidity, stablecoin swap desks, and DeFi protocols trying to manage redemption queues. Steve Ehrlich sits down with Craig Burchell, head of lending at FalconX, and Matteo Pandolfi, CEO of on-chain credit infrastructure provider Pareto, to map exactly how repo works, what broke in 2019, why it translates extremely well into onchain finance. Matteo puts a $1 trillion figure on where on-chain repo gets in five years. Craig gives you one reason it gets there and one very honest reason it might not. Host: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Steve Ehrlich, Head of Research at SharpLink and Host of Bits + Bips: The Interview - https://x.com/Steven_Ehrlich Guest: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Craig Burchell — Head of Lending, FalconX; previously Head of Lending at Membrane Finance. @_CraigBirchall ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Matteo Pandolfi — CEO & Co-Founder, Pareto (on-chain credit infrastructure). @pan_teo_ Learn more about your ad choices. Visit megaphone.fm/adchoices

The repo market is $16 trillion globally and most people have never heard of it — until the plumbing breaks. Craig Burchell of FalconX and Matteo Pandolfi of Pareto explain how it works and why bringing it on-chain is the next big unlock for DeFi. --- Heads up! If you haven't yet, be sure to subscribe to Bits + Bips, since the show will migrate there in a few weeks. Follow us on ⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠Unchained⁠⁠⁠⁠⁠ and wherever you get your podcasts. ---- The repo market is $16 trillion globally and it is, as Craig Burchell puts it, the oil that makes everything go. It is also almost entirely absent from on-chain finance — and that gap is creating real problems for RWA liquidity, stablecoin swap desks, and DeFi protocols trying to manage redemption queues. Steve Ehrlich sits down with Craig Burchell, head of lending at FalconX, and Matteo Pandolfi, CEO of on-chain credit infrastructure provider Pareto, to map exactly how repo works, what broke in 2019, why it translates extremely well into onchain finance. Matteo puts a $1 trillion figure on where on-chain repo gets in five years. Craig gives you one reason it gets there and one very honest reason it might not. Host: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Steve Ehrlich, Head of Research at SharpLink and Host of Bits + Bips: The Interview - https://x.com/Steven_Ehrlich Guest: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Craig Burchell — Head of Lending, FalconX; previously Head of Lending at Membrane Finance. @_CraigBirchall ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Matteo Pandolfi — CEO & Co-Founder, Pareto (on-chain credit infrastructure). @pan_teo_ 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

Get the free Core Drives in the Wild guide, behavioral design applied to real products: professorgame.com/WildCD Episode Summary Tetiana Kobzar, product designer with 18 years of experience and creator of the Comportance Framework, joins Rob to share how behavioral design turns clinical and educational software into products people actually want to use. She walks through the seven steps of Comportance (goal, baseline, emotion, hypothesis, minimum validation, cadence, and iteration) and shows how it shaped a gamified speech therapy app for Alder Hey Children's Hospital and a mini-game replacement for 27 cognitive assessment tests. The conversation covers why founders overload products with functionality, why Duolingo's Black Hat motivation works for some users and burns out others, and how Octalysis fits inside a wider behavioral design practice. Listeners leave with a practical structure for designing engagement and a sharper read on when game-based beats gamified. About the Host Rob Alvarez is Head of Engagement Strategy, Europe at The Octalysis Group (TOG), a leading gamification and behavioral design consultancy. A globally recognized gamification strategist and TEDx speaker, he founded and hosts Professor Game, the #1 gamification podcast, and has interviewed hundreds of global experts. He designs evidence-based engagement systems that drive motivation, loyalty, and results, and teaches LEGO® SERIOUS PLAY® and gamification at top institutions including IE Business School, EFMD, and EBS University across Europe, the Americas, and Asia. Key Takeaways The Comportance Framework runs seven steps in order: define the goal, set the baseline metrics, design the emotion (motivation and positioning), state one hypothesis, build the minimum validation, set the measurement cadence, and iterate. Most founders skip the goal and emotion steps and jump straight to functionality. Tetiana's team at Alder Hey Children's Hospital replaced weekly-only speech therapy with a gamified app where clinicians set tasks as mini games, letting kids practice pronunciation between sessions while the therapist tracks progress. A separate Tetiana project replaced 27 pen-and-paper cognitive assessment tests with mini games on tablets, capturing extra signal (timestamps, finger tremor, voice recordings) that paper tests cannot measure. Most products fail not because users are irrational but because founders treat them as rational agents. Behavioral biases and cognitive overload kill engagement faster than missing features. The Pareto trap in client work: founders spend 80% of their attention on the 20% of clients who complain, while the 80% of healthy clients who quietly bring most of the revenue get under-served. Reverse the ratio to protect recurring revenue. Duolingo's streak mechanic is heavy Black Hat motivation. It drives high retention but creates rage-quit risk: a user who loses a 4,000-day streak rarely returns. The near-miss has to threaten loss without delivering it. Game-based design (where the experience itself feels like a game) opens more creative options than gamification (points, badges, leaderboards bolted onto a non-game product), but both belong inside a wider behavioral design practice. Topics Covered 0:00 — Why Duolingo's Black Hat motivation backfires 0:24 — Rob's intro and the Core Drives in the Wild guide 2:47 — Daily life after the acquisition 4:14 — Favorite fail: design for the end game 8:16 — Alder Hey speech therapy app and 27 cognitive tests as games 11:26 — Game-based versus gamified, and where the line blurs 15:44 — Where Octalysis fits inside the Comportance Framework 17:11 — The seven steps of Comportance, walked end to end 23:50 — Cognitive overload and treating users as humans 27:24 — Duolingo streaks, near-miss design, and rage-quit risk 31:42 — Book picks: Cialdini, Yu-kai Chou, Don Norman 33:29 — Civilization, board games with the kids, final advice Get the free Core Drives in the Wild guide, behavioral design applied to real products: professorgame.com/WildCD About Tetiana Kobzar Tetiana Kobzar is a product strategist and behavioral designer with 18 years of experience building software for healthcare, wellness, and education. She is the creator of the Comportance Framework, a seven-step methodology that brings behavioral science structure to product design. Her recent work includes a gamified speech therapy app for Alder Hey Children's Hospital and a tablet-based replacement for 27 cognitive assessment tests, and she shares behavioral design ideas through her #BehaviouralDesignThursday LinkedIn series and industry talks. Find the Guest Online LinkedIn Tetiana-kobzar.com Instagram TikTok Mentioned in This Episode Proposed guest: someone from Duolingo Recommended book: Actionable Gamification by Yu-kai Chou Recommended book: Influence by Robert B. Cialdini Recommended book: The Design of Everyday Things by Don Norman Favorite game: Civilization series Duolingo Is Not A Free Language Learning App, It Is... (The Octalysis Group) Alder Hey Children's Hospital speech therapy app (Tetiana's project) Comportance Framework (Tetiana's seven-step methodology) Octalysis Framework by Yu-kai Chou Free Resources and Get in Touch Core Drives in the Wild: Professor Game Free Guide Get Daily Value on Your Email Let's chat about your gamification project YouTube LinkedIn Instagram Facebook Start Your Community on Skool for Free Ask a question

La diferencia entre ser bueno y dominar no suele ser enorme. A veces es solo un 1%. En este episodio analizamos por qué pequeñas diferencias de rendimiento generan resultados desproporcionados en deporte, trabajo y vida real. Desde Pogacar, Nadal o Djokovic hasta la ley de Pareto, la distribución normal o el efecto Mateo. Por qué cada vez ganan más los mismos. Por qué mejorar se vuelve más difícil… pero también muchísimo más valioso. Y por qué el largo plazo sigue siendo la ventaja más infravalorada del rendimiento humano. Basado en ciencia, estadística y teoría del entrenamiento aplicada a sistemas complejos adaptativos. Y si quieres aprender más... Cada semana escribo un email para ayudarte a ser mejor en este mundo moderno mientras obedeces y respetas tu biología: ✉️ https://solaarjona.com/lista/ Puedes conseguir mi nuevo libro aquí: ENTRENAR SISTEMAS COMPLEJOS: OBEDECE TU BIOLOGÍA PARA DOMINAR TU RENDIMIENTO https://amzn.eu/d/04Fu62bd

Nick and Dan unpack Real Brokerage's acquisition of RE/MAX and argue the market reaction tells the real story, RMAX trading ~30% below the headline $13.80 deal value and REAX selling off signals investors aren't convinced the combination creates shareholder value. They frame it as two stressed models trying to solve each other's problems: RE/MAX needs modernization, Real needs distribution, but both are operating in a transaction recession (US existing-home sales at 30-year lows, CREA forecasting just 1% volume growth in 2026). The bigger thesis: we hit "peak Realtor" in 2022, and the brokerage subscription model, where agents are the customer, not just the labour, is starting to unwind in a Pareto-distributed industry full of net losers. Closes on the innovation paradox: brokerages need AI to retain agents, but not so much AI that consumers start questioning why they need the intermediary at all. EDMONTON MULTIPLEX EVENT Try it NordVPN risk-free now with a 30-day money-back guarantee! Use our code "realestate" to get 4 extras months from a 2 years plan Exchange-Traded Funds (ETFs) | BMO Global Asset Management LISTEN AD FREESee omnystudio.com/listener for privacy information.

Angus and Graham detail the 80:20 life. Graham notices that Angus' knowledge of Pareto is lacking. Some people go all in and some do nothing. Life is all about balance in nutrition, fitness and fun. They discuss their own journeys and how they have come to understand the joy of a healthy, balanced life where your fitness amplifies the rest of your time.-Ready to stop winging it and start training with purpose, clarity and support? https://jc9cc7oe7jf.typeform.com/to/feOjPDNj-⚫ Get in touch in the comments below or head to...Instagram: Lift For Life - https://www.instagram.com/the_liftforlife_podcast/Instagram: Angus Warburton - https://www.instagram.com/angus_warburton/Instagram: Graham Ambrose - https://www.instagram.com/liftforlife.gdog/

Je réussis à maîtriser mon temps et à booster ma productivité grâce à 6 principes de gestion de temps universels:

The boys talk the news of the week in gaming including a substantial amount of time on the much-anticipated GTA 6. 0:00 — Intro 5:00 — Format explanation: public episodes vs. Patreon 5:58 — Grey Zone Warfare / Tarkov fail story 9:10 — Active Matter extraction shooter preview 15:44 — Black Ops 7 review bombing + AI in game assets controversy 27:59 — Windows Recall (K2) / Microsoft bloatware story 37:44 — Gaming industry layoffs vs. $195B record profits 44:55 — "Gaming's never been worse" + expectation inflation debate 48:59 — TikTok brain rot / gamer attention span discussion 51:55 — Baldur's Gate 3 Honor Mode debate (turn-based vs. real-time) 53:08 — AI causing most gaming layoffs theory 56:58 — "Homeopathy = indie games" analogy 58:38 — Subnautica 2 preview (May 14, co-op) 1:02:32 — GTA 6 trailer (May 21) + release hype 1:03:00 — GTA 6 expectations are actually justified 1:06:58 — GTA 6 economic impact / people calling out of work 1:09:37 — GTA 6 $3 billion development cost revealed 1:10:00 — GTA 6 as a gaming platform / meta-game ecosystem 1:13:51 — GTA extraction shooter tangent 1:14:00 — NVIDIA DLSS 5 announcement 1:21:39 — Highguard failure 1:25:05 — Sykkuno cheating scandal / streamer parasocial drama 1:31:35 — Streaming culture getting too big 1:33:52 — Fortnite Star Wars game modes (Galactic Siege, Escape Vader, Droid Tycoon) 1:37:44 — GTA 6 as a monopoly / Pareto principle / indie games can't compete 1:40:22 — Outro: Discord feedback, Patreon plug, short-form content plans _Note: timestamps may be slightly misaligned on podcast apps (but not on YouTube) due to dynamic ads._ The podcast is available wherever you listen to podcasts, and ad-free & early access versions - as well as bonus episodes - are available to all of our Patreon (https://www.patreon.com/thedropshot) supporters. We stream the podcast live on our website (https://www.thedropshot.com/live), on YouTube (https://www.youtube.com/c/thedropshotpodcast), and on Twitch (https://www.twitch.tv/thedropshotpodcast) simultaneously every Thursday and Saturday afternoon at ~12 o'clock Pacific Time. We typically start the stream 30 minutes early to answer viewer questions, banter, and chat. Links for everything are below. Thanks for checking us out!

À l'échelle mondiale, des milliers de milliards de dollars vont changer de mains dans les prochaines décennies, transmis des baby-boomers vers les générations suivantes. En Belgique, où les ménages comptent parmi les plus fortunés du monde, la question de la planification successorale est plus que jamais d'actualité. Vincent Garitte, associé chez Pareto, décrypte, au micro de Frédéric Clavir, les mécanismes des donations, les pièges à éviter et les outils pour transmettre dans les meilleures conditions fiscales. Les journalistes vous proposent différents podcasts sur les thèmes qui dominent notre monde et notre société. Sous différents angles et avec un accent clair sur l'économie et les entreprises, sur les affaires, les finances personnelles et les investissements. De manière indépendante, pertinente, toujours constructive et tournée vers l'avenir. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

Parable of the Sower & Pareto's Principle Education is useless without action. The actions we take determine the results we get. Sometimes working longer & harder only gives you more work & no more results. To get better results you need to find the things & areas that can provide the results you want to achieve.  Sponsors: American Gold Exchange Our dealer for precious metals & the exclusive dealer of Real Power Family silver rounds. Get your first, or next bullion order from American Gold Exchange like we do. Tell them the Real Power Family sent you! Click on this link to get a FREE Starters Guide. Or Click Here to order our new Real Power Family silver rounds. 1 Troy Oz 99.99% Fine Silver Abolish Property Taxes in Ohio: www.AxOHTax.com  Get more information about abolishing all property taxes in Ohio. Our Links: www.RealPowerFamily.com Info@RealPowerFamily.com 833-Be-Do-Have (833-233-6428)

Är vi på väg att bli glorifierade kvalitetsgranskare åt maskiner, eller ger AI oss superkrafter att lösa mer komplexa problem? I detta avsnitt gästar Jan Bidner, förändringsledare och digitaliseringsstrateg på IT-företaget Dizparc. Vi reflektera över hur generativ AI förändrar allt från yrkesidentitet till kundförväntningar. Samtalet kretsar kring den paradoxala stressen i AI: samtidigt som vi kan producera mer på kortare tid, brottas vi med frågan om vad kunden egentligen betalar för. Är det vår tid, eller summan av vår erfarenhet? Jan varnar för risken att vi ”backar ut kognitivt” – att vi blir så bekväma med AI-genererade utkast att vi tappar förmågan att argumentera för besluten bakom dem. Vi diskuterar begrepp som ”Vibe-coding”, där juniora utvecklare skapar resultat de inte fullt ut förstår, och vikten av att gå från ”Human in the loop” till ”Expert in the loop”. Yrkesidentitet i förändring: Hur förändras konsultens roll när hastigheten skruvas upp och kognitiva processer automatiseras? Kundens förväntningar: Om AI sparar tid, förväntar sig kunden då lägre priser eller högre kvalitet? Risken med ”Snabba kolhydrater”: Jan uttrycker en oro för att vi tränar oss på AI (blir mer generiska) snarare än att AI bara tränar på oss. Vibe-coding & Junioritet: Utmaningen i att producera resultat (kod, text, design) utan att ha den djupa förståelsen för varför det fungerar. Pareto-principen i AI-åldern: Hur vi kan använda de vunna 80 procenten tid till reflektion, empati och djupanalys istället för att bara trycka på ”OK”. Juridiskt och moraliskt ansvar: Varför AI aldrig kan bära ansvaret för ett felaktigt beslut och varför experten behövs vid knappen. ”Det är inte AI som kommer ta ditt jobb, utan någon som använder AI.” – Jan Bidner Vill du förstå hur du behåller din relevans som expert i en algoritmiserad värld? Lyssna på hela samtalet här eller i din favoritapp för poddar. Jan Bidner, Jonas Jaani (27:50) Videoversion av avsnittet: https://youtu.be/5CpioaMiSS4 Länkar / mer information: Om Jan Bidner Jan är tjänstedesigner, förändringsledare och digitaliseringsstrateg på IT-företaget Dizparc som finns på 9 orter i Sverige samt i Polen och Indien. Och även i Umeå där Jan är stationerad. På fritiden är han kreativt lagd och jobbar bland extra annat som sjukhusclown och skapar musik som han spelar i olika band (kolla in Ulgebräk på Spotify). Men på jobbet är det mycket fokus just nu på rågivning kring digital inriktning och att utbilda företag i generativ AI i allmänhet och Copilot i synnerhet och i sin yrkesroll reflekterar han ofta kring människans plats i digitaliseringen. Trenden att vi digitaliserar bort människan ur loopen t.ex. och att det finns en nationalekonomisk förväntan på linjär vinst och effekt. Men är det verkligen så enkelt? Så klart inte! Mer i ämnet: https://open.spotify.com/episode/0zTffasQYs4MWwo12UoL9G?si=mFuPgBWzRdidje3yTcKBxw&pi=DKppjLaFT36Nv&t=616 Podcasten POSSIBLE med Aria Finger & Reid Hoffman co-founder LinkedIn, bidrar med kloka perspektiv om ansvar, tid och effekt) Fokus mer på healthcare professionals och medical writers men med lite samma reflektioner. Vad är effektivitet och hur ska man se på vinsterna av AI? AI doesn't reduce work – it intensifies it https://open.spotify.com/episode/1Rg1WDfFz0SnXHKrI85Aa9?si=ge9yGxtjRG6Lvn3QSmjD_Q AI SAVES TIME – Does that mean you owe your clients a discount https://open.spotify.com/episode/5o3r0XmNbPFSqDBAo2D6iB?si=gBrTV5u5SNeVsxvKqPVMvg&t=627 Man pratar inom nationalekonomin om ”produktivitetsparadoxen”. Här omnämns rapporten från National Bureau of Economics. Den presenteras i ett sammanhang som skapar kontext: https://fortune.com/article/why-do-thousands-of-ceos-believe-ai-not-having-impact-productivity-employment-study Alla avsnitt av digitaliseringens podcast Effekten Prenumerera: Apple Podcasts Spotify: https://open.spotify.com/show/5Z49zvPOisoSwhwojtUoCm Är du vår nästa gäst? Maila oss på info(a)effekten(punkt)se

Duncan MacPherson is joined by Pareto coach and team dynamics specialist Elaine Christakos for a practical conversation on one of the most overlooked drivers of growth in financial advisory businesses: building and leading a high-performing team. Together, they explore the shift from advisor to CEO, where leadership, delegation, and structure become the real drivers of scale. As firms grow more complex, Elaine shares how intentional team design, clear roles, and aligned communication create consistency in the client experience while freeing up capacity. The conversation also dives into hiring, retention, and team cohesion, highlighting why behavioral alignment often matters more than technical skill, and how the wrong hires can quietly erode culture, trust, and enterprise value. Elaine breaks down the mindset shift required to let go, empower the right people, and build a business that can grow beyond the advisor. Key highlights include: Why scaling requires a shift from doing more to leading differently How team dynamics impact productivity, consistency, and enterprise value Hiring for alignment, not just experience Using behavioral insights to strengthen team communication Why letting go is essential to becoming a CEO This is a practical discussion for financial advisors looking to build a more scalable, self-sustaining business and lead with greater clarity and control. Tune in for actionable insights on leadership, team structure, and scaling the right way. Promotions: Toolkit CRM by Pareto: www.toolkitcrm.com Pareto Systems: Turnkey Advisor Membership Connect With Duncan MacPherson: Website: ParetoSystems.com Toll Free: 1.866.593.8020 Learn More: Schedule a Call LinkedIn: Duncan MacPherson Connect With Elaine Christakos: LinkedIn: Elaine Christakos Website: paretosystems.com/coaches/coach-elaine-christakos About Our Guest: Elaine Christakos is a senior level results-oriented professional and strategist with two decades of management and coaching experience in the financial services sector. She has designed and implemented successful and proven practice management and relationship management training programs. Elaine is also a behavioral strategist and high-performance team coach who helps financial advisory teams hire the right people, build strong team dynamics, and retain top talent. Her expertise in behavioral profiling, especially DISC, Emotional Intelligence, and Driving Forces, gives her clients a clear competitive edge in attracting and developing cohesive, high-functioning teams. A Certified DISC Specialist and trainer, Elaine uses a practical, science-based approach to decode human behavior in a way that’s immediately applicable to hiring decisions, communication strategies, and leadership development. She works with elite advisors and their teams to build intentional cultures where each person operates in alignment with their natural strengths, leading to better fit, faster trust, and longer-term engagement. Elaine’s foundation in practice management was shaped by early exposure to structured, client-centric systems, which ignited her passion for coaching and optimizing team performance. Today, as a coach with the Pareto Systems network, she blends behavioral insights with strategic consulting to help advisory teams grow with clarity and confidence.

Key Account Management (KAM) isn't just about maintaining relationships and securing renewals. Today's business environment demands a new approach—one rooted in strategic growth, deep customer understanding, and proactive leadership. I sit down with Alex Raymond, founder of Amplify, author of "The Growth Department," and leading expert in account management and client engagement, to explore what sets world-class key account managers apart and how organizations can improve their KAM strategies. We discuss how to define and segment key accounts, ways to align strategies with customer objectives, and the best way to access senior decision-makers through stakeholder mapping. Alex also shares his top dos and don'ts for effective account management and shares a real-world example illustrating relentless curiosity and how it leads to strategic growth.   Outline of This Episode [00:00] Mindset, relationships, and strategic focus in key account management [01:38] Power law versus Pareto principle in account management  [03:10] Differences in skill sets and approaches—hunters vs. farmers [04:34] Understanding customer goals and challenges [07:07] Risks of communicating only with lower-level stakeholders  [09:25] Adopting a growth rather than a support mentality  [15:37] Key questions for impactful account plans  [21:09] A real-world example of growing a strategic account Clear Segmentation in Key Accounts Too many companies default to the assumption that their largest customers are automatically "key accounts." However, identifying key accounts digs deeper, weighing not just current size but growth potential, strategic alignment, and the strength of mutual commitment. By focusing on the 10–20% of accounts that generate 80–90% of results, companies can use the power law to prioritize resources and attention where they matter most.   The Hunter–Farmer Divide: Why Role Specialization Matters One of the most common mistakes in account management is assuming that the same employee can seamlessly transition from a new-business "hunter" to a relationship-building "farmer." These roles require fundamentally different skillsets and mindsets. Hunters sell a compelling vision of the future; farmers deliver sustained value, focusing on whether customers are realizing the promised benefits, moving closer to their objectives, and overcoming real-world obstacles. Recognizing this distinction helps organizations assign the right people to the right roles and ensures that post-sale relationships receive the expertise and attention they deserve.   A Customer-Centric Key Account Strategy Building a strategy that aligns with customer objectives requires more than guesswork—it demands insight direct from the source. Often account managers neglect the most obvious step: talking to the customer. Alex recommends structured conversations to uncover not just stated goals but underlying drivers, ongoing initiatives, and pressing challenges. Supporting techniques like SWOT analysis or internal research can help, but nothing replaces genuine, curiosity-driven dialogue.   Unlocking Stakeholder Access and Mapping Relationships Strong, resilient relationships create the safety net for account success. Alex points out two major risks: having too few contacts and being confined to lower levels of the customer's organization. Effective stakeholder mapping means expanding both breadth and depth, forging connections at all relevant levels, especially with the most senior decision-makers. When you target strategic issues, you naturally gain access to those with broader authority and larger budgets.   Making Account Plans Living Documents Too often, account plans become static corporate theater, written once and forgotten. Alex suggests moving to agile, actionable plans that center on high-impact questions: What big problems are we solving? What assumptions need validation? What specific results are we driving? Practical, concise account plans, not cumbersome spreadsheets, help teams stay aligned and responsive. Key account management today is about more than retention; it is strategic, consultative, and growth-oriented. By segmenting strategically, specializing roles, practicing curiosity, leveraging the right tools, and living the owner's mindset, organizations can turn KAM into a true engine for business success.   Resources & People Mentioned The Growth Department by Alex Raymond Account Management Secrets Podcast  Sales Reinvented Episode 233: Connie Kadansky    Connect with Alex Raymond Alex Raymond on LinkedIn    Connect With Paul Watts  LinkedIn Twitter    Subscribe to SALES REINVENTED Audio Production and Show Notes by PODCAST FAST TRACK https://www.podcastfasttrack.com  

Imagine you were able to transform your relationship with time so that you had more balance, were better organized and focused so that you were able to work less and accomplish more.   How would that impact your life?    Well, that's what we are going to talk about today as I speak with Louise Bedford about mastering time for wealth creation.   We explore how effective time management is crucial for achieving success in all life areas.   We discuss the difference between time-for-money and leverage-based economies.   We highlight the importance of prioritizing oneself and maintaining time integrity.   We also delve into strategies for eliminating time leaks and distractions.   Join us as we provide insights to help you make informed decisions about time management.   Takeaways   Effective time management is key to success in all areas of your life. Prioritise your activities to maintain time integrity. Leverage-based economies outperform time-for-money models. Use the Pareto principle for better results. Delegate routine tasks to save time. Manage digital distractions effectively. Overcome procrastination with task chunking. Design your life with purpose. Focus on high-impact activities for growth.   Links and Resources:   Michael Yardney – Subscribe to my Property Update newsletter here.     Get the team at Metropole to help build your personal Strategic Property Plan. Click here and have a chat with us     Louise Bedford – The Trading Game https://www.tradinggame.com.au/   Join Michael Yardney, Louise Bedford plus a team of experts, at Wealth Retreat 2026 on the Gold Coast in May. Find out more about it here and register your interest www.wealthretreat.com.au It's Australia's premier event for successful investors and business people.   Get a bundle of eBooks and Reports at: www.PodcastBonus.com.au      Also, please subscribe to my other podcast Demographics Decoded with Simon Kuestenmacher – just look for Demographics Decoded wherever you are listening to this podcast and subscribe so each week we can unveil the trends shaping your future.   About The Michael Yardney Podcast | Property Investment And Wealth Creation Australia The Australian property market doesn't move in isolation - it's shaped by demographics, economic forces and long-term structural trends. The Michael Yardney Podcast dives into: • Australian economic outlook• Demographic trends shaping housing demand• Population growth and migration impacts• Housing affordability debates• Interest rates and inflation• Supply shortages and construction cycles• Government policy and property markets• Future trends in Australian real estate• Strategic property investment planning If you want to understand what's really driving property prices in Melbourne, Sydney, Brisbane and around Australia, and how to position your portfolio for the future, this podcast delivers data-driven insights and practical strategy. Explore more at:https://propertyupdate.com.auhttps://metropole.com.au

Most people fail to achieve long-term goals because their goals stay foggy, vague, not deconstructed, sequenced, selected and kept accountable.Achievement that lasts has very little to do with talent and everything to do with the process.When “get healthy,” “become a better leader,” or “grow my business” is still a blurry vision, it's almost impossible to know what to do on any day, let alone what to track, what to practice, and what to improve. And how to put the whole thing together.I walk you through one of the most fundamental coaching skills I use with clients: deconstruction (goal decomposition). We take any complex goal and break it into smaller, defined milestones and trainable subskills you can act on today. I ground it with a practical health example using the big four pillars of well-being: sleep, nutrition, exercise, and stress management, plus the real subskills inside nutrition like meal planning, protein, hydration, and emotion regulation.Then I bring in Tim Ferriss's DISSS learning framework: Deconstruction, Selection, Sequencing, and Stakes. We talk about the 80/20 rule (Pareto principle) so you focus on the few actions that create the biggest return, how to sequence skills so you're not “building a tabletop with no legs,” and why stakes and accountability are the difference between ideas and results. I also share how to use AI tools like ChatGPT or Claude to identify components, prioritize the high-leverage pieces, and draft a plan you can schedule and measure.If you want better goal setting, skill building, and a simple system for personal growth that actually works in real life, hit play and share it with someone who needs it.  Text Me Your Thoughts and IdeasSupport the showBrought to you by Angela Shurina  Behavior-First, Executive, Leadership and Optimal Performance Coach 360, Change Leadership & Culture Transformation Consultant  

Most rental companies compete on equipment. The ones pulling ahead are competing on something harder to copy. In this episode, Kyle sits down with Elliott Vigil, one of the most respected sales coaches in the construction equipment industry, to break down why trust is the only competitive advantage that truly compounds, how to apply the Pareto principle to your customer base, and why Elliott believes AI is still under hyped in rental.

What does it look like when a financial advisor builds a practice around purpose, not just profit? Join host Duncan MacPherson as he sits down with Kimberly Safoyan, founder of Anchor Wealth Management Group in Palm Desert, California, and a seasoned advisor within The Wealth Consulting Group (WCG), affiliated with LPL Financial. Kimberly shares how she has built a values-driven wealth management practice focused on philanthropy, community involvement, and purpose-driven financial planning to strengthen client relationships and drive long-term retention. From launching the American Heroes UIT with First Trust to guiding clients through complex life transitions, Kim brings a practical, client-centric approach to modern advisory firms. Key Takeaways for Financial Advisors: Build deeper client relationships through philanthropy Differentiate with values-based financial planning Strengthen client retention with legacy planning strategies Better serve women through divorce and widowhood Leverage mentorship to grow advisory teams Use technology to enhance the client experience Kimberly Safoyan demonstrates how top financial advisors go beyond portfolio management by aligning wealth with purpose and delivering a more meaningful client experience. Promotions: Toolkit CRM by Pareto: www.toolkitcrm.com Pareto Systems: Turnkey Advisor Membership Connect With Duncan MacPherson: Website: ParetoSystems.com Toll Free: 1.866.593.8020 Learn More: Schedule a Call LinkedIn: Duncan MacPherson Connect With Kimberly Safoyan: LinkedIn: Kimberly Safoyan Website: www.Anchor-Wealth.com WCG Website: www.wealthcg.com About Our Guest: Kimberly Safoyan is President and founder of Anchor Wealth Management Group, LLC and has over 30 years of financial services experience. She has served her clients as an independent wealth advisor since 1991. Her focus is to serve as your personal CFO, seeking to bring a full spectrum of wealth management capabilities and resources necessary to address your complex financial needs. Some of Kim's achievements include: Five Star Wealth Manager Award as featured in Palm Springs Life Magazine for years 2012, 2013, 2016 – 2021. She holds the Series 7, 24, and 63 securities registrations with LPL Financial, the Series 65 securities registration with WCG Wealth Advisors, and is registered to transact securities business with residents of the following states: AR, AZ, CA, CO, FL, ID, MI, NV, NY, TX, and WA. She also holds a California insurance license. Kim earned her Bachelor of Arts degree in Communication Studies with an emphasis in business from California State University Northridge. Raised in Michigan, Kim moved to the desert in 1982 and graduated from Indio High School. Kim is a strong community services advocate. She serves as an advisory board member for the Cathedral City Salvation Army Corp, is a past board member for the Palm Desert High School PTO and PDHS Foundation board member.

¿Sientes que tu negocio depende 100% de tus "trucos" diarios?. Llegó la hora de ignorar el humo del marketing, dejar de inventar la rueda y volver a las bases.En esta sesión de Mentoría Grupal, destruimos los mitos de las "herramientas mágicas" y nos enfocamos en aplicar el Principio de Pareto: el 20% del esfuerzo que te va a traer el 80% de los resultados.EN ESTE EPISODIO VAS A DESCUBRIR:

Most coaches hit this step and immediately think they need a website, a podcast, a blog, paid ads, and 10,000 Instagram followers. They don't. And chasing all of that before understanding who they actually need in the room is exactly why their group never fills.This is Step 3 in our 7-part series on scaling from one-to-one to one-to-many. If you haven't listened to Steps 1 and 2 on payoff and math, go back and start there first. This one won't land without that foundation.In this episode, Adam and Jess break down what "audience" actually means in the context of group coaching, why your follower count is probably lying to you, and how to use Pareto's Principle to get a real number you can actually work toward.What You'll LearnWhy the world doesn't care that you're a coach yet, and what to do about itHow Pareto's Principle (the 80-20 rule) translates to a concrete audience size you need to reach your group goalThe simple formula: multiply your desired group size by 5 to find how many real conversations you needWhy your Instagram followers, email list, and phone contacts are almost certainly not full of your ideal avatarThe difference between unknown, known, like, and trust audiences, and which ones actually matter hereWhy building a massive media empire won't get you to 500 warm avatars faster than showing up in the right roomsHow to audit what you already have and identify the gap between where you are and where you need to beWhy reconnecting with someone you haven't talked to in years is simpler than you thinkA preview of the next step: playing the contact sport in a way that actually fits who you areTimestamps00:01 Welcome to Step 3: Audience01:22 Why math and audience go hand in hand02:10 Pareto's Principle explained simply04:07 The group size formula: desired number x 505:15 Why you don't need a media empire to reach your number06:53 What "warm audience" actually means08:00 Why your follower count isn't your avatar count10:47 The four audience tiers: unknown, known, like, trust11:18 Being in proximity vs. cold outreach15:06 How to audit your existing warm audience16:15 The warm names list in the blueprint18:28 You have more avatars under your nose than you think21:21 Audience building is a contact sport22:12 Why how you play the contact sport matters as much as playing it24:15 Episode recap and call to actionQuotes From This Episode"The world does not care right now that you are a coach. So before we dive into the solution behind that, let's talk about how math and audience go hand in hand." - Adam"If you want 100 people in your group, multiply that by five. That tells you how many whole conversations you have to have." - Jess"It's not about every person. It's about enough of the right people." - Jess"You don't realize how many of your avatars are right under your nose because you've actually never stepped into this in an intentional way." - Adam"If you are not acting as if you have to be in a contact sport to build your audience, it will be a hard uphill battle." - Adam"There is a way to do this authentically that aligns with who you are and how you already show up in the world." - JessResources + Next StepsDownload the free Get Paid to Coach guide at ilovecoachingco.comJoin the $10K+ Coaching Offer Challenge at ilovecoachingco.com/challengeREAL Coach Method Membership: ilovecoachingco.com/discoverMissed Steps 1 or 2? Go back and listen to the payoff and math episodes first

What happens when you try to grow faster than your foundation can support?In this episode, Kevin Palmieri and Alan Lazaros break down why so many people get stuck trying to jump ahead in self-improvement. Based on their own journey, years of coaching, and thousands of episodes, they explore what happens when you chase advanced strategies before mastering the basics. The result is usually frustration, inconsistency, and slower progress than expected. This conversation will shift how you think about growth, goals, and what it actually takes to build momentum that lasts. If you want real progress, you need a foundation strong enough to hold it. Hit play and check the level you're really building from._______________________Learn more about:Book Alan's Business Breakthrough Session. Your first 30-minute coaching call is FREE. Learn how to prioritize success and let your quality of life become the byproduct - https://calendly.com/alanlazaros/30-minute-breakthrough-session_______________________NLU is not just a podcast; it's a gateway to a wealth of resources designed to help you achieve your goals and dreams. From our Next Level Dreamliner to our Group Coaching, we offer a variety of tools and communities to support your personal development journey.For more information, check out our website and socials using the links below.

Episode SummaryMost coaches want to build a group offer. Very few sit down and do the math first. That's exactly why this episode exists.This is Step 2 in our 7-part series on scaling your coaching business from one-to-one into a one-to-many model. If you haven't listened to Step 1 on defining your payoff, start there first. The math in this episode won't land without it.Adam and Jess break down the actual numbers behind transitioning from one-to-one into group coaching, and why skipping this step is the reason so many coaches burn out instead of scaling up.What You'll LearnWhy $15K/month in one-to-one revenue is the benchmark before group makes strategic senseThe replacement math rule: your group must equal or exceed the one-to-one slot it replacesWhy pricing your group at $97 to "make it accessible" creates a harder lift, not an easier oneHow to use group to buy back your time without sacrificing incomeWhat the ascension model actually looks like when you build it in the right order (hint: it's backwards from what most people teach)The difference between independent coaching and being a delivery vehicle for someone else's payoffWhy imposter syndrome around pricing almost always points back to a payoff problem, not a confidence problemPareto's Principle applied: why you may only need 25 real conversations to fill a group of fiveTimestamps00:00 Why we're talking about math (and why it's not as scary as it sounds)01:12 Where you should be before building a group: the one-to-one foundation03:06 The $15K/month benchmark and what it actually requires in time05:49 The replacement math rule explained07:05 Why low-ticket group pricing creates a bigger problem than it solves08:02 Playing with the math: replacing all your one-to-ones vs. some of them09:40 What "ascension" actually means and why ILC builds it backwards13:21 Why group creates pricing power in your one-to-one14:41 Dollar-per-hour productivity and how group changes the equation16:36 The dependent coaching model and why it's costing you more than money18:29 How to know if your pricing fear is actually a payoff problem27:40 Pareto's Principle: the 25 conversations framework for filling your group30:19 Why time is the only non-renewable asset in this businessQuotes From This Episode"The numbers inform the decision. Most people will get so excited by the opportunity and they'll have big vision and they'll want to build something, and yet they won't know the numbers behind it." - Jess"Your group coaching has to be equal to or greater than one one-on-one coaching client. Equal or greater than." - Adam"If you're in a dependent model, you are not actually coaching. You are the vehicle to deliver the payoff defined by the company you're coaching for." - Jess"I truly believe that if you're gagging on the idea of charging somebody $5,000, you don't know your payoff. That's why you have imposter syndrome around these numbers." - Jess"Time is the only non-renewable asset. You can spend money, you can lose money, you can make it back. But if you spend time, you can't get it back." - Jess"You don't have to know everybody. You don't have to get everybody in your group. You only need a percentage, a fraction of the people you think you need to fill that group." - JessResources + Next StepsDownload the free Get Paid to Coach guide at ilovecoachingco.com (start here if you haven't already)Join the $10K+ Coaching Offer ChallengeBecome a member of the REAL Coach Method communityMissed Step 1? Go back and listen to the payoff episode before this one

Si quieres sentirte más fuerte, con más energía, más sano e incluso poder vivir más años con buena calidad de vida, hoy voy a explicarte la mejor forma de hacerlo y cómo puedes mejorar tu salud en las próximas 6 semanas mucho más que en los últimos 6 meses. Y no me refiero a hacer más cosas, me refiero a evaluar las cosas que tienes que hacer, porque seguro que ahora mismo hay algo, una única cosa, que si la hicieras mejorarías mucho tu salud. Lo difícil es encontrarla y es lo que voy a enseñarte hoy usando el mismo sistema que uso con mis clientes en mis mentorías. A simple vista, esto de mejorar la salud parece fácil, y mucho más fácil aún desde que tenemos las redes sociales y hay cientos de individuos, como yo, que te dicen qué hacer para mejorar tu salud. Así que ahora la información no es un problema, el problema es la sobreinformación. Y te voy a poner un ejemplo. El otro día estaba en una comida familiar y alguien empezó a decir que en su casa no quería plástico. Que todos los alimentos los quería almacenar en vidrio, nada de plástico porque estamos sobreexpuestos a los microplásticos y eso es muy malo para la salud y todo eso. Que es un buen mensaje, pero pierde fuerza cuando me lo dices comiéndote una tarta de zanahoria del mercadona y tienes un sobrepeso más que notable. Y esto no es por estigmatizar a nadie, pero creo que si quieres mejorar tu salud necesitas priorizar las cosas que son más importantes. Porque hay 50.000 cosas que puedes hacer para mejorar tu salud y esto les sirve a los influencers de las redes sociales para crear 50.000 reels de cosas que puedes hacer para mejorar tu salud. Pero lo que no te cuenta nadie es que no todas esas 50.000 cosas tienen el mismo impacto. Porque la gente se preocupa por los microplásticos y por comprar sartenes de acero inoxidable pensando que eso va a mejorar su salud, y realmente lo va a hacer ¿Pero en cuánto? Y ahí está el problema, que estás poniendo el foco en las cosas que tienen un impacto muy marginal, hasta el punto que realmente no cambia nada. Y aquí entra el principio de Pareto, el 20% de las cosas que hagas pueden darte el 80% de las mejoras en salud. Entonces ¿Crees de verdad que poner tuperes de vidrio en lugar de plástico es una de las cosas que va a darte el 80% de las mejoras en salud? Porque yo creo que no. Y creo que cualquiera que haga este ejercicio que te voy a enseñar ahora será capaz de darse cuenta de cuáles son las cosas que necesita hacer. Y este ejercicio es muy simple, yo lo uso para encontrar las cosas que tengo que priorizar en mi negocio precisamente para evitar centrarme en todas las cosas que me van a robar el tiempo, el dinero o la energía, pero no me van a dar apenas resultados. Y esto mismo lo puedes aplicar tú para mejorar tu salud. Se trata de apuntar todas las cosas que puedes hacer para mejorar tu salud: Apunta, cambiar tuperes de plástico por tuperes de vidrio, cambiar sartenes de teflón por sartenes de acero, comprarme unas gafas rojas para dormir, usar ropa de algodón orgánico en lugar de poliéster, tomar melatonina para dormir, tomar colágeno para los huesos, apúntalo todo. Cuanto más fan seas de las redes sociales y más expertos de estos sigas, más pájaros tendrás en la cabeza y más cosas podrás apuntar. Y ahora en un papel haces un eje sencillo. En el eje vertical pones el impacto (de menos impacto a más impacto) y en el eje horizontal pones la facilidad (a la izquierda poca facilidad, y a la derecha muy fácil). Y el último paso es calificar todas esas cosas que tienes en la cabeza en función de su impacto y su facilidad para aplicarlas. Por ejemplo: Cambiar tupperes de plástico por tupperes de vidrio, es súper fácil de hacer, pero al mismo tiempo el impacto que tiene en tu salud general es ridículo. Estaría abajo a la derecha. Empezar a aplicar entrenamientos de fuerza, es igualmente fácil, porque hay gimnasios por todos sitios, puedes hacerlo en casa en el gimnasio, en un parque, donde tú quieras, solamente incluso 2 veces por semana. Es decir, es muy fácil, y el impacto que tiene es altísimo. Por tanto estaría arriba a la derecha. Y el objetivo es ese, el objetivo es encontrar aquellas cosas que puntúan cuanto más arriba y cuanto más a la derecha. Por ejemplo si tienes sobrepeso, llevar tu cuerpo a un punto donde no tengas ese exceso de grasa y tu cintura sea como mucho la mitad que tu altura, va a tener un impacto muy alto ¿Es fácil? Bueno, montar tu plan de alimentación para llegar a ese punto sí que es fácil. En definitiva, el ejercicio es encontrar qué cosa en singular puedes hacer ahora mismo para mejorar tu salud, que tenga el máximo impacto posible y sea lo más fácil de implementar posible. Y cuando hayas hecho eso, puedes volver a repetir el ejercicio y centrarte en la siguiente cosa que tenga más impacto y más facilidad. Y así hasta que llegues a las cosas que aunque sean muy fáciles de hacer no van a tener apenas impacto, pero al menos sabes que las cosas gordas ya las tienes implementadas. Porque yo cocino en sartenes de acero inoxidable, y tengo tuperes de vidrio. Pero tampoco bebo alcohol, llevo entrenando fuerza como 15 años, no tengo sobrepeso, cuido mucho mi descanso, salgo a pasear al sol todas las mañanas y tengo mi alimentación bien controlada. Pero todo esto lo hice antes. Y ese es el mensaje que quiero transmitir, quiero transmitir que primero va el 1 y luego va el 2, y si quieres mejorar tu salud, no puedes abrumarte con todas las cosas que puedes hacer ni tampoco obsesionarte con aquellas que haciéndolas apenas te va a cambiar nada tu vida. Céntrate en las que más impacto tienen. Por eso yo sueno como un disco rallado, porque siempre te digo lo mismo: Empieza a entrenar fuerza, evalúa tu dieta actual y hazte un plan de alimentación para mejorar tu forma física, que por cierto esto lo puedes hacer con el planificador nutricional y esta herramienta te va a servir para analizar tu dieta actual y ver si es tan saludable como pensabas y también para crearte un plan de alimentación que te sirva entre otras cosas para eliminar el sobrepeso si es que lo tienes o mejorar tu forma física igualmente. En otras palabras, en mi contenido intento hablarte de lo sustancial, de lo importante, de lo que te va a cambiar la vida. Me da igual si usas tuperes de plástico o tuperes de vidrio, me da exactamente igual, porque yo no quiero hacer un reel viral, quiero ayudarte en lo que pueda y creo que la forma de ayudarte no es liarte con decenas de cosas que puedes hacer que si no bebas agua embotellada, pero tampoco bebas agua del grifo, que si la pasta de dientes tiene no se qué compuestos que te destrozan los dientes… O sea, yo te compro el mensaje, pero si alguien se preocupa de todo esto, sin tener controlados los pilares básicos como son su alimentación, su actividad física y su recuperación, hablar de todo esto solamente es ruido. Y haciendo el ejercicio que te he enseñado puedes ver cuáles son las cosas realmente útiles para mejorar tu salud. Y una vez que encuentres esa cosa con más impacto y más fácil de implementar que puedes hacer ahora mismo, si lo aplicas 6 semanas seguidas, eso va a darte más resultados en las próximas 6 semanas que los resultados que has tenido en los últimos 6 meses. Porque como digo siempre, cuida de tu cuerpo y tu cuerpo cuidará de ti. Pero para cuidar de tu cuerpo, yo al menos te aconsejo que empieces por el 1 y luego vayas al 2, y no al revés. Origen

Send a textSpacemen, speak truth. On today's episode, we go a little deeper into a previously explored topic--honesty. We've been working with more men lately who may struggle to be honest, fearing the repercussions, or just feeling stuck in the habit of white lies or omission. So, we diagnose your problem and give you the familiar Manspace Tri-Tip to help you be more honest. You can't wait. Admit it. Keywordshonesty, lies, relationships, communication, vulnerability, trust, self-awareness, social science, honesty exercisesKey  TopicsTypes of lies: black, white, ParetoReasons behind dishonesty in relationshipsImpact of honesty and deception on trustExercises to promote honesty and vulnerabilitySound Bites"The drummer's stamina in live shows is incredible.""Normalize honesty to build trust and intimacy.""Share small vulnerabilities to build connection."Chapters00:00 Introduction to Honesty and Lies01:11 Discussion of the song 'White Lies' and band RxBandits02:02 The significance of the album 'And the Battle Begun'03:10 Band preferences and musical insights04:11 The drummer's incredible stamina and live performance05:01 Children, honesty, and self-protection06:19 Innovative guitar techniques and slide guitar07:22 The emotional impact of slide guitar and harmonica08:30 Review of the series 'Scrubs' and its seasons09:59 Honesty in relationships and the importance of vulnerability11:54 Types of lies: black, white, Pareto white lies14:10 Why people lie and the motivations behind dishonesty16:23 Gender differences in lying and honesty18:28 Studies on lying: social science insights22:17 The role of masking and social performance24:34 The importance of honesty for connection and trust28:28 Practical exercises to foster honesty in relationships36:41 Addressing shame, self-deception, and honesty barriers43:58 Normalizing honesty and emotional expression52:24 Building a culture of honesty and repair55:58 The importance of owning feelings and reactions01:00:18 Sharing vulnerabilities and small honest acts01:02:51 Conclusion and encouragement to practice honesty ResourcesRxBandits - https://en.wikipedia.org/wiki/RxBanditsScrubs Series - https://en.wikipedia.org/wiki/Scrubs_(TV_series)Honesty and Vulnerability Exercises - https://www.psychologytoday.com/us/blog/the-moment-youth/201911/the-power-honesty-in-relationshipsSpread the word! The Manspace is Rad!!

Dr. Beckett hosts Josh Luber about his 136 page white paper on “BlindBoxification”. They debate Shohei Ohtani's “GOAT” case in comparison to Babe Ruth, including Ruth's influence on Japanese baseball, and discuss hobby myths and legends surrounding iconic cards like the 1952 Topps Mantle and T206 Wagner, arguing the myths are “frosting” on already great cards. The discussion covers Bruce McNall's perceived wealth and relationship with Gretzky, PSA grade price spreads in bull vs. bear markets (especially the gap between 9 and 10), and the Pareto principle as collectors consolidate toward “best of the best” items. Beckett connects blind products to buyers overestimating odds of landing grails and explores an analogy between collecting decisions and Pascal's Wager, including opportunity cost of staying out of the hobby and why 2021 is cited as the only year a new entrant might regret. Beckett also shares a personalized ChatGPT critique of Josh's arguments, touching on novelty, collector intent, information asymmetry changing over time, liquidity vs. hobby health, and saturation risk, while both agree markets adapt and digital repacks may dominate.   00:48 Ohtani vs Babe Ruth 02:30 Mantle and Wagner Myths 03:45 McNall and Gretzky Scandal 04:17 Grading Spreads in Markets 06:14 Pareto and Blind Packs 07:35 Pascal Wager for Collectors 10:59 ChatGPT Critiques the Thesis    

No todos en tu equipo están listos para crecer, y un buen líder sabe reconocerlo. En este episodio hablamos de cómo aplicar el Principio de Pareto para enfocar tu tiempo en ese 20% que genera el 80% de los resultados, formando líderes que multipliquen tu impacto. Escucha Descubre tu Potencial de Liderazgo todos los martes y jueves a las 9:00 a.m. por supresenciaradio.com.

Most financial advisors are really good at their job, and that might be exactly what’s holding them back! Duncan MacPherson is joined by Pareto coaches Jason Westover and Mike “Cy” Cajthaml Jr. for a candid, high-level conversation on what the best financial advisors are doing right now to stay ahead in a rapidly evolving industry. Together, they unpack what it truly means to become the “advisor of the future”,  from growing up-market and attracting ideal clients, to making the pivotal shift from technician to CEO. As disruption accelerates and AI reshapes workflows, the discussion centers on how top-performing advisors are leveraging both technology and human insight to build scalable, enterprise-value businesses. The conversation explores the widening gap between complacency and ambition, the power of intentional practice management, and why relationship excellence, not technical expertise alone, remains the ultimate differentiator. Jason and Mike also share real-world observations from coaching some of the most sophisticated advisory teams in North America, highlighting the habits, structures, and mindset shifts that separate sustainable firms from stalled practices. Key highlights include: Why growing “up-market” often starts with refining your top 50 relationships The transition from advisor to CEO, and why delegation unlocks scale How leading teams are using AI to compress time without compromising trust The importance of client advisory councils and feedback loops Why no one wants to buy your job, only your business This is a practical, forward-looking discussion for financial advisors who want to avoid plateauing, build enterprise value, and design a business that ultimately serves their life, not the other way around. Tune in for strategic insight, tactical ideas, and a clear roadmap for what's next in advisory leadership. Promotions: Pareto Systems: Turnkey Advisor Membership Connect With Duncan MacPherson:  Website: ParetoSystems.com Toll Free: 1.866.593.8020 Learn More: Schedule a Call LinkedIn: Duncan MacPherson Connect With Jason Westover: LinkedIn: Jason Westover Website: paretosystems.com/coaches/coach-jason-westover Connect With Mike “Cy” Cajthaml Jr.: LinkedIn: Mike “Cy” Cajthaml Jr. Website: www.paretosystems.com/coaches/coach-mike-cy-cajthaml-jr About Our Guests: Jason Westover has spent over 20 years helping financial advisors, sales teams, and wholesalers perform at their best. After discovering Pareto Systems 15 years ago, he became one of its strongest advocates, using its proven coaching methods to help top performers elevate their businesses. Today he’s also leading conversations on how AI tools can transform advisor effectiveness and client outcomes across the industry. Jason lives near Kansas City with his wife and three children. Outside of work he’s a competition BBQ cook and Brazilian Jiu-Jitsu competitor. Mike “Cy” Cajthaml Jr. brings 17 years of financial services experience to his role as a Pareto coach. His background spans insurance marketing, nationwide advisor consulting, and working alongside his father as a financial advisor in Overland Park, KS. That blend of wholesale and retail experience gives Mike a unique perspective in helping advisory firms integrate the Pareto Process and build toward their ideal practice. Mike lives in Overland Park with his wife Ashley and their two sons, Cameron and Carson. Outside of work he enjoys golf, a good cigar, and cheering on the Chicago Bears. Listen on Apple Podcasts

Want to scale your business faster?Join our 2-day, interactive workshop: https://www.acquisition.com/workshop-yt-d?el=yt-alex-485w&htrafficsource=youtubeMost business owners aren't “bad at business.” They're just selling to broke people and then act surprised when the close rate is trash, churn is high, and customers complain nonstop. In this episode of The Game, Alex breaks down the uncomfortable truth: if you want to make money, you have to go where the money is. A small percentage of buyers control a massive percentage of the wealth, which means if you price and position your business for “everyone,” you end up building a business for the people who can't pay. The goal is simple. Pick a better customer, build a bigger offer, and charge in a way that makes you more money with fewer sales.YouTube Timestamps00:00 Why businesses struggle to make money04:32 Applying the Pareto principle in profits07:21 Top-down business and pricing strategy16:10 Sell to the rich - they pay better, complain less28:47 Picking price points: value over cost32:50 How close rates reveal underpriced commodities38:41 Stop selling commodities and raise prices systematicallyMore Value:Discover The Easiest Business I Can Help You Start (Free Trial): https://www.skool.com/hormoziJoin The In-Person Scaling Workshop In Las Vegas: https://www.acquisition.com/o-vegasDownload your free $100M scaling roadmap here: https://www.acquisition.com/roadmap?el=yt-alex-486r&htrafficsource=youtubeGet the $100M Book Bundle: https://shop.acquisition.com/pages/100m-book-bundleTake the $100M Lead Generation Course: https://www.acquisition.com/training/leads?hsLang=enLearn How to Make Offers People Cannot Refuse: https://www.acquisition.com/training/offers?hsLang=enFollow Alex Hormozi's Socials:⁠⁠LinkedIn ⁠⁠ | ⁠⁠Instagram⁠⁠ | ⁠⁠Facebook⁠⁠ | ⁠⁠YouTube ⁠⁠ | ⁠⁠Twitter⁠⁠ | ⁠⁠Acquisition ⁠

Are you the only person your builders call when something goes wrong? Most subcontractor owners don't realize they've accidentally become their company's full-time account manager, and it's the reason they can't step away from the business.In this episode, Khalil and Martin break down why this happens, what an account manager actually does (and how it's different from a project manager), and how to build this role into your company so you can stop being the bottleneck.What You'll LearnThe critical difference between an account manager and a project manager, and why confusing them creates chaosWhat the full account manager workflow looks like from discovery and onboarding through post-installHow to find, develop, and compensate the right person for this role inside your companyWhy proactive communication changes the power dynamic between subs and buildersHow to use the 80/20 rule to decide which builder accounts deserve dedicated managementKey Topics & Timestamps01:00 - Episode Intro06:54 - Account Manager vs. Project Manager: Process vs. People + One Point of Contact14:48 - Hiring & Incentivizing Great Account Managers (Homegrown Traits + Pay Structure)18:49 - What Great Account Managers Actually Do (Advocate, Proactive, Problem-Solver)23:47 - Defining the Role: Not Sales, Not PM — Owning the Builder Relationship26:24 - The Account Manager Workflow: Onboarding → Pipeline → Quote → Production → Post-Install + Scaling Tips Key TakeawaysIf every builder calls you directly when something goes wrong, you've become your company's account manager, whether you intended to or notStart building this role by documenting exactly what you do for your top builder relationships so the process can eventually be transferredGrow your account manager internally; external hires lack the institutional context needed to be effectiveThe account manager must have full context across sales, production, and install to make the same quality decisions you wouldCompensate this role well with a strong base plus account-based incentives; they are essentially an inside salesperson for your most valuable relationshipsBegin with one key account and your most reliable employee before expandingResourcesTodd Hagopian and the 80/20 (Pareto) principle for prioritizing accounts⁠⁠⁠Implementing AI in Your Business Workshop Sign-Up ⁠⁠⁠24 Things⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Construction Business Owners Need to Successfully Hire & Train an Executive Assistant⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Schedule⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ a 15-Minute Roadblock CallBuild a Business that Runs without you. Explore our⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ GrowthKits⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Need Marketing Help? We Recommend⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Benali⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Need Help with podcast production? We recommend⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Demandcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Checkout ⁠⁠⁠⁠⁠Quo⁠⁠⁠⁠⁠ More from Martin Holland⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠theprofitproblem.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠annealbc.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠   ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Email Martin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Meet With Martin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠More from Khalil⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠benali.com ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Email Khalil⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Meet With Khalil⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠More from The Cash Flow ContractorSubscribe to our⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube channel⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe to our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow On Social:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X(formerly Twitter)⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Visit our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Email⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ The Cashflow Contractor

What if the very laws you were taught to follow are actually keeping you from the life you're meant to live? In Episode 101 of Love Learning You™, cultural psychology researcher, author, and speaker Justine Gonzalez examines the viral "7 Laws for a Liberated Life" posts and goes deep into the heart and intent behind some of the laws we follow. Most people scroll past posts like these without ever asking: Who created these laws? Who benefits from them? And what gets left out when the wisdom of entire cultures, ancestral traditions, and spiritual lineages is replaced with a list of Eurocentric/capitalistic productivity hacks?In this episode, Justine models what it looks like to think critically as a cultural psychology researcher and then offers something radically different: her own 5 personal laws for liberation, built from 15+ years of research, lived experience, and cross-cultural spiritual study.

This week, Bob walks through Javier Milei's 2026 address to the World Economic Forum, explaining the Austrian and neoclassical ideas behind Milei's defense of capitalism—from Rothbard and Kirzner to Pareto efficiency and the welfare theorems.Related:Bob's Breakdown of The Intra-Austrian Debate over Milei: Mises.org/HAP539aThe Mises Institute is giving away 100,000 copies of Hayek for the 21st Century. Get your free copy at Mises.org/HAPodFree

This week, Bob walks through Javier Milei's 2026 address to the World Economic Forum, explaining the Austrian and neoclassical ideas behind Milei's defense of capitalism—from Rothbard and Kirzner to Pareto efficiency and the welfare theorems.Related:Bob's Breakdown of The Intra-Austrian Debate over Milei: Mises.org/HAP539aThe Mises Institute is giving away 100,000 copies of Hayek for the 21st Century. Get your free copy at Mises.org/HAPodFree

From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

Andrew Hulbert built Pareto from scratch, scaled it to about £50M in revenue, and exited for around $100M, retiring at 37. But the most interesting part of his story is what happened next. In this episode, Andrew explains how he avoided the post-exit crash many founders experience by preparing himself personally, not just preparing the business. We talk about working with a business psychologist, the “Exit Island” concept, how he decompressed after closing, and why the things that looked like success (cars, status, noise) were far less fulfilling than reconnecting with his wife, kids, friends, and health. This is a practical, honest conversation for founders who are approaching an exit and wondering: Who am I without the business, and what comes next? We cover: preparing for exit mentally, clean exits vs earn-outs, identity after exit, relationship repair, health during the sale process, significance and meaning, and what Andrew would do differently if he built it again. Guest: Andrew Hulbert Host: Jerome Myers Learn more about your ad choices. Visit megaphone.fm/adchoices

Hosts Kevin Palmieri and Alan Lazaros expose a subtle trap that keeps high performers stuck longer than failure ever could. Holding onto what once worked. After years of building Next Level University and coaching thousands through real growth phases, they have seen how progress turns into comfort, and how comfort quietly caps results.This episode cuts through surface-level self-improvement advice and reframes what it actually takes to move from momentum to mastery. The focus is on leverage, standards, and long-term consistency across health, wealth, and relationships. No hacks. No hype. Just the principles required to reach the next level without burning out or drifting backward.Learn more about:Your first 30-minute “Business Breakthrough Session” call with Alan is FREE. This call is designed to help you identify bottlenecks and build a clear plan for your next level. - https://calendly.com/alanlazaros/30-minute-breakthrough-sessionJoin our private Facebook community, “Next Level Nation,” to grow alongside people who are committed to improvement. - https://www.facebook.com/groups/459320958216700_______________________NLU is not just a podcast; it's a gateway to a wealth of resources designed to help you achieve your goals and dreams. From our Next Level Dreamliner to our Group Coaching, we offer a variety of tools and communities to support your personal development journey.For more information, check out our website and socials using the links below.

Mentor Sessions Ep. 049: Jeff Booth vs Simon Dixon: Bitcoin's Abundant Future or Total Dystopian Nightmare?What if Bitcoin's promise of a deflationary free market utopia crushes the global surveillance state—or traps 95% in a multipolar prison of programmable money, elite control, and endless chaos? In this must-watch, visionary Jeff Booth clashes with geopolitical expert Simon Dixon on whether Bitcoin enforces abundance for all through unstoppable privacy tech like Fedi and Nostr, or merely offers an escape hatch for the few amid dollar demise, AI weaponization, and financial industrial complex capture. From Venezuela's turmoil and Iranian protests to UK thought police and precious metals surges, they expose how centralized custody in ETFs and treasury companies co-opts Bitcoin, risking chain forks and surveillance nightmares. Jeff's optimistic blueprint for agency-driven freedom battles Simon's stark warnings of hybrid systems where the masses "own nothing and be happy," tying into Bitcoin self-custody, decentralized mining, and circular economies as your shield against fiscal dominance and currency wars. If you're stacking sats in a Bitcoin-only world, this debate reveals why privacy isn't optional—it's your path to sovereignty or subjugation. Don't miss the white pill vs. black pill showdown that could redefine your Bitcoin strategy!About Jeff BoothWebsite: https://jeffbooth.ca/Nostr: jeffbooth@nostrverified.comAbout Simon DixonX: @SimonDixonTwittYouTube: https://www.youtube.com/@SimonDixon21Chapters:00:01:05 Hook & Guest Introduction00:01:13 Global Chaos Overview00:01:40 Jeff: System Collapse & Deflation00:04:02 Simon: Multipolarity & Dollar Strategy00:07:22 Surveillance State & Resistance00:08:55 Jeff: Control Structures & Elite00:11:02 Fear vs Optimism Messaging00:12:35 Bitcoin Centralization Risks00:14:44 Privacy & Cypherpunk Roots00:16:37 Simon: Banking to Bitcoin Journey00:19:29 Jeff: Parallel Ecosystems00:25:35 Trump's Surveillance Ties00:26:25 Node Risks00:29:30 Bitcoin Protocol Stack00:33:08 Chain Forks & Resistance00:35:40 Federations & Decentralized Banks00:36:43 Imposition vs Escape Hatch00:38:50 Systems Non-Coexistence00:40:02 Pareto & Prison Debate00:42:43 Black Markets & Emergence00:45:21 Gold Lessons & Ethics00:49:18 Free Market Spirituality00:52:03 Thought Traps & Sovereignty00:58:57 Distractions & Community Building01:00:01 Bitcoin's Voluntary Ethics01:01:27 Agency & Time Value01:03:36 Force & Confiscation Risks01:04:46 Privacy Attack Costs01:06:40 Custody Fears01:07:01 Hope vs Fear01:08:29 Simon's Financial Obsession01:10:17 Jeff's Optimism Shift01:13:24 Decentralization Threats01:15:17 VC Journeys01:17:02 Outperforming Bitcoin01:21:23 Spiritual Free Market01:24:08 Decentralized Banks Concept01:24:44 E-Cash & Federation Risks01:29:30 Fedi Privacy Layers01:30:12 Final Advice01:33:50 Guest Contacts & Wrap-Up⚡ POWERED by Abundant Mines: Fully managed Bitcoin mining. Learn more at https://qrco.de/bgYKPB

What if financial planning were approached the same way engineers design aircraft, medical treatments, or complex systems—with clearly defined objectives, constraints, and rigorous trade-off analysis? In this episode, Benjamin Felix is joined by Braden Warwick for a deep dive into what it means to engineer financial outcomes. Drawing on Braden's background as a PhD-trained mechanical engineer and his work building financial planning software at PWL Capital, the conversation reframes financial planning as a design problem rather than a speculative exercise. They explore the critical distinction between a financial plan and a financial projection, why uncertainty does not invalidate good planning, and how professional communication under uncertainty can build trust with clients—especially those from technical backgrounds. The discussion highlights the importance of goals-based planning, sensitivity analysis, and explicitly quantifying trade-offs when clients have multiple competing objectives. Key Points From This Episode: (0:00:04) Introduction to Episode 393 and the return of Braden Warwick (0:02:50) Braden's role at PWL and his experience deploying Conquest Planning software (0:05:46) The tension between low industry entry barriers and professional standards in financial planning (0:07:54) Braden's background in mechanical engineering and academia 0:09:33) Financial plans vs. financial projections: why uncertainty doesn't make a plan "wrong" (0:12:59) Lessons from medicine and engineering on communicating decisions under uncertainty (0:15:15) An engineering framework for financial planning: objectives first, then solutions (0:18:42) Why surface-level goals like "minimize tax" or "maximize returns" often miss what really matters (0:21:19) Evaluating plans against goals using projections, scenario analysis, and sensitivity analysis (0:24:28) Why sensitivity analysis helps planners focus on what actually drives outcomes (0:29:27) Handling multiple competing goals using trade-off analysis and Pareto frontiers (0:36:46) Practical ways planners can present trade-offs without complex math (0:39:25) Case study setup: professional financial planning with corporate clients (0:40:20) Salary vs. dividends for business owners when optimizing for legacy goals (0:44:26) Why financial planning software outputs can be misleading without context (0:48:23) The importance of understanding how planning software calculates key metrics (0:50:22) Using PWL's free retirement tool to analyze CPP and OAS timing decisions (0:53:44) Approximating Monte Carlo outcomes using standard error of the mean (0:56:16) Linking "bad" and "terrible" outcomes to plan success probabilities (0:58:44) How CPP and OAS deferral affects sustainable spending and downside protection (1:02:46) What makes PWL's CPP calculator different from typical break-even tools (1:05:15) Why wage inflation assumptions materially affect CPP deferral decisions (1:07:46) Closing framework: goals, constraints, sensitivity analysis, and quantified trade-offs (1:09:36) Financial planning as an emerging discipline rooted in engineering-style thinking Links From Today's Episode: Meet with PWL Capital: https://calendly.com/d/3vm-t2j-h3p Rational Reminder on iTunes — https://itunes.apple.com/ca/podcast/the-rational-reminder-podcast/id1426530582. Rational Reminder on Instagram — https://www.instagram.com/rationalreminder/ Rational Reminder on YouTube — https://www.youtube.com/channel/ Benjamin Felix — https://pwlcapital.com/our-team/ Benjamin on X — https://x.com/benjaminwfelix Benjamin on LinkedIn — https://www.linkedin.com/in/benjaminwfelix/ Editing and post-production work for this episode was provided by The Podcast Consultant (https://thepodcastconsultant.com)

คนสองคนที่เกิดวัน เดือน ปีเดียวกัน แต่ชีวิตอาจไปกันคนละทาง บางครั้งอาจไม่ใช่เพราะดวง ไม่ใช่เพราะฐานะ แต่อาจเป็นเพราะ “วิธีคิดในการตัดสินใจ” ที่ต่างกัน Sci x Fi EP. นี้ ต้อง นนทพงศ์ ชวน ดร.โก้ พงศกร สายเพ็ชร์ อาจารย์พิเศษ Scientific Research and Presentation มหาวิทยาลัยมหิดล หลักสูตรนานาชาติ มาเล่ากฎ 5 ข้อ ที่จะช่วยให้คุณตัดสินใจเรื่องงาน เงิน และชีวิต ได้ดีขึ้น แล้วคุณจะเข้าใจว่า...ทำไมบางคนแม้เริ่มจากศูนย์ แต่ไปได้ไกลกว่าคนอื่น 0:00 Intro 0:54 เปิดรายการ 6:31 ‘Opportunity Cost' ต้นทุนค่าเสียโอกาส  13:18 ‘Inversion' การคิดย้อนกลับ 24:18 ‘Pareto' กฎ 80/20 33:09 ‘Probabilistic Thinking' คิดเชิงความน่าจะเป็น  44:43 First Principles คิดจากหลักการพื้นฐาน #WealthMeUp #ScixFi  #DecisionMaking #MentalModels #การเงิน #การลงทุน

What if the reason you're not achieving extraordinary results isn't because you're doing too little, but because you're doing too much? In this encore episode of The Game Changing Attorney Podcast, Michael Mogill sits down with Jay Papasan, Vice President at Keller Williams Realty and bestselling author of The One Thing: The Surprisingly Simple Truth Behind Extraordinary Results. Jay breaks down why the popular concept of balance is a fallacy, how multitasking is actually killing your productivity, and why discipline is not what you think it is. From understanding the truth about willpower to mastering the focusing question that changes everything, this conversation delivers a master class in achieving more by doing less. Here's what you'll learn: Why multitasking is a lie that's costing you 28% of your day and lowering your IQ by 11 points How to use selective discipline and the 66-day habit formation principle to make success automatic What the focusing question is and how it creates clarity around your most leveraged activities Want to achieve extraordinary results? This episode shows you exactly how to get there. ---- Show Notes: 03:52 – The origin story of The One Thing, from a 14-page handwritten essay to a bestselling book 05:59 – Why focusing on one thing is such a challenge despite being simple 09:01 – Walking through the process of using extreme Pareto to narrow down priorities 13:04 – Debunking the myth of multitasking and why it's costing you 28% of your day 19:36 – The Green Beret story: how training creates habits that last decades 28:26 – Defining willpower as different from discipline and why it's a limited resource 30:16 – A powerful study on parole judges that proves willpower depletion is real 36:47 – Counterbalancing instead of balance and why it matters for business and life 47:23 – How purpose gives you direction and a clear sense of priority ---- Links & Resources: The One Thing by Jay Papasan Atomic Habits by James Clear Better Than Before by Gretchen Rubin Willpower Doesn't Work by Benjamin Hardy Grit by Angela Duckworth Eat That Frog by Brian Tracy The Miracle Morning by Hal Elrod The Pareto Principle ---- Do you love this podcast and want to see more game changing content? Subscribe to our YouTube channel. ---- Past guests on The Game Changing Attorney Podcast include David Goggins, John Morgan, Alex Hormozi, Randi McGinn, Kim Scott, Chris Voss, Kevin O'Leary, Laura Wasser, John Maxwell, Mark Lanier, Robert Greene, and many more. ---- If you enjoyed this episode, you may also like: 383. AMMA — Why Comfort Will Quietly Destroy Your Law Firm 334. Dr. Benjamin Hardy — From Limiting Beliefs to Limitless Potential: A Guide to Personal Growth 78. Dr. Katy Milkman — How to Change: The Science of Getting From Where You Are to Where You Want to Be

El Principio de ParetoConviértete en un supporter de este podcast: https://www.spreaker.com/podcast/productividad-maxima--5279700/support.Newsletter Marketing Radical: https://marketingradical.substack.com/welcomeNewsletter Negocios con IA: https://negociosconia.substack.com/welcomeMis Libros: https://borjagiron.com/librosSysteme Gratis: https://borjagiron.com/systemeSysteme 30% dto: https://borjagiron.com/systeme30Manychat Gratis: https://borjagiron.com/manychatMetricool 30 días Gratis Plan Premium (Usa cupón BORJA30): https://borjagiron.com/metricoolNoticias Redes Sociales: https://redessocialeshoy.comNoticias IA: https://inteligenciaartificialhoy.comClub: https://triunfers.com

Most flower farmers head into a new year full of hope but without a clear plan... and that's how burnout and unpredictable income sneak right back in! In this episode of the Six Figure Flower Farming Podcast, Jenny Marks shares five strategic shifts to help you build a more profitable, efficient, and sustainable flower farm in 2026 - without more land, more flowers, or more chaos. This conversation is about stepping out of busywork and into clarity so your farm supports your life, not the other way around. You'll hear how to plan your flower farm around outcomes instead of endless tasks, choose one high-impact sales outlet to focus on, forecast crops based on real profit data, build simple systems that protect your time and margins, and create a consistent marketing and sales engine before the season starts. If you want steadier revenue, better boundaries, and a farm business that feels intentional and manageable, this episode will help you set the foundation for long-term flower farming success. Listen to Episode 42: Pareto's Law Did you enjoy this episode? Please leave a review on Apple or Spotify. Follow Jenny on Instagram: @trademarkfarmer Find free flower business resources: www.trademarkfarmer.com ​

In today's episode of Next Level University, hosts Kevin Palmieri and Alan Lazaros challenge the way most people think about effort, focus, and progress. The idea of “less is more” is familiar, but rarely understood correctly. This conversation reframes the Pareto Principle as a decision-making standard, not a productivity trick. It confronts why most people stay busy yet stagnant, why real progress feels slower than expected, and why long-term results demand a different mindset entirely. This episode is about leverage, patience, and choosing what actually matters when the payoff is far away. Listen closely. Then cut the noise and commit to the work that compounds when no one is watching.Learn more about:Where learning turns into action. “Next Level Book Club”  every Saturday:https://zoom.us/meeting/register/tJMkcuiupjIqE9QlkptiKDQykRtKyFB5Jbhc_______________________NLU is not just a podcast; it's a gateway to a wealth of resources designed to help you achieve your goals and dreams. From our Next Level Dreamliner to our Group Coaching, we offer a variety of tools and communities to support your personal development journey.For more information, check out our website and socials using the links below.

In this episode of the Always On Podcast, Duncan MacPherson speaks with Natasha Kennedy, Pareto-certified coach and family legacy specialist, for a timely conversation on the greatest risk facing financial advisors during the great wealth transfer. Natasha breaks down why most heirs ultimately leave their parents' advisor, how silence and avoidance around legacy planning erode trust, and why preparing heirs has become a critical differentiator in today's advisory landscape. Together, they unpack how advisors can embed continuity, succession, and family investment legacy into their value proposition in a way that deepens relationships, strengthens referability, and positions them as indispensable stewards of multi-generational wealth. Key takeaways: Why nearly 90% of heirs don't stay with their parents' advisor How legacy and succession conversations increase advisor retention and fee worthiness Practical ways to engage heirs without creating entitlement How continuity planning protects relationships through generational transitions Tune into now to unlock the secrets to keeping clients for generations by addressing the blind spot most advisors overlook. Promotions: Pareto Systems: Turnkey Advisor Membership Connect With Duncan MacPherson: Website: ParetoSystems.com Toll Free: 1.866.593.8020 Learn More: Schedule a Call LinkedIn: Duncan MacPherson Connect With Natasha Kennedy: LinkedIn: Natasha Kennedy Presentation: The Legacy Plan Speaker Introduction: Natasha Kennedy About Our Guest: Natasha Kennedy, who brings more than a decade of financial services experience, beginning her career on Wall Street and then working with First Trust Advisors, and now focused on psychology and counselling. Today, Natasha is a consultant with Pareto Systems helping advisors to strengthen client relationships, and build purposeful businesses. With her background in both finance and psychology, Natasha is the ideal person to guide us through the human side of this great wealth transfer, specifically on adding Intergenerational Planning & Family Meetings into your process.

The holidays are here, and while they are meant to be a time of joy and connection, they often bring along digital distraction, fear of overeating, and anxiety about the impending New Year.In this short and sweet episode, Andrés Preschel breaks down the three most important themes for navigating the holiday season intentionally. You'll learn how to optimize your physiology to enjoy family meals without the guilt, how to create a digital barrier to ensure you are truly present with your loved ones, and exactly how to execute a "Past Year Review" to set yourself up for massive success in the year ahead.Discover your science, optimize your life, and enjoy your holidays.In This Episode, You'll Learn:1. The Gift of Presence (Digital Detox Strategies)Why you should delete social media for the last week of the year (less than 2% of your life!).How to use "intervention" tools to break the dopamine loop and stop doom-scrolling.Tools mentioned: The One Sec app and Shift technology.2. Preventing Holiday Weight Gain (Without Dieting)How to enjoy potlucks and home-cooked meals without "dieting" or counting calories.The Pre-Meal Protein Primer: Consuming 20-30g of lean protein (Greek yogurt, whey, lean beef) 30–60 minutes before a meal to suppress Ghrelin and boost GLP-1 (satiety).Fiber & Bitters: Why eating handfuls of dark leafy greens (arugula, spinach) before your meal slows gastric emptying and reduces glucose spikes.Food Sequencing: The correct order to eat your food to manage insulin response (Fiber/Protein first, Carbs last).The Postprandial Stroll: How a 10–15 minute walk after dinner pulls glucose into the muscles without insulin.3. The "Past Year Review" (Strategic Planning)Why New Year's Resolutions often fail and what to do instead.A step-by-step guide to Tim Ferriss's Past Year Review exercise.How to use the 80/20 rule (Pareto's Law) to identify the people and activities that bring you peak joy—and how to schedule them immediately.Creating a "Not-To-Do" list to eliminate the negative triggers from your life.

A rapid-fire tour through a packed week in AI, from Google's surprise Gemini 3 Flash release and its implications for the model Pareto frontier, to bombshell OpenAI fundraising talks involving Amazon and trillion-dollar valuations, major AI leadership and org changes at Amazon, early signs of stress in data-center financing markets, ChatGPT's push toward an app-platform future, fresh details on the OpenAI–Disney deal, a new US Tech Force for government AI infrastructure, Nvidia's China chip calculus, and Bernie Sanders' call for a data-center construction moratorium. This episode connects the dots between models, money, infrastructure, and politics shaping where AI heads next. Brought to you by:KPMG – Discover how AI is transforming possibility into reality. Tune into the new KPMG 'You Can with AI' podcast and unlock insights that will inform smarter decisions inside your enterprise. Listen now and start shaping your future with every episode. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.kpmg.us/AIpodcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Rovo - Unleash the potential of your team with AI-powered Search, Chat and Agents - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://rovo.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Zenflow by Zencoder - Turn raw speed into reliable, production-grade output at ⁠⁠⁠https://zenflow.free/⁠⁠⁠LandfallIP - AI to Navigate the Patent Process - ⁠⁠⁠https://landfallip.com/⁠⁠⁠Blitzy.com - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to build enterprise software in days, not months Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Interested in sponsoring the show? sponsors@aidailybrief.ai

I'm a woman trapped in a man's body analysing dating advice for women given by a man. In #505 of 'Meanderings', Juan and I discuss: three dating and relationships books (The New Rules, Get the Guy by Matthew Hussey and Attached), how prescriptive “rules” aimed at women can backfire, why some advice feels outdated (Facebook walls and BBM), how scarcity games tend to attract the very players you might want to avoid, why attachment styles are useful as a lens but less so as a to‑do list, a focus on authenticity over mere effectiveness, watch the influence of your friend circle, understand how strong male sexual drive can shape dating dynamics, apply Pareto principles to health and appearance first, build an interesting life (travel, skills, community) and learning to read yourself so you don't try to fill loneliness with just anyone.No boostagrams this week, very sad puppy.Stan Link: https://stan.store/meremortalsTimeline:(00:00:00) Intro(00:00:47) The books: The New Rules, Get the Guy & Attached(00:04:21) Lot's of Don'ts(00:07:12) Perfectionism and the hunt for Mr Right(00:11:09) Who this attracts: playing games gets game players(00:16:21) What men reportedly dislike(00:20:25) Quick verdict on The New Rules & Switch To Matt Hussey(00:25:48) Practical prompts: compliments, conversations, and friendly vibes(00:30:15) Brief detour to Attached: anxious, avoidant, secure(00:39:24) Boostagram Lounge(00:41:15) Effectiveness vs authenticity: advice for daughters(00:45:00) Masks, faking confidence and why acts won't last(00:48:00) Be interesting: travel, stories and easy conversation openers(00:55:14) Broad advice: the male mind, sex drive, and expectations(01:02:26) Pareto squared: health and appearance(01:07:09) A raw moment: walking through Brisbane and feeling loneliness(01:11:24) Closing reflections Connect with Mere Mortals:Website: https://www.meremortalspodcasts.com/Discord: https://discord.gg/jjfq9eGReUTwitter/X: https://twitter.com/meremortalspodsInstagram: https://www.instagram.com/meremortalspodcasts/TikTok: https://www.tiktok.com/@meremortalspodcastsValue 4 Value Support:Boostagram: https://www.meremortalspodcasts.com/supportPaypal: https://www.paypal.com/paypalme/meremortalspodcast

✅ The biblical reason dads are called to bring order to their homes ✅ How to train your kids like a football coach (M&Ms included!) ✅ The power of a weekly family meeting to solve your biggest friction points ✅ Why setting "impossible" goals actually works SUMMARY Chaos doesn't have to be the norm in your home. In Part 1 of this conversation, Army Ranger turned fatherhood coach Chris Cirullo unpacks the biblical call for fathers to bring order—and shares the practical systems he's built to lead his five sons with both fun and discipline. You'll also hear why setting impossible goals might be the key to real growth. TAKEAWAYS God designed fathers to bring order and strategy to their homes—it's part of our calling, not just a nice-to-have. Training kids in specific behaviors with immediate rewards (like M&Ms) can save decades of frustration. Weekly family meetings with your wife help you identify and solve one key friction point at a time. Setting "impossible" goals narrows your options and forces clarity on what actually needs to change. What gets measured improves—but what gets measured and reported improves exponentially. GUEST Chris Cirullo is a former Army Ranger with four combat tours in Afghanistan, a former collegiate football player, fitness coach, and tech startup leader. He now coaches men through Mission Fit and serves on the team at Forming Men. Chris and his wife Justine homeschool their five sons in Eugene, Oregon, and are expecting their sixth child. LINKS Send a Voice Message to DadAwesome Apply to join the next DadAwesome Accelerator Cohort: Email awesome@dadawesome.org Subscribe to DadAwesome Messages: Text the word "Dad" to (651) 370-8618 FREE copy of Chris' book: https://www.missionfit.co/free15 Mission Fit Scorecard: missionfit.co/scorecard Forming Men Quotes: "Minutes of training can sometimes save decades of headaches for a father." "I have this innate responsibility as a father to bring order. We're not all great at it, but we do have to find ways to make efforts unto that end." "Setting impossible goals is one of the most effective ways to actually make meaningful growth." "What gets measured improves, but what gets measured and reported improves exponentially." "God wanted to partner with Adam to bring about order in the world, and He stopped short of producing complete order so that man as a father and a husband could do some of that work." TAGS fatherhood, intentional parenting, family systems, discipline, order, army ranger, coaching dads, homeschool dad, training kids, goal setting, Parkinson's law, Pareto principle, Pearson's law, accountability, family mission, Christian dad, family meetings, parenting hacks, dadlife, Genesis