Podcasts about CRO

We're really at a crisis point for a lot of marketers.It's not just that ads keep getting more expensive.It's that it just gets harder and harder to get and keep prospects' attention.And with everything being engineered and optimized by AI and CRO, stuff ends up looking more and more the same. And that only works against you.You know you need to stand out–but how?Well, the best way to get and keep attention is, and always has been, a story.But how long is a story?I mean, a Hero's Journey story can take hours.And even the type of compact tales I introduced in my book The Persuasion Story Code can take two to three minutes. That's not very long, but at a time of shrinking attention spans, it's still too long.Now, you can try using outrageous hooks. But in addition to shrinking attention spans, you're also fighting against rising levels of skepticism and outright distrust.If you say something that gets attention but just isn't believable, you're still sunk.So, what would be ideal to solve this problem?It would be a persuasion story you could tell in 15 or 20 words.Impossible, you say?That's what I thought until I really started working on it.One of my clients, Ari Nirsissian, helped me quite a lot in the development of my thinking and writing of these new kind of attention magnets, the one-sentence microstory.It really is a story. It really is persuasive.And it really is short!Just the right size for today's attention spans.Today I'm going to show you, step by step, how I developed three of them… and how I combined them into one electric three-sentence paragraph, which takes less than a minute to read out loud.Resources:To find out more about my book The Persuasion Story Code, check out this link to the Amazon page:https://www.amazon.com/dp/B0CFD2KXNQAnd to find out more about my coaching for experienced copywriters and business owners, go to:https://garfinkelcoaching.com

AI is forcing a leadership choice. You can treat it like a stack of use cases and end up with a lot of motion and a little progress. Or you can start with a clear vision of the future you want, make strategy visible, and use that to align decisions across the business.  In this episode, Drew Neisser talks with Brian Evergreen, author of Autonomous Transformation, about why the AI conversation so often collapses into tools and use cases, and how leaders can pull it back to vision, outcomes, and the kind of alignment that drives transformation. What you'll take away:  Why optimization can keep you busy while you stay stuck  How to make a future vision concrete enough to act on  What "no strategy without vision" means, and how to spot fake strategy  Why leaders default to scorecards, and how it stalls transformation  How Brian's "nindrant" separates "we can do" from "we need alignment"  Why use case first AI limits gains, and how to shift to value creation Plus:  A simple workshop to surface visions before projects  A clean split between what marketing can do now and what needs CRO and CFO alignment  How to move AI from tool talk to a value creation leadership conversation If you are tired of AI conversations that start with tools and end with small wins, listen to this episode for a vision first approach that changes what you do next.  For full show notes and transcripts, visit https://renegademarketing.com/podcasts/ To learn more about CMO Huddles, visit https://cmohuddles.com/

Send a textHow to turn founder instincts into a repeatable pipeline engine. Guest: Javier Lozano, Fractional CMO & GTM Leader  --  Founder-led sales is often the fastest way to get an early-stage SaaS company off the ground. But at some point, the very thing that helped you close your first customers becomes the bottleneck preventing your company from scaling.In this episode of SaaS Backwards, Ken Lempit sits down with fractional CMO and GTM leader Javier Lozano of Bolder Media to break down why founder-led sales eventually stop working—and how SaaS leaders can turn founder instincts into a repeatable revenue engine.They discuss how to extract the winning patterns inside a founder's head, transform those insights into positioning and messaging, and build a predictable pipeline that sales teams can execute at scale.You'll also learn why hiring sales leaders too early often backfires, how to create a “blue ocean” positioning that separates your SaaS product from crowded markets, and what investors really look for when evaluating early-stage SaaS growth.If you're a SaaS founder, CRO, or GTM leader trying to move beyond founder-led growth, this episode provides a practical framework for building a scalable go-to-market engine.Key Topics CoveredWhy founder-led sales works early but breaks at scaleTurning founder knowledge into a repeatable SaaS GTM playbookHow positioning and messaging create predictable pipelineWhy hiring a CRO too early can stall growthBuilding a scalable revenue engine before raising capital---Not Getting Enough Demos? Your messaging could be turning buyers away before you even get a chance to pitch.

Most sellers obsess over asking the perfect discovery question. In this episode, Gal Aga breaks down why great discovery has nothing to do with memorized questions and everything to do with understanding the problem, finding the root cause, and guiding the buying process.

L'IA est-elle vraiment prête à générer vos A/B tests ? Dans cet épisode du Shop des Titans, on décortique 5 mises à jour majeures du back-office Shopify qui vont impacter votre feuille de route technique. On vous partage d'abord notre retour d'expérience concret sur SimGym, le nouvel outil d'A/B test par IA, après l'avoir éprouvé sur nos clients. Au programme de cette revue d'actus : la migration obligatoire vers les nouveaux comptes clients, l'intégration très attendue des Metafields dans Analytics, l'explosion de la limite d'architecture à 1250 blocs, et les vraies capacités de "vibe coding" de l'assistant Sidekick.Hébergé par Ausha. Visitez ausha.co/politique-de-confidentialite pour plus d'informations.

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Did you know that consuming alcohol, even casually, is classified in the same cancer risk category as tobacco and asbestos? Today's conversation is all about the alcohol-cancer connection, the sober curious movement, and how we can make informed choices about our health.On Salad With a Side of Fries, Jenn Trepeck welcomes Cecily Mak, a former Silicon Valley attorney, breast cancer survivor, and author of Undimmed, for a conversation that is equal parts eye-opening science and deeply personal storytelling. Cecily shares how losing her mother to esophageal cancer and later facing her own breast cancer diagnosis led her to uncover this critical, under-discussed connection between alcohol and cancer risk, and what all of us can do with that information today.What You Will Learn in This Episode:✅ The five distinct biological mechanisms that directly link alcohol and cancer.✅ How the alcohol industry has followed a playbook similar to Big Tobacco, suppressing updates to alcohol labeling laws and lobbying against stronger public health disclosures for decades.✅ What the sober curious movement looks like beyond the AA model and how alcohol moderation rather than full abstinence can still make a meaningful, measurable difference in your long-term health.✅ How Cecily Mak's Eight Awarenesses framework helps individuals break free from unwanted habits by building agency, self-compassion, and intentional choice rather than relying on willpower or labels.The Salad With a Side of Fries podcast, hosted by Jenn Trepeck, explores real-life wellness and weight-loss topics, debunking myths, misinformation, and flawed science surrounding nutrition and the food industry. Let's dive into wellness and weight loss for real life, including drinking, eating out, and skipping the grocery store.TIMESTAMPS:00:00 The truth about alcohol and cancer risk, why metabolizing alcohol releases a DNA-damaging carcinogen04:34 Cecily's mother's cancer diagnosis and how years of alcohol dependency shaped her path forward06:31 A 30-day experiment of an alcohol-free lifestyle reveals transformative benefits on sleep and relationships08:29 Cecily's breast cancer diagnosis and the discovery linking her drinking history to breast cancer risk factors12:40 Removing the dimmers and dependence on alcohol, and Cecily shares her journey of writing Undimmed: The Eight Awarenesses for Freedom from Unwanted Habits17:55 Alcohol classified as a group one carcinogen and how it ranks alongside tobacco, asbestos, and UV radiation20:14 A discussion on the fight to update the outdated alcohol labeling laws25:54 The five biological pathways: acetaldehyde, elevated estrogen, oxidative stress, impaired DNA repair, and increased permeability32:55 Choosing clarity and alcohol-free living as the foundation for personal agency40:26 Releasing judgment and cultivating self-compassion as tools for sustainable habit change45:39 Cecily's one most important takeaway: learning to listen to ourselves as the most powerful tool in breaking unwanted habitsKEY TAKEAWAYS:

 Study start-up remains one of the most persistent bottlenecks in clinical trials—slowing site activation, delaying patient enrollment, and adding pressure across already stretched teams. In this episode of WCG Talks Trials, host Jamie Harper, Vice President of Site Solutions & Engagement at WCG, is joined by Stephanie Held, Associate Director of Coverage Analysis, and Jody Ingebritsen‑Howe, Director of Contracts & Budgets, to unpack why start-up timelines continue to stall and what can be done to improve them.Together, they explore how Medicare Coverage Analysis (MCA) serves as the foundation for compliant and efficient study start-up, how downstream processes like budgeting, contract negotiations, and CTMS build are impacted by early decisions, and where “hidden” white space can quietly derail timelines.The conversation also highlights practical, actionable steps sites and sponsors can take today—from mapping end‑to‑end workflows to better equipping negotiators with the information they need.Looking ahead, the panel discusses how technology and AI may reshape study activation, while emphasizing the importance of human expertise and alignment across teams. Whether you're a site, sponsor, or CRO, this episode offers timely insights to help accelerate activation and reduce friction in an increasingly complex trial landscape.Host:Jamie Harper, vice president, Site Solutions & EngagementGuests:Stephanie Held, associate director, Coverage AnalysisJody Ingebritsen-Howe, director, Contracts & Budgets

Full video HERE A seasoned CRO grabs the phone, goes undercover as “Bob Smith,” and makes live cold calls trying to book five connects as fast as possible. No script reading. No brand flex. Just real dials, real objections, and real pressure. You'll hear the exact opener that gets people talking, the provocative question that instantly hooks sales leaders, and the “Green Turkey” move that turns any answer into momentum. There's a meeting booked by backing off instead of pushing, referrals created on the fly, and a few brutal rejections along the way. If you want to know what cold calling actually sounds like in the wild and what it really takes to generate pipeline on the phone, this episode delivers. These Courses Will Get You to President's Club:

From accidental quality professional to global quality leader - Valerie Brown's story is one of courage, curiosity, and conviction.In today's episode I was joined by Valerie Brown, Head of Global Quality Assurance and Compliance at Thermo Fisher Scientific's Clinical Research Group.I really wanted to speak to Valerie because she brings something different to the quality leadership conversation. Yes, she has held senior quality roles across innovator companies, CDMOs, and now one of the largest CROs in the world. But what makes her story compelling is how she got there - and what she learned along the way.Valerie didn't plan to work in quality. At 22, she was asked to be a scribe for an FDA inspection. The host fell ill on the day. She stepped in - no preparation, no safety net - and handled it.Someone told her she had a knack for it. She wasn't sure she agreed. She still wanted to be in the lab, in manufacturing, doing what she knew. But that moment planted a seed.What followed was a career that took her across CDMOs, innovator companies including Gilead Sciences, and now Thermo Fisher - where she leads global quality assurance and compliance for the clinical research group. She has sat on both sides of the table, as sponsor and as service provider, and that experience shapes everything about how she leads.We talk about the following:How Valerie accidentally became a quality professional, and why that unplanned start shaped everything that followedWhat it felt like to host an FDA inspection at 22, with no preparation and no safety netHer philosophy of servant leadership and what it really means to lead with empathy in a regulated environmentThe challenge of transforming a fragmented quality organisation into a connected, strategic function at Thermo FisherThe difference between working on the innovator side versus the CRO side - and the unique skill set the latter demandsWhy speed and quality are not in conflict, and how embedding quality by design from the outset actually accelerates deliveryHer approach to talent development - why she prefers to grow leaders from within and how she identifies that potential earlyThe growing importance of AI and digital governance in regulated environments, and why quality professionals need to engage with these tools nowWhat keeps her up at night heading into 2026 - from talent gaps to trial complexity to the pace of regulatory changeThe advice she would give her younger self, and what she believes every aspiring quality leader needs to understandValerie Brown is a highly accomplished global quality leader whose career is a masterclass in adaptability, influence, and patient-centric thinking. She leads with purpose, develops people with intention, and approaches every challenge with the mindset of a problem solver - exactly the kind of leader our industry needs more of.Thank you Valerie for sharing your incredible journey. Hope everyone enjoys the show!

Today, we are dropping another episode in our "chats" series, but expanding the audience set to include more folks. This episode is Founder Chats - hearing from those scaling the companies themselves.In this episode, we are talking with Max Denevich, Co-founder and CRO of LoyaltyPlant. Max is going to share with us to road he travelled, entering into this industry, his go to market strategies, scaling across geographic region - and much, much more.QuestionsBefore we talk about products and scale, tell us a bit about your path to this point. What experiences shaped the way you think about business and leadership before LoyaltyPlant?At what point did you realise you wanted to work with complex, traditional industries rather than consumer apps or “easy” tech?Why foodtech, and specifically Quick Service Restaurants? What made you believe this industry had deep structural problems worth solving with technology?What made you decide to join LoyaltyPlant, and what potential did you see that others might have missed?You're often referred to as a co-founder today. How did the transition happen from an executive role to shaping the company's future at that level?LoyaltyPlant was close to running out of investment at one point. What were the first decisions that fundamentally changed the company's trajectory?What were the key milestones that turned LoyaltyPlant from a struggling company into a global enterprise business, from the first major client to scaling across 30 countries?You've worked across the US, UK, MENA, Europe, and CIS. What did you learn about scaling the same product across very different markets, and what absolutely doesn't translate?You built new go-to-market strategies that now generate over 90% of new sales. What did you change compared to a classic SaaS sales playbook, and why did it work in enterprise QSR?Margins are shrinking, aggregators dominate, and costs are rising. What's actually happening on the ground right now in QSR and foodtech, and how should companies adapt?Tell us about a decision you got wrong. What did it cost the business, and what did it teach you as a leader?What advice would you give founders building B2B products for traditional industries today, especially around scale, partnerships, and staying relevant?SponsorsUnblockedBraingrid.TECH DomainsMezmoLinkshttps://loyaltyplant.com/https://www.linkedin.com/in/denevich/Support this podcast at — https://redcircle.com/codestory/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

In this minisode, Cedric Pech, President of Field Operations at MongoDB and former CRO, shares a formative leadership moment from early in his career at PTC that shaped how he thinks about building revenue organizations. He tells the story of a manager who invested in him personally before he had proven himself professionally. It is a lesson in what real leadership looks like under pressure. For CROs and frontline leaders alike, this clip is a reminder that culture is built in moments like these, not in mission statements. Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

Send a textWhat if the best leadership training you'll ever get happens at your kitchen table? We sit down with Michael Clark—CRO of Asymbl, former Salesforce leader, TEDx speaker, and proud dad of three—to unpack a practical, heart-forward playbook for leading a family with the same intention you'd bring to a high-performing team.Michael shares the simple structure that guides him: a personal why statement and three values—authenticity, accountability, and action. You'll hear how honest check-ins with his daughters build real trust, why delivering on promises lays a foundation for hard conversations, and how “big speak-ups” like ordering their own food help kids practice courage in everyday life. We trade stories about vulnerability—teens seeing their dad admit fear or shed tears—and how those moments shape emotional fluency. We also explore the language shift from “need to” and “should” to “I will,” a small change that lowers anxiety and raises ownership for both parents and kids.The conversation reaches beyond home into work and purpose. Michael reframed sales as outcome-driven service—less about pushing products, more about solving human problems. From pharma to Salesforce to his role at Assemble, he shows how aligning work with values makes impact sustainable. We dig into workforce orchestration and how human-plus-digital teams free people for empathy, creativity, and relationship building—skills that win at home and in business. Along the way, we cover modeling independence without overhelping, protecting sleep as a leadership habit, and using curiosity to guide teens through team dynamics and identity.You'll leave with tools you can use tonight: ask one better question, keep one small promise, and take one action that reflects your family values. If the message resonates, share this episode with a friend, subscribe for more conversations like this, and leave a quick review so others can find the show. What's the one “A” you'll lead with this week?Support the showPlease don't forget to leave us a review wherever you consume your podcasts! Please help us get more dads to listen weekly and become the ultimate leader of their homes!

Nick Turner, CEO of Dreamdata, joins Sam in this special episode of Topline Spotlight. They unpack a challenge many B2B leaders are facing right now: how do you raise capital in a market obsessed with AI when you're not an "AI-native" company? Nick shares his journey from CRO to CEO and what it was like stepping into the top job—only to immediately lead a $55M Series B raise in one of the toughest venture environments in recent history. After speaking with 73 investors in six weeks, he reflects on the realities of fundraising today, investor skepticism around revenue durability, and why profitable, efficient growth still wins. Nick brings nearly 20 years of commercial leadership experience scaling martech companies from Seed and Series A to $75M in revenue. Now leading Dreamdata—a Copenhagen-based B2B marketing attribution and activation platform—he's helping marketers prove what's working and take action on it.

Chris Degnan was the first sales hire at Snowflake and spent 11 years scaling the company from zero to $3.5 billion in revenue as its CRO, working alongside four different CEOs and learning from each one. In this episode, Chris breaks down what it actually takes to scale an enterprise sales organization, why MEDDIC is the methodology every founder should know, and what working under Frank Slootman taught him about firing fast, taking feedback and finding the fakers in your team. In today's episode, we discuss: What the CRO job looks like at $10M vs. $1B+ Why sales leaders must know how to sell the product themselves The MEDDIC methodology and why it's a founder's best insurance policy How to find the fakers, manage-uppers and passengers in your org What Frank Slootman got right — and wrong — about scaling Snowflake Why most AI companies will face a go-to-market reckoning References: Amazon: https://www.amazon.com/ Bob Muglia: https://www.linkedin.com/in/bob-muglia-714ba592/ Carl Eschenbach: https://www.linkedin.com/in/carl-eschenbach-980543/ Christian Kleinerman: https://www.linkedin.com/in/christian-kleinerman-a973102/ Denise Persson: https://www.linkedin.com/in/denisepersson/ Dell: https://www.dell.com/ Frank Slootman: https://www.linkedin.com/in/frankslootman/ John McMahon: https://www.linkedin.com/in/johnmcmahon1/ Michael Scarpelli: https://www.linkedin.com/in/michael-scarpelli-1b289b9/ Microsoft: https://www.microsoft.com/ Oracle: https://www.oracle.com/ Salesforce: https://www.salesforce.com/ Snowflake: https://www.snowflake.com/ Sridhar Ramaswamy: https://www.linkedin.com/in/sridhar-ramaswamy/ Stanford Graduate School of Business: https://www.gsb.stanford.edu/ Where to find Chris: LinkedIn: https://www.linkedin.com/in/chris-degnan/ Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps: 00:00 What is the job of a CRO? 01:12 What excellence looks like at different revenue stages 02:59 Sales leaders need to know how to sell the product 04:52 The hardest skill leaders have to learn 08:17 You need to stay open to feedback - at all levels 14:01 Sales, segmentation, and international expansion 16:17 Why MEDDIC is the foundation for every sales org 20:32 The metrics that actually matter 22:56 A week in the life of a CRO at scale 28:32 Navigating compensation at a GTM organization 31:45 What technical CEOs get wrong about GTM 36:01 The role of hunger in great sales leaders 40:35 What makes an exceptional IC sales rep 46:41 Dysfunctional vs. high-performing executive teams 48:01 Chris' most impactful decisions at Snowflake 49:53 "When there's doubt, there's no doubt" 54:49 Learning from world-class leaders

Scaling from regional VP to global CRO is not a promotion. It is a shift from managing execution to defining meaning at scale. In this replay conversation, Cedric Pech reflects on leading a 2,000-person global sales organization at MongoDB, integrating complex routes to market, and building culture that withstands market volatility. He breaks down the difference between compensation-driven leadership and purpose-driven leadership, why execution alone creates burnout, and how resilient organizations are built long before downturns arrive. For CROs and revenue leaders navigating scale, volatility, or retention pressure, this episode offers a grounded perspective on building durable teams without burning them out. Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

I was halfway through writing an article about generic website copy when something uncomfortable occurred to me. I should probably check my own website. My headline at the time read: "Helping You and Your Users Succeed." On the face of it, that doesn't sound terrible. It's positive, it's benefit-focused, and it sounds like exactly the kind of thing a UX consultant should say. The problem is that it also sounds like exactly the kind of thing every other UX consultant says. And their accountant. And possibly even their office cleaner! Generic copy is one of the most common problems I encounter doing conversion rate optimization work, and like a doctor who ignores their own symptoms, I had been sitting on a headline that failed every test I apply to client websites. So let's talk about how to spot problems and how to fix them. Three Questions That Will Expose Weak Copy When I'm reviewing website copy with clients, I use 3 simple questions to find out whether a value proposition is doing any real work. Could this statement apply to other products or services? A value proposition should be specific enough that it only makes sense in your context. “Help you and your users succeed” could work just as well on a SaaS website or on the site of a user researcher. If it can work on a different kind of website, it isn't a proposition at all. It's just a sentence. Could a competitor make this claim? If your direct competitors could copy-paste your headline and it would work just as well for them, it isn't differentiating you. It's just noise. Would the opposite statement be ridiculous? This is my favorite test, because it exposes just how empty a claim can be. If no company would ever say "We're helping your users fail" or "We provide terrible customer service," then the positive version isn't telling anyone anything. You're essentially saying "We are not actively terrible," which is not much of a selling point. Apply those 3 questions to my old headline. "Helping You and Your Users Succeed." Could it apply to other services? Absolutely. A web developer, a copywriter, and a business coach could all put it on their homepage without anyone raising an eyebrow. Could competitors claim it? Every UX consultant on the planet already does. Would the opposite be valid? No company would ever say "Helping You and Your Users Fail," which means the positive version communicates precisely nothing. It fails all 3 tests, which was enough to make me start over. Being Specific Is Harder Than It Sounds The fix sounds simple. Just be more specific. But that's where most people get stuck, because specificity requires you to actually commit to a position. Vague copy is often a symptom of vague thinking about what you offer and why it matters, and confronting that is a bit uncomfortable. In my case, getting specific meant being honest about what I actually do and why it's different. I work across 3 disciplines that most consultants treat as entirely separate. Conversion rate optimization is about improving customer acquisition. UX strategy is about improving retention once customers arrive. Design leadership is about getting the organizational buy-in to implement changes at all. Most consultants offer one of those. I work across all three. That led to a new headline: "Your Digital Funnel Leaks in 3 Ways. I Fix Them All." It passes the first 2 tests cleanly. It couldn't apply to a web developer or a copywriter, and a pure CRO specialist or a pure UX designer couldn't honestly claim it. The third test is more nuanced. If you literally flip it, "Your digital funnel works perfectly, and I'll make it worse" is clearly absurd. But a specialist could legitimately say "Your funnel leaks in one place, and that's what I fix," which is a valid positioning rather than a ridiculous one. That's worth being aware of: the third test is good at catching empty aspirational claims, but specific copy can still be outflanked by variations rather than direct opposites. The real differentiating work happens in tests 1 and 2. Back Up Your Claims With Evidence Specificity is a strong start, but evidence makes claims even harder to ignore. The more proof you can attach to a statement, the more credible it becomes. "We provide great customer service" is vague. "Our clients rate us 4.9 out of 5 for responsiveness" is specific and verifiable. "We're experienced professionals" is empty. "We've delivered over 200 UX audits for organizations ranging from NHS trusts to e-commerce startups" gives the reader something real to hold onto. I won't pretend I always have perfect statistics to hand. Often I don't, and in those cases I try to ground claims in specific outcomes or named examples rather than numbers. But any evidence is better than a confident assertion with nothing behind it. Try This on Your Own Homepage Pull up your website's homepage right now and read your headline and opening paragraph. Then apply those 3 questions. If your copy could live comfortably on a competitor's site, or would work equally well for a plumber and a UX consultant, it's time to be more specific about what you actually do and who you actually do it for. The good news is that this doesn't have to take as long as you might expect, especially if you work alongside an AI tool. Give it the 3 questions from this newsletter, tell it what you actually do and who you do it for, and ask it to generate a dozen variations. It will produce far more options than you'd come up with alone, and far faster. Your job then is to apply the tests and pick the one that passes. The thinking is yours. The writing of dozens of variations doesn't have to be.

Welcome back to The Cashflow Project Podcast! In this episode, we sit down with Matt Medrano, managing partner and CRO of Dynamo Capital, Kansas' leading private lender. Matt shares his journey from working in foundation repair to building a fast-growing lending company focused on creative financing solutions. We dive into how Dynamo's approach—including DSCR loans, portfolio consolidation, and entity-only lending—gives investors flexible capital beyond traditional banks. Packed with real-world lessons on resilience, relationship-building, and smart problem-solving, this episode delivers practical insights for investors and entrepreneurs ready to level up. [00:00] "Reframing Goals and Career Realizations" [06:28] "Sales Role in Home Solutions" [07:37] Costly Home Repairs Explained [11:37] Adapting to Work Challenges [15:27] "Redefining Success Through Storytelling" [19:14] Perseverance, Pivoting, and Hindsight [21:21] "Connections Through Collected Jerseys" [25:47] "Rethinking Lending with a 'Why?'" [26:46] "Challenges in Midwest Loan Brokering" [30:43] "DSCR Loans for Investors" [34:42] $50K Profit Investment Deal [38:40] Startup Struggles and Triumphs [41:38] "Scrappy, Solution-Driven Fund Managers" [44:11] "Real Estate to Wall Street" [46:03] Raising Lending Standards Locally [51:01] "Future Success and Growth Ahead" [52:38] "Connect, Act, & Stay Tuned" Connect with Matt Medrano! LinkedIn Website Instagram Connect with The Cashflow Project! Website LinkedIn YouTube Facebook Instagram

Warren Zenna is joined by Eric Steele, CRO at SIB, to pull back the curtain on the often-chaotic reality of stepping into your first Chief Revenue Officer role. Eric shares why these initial appointments are rarely "sexy" and often come with significant organizational challenges that others might avoid. They discuss the mental shift required to move from a sales leader to a true executive, treating the first role as a critical lab for learning.The conversation digs into the paramount relationship between the CRO and the CEO, which Eric describes as the ultimate unlock for success. He explains how to build a foundation of trust that allows for healthy disagreement and strategic alignment. By positioning yourself as an integrator of the CEO's vision rather than just a department head, you can secure the autonomy and resources necessary to navigate the high-pressure environment of private equity.Eric also highlights the strategic necessity of financial fluency, emphasizing that a CRO must speak the language of the CFO to be taken seriously. They discuss the common friction point of Revenue Operations and why this function must report to the revenue leader to drive growth rather than just board reporting. Eric argues that alignment on EBITDA and margins is just as important as hitting sales targets when you are operating at the C-suite level.The episode concludes with a look at how SIB uses AI-driven "spend ontologies" to help companies find hidden capital. Eric describes how their SpendBrain technology identifies deep errors in invoices—from waste hauling to logistics—allowing CEOs to fund new hires and technology through recovered savings. By combining human expertise with "kinetic cost control," Eric shows how modern CROs can impact the bottom line by turning the tables on a spend-more world.

Saíram por amor. Por oportunidade. Por acaso. No caminho descobriram que "casa" não é só onde nascemos. É onde decidimos ficar. Croácia, Suíça, Polónia. É lá que estão o Sérgio Salvaterra, a Joana Roda e o José Teixeira.

Welcome to RIMScast. Your host is Justin Smulison, Business Content Manager at RIMS, the Risk and Insurance Management Society.   In this episode, Justin interviews Cynthia Garcia about her career journey. She credits mentors and sponsors for paving the way for her success. Justin and Cynthia discuss the demands of the Chief Risk Officer role and how Cynthia works with stakeholders who have competing priorities. Cynthia shares her perspective on construction risk and safety. She is seeing more diversity in the rising generation of risk professionals, with amazing opportunities for all.   Cynthia shares how her Confucianist upbringing still makes it a struggle for her to receive recognition. Despite that, she posted on LinkedIn about receiving the 2025 Bill McIntyre Leadership Award at the International Risk Management Institute (IRMI) Construction Risk Conference. That post led Justin to reach out to her. Cynthia speaks of her involvement with the Spencer Educational Foundation, including being a Risk Manager on Campus. Justin and Cynthia talk about the March 6th Webinar, "Hard Hats & High Stakes: Women Leaders Shaping Construction Risk Management", that she joins as a featured panelist. Listen for tips on careers in risk management for construction.   Key Takeaways: [:01] About RIMS and RIMScast. [:16] About this episode of RIMScast. Our guest is Cynthia Garcia, the award-winning Chief Risk Officer for Bernards. We will talk all about her career in construction risk and get some "inspirado." But first… [:44] RIMS Virtual Workshops. On March 10th and 11th, we have a two-day course led by John Button for the RIMS-CRMP Exam Prep. [:55] On March 17th and 18th, RIMS will align with AFERM for a two-day RIMS-CRMP-FED Exam Prep Course. [1:02] On March 4th and 5th, we have a virtual workshop, "Facilitating Risk-Based Decision Making", with Joe Milan. On April 15th, we have a virtual workshop covering "Emerging Risks", led by Joseph Mayo. [1:20] Register today and strengthen your risk knowledge. RIMS members always enjoy deep discounts on the virtual workshops. [1:27] Webinars. On March 6th, RIMS presents "Hard Hats & High Stakes: Women Leaders Shaping Construction Risk Management". We'll be joined by a Chief Risk Officer, an underwriter, and a broker. [1:42] They will explore their career paths, risk and safety philosophies, and lend some insight as to why this is the time for the next generation of leaders to rise. [1:53] On March 12th, Global Risk Consultants returns with "Don't Waste the Soft Market: Where to Reinvest Insurance Savings Before the Window Closes". Register for these and other webinars by visiting RIMS.org/webinars and the links in this episode's show notes. [2:14] On with the Show! Our guest today is Cynthia Garcia. She is the Chief Risk Officer for Bernards. [2:22] Cynthia made a big impact on the risk landscape in 2025 when she received the Bill McIntyre Leadership Award from the International Risk Management Institute during its Construction Risk Conference. [2:35] I wanted to learn all about her career and what it's like to be the risk officer for a major construction company. [2:42] Earlier, I mentioned the March 6th RIMS Webinar, "Hard Hats and High Stakes," and Cynthia will, in fact, be the Chief Risk Officer mentioned there. [2:51] If you like what you hear in this episode and want to learn more about career development, construction risk, and why rising risk professionals should seize the opportunities in the construction sector, you can register for that Webinar. [3:04] Cynthia is a fascinating individual, and I am so pleased to present this interview! Let's get to it! [3:09] Interview! Cynthia Garcia, welcome to RIMScast! [3:27] Justin and Cynthia are going to be collaborating on a RIMS Webinar on March 6th, "Hard Hats and High Stakes." It's all about how women have and can continue to thrive in construction risk management. Cynthia is the ideal Chief Risk Officer to have on that panel. [3:46] Justin thanks Cynthia in advance for being on that panel and being a guest on RIMScast. [4:07] Cynthia is the CRO for Bernards, based in California. [4:33] Like many in her generation, Cynthia stumbled into risk management. She started as an administrative assistant for Morley Builders, an amazing employee-owned general contractor in Santa Monica, California. [4:52] She was fortunate to have several sponsors and mentors within the organization. They helped her see that she belonged at the table. They saw something in her that she hadn't seen in herself, which is the beauty of a mentor. [5:16] In spaces she was not in, they advocated for her and said, Why don't we give this to Cynthia? That's the beauty of a sponsor. Cynthia says she was blessed to be in the right place at the right time. She was able to lean in. [5:32] Cynthia says that the thing that attracts her about risk management and what she does is finding the hard yes. Risk management doesn't say, "No." [5:50] Risk management, when practicing its craft, is fully integrated with operations and understanding what the business needs. It is strategically aligned and helps make sure the organization is making those thoughtful business decisions that allow taking risks. [6:11] Then, risk management takes it to the next step to ask how this adds to our shareholder equity, how this aligns with who we want to be as a company and as people. Risk management threads the needle between entrepreneurship and "cowboyism." [6:28] Risk management leads with "Help me understand, and help us get to the hard yes. We can do it, but here are some of the things we need to do to make sure that it's successful."  [6:50] Cynthia always likes to start by making sure she is coming in with a lot of curiosity. She asks for help to understand what she's not seeing to try to connect the dots. If Cynthia doesn't understand the needs of her business partners, she's not creating value. [7:11] Cynthia joined Bernards as Chief Risk Officer four years ago next month (March). Bernards created the position for her. She says she's blessed to work with talented people. She credits an amazing group of rockstar individuals. She says a rising tide lifts all boats. [8:00] Cynthia says her team carries the weight and does it beautifully. She says the genius of true leadership is understanding we're paving the way for our replacement. Leaders who are afraid of talent need to pause and rethink what that means. [8:26] Cynthia's Risk and Safety team has 13 staff members. [8:45] Cynthia has a VP of Risk and Safety who is definitely a genius at making the wheels turn. He is Cynthia's only direct report. He does an amazing job setting the tone and the pace. [9:03] Cynthia says, We focus on listening to the voices of our internal and external customers. As an employee-owned company, we try to understand what our business partners need, whether it's accounting, finance, human resources, operations, or estimating. [9:22] Cynthia focuses on what our business partners need from risk management to help achieve mission success. [9:27] Cynthia says, from day to day, it's everything from safety to claims, to insurance issues, to coverage questions, but a fair part of the job is when business teams proactively reach out with questions about issues that have come up. [9:50] Cynthia says the beauty of being in a smaller organization is that Risk Management is not siloed. It's not just insurance and claims but also litigation management and contracts. Risk partners closely with the CHRO on policies and employment practices. [10:13] Risk partners closely with Finance and Accounting on a variety of issues. Cynthia feels it is fortunate that Risk is viewed and valued as an internal resource to its business partners and part of the critical strategy to achieve the company's goals. [10:41] Bernards has a little fewer than 400 employee-owners. Cynthia credits Finance and Accounting for paying vendors on time and treating trade partners fairly. She credits Marketing for helping the brand, highlighting company accomplishments, and creating community buzz. [11:30] Cynthia credits the very customer-centric Tech team, who have helped her a lot, and the Virtual Construction Design team, who help with clash detection and getting ahead of constructability issues early on. [11:59] She notes the estimating team getting ahead of what's out there and making sure we have the right projects to go after. It takes a village. [12:14] Cynthia says we like to think all of us employee-owners have a vested interest in mission success. We're all in construction. [12:27] Quick Break! RISKWORLD 2026 will be held from May 3rd through the 6th in Philadelphia, Pennsylvania. RISKWORLD attracts more than 10,000 risk professionals across the globe. It's time to Connect, Cultivate, and Collaborate with them. [12:45] Booth sales are open now. General registration and speaker registration are also open right now. Marketplace and hospitality badges will be available starting on March 3rd. Links are in this episode's show notes, and be sure to check out RIMS.org for more information. [13:04] Save the dates March 18th and 19th, 2026, for the RIMS Legislative Summit, which will be held in Washington, D.C.! Join us in Washington, D.C. for two days of Congressional meetings, networking, and advocating on behalf of the risk management community. [13:20] Visit RIMS.org/advocacy for more information and to register. Also, check out the prior episode of RIMScast, Episode 378, featuring RIMS General Counsel and Vice President of External Affairs, Mark Prysock, as we discuss the top priorities for RIMS in 2026 and beyond. [13:41] Let's Return to Our Interview with Bernards' Chief Risk Officer, Cynthia Garcia! [13:58] When Cynthia joined Bernards, there were about 10 people on the Risk and Safety team. Then they went into remodel mode, with a different strategic vision. Continuous improvement is a Bernards core value. It's a 52-year-old company with processes and talent in place. [14:27] Cynthia says we've been looking at the areas where we can have the greatest impact, picking off the low-hanging fruit first, and then building out processes that allow us to scale without reinventing ourselves every few years. [14:57] Cynthia says safety is our priority. Bernards added safety to its core values this year. Cynthia says it was a grass-roots movement. It percolated up through Operations and said, This is who we need to be. [15:24] Cynthia says a risk management team's job is to safeguard all the resources of the organization. That includes people and things, clients, and trade partners. The Risk and Safety team has a holistic view. They can't be good by themselves. They can't be safe by themselves. [15:42] For Cynthia, safety takes on a larger meaning than physical well-being, including creating spaces where people are allowed to be vulnerable. [15:57] Cynthia talks about leading with empathy, with top priority not only for physical safety but also for a psychologically safe environment, where you can show up, be seen, heard, and thrive. [16:41] Cynthia says she works on building connections through conflict. For what could be tough conversations, it helps if you are willing to check your ego at the door and come in curious. Cynthia often states her intention up front. [17:01] Cynthia might say, "My intention isn't to challenge you, it's to have you help me understand your perspective and help me see what I'm missing." Cynthia says she asks a billion questions because there is so much she doesn't know. She always tries to get with the "why." [17:32] Cynthia says, When I try to understand what it is that my counterpart needs to happen, then we can figure out the path forward together. As employee-owners, our goals are aligned. We're looking in the same direction. [17:52] Cynthia says, We may fuss with the GPS a little bit, but we know the destination is set and we have a commitment to one another. Once we are willing to shut up, listen, and ask the questions to learn, then we can figure out how to be of service. [18:16] Cynthia says her job isn't to convince, it's first to understand. [18:22] A Quick Break! The Spencer Educational Foundation's Risk Manager on Campus application period will open on April 1st, 2026, and it will close on June 30th. Grant awardees, colleges, and universities are typically notified in September. [18:51] The Course Development Grant application deadline for Interval Number 2 will be on June 15th, 2026. Award notifications will be sent out in late July. [19:06] General Grant applications will open on May 1st, 2026, and the application deadline is July 30th. Internship Grant applications open on August 15th and close on October 15th. [19:18] Links to each of these grants are in this episode's show notes. Visit SpencerEd.org for more information. [19:27] Let's Conclude Our Interview with Bernards' Chief Risk Officer, Cynthia Garcia. [19:41] As Cynthia mentioned earlier, Bernards is employee-owned. Cynthia thinks that Bernards being 100% employee-owned makes all its employee-owners better businesspeople. The heart of risk management is making those good choices. [20:27] Looking across the table and knowing she is betting with her fellow owner's retirement, makes Cynthia think about that a little bit differently. She thinks the employee ownership structure lends itself to amazing risk management. [20:49] Cynthia says you have to be disciplined. You're not spending somebody else's money on this. We're working together, and when we all make good choices, we are ultimately rewarding ourselves and impacting future generational owners, too. That's quite meaningful. [21:09] Cynthia says it's the best of both worlds. You have the umbrella of a big company paying the bills, but you're rewarded for smart entrepreneurism. [21:27] Cynthia has a long-term view when making decisions. It's not about what's in it for her. It's how does this support who we want to be today, and who we want to try to be tomorrow? It makes us look further into the horizon. [22:24] May 4th through May 8th, 2026, is Safety Week, here in the U.S. That coincides with RISKWORLD 2026. Cynthia will be at RISKWORLD. [22:41] Cynthia says for Safety Week, Bernards has planned activities on each job site to highlight the good things that men and women are doing to build the communities in which they work and live, and doing them in such a way that they go home to families and loved ones. [23:01] Justin notes that settlements from construction site accident injuries can be astronomical. Part of Cynthia's job is to minimize accidents from the outset, which connects to Bernards' core value safety-first mindset. [23:34] Cynthia says client response has been amazing. Recently, one of the project executives at Bernards was invited to the school district and won an award acknowledging their efforts on safety. That felt good because it wasn't Bernards saying it, but the clients saying we see it. [23:58] Bernards has trademarked "A Better Experience." It's a phrase they are proud of. They're building not only to create a better experience for their employee-owners, but also for project success for owners who value safety. [24:15] Bernards is a large school builder, working on many programs up and down the state. Bernards is cognizant of the impact they are having on the future generation of leaders and citizens. They're very grateful to have that acknowledgement from their clients. It's special. [25:29] Cynthia says she is absolutely seeing more opportunities for women in risk management and in construction. Construction tends to be inclusive. It's an industry filled with optimists. Its people bring that can-do attitude. They are very generous and gracious with their support. [26:13] Cynthia says she has been in the risk profession for about 30 years. The demographics have changed, and she sees diversity in the new young talent permeating the industry. [27:10] Cynthia thinks the work that the Spencer Educational Foundation does in partnership with RIMS is tremendous. She says it is amazing that colleges and universities are offering the Risk Management and Insurance degree and concentration. Cynthia never heard of that before. [27:35] Cynthia says that people her age moved into risk management from adjacent areas. She is pleased that now people come into risk management intentionally. She talks about risk managers trying to figure out how to help businesses thrive and grow to the next level. [28:47] Cynthia is one of Spencer's Risk Managers on Campus. She explains how the grants to colleges work. Spencer works tirelessly to make sure the next generation of leaders know what an amazing career this is and the opportunities it offers. Cynthia is grateful to be part of it. [30:15] Justin mentions that other Risk Manager On Campus risk professionals have been guests on RIMScast, and they have inspiring stories to tell. They love reaching the young people who are going to be the future of the profession. [30:35] Megan Miller, Spencer CEO, was a recent RIMScast guest. Check out SpencerEd.org for grants and opportunities. If you know somebody interested, send them the link to explore. If they connect with people like Cynthia through the RMOC grant, their experience will be richer. [31:28] Cynthia came to Justin's attention through a LinkedIn post about her being honored as the 2025 Bill McIntyre Leadership Award recipient at the International Risk Management Institute (IRMI) Construction Risk Conference. [32:08] Cynthia says you're always a little bit surprised but so pleased when you get acknowledged by your peers. As IRMI is pre-eminent in the construction risk management space, it was more special to Cynthia, as she knew of the great work they did. [32:33] Cynthia remembers starting in risk management and going to them as a resource. She knows the people who make IRMI thrive. They're people Cynthia looks up to. She is very grateful that it was her turn to be acknowledged. She feels there are way more qualified folks out there! [33:41] Cynthia says she is an immigrant. English is her second language. She is Korean and grew up in a Confucianist household. In terms of philosophy, you should be seen, not heard. The collective win is celebrated. [34:06] Cynthia has had to work to get over the heebie-jeebies about self-promotion or what could be viewed as arrogance. She's working on it and doing better at accepting compliments. It's an opportunity to show others who are coming up behind her that diversity exists. [34:45] Cynthia says it's hard for us to visualize ourselves in a role without models who came before us. What are the opportunities that exist? Can I also think about this? Cynthia said the marketing team is genius. Justin said that was what caught his eye on LinkedIn. [35:19] Cynthia says she is very fortunate to be supported by so much talent and such a community that helps uplift you. [35:27] Justin comments that the "seen and not heard" thing is not just Confucianism, but also old-world Brooklynism. His old relatives said, "Children should be seen and not heard." [35:52] Cynthia says we all have shared experiences within our collective. People tend to focus on the differences. It is important to celebrate our differences, but there's so much more in common, regardless of the geography and the generation in which we were raised. [36:10] There is so much in shared value. Cynthia says she is constantly inspired by those stories of people who saw a different future or leaned into a hand up. That motivates her to try to be better and drives her. [36:35] Justin says posting is a networking opportunity too. If that post had not gone up, Justin would not have met Cynthia. It's a way to broaden your network and meet more people. Justin says it's OK to do a humblebrag. Justin is known as the shameless self-promoter. [37:11] Justin says it is very special when you are acknowledged outside your company. [37:20] Cynthia's post triggered a series of events, one of which is, in recognition of Women's History Month, RIMS will present the webinar on March 6th, "Hard Hats & High Stakes: Women Leaders Shaping Construction Risk Management", with Cynthia as a featured panelist. [37:38] Cynthia will provide the CRO perspective. Also on the panel are Danette Beck from Astrus and Jessica Risullo from WTW. Cynthia shares how she knows these amazing, trailblazing women. Cynthia is grateful to be on a panel with them. They're rockstars! [38:47] Justin says it's going to be excellent! The link is in this episode's show notes, or visit RIMS.org/webinars. Megan Miller, the CEO of the Spencer Educational Foundation, will kick things off with a special introduction. [39:15] It's going to be a wonderful way to observe and celebrate Women's History Month, ahead of RISKWORLD and Construction Safety Awareness Week. [39:30] Justin thanks Cynthia for joining us on RIMScast, sharing with listeners her construction risk perspective and career path. There's a lot to take away. Justin thanks Cynthia for her perspective and her time. [39:45] Cynthia says she appreciates Justin and the work RIMS is doing to put a spotlight on our amazing industry and the opportunities that exist. She says she is grateful for the opportunities Justin and RIMS are creating and thoughtfully curating. [40:04] Special thanks again to Cynthia Garcia for joining us here on RIMScast. You can hear more from her directly on March 6th during the RIMS Webinar "Hard Hats & High Stakes: Women Leaders Shaping Construction Risk Management". [40:17] RIMS members, keep in mind that RIMS Webinars are complimentary for you. That is one of the many benefits of a RIMS membership. Visit RIMS.org/webinars and the link in this episode's show notes to register. That's going to be a fantastic session! [40:34] Plug Time! You can sponsor a RIMScast episode for this, our weekly show, or a dedicated episode. Links to sponsored episodes are in the show notes. [41:03] RIMScast has a global audience of risk and insurance professionals, legal professionals, students, business leaders, C-Suite executives, and more. Let's collaborate and help you reach them! Contact pd@rims.org for more information. [41:21] Become a RIMS member and get access to the tools, thought leadership, and network you need to succeed. Visit RIMS.org/membership or email membershipdept@RIMS.org for more information. [41:38] Risk Knowledge is the RIMS searchable content library that provides relevant information for today's risk professionals. Materials include RIMS executive reports, survey findings, contributed articles, industry research, benchmarking data, and more. [41:55] For the best reporting on the profession of risk management, read Risk Management Magazine at RMMagazine.com. It is written and published by the best minds in risk management. [42:09] Justin Smulison is the Business Content Manager at RIMS. Please remember to subscribe to RIMScast on your favorite podcasting app. You can email us at Content@RIMS.org. [42:21] Practice good risk management, stay safe, and thank you again for your continuous support!   Links: RIMS Legislative Summit — March 18‒19, 2026 on Capitol Hill, Washington, D.C. | Register now! RISK PAC | RIMS Advocacy RISKWORLD 2026 Registration — Open for exhibitors, members, and non-members! Reserve your booth at RISKWORLD 2026! Construction Safety Week RIMS-CRO Certificate Program In Advanced Enterprise Risk Management | April‒June 2026 Cohort | Led by James Lam RIMS Compensation Survey 2025 — Download Today RIMS Risk Management magazine | Contribute RIMS Now RIMS-Certified Risk Management Professional (RIMS-CRMP) | Insights Video Series Featuring Joe Milan! The Strategic and Enterprise Risk Center RIMS Diversity Equity Inclusion Council RIMS-CRMP Story, featuring John Button RIMScast Canada — Debut Episode Now Live Spencer Educational Foundation — Scholarships and Grants RIMS Texas Regional Conference 2026 Education Content Submission — Deadline March 18, 2026! Hard Hats & High Stakes: Women Leaders Shaping Construction Risk Management | March 6 | Presented by RIMS — Featuring Today's Guest, Cynthia Garcia! Upcoming RIMS-CRMP Prep Virtual Workshops: RIMS-CRMP Exam PrepMarch 10‒11 | April 21‒22 | June 9‒10 RIMS-CRMP-FED Exam Prep with AFERM | March 17‒18 Full RIMS-CRMP Prep Course Schedule See the full calendar of RIMS Virtual Workshops RIMS Virtual Workshop — "Facilitating Risk-Based Decision Making" | March 4‒5 | Register Now "Risk Appetite Management" | March 25‒26 "Claims Management" | April 7‒8 "Emerging Risks" | April 15 | Register Now! Upcoming RIMS Webinars: "Hard Hats & High Stakes: Women Leaders Shaping Construction Risk Management" | March 6 | Presented by RIMS "Don't Waste the Soft Market: Where to Reinvest Insurance Savings Before the Window Closes" | March 12 | Sponsored by Global Risk Consultants RIMS.org/Webinars   Related RIMScast Episodes: "Investing In Yourself with RIMS 2026 President Manny Padilla" "Strategic Risk Career Transitions with Susan Hiteshew" "Supply Chain Integrity and Sustainability with Nicole Sherwin of EcoVadis"   Sponsored RIMScast Episodes: "Secondary Perils, Major Risks: The New Face of Weather-Related Challenges" | Sponsored by AXA XL (New!) "The ART of Risk: Rethinking Risk Through Insight, Design, and Innovation" | Sponsored by Alliant "Mastering ERM: Leveraging Internal and External Risk Factors" | Sponsored by Diligent "Cyberrisk: Preparing Beyond 2025" | Sponsored by Alliant "The New Reality of Risk Engineering: From Code Compliance to Resilience" | Sponsored by AXA XL "Change Management: AI's Role in Loss Control and Property Insurance" | Sponsored by Global Risk Consultants, a TÜV SÜD Company "Demystifying Multinational Fronting Insurance Programs" | Sponsored by Zurich "Understanding Third-Party Litigation Funding" | Sponsored by Zurich "What Risk Managers Can Learn From School Shootings" | Sponsored by Merrill Herzog "Simplifying the Challenges of OSHA Recordkeeping" | Sponsored by Medcor "How Insurance Builds Resilience Against An Active Assailant Attack" | Sponsored by Merrill Herzog "Third-Party and Cyber Risk Management Tips" | Sponsored by Alliant   RIMS Publications, Content, and Links: RIMS Membership — Whether you are a new member or need to transition, be a part of the global risk management community! RIMS Virtual Workshops On-Demand Webinars RIMS-Certified Risk Management Professional (RIMS-CRMP) RISK PAC | RIMS Advocacy RIMS Strategic & Enterprise Risk Center RIMS-CRMP Stories — Featuring RIMS President Manny Padilla!   RIMS Events, Education, and Services: RIMS Risk Maturity Model®   Sponsor RIMScast: Contact sales@rims.org or pd@rims.org for more information.   Want to Learn More? Keep up with the podcast on RIMS.org, and listen on Spotify and Apple Podcasts.   Have a question or suggestion? Email: Content@rims.org.   Join the Conversation! Follow @RIMSorg on Facebook, Twitter, and LinkedIn.   About our guest: Cynthia Garcia, Risk Manager at Bernards   Production and engineering provided by Podfly.

Saíram por amor. Por oportunidade. Por acaso. No caminho descobriram que "casa" não é só onde nascemos. É onde decidimos ficar. Croácia, Suíça, Polónia. É lá que estão o Sérgio Salvaterra, a Joana Roda e o José Teixeira.

En este episodio conversé con David Peña, cofundador y CRO de Comunidad Feliz, que ha logrado convertir comunidades de copropietarios, desordenadas y poco transparentes en organizadas y felices.Su empresa, según el diario financiero, fue vendida en alrededor de 70 millones de dólares al grupo noruego Visma.

What began as a 14 year old fixing infected computers became Malwarebytes, an 800 person cybersecurity company trusted by millions of customers.On Grit, Marcin Kleczynski joins Joubin Mirzadegan to explore AI driven cyber threats, strategic reinvention, and the discipline of evolving before the market forces you to.“We've exceeded. Now, what do we do to protect individuals against the next wave of threats, which are plentiful?”Guest: Marcin Kleczynski, CEO at MalwarebytesConnect with Marcin KleczynskiX: https://x.com/mkleczynskiLinkedIn: https://www.linkedin.com/in/marcinkleczynski/Connect with JoubinX: https://x.com/JoubinmirLinkedIn: https://www.linkedin.com/in/joubin-mirzadegan-66186854/Email: grit@kleinerperkins.comFollow on LinkedIn:https://www.linkedin.com/company/kpgritFollow on X:https://x.com/KPGrit​Learn more about Kleiner Perkins: https://www.kleinerperkins.com/

I'm not gonna lie…I needed this chat. In an era where there's so much emphasis on growth plans (like SEO, CRO, Email Marketing, Ads, Social, etc), it's easy to second-guess yourself or just abandon web design completely if you prefer to just build awesome websites and support that work.Well good news, just offering high-converting web design + hosting and maintenance plans is still a VERY VIABLE model in 2026 and beyond.Jake Kramer of Artillery Media (along with many members in my community Web Designer Pro) are proof.Jake and his partner John, along with their lean team, are currently hosting and maintaining 500+ websites with their plan, using the same toolstack I've been using since 2015. Divi website builder + SiteGround hosting. We get into how they're running and managing things practically in this one so if you're interested in simply designing sites and supporting clients month to month but not becoming a full stack digital marketing agency, this one's for you.Note: I don't disparage anyone wanting to get into growth plans or digital marketing, and that is a faster path to revenue growth but if you're more in the “slow and steady” camp, and you're ok with delayed gratification, website design + hosting and maintenance plans are still a great way to go.Head to the show notes to get all links and resources we mentioned, along with a full transcription of this episode at joshhall.co/419

John LeBaron is the CRO at Pattern, the leading e-commerce accelerator that helps brands scale profitably across marketplaces worldwide. John runs the SaaS and Services business units for Pattern and oversees all global go-to-market activities for the company and its partners. Prior to joining Pattern, John ran marketing for the Google Cloud business at Rackspace and has held a variety of global marketing roles with leading tech companies including Apple, Cisco, and Ciena. He holds an MBA from the Kellogg School of Management, an MSW from Columbia University, and a B.A. in Communications from Brigham Young University.Highlight Bullets> Here's a glimpse of what you would learn…. Challenges faced by e-commerce brands, particularly on Amazon, including competition and pricing pressures.The importance of inventory management and maintaining stock levels to avoid losing market share.Strategies for optimizing conversion rates, focusing on product imagery and continuous testing.The role of data-driven approaches in improving traffic, conversion, price, and availability.The significance of strategic pay-per-click (PPC) advertising and its relationship with organic rankings.Insights on leveraging AI and technology for product listing optimization and advertising efficiency.The impact of overseas competitors on the e-commerce landscape and brand profitability.The concept of the "e-commerce equation" and its components: traffic, conversion, price, and availability.Best practices for managing logistics and shipping to enhance operational efficiency.The importance of continuous improvement and adapting to changes in the e-commerce environment.In this episode of the Ecomm Breakthrough Podcast, host Josh Hadley interviews John LeBaron, CRO at Pattern. They discuss how e-commerce brands can profitably scale on Amazon amid rising competition, pricing pressures, and operational challenges. John shares Pattern's data-driven strategies—optimizing inventory, pricing, traffic, and conversion—using advanced AI tools and logistics solutions. Key takeaways include the importance of inventory availability, rigorous conversion rate optimization, and strategic PPC management to build organic rankings. The episode offers actionable advice for brands seeking sustainable growth and highlights Pattern's role as a partner in navigating today's complex e-commerce landscape.Here are the 3 action items that Josh identified from this episode:Protect Your Availability or Lose the GameForecast demand aggressively, fix your inbound bottlenecks, and partner with fast-moving 3PLs—because every stockout destroys ranking, momentum, and profit.Obsess Over Conversion, Starting With the Main ImageRun continuous A/B tests on your hero image, audit your live content weekly, and optimize every element (titles, bullets, A+, coupons, bundles) to lift conversion without increasing ad spend.Use PPC to Own Keywords, Not Rent Them ForeverShift ad spend toward keywords that improve organic rank, monitor Buy Box and conversion signals, and prioritize long-tail opportunities to build profitable, compounding visibility.Resources mentioned in this episode:Josh Hadley on LinkedIneComm Breakthrough ConsultingeComm Breakthrough PodcastEmail Josh Hadley: Josh@eCommBreakthrough.comTmallTikTokWalmartPickFuLovable AIPatternLinkedInThe E-MythAtomic HabitsAll In PodcastSpecial Mention(s):Adam “Heist” Runquist on LinkedInKevin King on LinkedInMichael E. Gerber on LinkedInRelated Episode(s):“Cracking the Amazon Code: Learn From Adam Heist's Brand Scaling Secrets” on the eComm Breakthrough Podcast“Kevin King's Wicked-Smart Tips for Building an Audience of Raving Fans” on the eComm Breakthrough Podcast“Unlocking Entrepreneurial Greatness | Insider Secrets With E-myth Author Michael Gerber” on the eComm Breakthrough PodcastEpisode SponsorSponsor for this episode...This episode is brought to you by eComm Breakthrough Consulting where I help seven-figure e-commerce owners grow to eight figures. I started Hadley Designs in 2015 and grew it to an eight-figure brand in seven years.I made mistakes along the way that made the path to eight figures longer. At times I doubted whether our business could even survive and become a real brand. I wish I would have had a guide to help me grow faster and avoid the stumbling blocks.If you've hit a plateau and want to know the next steps to take your business to the next level, then go to www.EcommBreakthrough.com (that's Ecomm with two M's) to learn more.Transcript AreaJohn Lebaron 00:00:00  We're absolute zealots around something we call the e-commerce equation, which is revenue as a function of traffic times, conversion times, price times, availability. And I think that's very much the way that we think about accelerating brands is just isolating those specific variables of the equation and really going to work on okay for traffic, for example, there's paid traffic. There's, you know, organic traffic, there's off platform traffic. And what are all the hundreds of different kind of atomic levers that we want to pull and automate increasingly via AI for the brands that we represent. And and then helping them set an expectation, helping them forecast appropriately, helping them understand what is their ops upside.Speaker 2 00:00:47  Welcome to the E-comm Breakthrough Podcast. Are you ready to unlock the full potential and growth in your business? You've already crossed seven figures in sales, but the challenge is knowing how to take your business to the next level.Josh Hadley 00:01:00  Are you tired of getting squeezed by Amazon, watching your sales fall? Watching more overseas competitors come in to overtake your market share? Watching the race to the bottom pricing.Josh Hadley 00:01:12  Well, today's guest has the answer for you of how to di...

In today's minisode, Football coach and author Brian White shares essential leadership lessons on building winning cultures that apply far beyond the field. Brian breaks down why trust must flow both ways, from the individual entering a new organization and from the team itself, and reveals why assimilating into an existing culture before trying to change it is the key to lasting impact. Whether you're a sales leader establishing yourself in a new company, a manager building team cohesion, or a CRO creating a culture where people compete selfishly but give selflessly, this episode delivers actionable insights on peer leadership, the power of direct human engagement, and why the huddle is always more important than the position. Brian White is a veteran Division I football coach, Assistant Coach of the Year, and author of The Locker Room Is Not for Sale. Over 55 years in and around elite programs including Notre Dame, he has coached national champions, developed NFL talent including Heisman Trophy winner Ron Dayne, and built cultures grounded in respect, accountability, and the human touch. Resources mentioned: The Locker Room Is Not for Sale by Brian White The Qualified Sales Leader by John McMahon Want to know how top-performing organizations create a culture of consistent success? Check out Force Management's guide to the Predictable Revenue Framework:  https://hubs.li/Q03-T6NH0 Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

Net revenue retention is under pressure. SaaS growth has slowed. Sales and marketing budgets are shrinking. And AI is forcing companies to rethink everything, from seat-based pricing models to how they engage, retain, and grow customers.In this episode of TECHtonic, host Thomas Lah welcomes back Brent Grimes, CEO of Reef.ai, for a direct and timely conversation about what's actually happening in the AI revenue landscape.Since their last discussion, AI capabilities have advanced rapidly, but the bigger shift isn't just better models. It's how companies are using those models to survive and win in a far more demanding market. With declining net revenue retention across public SaaS companies and mounting pressure to “do more with less,” leaders can no longer rely on intuition, last-call sentiment, or broad segmentation strategies. The era of guessing is ending.Thomas and Brent explore the rise of model-driven revenue management, where predictive AI doesn't just improve forecasting accuracy but identifies churn risk months in advance, pinpoints expansion opportunities with statistical precision, and helps teams prioritize their time where it matters most. They discuss how upsell intelligence may actually unlock more upside than churn reduction alone, and how organizations are beginning to move from dashboards and insights toward autonomous workflows powered by renewal and expansion agents.The conversation also dives into the practical realities of making this shift—from solving data quality challenges to building trust with revenue teams who must learn to work alongside models and agents rather than rely solely on instinct. As AI adoption compounds quarter over quarter, the gap between early adopters and laggards is widening, and 2026 may mark the moment when that divide becomes unmistakable.If you own revenue, lead customer success, or sit in the CRO seat, this episode will challenge how you think about forecasting, expansion, resource allocation, and the future of go-to-market execution.

Most consultants don't fail dramatically. They burn out slowly, lose faith in what they've built, and one day find themselves dusting off their resume to go back to corporate.In this episode, Melisa introduces the concept of self-retention: treating yourself with the same intentionality a great company would extend to its top performer. In your business, you're also the manager, the CHRO, and the one responsible for making sure that top talent doesn't walk out the door. High performers, like you, stay when the conditions are worth staying for. You get to build those conditions.You'll learn the five retention drivers that keep high performers loyal in corporate environments and exactly how to translate each one into your consulting business so you can build something you actually want to stay in.Stay for the exercise at the end.The episode closes with a practical three-pass exercise to help you build your own self-retention plan by stepping into three distinct roles (owner, CRO, and delivery consultant) so you can see your business clearly from every angle.What you will learn in this episode:[05:00] What “self-retention” means and why consultants often leave because conditions become unsustainable[10:00] Retention Strategy 1. Compensation and security, and how to stop treating revenue like a mystery[15:00] Retention Strategy 2. A growth path and plan so you are not “failing” at skills you never trained for[20:00] Retention Strategy 3. Meaningful work, including the client red flags that create a retention risk[25:00] Retention Strategy 4. Recognition, and why your client is the wrong person to rely on for it[30:00] Retention Strategy 5. Sustainable expectations, so your business stops requiring you to be “on” all the time[35:00] How to build your self-retention plan with a 3-pass exercise you can repeat over timeTune into Episode 258 to learn how to build a consulting business that aligns with your goals, leverages your expertise, and sets you up for long-term success.Mentioned ResourcesCompanion Resource: Read Melisa's Book Grow Your Consulting Business: The 14-Step Roadmap to Make Your Independent Consulting Goals a Reality, https://www.amazon.com/dp/B0CSXJBGVB Full Show Notes: https://shownotes.melisaliberman.com/episode-258Melisa's Books, Planners & Journals: https://linktr.ee/melisalibermanMentioned in this Episode:Episode 176 - Set a Compound Goal for Sustainable Consulting Business Growth, https://shownotes.melisaliberman.com/episode-176/#more-2463 ️Episode 088 – The Burnout Formula for Independent Consultants, https://shownotes.melisaliberman.com/episode-88/#more-1326  Want help achieving your consulting business goals? Melisa can help. Click here for more on coaching tailored to you as an independent consulting business owner.

Stevie Case is the CRO of Vanta, the trust management platform serving everyone from founders to Fortune 100 CISOs. A former pro-video gamer who stumbled into sales through a mentor's bet, Stevie has built one of the most unconventional paths to the C-suite in tech. In this episode, she unpacks why early revenue hires fail, what separates a true CRO from a VP of Sales, and why she believes fewer than 10% of current CROs will thrive by 2028. In today's episode, we discuss: Why early revenue hires fail What a top 1% CRO actually does The scaling mistake Stevie made by copying Twilio's playbook at Vanta Why Vanta remains 100% sales-led at every segment AI vs. humans in go-to-market References: Cursor: https://cursor.sh/ Gong: https://www.gong.io/ Salesforce: https://www.salesforce.com/ Twilio: https://www.twilio.com/ Vanta: https://www.vanta.com/ Where to find Stevie: LinkedIn: https://www.linkedin.com/in/steviecase/ Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps: 00:00 Why early revenue hires fail 02:23 Who to hire at $5M in revenue 04:16 Coin-operated sellers vs. long-term builders 05:57 What excellence looks like in the CRO role 07:44 Metrics, confidence, and velocity 12:04 Should CROs lead sales? 14:39 From shy seller to revenue leader 16:36 Learning to scale at Twilio 17:44 "There is no CRO playbook" 19:58 Stevie's scaling mistake at Vanta 22:16 Why Vanta stays 100% sales-led 23:16 The value of planning 24-26 months ahead 29:54 When trusting intuition was the wrong call 30:49 Do humans still have a place in the future of GTM? 33:33 Stevie's leadership non-negotiables 36:36 The myth of hiring for industry expertise 40:00 What stays centralized in a 600-person company 47:09 The hidden leverage of a customer's first 30 days 53:42 Why the CRO role will face enormous changes by 2028 58:42 What leaders must do now to stay relevant 01:02:30 Unpacking the CEO-CRO dynamic

If you work in conversion optimization, user experience design, or design leadership, you probably think of these as separate disciplines. Different skill sets, different tools, different conversations.But treating them as separate is precisely what limits your impact.These three areas are deeply interconnected, and they build on top of one another in ways that make each more effective. If you're only working in one of these areas without considering the others, you're solving the wrong problems, or at best, only solving part of the right problem.I know this because my work spans all three, which makes me sound like I'm either a confused generalist or cobbling together random consulting gigs.People often ask what I actually do, because it doesn't fit neatly into a single box. When I list the three areas, I can see the confusion on their faces. I sometimes feel like that conspiracy theorist from the meme, standing in front of a pin board covered in red string, ranting about how it's all connected.But it is all connected. And if you work in any of these fields, you should be taking this holistic, interconnected approach as well.Let me walk you through how this actually works in practice, and why you should be thinking this way too.It starts with conversionUltimately, the goal of almost every project I take on is to improve a company's conversion rate through their website or app. Sometimes that means acquiring new customers, sometimes it means retaining existing ones, but the end goal is always the same: make the company more profitable through digital channels.In straightforward cases, I can achieve that with traditional conversion optimization techniques:A/B testingInterface design improvementsRefined copy and messagingThese are the tools you'd expect from anyone doing CRO work, and often they're enough to move the needle.But more often than I'd like to admit, those surface-level fixes aren't sufficient. The conversion problem runs deeper than a poorly worded call-to-action or a confusing checkout flow. When that happens, I need to look at the entire user experience, which means examining usability issues, carrying out proper user research, mapping out all the other touchpoints where customers interact with the brand, and understanding the full journey they're on.That's where the user experience design and strategy work comes into play.When UX goes beyond the screenHowever, sometimes even comprehensive user experience work isn't enough, because the real problems exist beyond the screen entirely.I once worked with a company that sold frozen ready meals to elderly customers. They wanted me to improve their website conversion rates, which seemed like a straightforward brief. We carried out user research and discovered that the elderly audience was nervous about multiple aspects of the experience, none of which had anything to do with the website design itself:Entering credit card details online because of fraud and scamsA strange delivery driver they didn't know turning up at their houseUnloading heavy trays of frozen products into their freezersNow, in most companies, a user experience designer would hit a wall at this point. You can't redesign a website to make someone feel safer about delivery drivers or less anxious about lifting heavy boxes. The best you could do would be to make the existing service as palatable as possible through clever messaging and reassurance copy.But in a company with a strong culture of design leadership, a UX designer can be instrumental in shaping solutions to these kinds of problems. Solutions that go way beyond polishing existing products to fundamentally reshaping the service itself.This is where the design leadership coaching aspect of my work becomes essential.Design leadership changes what's possibleIn that frozen meal company, we didn't just optimize the website. We fundamentally changed the offering based on what we learned from users:Customers got the same delivery driver every time, and when that wasn't possible, they'd be notified in advance and shown a photo of their driverAll drivers were police-checked so customers could feel confident about safetyThe driver didn't just dump the products and leave but actually unpacked everything into the customer's freezerCustomers could even reorder directly from their driver if they didn't want to use the website and enter card details onlineThe user experience shaped the product, and by extension, delivered the improved conversion rate the client originally asked for.You can see how these three areas that appear unrelated are actually deeply entwined. This interconnected approach is much more representative of what real user experience design should be about, rather than just pushing pixels around a screen.What this means for your workIf you're working in conversion optimization: Start asking deeper questions about the user experience.If you're doing UX work: Understand how it connects to business outcomes and conversion.If you're in design leadership: Recognize that your influence should extend beyond the screen to reshape products and services based on what users actually need.Because at the end of the day, conversion optimization teaches you what matters to the business, user experience design teaches you what matters to customers, and design leadership gives you the organizational influence to actually do something meaningful about both.And once you start seeing those connections, you can't unsee them.If you're thinking about how to bring these different elements together in your own work, drop me an email. I'm always happy to chat it through.

In this episode, Peg Crowley-Nowick speaks with Joseph Elassal, MD, MBA, Chief Medical Officer of Ankyra Therapeutics, about the strategic, operational, and financial realities of leading clinical development from pre-IND through proof of concept and toward commercialization.   Drawing on experience across large pharma, biotech partnerships, and early-phase oncology, Joe shares a practical roadmap for new and aspiring CMOs. They discuss how to prioritize essential capabilities, including clinical operations, regulatory strategy, biostatistics, and pharmacovigilance, while determining the right time to introduce Medical Affairs. The conversation outlines how to scale teams at critical inflection points such as IND clearance, Phase 2 proof of concept, and advancement into Phase 3.   The episode also examines investor and board expectations, CRO selection, capital efficiency, cash runway management, and the performance metrics CMOs are ultimately judged on—from disciplined milestone execution to generating meaningful clinical data. This episode offers actionable insight for biotech founders, clinical development leaders, medical affairs professionals, and emerging CMOs navigating the path from early development to launch.

The event didn't start on the show floor.By the time Accelevents showed up with a 10×10 booth, much of the pre-show groundwork was already in motion—accounts targeted, meetings scheduled, and conversations planned. The result? 116 meetings driven by intentional strategy, not booth size or blind hope for foot traffic.In this live conversation, Matt is joined by Jonathan Kazarian, CEO of Accelevents, and Michael Burns, CRO, to walk through a real case study. They'll break down the pre-show strategy behind that outcome:✅ Why booth size had nothing to do with meeting volume✅ How targeted outreach and community plays drove qualified conversations✅ What changes when sales and marketing co-own pre-show strategy✅ How pre-booked meetings should influence booth design and on-site experienceIf you want your next event to work BEFORE you arrive and not after, this episode will show you how to rethink your approach.----------------------------------Connect with Jonathan KazarianLinkedIn: https://www.linkedin.com/in/jkazarian/ Connect with Michael Burns LinkedIn: https://www.linkedin.com/in/michael-burns-0208/ Connect with Matt KleinrockLinkedIn: https://www.linkedin.com/in/matt-kleinrock-9613b22b/Company: https://rockwayexhibits.com/ 

Most foreign SaaS companies struggle to land even a handful of enterprise clients in Japan.Shay Khosrowshahi helped scale Ulife to 50+ B2B customers in just 18 months, largely through strategic partnerships.In this episode of the Scaling Japan Podcast, we break down how he did it.Shay is the co-founder of NXL and former Managing Director of Ulife APAC. After a 100M investment, he was sent to Japan to launch and scale the business in one of the most credibility-driven markets in the world.We explore what partnerships really mean in Japan, how to align incentives with distribution partners, and why most founders underestimate the level of commitment required to succeed here.Shay shares tactical insights on discovery calls, partner qualification, internal champions, cultural misalignment, and how to create momentum that compounds over time.If you are a SaaS founder, CRO, or GTM leader entering Japan, this episode offers a practical partnership blueprint grounded in real execution.In This Episode, We Cover:What a partnership actually means in the Japanese marketDistribution partners vs strategic alliancesWhy hunger and ambition matter more than brand sizeThe 70/30 discovery framework for qualifying partnersHow to forecast revenue impact to align incentivesManaging harmony culture while still driving urgencyWhy early wins create long-term momentumWhen to double down or exit a partnershipWhy getting direct customers first gives you leverageGuest Appearance:

In this episode of the CRO Spotlight, Warren Zenna sits down with Miya Mee-Lee Dias, Co-Founder of Beyond The Script, to discuss a transformative approach to sales training. Miya shares her unique background blending health science with performance arts, explaining how traditional methodologies often fail because they ignore the human element. She introduces the concept of the "sales gym," where reps practice role-plays like actors preparing for a scene, stripping away bad habits to build authentic character and confidence in their delivery.Warren and Miya dive deep into the parallels between professional acting and high-performance sales. They explore the idea that every salesperson brings personal "baggage" and history that influences their communication style. Miya explains that true proficiency isn't about memorizing lines but about internalizing the script to project a genuine persona. The conversation highlights the importance of adaptability, showing how top performers maintain a "beginner's mind" and remain open to molding their approach regardless of their experience level.A critical portion of the discussion centers on the elusive trait of coachability. Miya reveals her methods for identifying whether a rep is truly ready to learn, often spotting resistance through subtle cues like tone of voice and body language. The dialogue challenges Revenue Leaders to look beyond metrics and address the holistic human factors driving performance. They discuss the necessity of understanding a rep's intrinsic motivations and personal history to unlock their full potential and drive sustainable behavioral change.As technology automates more transactional aspects of business, Warren and Miya argue that human connection and emotional intelligence are becoming the ultimate competitive advantages. They emphasize that modern CROs must develop the "muscle" to have difficult, personal conversations with their teams to foster trust and growth. The episode concludes with a look at the intersection of creativity and business, encouraging leaders to embrace a coaching mindset that empowers their organizations through genuine human development.

If you're a physician with at least 5 years of experience looking for a flexible, non-clinical, part-time medical-legal consulting role… ...Dr. Armin Feldman's Medical Legal Coaching program will guarantee to add $100K in additional income within 12 months without doing any expert witness work. Any doctor in any specialty can do this work. And if you don't reach that number, he'll work with you for free until you do, guaranteed. How can he make such a bold claim? It's simple, he gets results…  Dr. David exceeded his clinical income without sacrificing time in his full-time position. Dr. Anke retired from her practice while generating the same monthly consulting income.  And Dr. Elliott added meaningful consulting work without lowering his clinical income or job satisfaction. So, if you're a physician with 5+ years of experience and you want to find out exactly how to add $100K in additional consulting income in just 12 months, go to arminfeldman.com.                                                          =============== Get the FREE GUIDE to 10 Nonclinical Careers at nonclinicalphysicians.com/freeguide. Get a list of 70 nontraditional jobs at nonclinicalphysicians.com/70jobs.                                                                                                 =============== Regulatory medical writer Dr. Keagen Hadley explains how a pre-med background, clinical research work at a small CRO, and graduate training in occupational therapy led him into a fully remote, high-earning career writing core documents for pharma and biotech. He describes how he first discovered regulatory writing, why it felt like the right balance of science, impact, and flexibility, and how that path allowed him to work, study, and eventually step away from traditional clinical roles. He then outlines what regulatory writers actually do: drafting protocols, investigator brochures, and clinical study reports. And why the work is a strong fit for clinicians who enjoy clear, technical writing and are willing to learn the drug-development process. Along the way, he talks about salary expectations, personality traits that help (discipline, proactivity, comfort with timelines), and the practical steps clinicians can take to move into the field and eventually build their own regulatory writing business. You'll find links mentioned in the episode at nonclinicalphysicians.com/regulatory-medical-writing/

Dave's guest this week is Chris Riedy, CRO at Ibotta, where he's helping evolve promotions and offers into a true performance marketing engine for CPG brands.In this episode, Chris breaks down why great sales is really about problem-solving, trust, and empathy - not transactions. He shares how Ibotta has evolved from a consumer app into a platform that connects manufacturers, retailers, and shoppers in moments that change behavior.Dave and Chris also dig into what makes promotions incremental instead of subsidized, how creative and offers can travel across retail media and even into CTV, and why AI and machine learning are most powerful when they help marketers learn faster, not guess better.Connect with Chris on LinkedInFollow Beyond the Shelf on LinkedInLearn More about It'sRapidGet the It'sRapid Creative Automation PlaybookTake It'sRapid's Creative Workflow Automation with AI surveyEmail us at sales@itsrapid.io to find out how to get your free AI Image AuditTheme music: "Happy" by Mixaud - https://mixaund.bandcamp.comProducer: Jake Musiker

In this masterclass episode, Favour Obasi-ike, MBA, MS delivers an in-depth exploration of web sales optimization (CRO - conversation rate optimization) through strategic search engine marketing (SEM). The episode focuses on the critical relationship between website speed and conversion rates, revealing how technical optimization directly impacts sales performance. Favour emphasizes that web sales are fundamentally a result of web speed, explaining that websites loading slower than 3 seconds can decrease conversion rates by at least 7%, with compounding effects reaching 20% for sites taking 10 seconds to load.The discussion covers comprehensive website optimization strategies, including image optimization (recommending WebP format over JPEG/PNG), structured data implementation with schema markup, and the importance of optimizing every website element from headers and footers to file names and internal linking structures. Favour introduces the concept of treating URLs like seeds that need time to grow, recommending a 2-3 month planning horizon for content strategy.The masterclass also explores collection pages, category optimization, and the strategic use of content hubs to create pathways for user navigation. Favour shares practical tools and resources for keyword research and competitive analysis, while emphasizing the importance of submitting websites to Google Search Console and Bing Webmaster Tools for maximum visibility. The episode concludes with actionable advice on implementing these strategies either independently or through professional SEO consultation.Book SEO Services | Quick Links for Social Business>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Book SEO Services with Favour Obasi-ike⁠>> Visit Work and PLAY Entertainment website to learn about our digital marketing services>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Join our exclusive SEO Marketing community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Read SEO Articles>> ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe to the We Don't PLAY Podcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠>> Purchase Flaev Beatz Beats Online>> Favour Obasi-ike Quick Links

In this two-part series, James Hatfield, CRO for LiveSwitch, gives you some ways that AI is currently being used and strategies you can implement now and prepare for in the future. Free P&L Statement and Balance Sheet https://tinyurl.com/2rjd6wxu Ruth King Facebook - https://www.facebook.com/ruthking1650 LinkedIn - https://www.linkedin.com/in/ruthking1/   Podcast Produced by Nick Uttam https://www.linkedin.com/in/nick-uttam-4b33a1147

Today's minisode features Carlos Delatorre as he shares two hard-earned leadership lessons that every sales leader scaling an organization needs to hear. He reflects on an early moment in his career when he learned the difference between being a top-performing rep and becoming a true manager, and why doing the work for your team might feel helpful in the moment but ultimately breaks scale. If you're a manager trying to transition into leadership, or a CRO navigating rapid growth and wondering whether your leadership bench is ready to scale, this clip is for you. Carlos Delatorre is a seasoned sales leader with over 25 years of enterprise software and SaaS experience. He has served as CRO at MongoDB (driving 100%+ annual revenue growth), TripActions/Navan, and ClearSlide, and as CEO of Vera. Carlos is also an active investor and advisor to high-growth software companies including Starburst, Outreach, and Modern Treasury, and serves on the board of Yalo.Connect with Carlos:LinkedIn Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

From Stanford and the RAND Corporation to leading revenue teams in the commercial world, Brent Keltner, PhD, has spent his career decoding how complex B2B deals are actually closed. As founder and president of Winalytics, Brent helps mid-market and enterprise teams move beyond product pitching to true account-based growth. He's the author of “The Revenue Acceleration Playbook” and the forthcoming “Journey First Marketing,” a book that challenges one of B2B's biggest bad habits: obsessing over individual personas when companies actually buy in committees. In this episode, Brent reveals why traditional contact-focused marketing leaves so much revenue on the table and how to flip your entire go-to-market motion around a simple idea: accounts buy, personas don't. You'll hear how to design websites that speak to every member of the buying committee, why customer stories should be your #1 content asset (not #5), and how to connect product value, business value, and corporate value so that users, budget owners, and risk-averse stakeholders all see themselves in your message. https://youtu.be/2dCBKj9vf88 Brent also breaks down a practical roadmap for teams stuck in contact scoring and lead chaos. He explains how to use tools like ChatGPT on top of your CRM to spot real buying committees (not just random clickers or competitors snooping), how to build three aligned content streams for your core buyer types, and how to reuse a single customer story across your entire funnel, website, social, sales decks, and beyond. Whether you're a CMO, CRO, founder, or product marketer, you'll come away with a clearer picture of what true account-based enablement looks like in the real world and how a few smart changes can unlock faster, more predictable growth. Quotes: "Accounts buy. Personas don't, and every part of your marketing should reflect that reality.” “If your customers aren't saying it consistently, it isn't true, no matter how often your CEO repeats it.” “Customer stories are the only asset that turn ‘me selling to you' into ‘we solving a problem together.'” Resources: Winalytics LLC Brent Keltner on LinkedIn The Revenue Acceleration Playbook: Creating an Authentic Buyer Journey Across Sales, Marketing, and Customer Success on Amazon

In this episode of the HR Like a Boss podcast, John is joined by Biz Williams, a seasoned professional in the HR tech space. Biz shares her journey through various roles in HR, her experiences with imposter syndrome, and her transition to entrepreneurship. The conversation highlights the importance of financial acumen in HR, the role of HR in business success, and the impact of tax credits on hiring practices. Biz emphasizes the need for effective communication between HR and leadership and the value of understanding business operations to drive success.ABOUT BIZBiz has been in the HR Tech space for 14 years - supporting talent acquisition, applicant tracking, onboarding, flex shift work and now the compliance side with tax credits, I9, Unemployment Claims Management and Employment & Wage Verifications. She created Ryze, a tax credit mapping tool in December, 2024 and merged with HRlogics in June, 2025 coming on board as CRO.

Climbing from individual contributor to CRO requires far more than strong execution. It demands disciplined leadership, intentional systems, and the ability to scale through complexity. In this replay episode, Carlos de la Torre joins John McMahon to unpack lessons from decades of enterprise sales leadership, including how he evaluates CRO opportunities, why complex selling environments demand sophisticated go-to-market engines, and how pipeline generation, leadership hiring, and management operating rhythm drive sustainable growth. Carlos also shares hard-earned insights on developing leaders, avoiding common scaling traps, and protecting personal sustainability as organizational demands increase.Carlos Delatorre is a seasoned sales leader with over 25 years of enterprise software and SaaS experience. He has served as CRO at MongoDB (driving 100%+ annual revenue growth), TripActions/Navan, and ClearSlide, and as CEO of Vera. Carlos is also an active investor and advisor to high-growth software companies including Starburst, Outreach, and Modern Treasury, and serves on the board of Yalo.Connect with Carlos:LinkedInForce Management resources on scaling predictably:The Predictable Revenue Framework: Guide for LeadersKey takeaways from this episode: 04:18 - The three non-negotiables Carlos uses to evaluate a CRO role: a market big enough to scale, a product that delivers real business value, and a leadership team capable of growing with the company.06:43 - Why complex selling environments require more than great reps, and how elite go-to-market engines translate technical products into business outcomes across multiple stakeholders while navigating internal politics.20:47 - The MongoDB lesson every scaling CRO needs to hear: why waiting 6-9 months too long to hire senior leaders creates capacity gaps, forces Q4 heroics, and caps your upside.34:00 - How defining clear stage criteria, tailoring messages by persona, and training the entire team on a single system fuels consistent 100%+ growth.41:44 - What to analyze after the quarter closes: how revenue mix, productivity per AE, and stage conversion rates reveal which reps and behaviors are actually driving outsized results.49:12 - Why blocking time by day, week, month, quarter, and year is the only way to protect focus and maintain execution.54:56 - Staying connected to what's really happening in the field, why office walks, open office hours, and time on sales calls give CROs earlier signal, better coaching moments, and stronger strategy. Hosted by five-time CRO John McMahon and Force Management Co-Founder John Kaplan, the Revenue Builders podcast goes behind the scenes with the sales leaders who have been there, done that, and seen the results. This show is brought to you by Force Management. We help companies improve sales performance, executing their growth strategy at the point of sale. Connect with Us: LinkedInYouTubeForce Management

This podcast features Gabriele Corso and Jeremy Wohlwend, co-founders of Boltz and authors of the Boltz Manifesto, discussing the rapid evolution of structural biology models from AlphaFold to their own open-source suite, Boltz-1 and Boltz-2. The central thesis is that while single-chain protein structure prediction is largely “solved” through evolutionary hints, the next frontier lies in modeling complex interactions (protein-ligand, protein-protein) and generative protein design, which Boltz aims to democratize via open-source foundations and scalable infrastructure.Full Video PodOn YouTube!Timestamps* 00:00 Introduction to Benchmarking and the “Solved” Protein Problem* 06:48 Evolutionary Hints and Co-evolution in Structure Prediction* 10:00 The Importance of Protein Function and Disease States* 15:31 Transitioning from AlphaFold 2 to AlphaFold 3 Capabilities* 19:48 Generative Modeling vs. Regression in Structural Biology* 25:00 The “Bitter Lesson” and Specialized AI Architectures* 29:14 Development Anecdotes: Training Boltz-1 on a Budget* 32:00 Validation Strategies and the Protein Data Bank (PDB)* 37:26 The Mission of Boltz: Democratizing Access and Open Source* 41:43 Building a Self-Sustaining Research Community* 44:40 Boltz-2 Advancements: Affinity Prediction and Design* 51:03 BoltzGen: Merging Structure and Sequence Prediction* 55:18 Large-Scale Wet Lab Validation Results* 01:02:44 Boltz Lab Product Launch: Agents and Infrastructure* 01:13:06 Future Directions: Developpability and the “Virtual Cell”* 01:17:35 Interacting with Skeptical Medicinal ChemistsKey SummaryEvolution of Structure Prediction & Evolutionary Hints* Co-evolutionary Landscapes: The speakers explain that breakthrough progress in single-chain protein prediction relied on decoding evolutionary correlations where mutations in one position necessitate mutations in another to conserve 3D structure.* Structure vs. Folding: They differentiate between structure prediction (getting the final answer) and folding (the kinetic process of reaching that state), noting that the field is still quite poor at modeling the latter.* Physics vs. Statistics: RJ posits that while models use evolutionary statistics to find the right “valley” in the energy landscape, they likely possess a “light understanding” of physics to refine the local minimum.The Shift to Generative Architectures* Generative Modeling: A key leap in AlphaFold 3 and Boltz-1 was moving from regression (predicting one static coordinate) to a generative diffusion approach that samples from a posterior distribution.* Handling Uncertainty: This shift allows models to represent multiple conformational states and avoid the “averaging” effect seen in regression models when the ground truth is ambiguous.* Specialized Architectures: Despite the “bitter lesson” of general-purpose transformers, the speakers argue that equivariant architectures remain vastly superior for biological data due to the inherent 3D geometric constraints of molecules.Boltz-2 and Generative Protein Design* Unified Encoding: Boltz-2 (and BoltzGen) treats structure and sequence prediction as a single task by encoding amino acid identities into the atomic composition of the predicted structure.* Design Specifics: Instead of a sequence, users feed the model blank tokens and a high-level “spec” (e.g., an antibody framework), and the model decodes both the 3D structure and the corresponding amino acids.* Affinity Prediction: While model confidence is a common metric, Boltz-2 focuses on affinity prediction—quantifying exactly how tightly a designed binder will stick to its target.Real-World Validation and Productization* Generalized Validation: To prove the model isn't just “regurgitating” known data, Boltz tested its designs on 9 targets with zero known interactions in the PDB, achieving nanomolar binders for two-thirds of them.* Boltz Lab Infrastructure: The newly launched Boltz Lab platform provides “agents” for protein and small molecule design, optimized to run 10x faster than open-source versions through proprietary GPU kernels.* Human-in-the-Loop: The platform is designed to convert skeptical medicinal chemists by allowing them to run parallel screens and use their intuition to filter model outputs.TranscriptRJ [00:05:35]: But the goal remains to, like, you know, really challenge the models, like, how well do these models generalize? And, you know, we've seen in some of the latest CASP competitions, like, while we've become really, really good at proteins, especially monomeric proteins, you know, other modalities still remain pretty difficult. So it's really essential, you know, in the field that there are, like, these efforts to gather, you know, benchmarks that are challenging. So it keeps us in line, you know, about what the models can do or not.Gabriel [00:06:26]: Yeah, it's interesting you say that, like, in some sense, CASP, you know, at CASP 14, a problem was solved and, like, pretty comprehensively, right? But at the same time, it was really only the beginning. So you can say, like, what was the specific problem you would argue was solved? And then, like, you know, what is remaining, which is probably quite open.RJ [00:06:48]: I think we'll steer away from the term solved, because we have many friends in the community who get pretty upset at that word. And I think, you know, fairly so. But the problem that was, you know, that a lot of progress was made on was the ability to predict the structure of single chain proteins. So proteins can, like, be composed of many chains. And single chain proteins are, you know, just a single sequence of amino acids. And one of the reasons that we've been able to make such progress is also because we take a lot of hints from evolution. So the way the models work is that, you know, they sort of decode a lot of hints. That comes from evolutionary landscapes. So if you have, like, you know, some protein in an animal, and you go find the similar protein across, like, you know, different organisms, you might find different mutations in them. And as it turns out, if you take a lot of the sequences together, and you analyze them, you see that some positions in the sequence tend to evolve at the same time as other positions in the sequence, sort of this, like, correlation between different positions. And it turns out that that is typically a hint that these two positions are close in three dimension. So part of the, you know, part of the breakthrough has been, like, our ability to also decode that very, very effectively. But what it implies also is that in absence of that co-evolutionary landscape, the models don't quite perform as well. And so, you know, I think when that information is available, maybe one could say, you know, the problem is, like, somewhat solved. From the perspective of structure prediction, when it isn't, it's much more challenging. And I think it's also worth also differentiating the, sometimes we confound a little bit, structure prediction and folding. Folding is the more complex process of actually understanding, like, how it goes from, like, this disordered state into, like, a structured, like, state. And that I don't think we've made that much progress on. But the idea of, like, yeah, going straight to the answer, we've become pretty good at.Brandon [00:08:49]: So there's this protein that is, like, just a long chain and it folds up. Yeah. And so we're good at getting from that long chain in whatever form it was originally to the thing. But we don't know how it necessarily gets to that state. And there might be intermediate states that it's in sometimes that we're not aware of.RJ [00:09:10]: That's right. And that relates also to, like, you know, our general ability to model, like, the different, you know, proteins are not static. They move, they take different shapes based on their energy states. And I think we are, also not that good at understanding the different states that the protein can be in and at what frequency, what probability. So I think the two problems are quite related in some ways. Still a lot to solve. But I think it was very surprising at the time, you know, that even with these evolutionary hints that we were able to, you know, to make such dramatic progress.Brandon [00:09:45]: So I want to ask, why does the intermediate states matter? But first, I kind of want to understand, why do we care? What proteins are shaped like?Gabriel [00:09:54]: Yeah, I mean, the proteins are kind of the machines of our body. You know, the way that all the processes that we have in our cells, you know, work is typically through proteins, sometimes other molecules, sort of intermediate interactions. And through that interactions, we have all sorts of cell functions. And so when we try to understand, you know, a lot of biology, how our body works, how disease work. So we often try to boil it down to, okay, what is going right in case of, you know, our normal biological function and what is going wrong in case of the disease state. And we boil it down to kind of, you know, proteins and kind of other molecules and their interaction. And so when we try predicting the structure of proteins, it's critical to, you know, have an understanding of kind of those interactions. It's a bit like seeing the difference between... Having kind of a list of parts that you would put it in a car and seeing kind of the car in its final form, you know, seeing the car really helps you understand what it does. On the other hand, kind of going to your question of, you know, why do we care about, you know, how the protein falls or, you know, how the car is made to some extent is that, you know, sometimes when something goes wrong, you know, there are, you know, cases of, you know, proteins misfolding. In some diseases and so on, if we don't understand this folding process, we don't really know how to intervene.RJ [00:11:30]: There's this nice line in the, I think it's in the Alpha Fold 2 manuscript, where they sort of discuss also like why we even hopeful that we can target the problem in the first place. And then there's this notion that like, well, four proteins that fold. The folding process is almost instantaneous, which is a strong, like, you know, signal that like, yeah, like we should, we might be... able to predict that this very like constrained thing that, that the protein does so quickly. And of course that's not the case for, you know, for, for all proteins. And there's a lot of like really interesting mechanisms in the cells, but yeah, I remember reading that and thought, yeah, that's somewhat of an insightful point.Gabriel [00:12:10]: I think one of the interesting things about the protein folding problem is that it used to be actually studied. And part of the reason why people thought it was impossible, it used to be studied as kind of like a classical example. Of like an MP problem. Uh, like there are so many different, you know, type of, you know, shapes that, you know, this amino acid could take. And so, this grows combinatorially with the size of the sequence. And so there used to be kind of a lot of actually kind of more theoretical computer science thinking about and studying protein folding as an MP problem. And so it was very surprising also from that perspective, kind of seeing. Machine learning so clear, there is some, you know, signal in those sequences, through evolution, but also through kind of other things that, you know, us as humans, we're probably not really able to, uh, to understand, but that is, models I've, I've learned.Brandon [00:13:07]: And so Andrew White, we were talking to him a few weeks ago and he said that he was following the development of this and that there were actually ASICs that were developed just to solve this problem. So, again, that there were. There were many, many, many millions of computational hours spent trying to solve this problem before AlphaFold. And just to be clear, one thing that you mentioned was that there's this kind of co-evolution of mutations and that you see this again and again in different species. So explain why does that give us a good hint that they're close by to each other? Yeah.RJ [00:13:41]: Um, like think of it this way that, you know, if I have, you know, some amino acid that mutates, it's going to impact everything around it. Right. In three dimensions. And so it's almost like the protein through several, probably random mutations and evolution, like, you know, ends up sort of figuring out that this other amino acid needs to change as well for the structure to be conserved. Uh, so this whole principle is that the structure is probably largely conserved, you know, because there's this function associated with it. And so it's really sort of like different positions compensating for, for each other. I see.Brandon [00:14:17]: Those hints in aggregate give us a lot. Yeah. So you can start to look at what kinds of information about what is close to each other, and then you can start to look at what kinds of folds are possible given the structure and then what is the end state.RJ [00:14:30]: And therefore you can make a lot of inferences about what the actual total shape is. Yeah, that's right. It's almost like, you know, you have this big, like three dimensional Valley, you know, where you're sort of trying to find like these like low energy states and there's so much to search through. That's almost overwhelming. But these hints, they sort of maybe put you in. An area of the space that's already like, kind of close to the solution, maybe not quite there yet. And, and there's always this question of like, how much physics are these models learning, you know, versus like, just pure like statistics. And like, I think one of the thing, at least I believe is that once you're in that sort of approximate area of the solution space, then the models have like some understanding, you know, of how to get you to like, you know, the lower energy, uh, low energy state. And so maybe you have some, some light understanding. Of physics, but maybe not quite enough, you know, to know how to like navigate the whole space. Right. Okay.Brandon [00:15:25]: So we need to give it these hints to kind of get into the right Valley and then it finds the, the minimum or something. Yeah.Gabriel [00:15:31]: One interesting explanation about our awful free works that I think it's quite insightful, of course, doesn't cover kind of the entirety of, of what awful does that is, um, they're going to borrow from, uh, Sergio Chinico for MIT. So he sees kind of awful. Then the interesting thing about awful is God. This very peculiar architecture that we have seen, you know, used, and this architecture operates on this, you know, pairwise context between amino acids. And so the idea is that probably the MSA gives you this first hint about what potential amino acids are close to each other. MSA is most multiple sequence alignment. Exactly. Yeah. Exactly. This evolutionary information. Yeah. And, you know, from this evolutionary information about potential contacts, then is almost as if the model is. of running some kind of, you know, diastro algorithm where it's sort of decoding, okay, these have to be closed. Okay. Then if these are closed and this is connected to this, then this has to be somewhat closed. And so you decode this, that becomes basically a pairwise kind of distance matrix. And then from this rough pairwise distance matrix, you decode kind of theBrandon [00:16:42]: actual potential structure. Interesting. So there's kind of two different things going on in the kind of coarse grain and then the fine grain optimizations. Interesting. Yeah. Very cool.Gabriel [00:16:53]: Yeah. You mentioned AlphaFold3. So maybe we have a good time to move on to that. So yeah, AlphaFold2 came out and it was like, I think fairly groundbreaking for this field. Everyone got very excited. A few years later, AlphaFold3 came out and maybe for some more history, like what were the advancements in AlphaFold3? And then I think maybe we'll, after that, we'll talk a bit about the sort of how it connects to Bolt. But anyway. Yeah. So after AlphaFold2 came out, you know, Jeremy and I got into the field and with many others, you know, the clear problem that, you know, was, you know, obvious after that was, okay, now we can do individual chains. Can we do interactions, interaction, different proteins, proteins with small molecules, proteins with other molecules. And so. So why are interactions important? Interactions are important because to some extent that's kind of the way that, you know, these machines, you know, these proteins have a function, you know, the function comes by the way that they interact with other proteins and other molecules. Actually, in the first place, you know, the individual machines are often, as Jeremy was mentioning, not made of a single chain, but they're made of the multiple chains. And then these multiple chains interact with other molecules to give the function to those. And on the other hand, you know, when we try to intervene of these interactions, think about like a disease, think about like a, a biosensor or many other ways we are trying to design the molecules or proteins that interact in a particular way with what we would call a target protein or target. You know, this problem after AlphaVol2, you know, became clear, kind of one of the biggest problems in the field to, to solve many groups, including kind of ours and others, you know, started making some kind of contributions to this problem of trying to model these interactions. And AlphaVol3 was, you know, was a significant advancement on the problem of modeling interactions. And one of the interesting thing that they were able to do while, you know, some of the rest of the field that really tried to try to model different interactions separately, you know, how protein interacts with small molecules, how protein interacts with other proteins, how RNA or DNA have their structure, they put everything together and, you know, train very large models with a lot of advances, including kind of changing kind of systems. Some of the key architectural choices and managed to get a single model that was able to set this new state-of-the-art performance across all of these different kind of modalities, whether that was protein, small molecules is critical to developing kind of new drugs, protein, protein, understanding, you know, interactions of, you know, proteins with RNA and DNAs and so on.Brandon [00:19:39]: Just to satisfy the AI engineers in the audience, what were some of the key architectural and data, data changes that made that possible?Gabriel [00:19:48]: Yeah, so one critical one that was not necessarily just unique to AlphaFold3, but there were actually a few other teams, including ours in the field that proposed this, was moving from, you know, modeling structure prediction as a regression problem. So where there is a single answer and you're trying to shoot for that answer to a generative modeling problem where you have a posterior distribution of possible structures and you're trying to sample this distribution. And this achieves two things. One is it starts to allow us to try to model more dynamic systems. As we said, you know, some of these structures can actually take multiple structures. And so, you know, you can now model that, you know, through kind of modeling the entire distribution. But on the second hand, from more kind of core modeling questions, when you move from a regression problem to a generative modeling problem, you are really tackling the way that you think about uncertainty in the model in a different way. So if you think about, you know, I'm undecided between different answers, what's going to happen in a regression model is that, you know, I'm going to try to make an average of those different kind of answers that I had in mind. When you have a generative model, what you're going to do is, you know, sample all these different answers and then maybe use separate models to analyze those different answers and pick out the best. So that was kind of one of the critical improvement. The other improvement is that they significantly simplified, to some extent, the architecture, especially of the final model that takes kind of those pairwise representations and turns them into an actual structure. And that now looks a lot more like a more traditional transformer than, you know, like a very specialized equivariant architecture that it was in AlphaFold3.Brandon [00:21:41]: So this is a bitter lesson, a little bit.Gabriel [00:21:45]: There is some aspect of a bitter lesson, but the interesting thing is that it's very far from, you know, being like a simple transformer. This field is one of the, I argue, very few fields in applied machine learning where we still have kind of architecture that are very specialized. And, you know, there are many people that have tried to replace these architectures with, you know, simple transformers. And, you know, there is a lot of debate in the field, but I think kind of that most of the consensus is that, you know, the performance... that we get from the specialized architecture is vastly superior than what we get through a single transformer. Another interesting thing that I think on the staying on the modeling machine learning side, which I think it's somewhat counterintuitive seeing some of the other kind of fields and applications is that scaling hasn't really worked kind of the same in this field. Now, you know, models like AlphaFold2 and AlphaFold3 are, you know, still very large models.RJ [00:29:14]: in a place, I think, where we had, you know, some experience working in, you know, with the data and working with this type of models. And I think that put us already in like a good place to, you know, to produce it quickly. And, you know, and I would even say, like, I think we could have done it quicker. The problem was like, for a while, we didn't really have the compute. And so we couldn't really train the model. And actually, we only trained the big model once. That's how much compute we had. We could only train it once. And so like, while the model was training, we were like, finding bugs left and right. A lot of them that I wrote. And like, I remember like, I was like, sort of like, you know, doing like, surgery in the middle, like stopping the run, making the fix, like relaunching. And yeah, we never actually went back to the start. We just like kept training it with like the bug fixes along the way, which was impossible to reproduce now. Yeah, yeah, no, that model is like, has gone through such a curriculum that, you know, learned some weird stuff. But yeah, somehow by miracle, it worked out.Gabriel [00:30:13]: The other funny thing is that the way that we were training, most of that model was through a cluster from the Department of Energy. But that's sort of like a shared cluster that many groups use. And so we were basically training the model for two days, and then it would go back to the queue and stay a week in the queue. Oh, yeah. And so it was pretty painful. And so we actually kind of towards the end with Evan, the CEO of Genesis, and basically, you know, I was telling him a bit about the project and, you know, kind of telling him about this frustration with the compute. And so luckily, you know, he offered to kind of help. And so we, we got the help from Genesis to, you know, finish up the model. Otherwise, it probably would have taken a couple of extra weeks.Brandon [00:30:57]: Yeah, yeah.Brandon [00:31:02]: And then, and then there's some progression from there.Gabriel [00:31:06]: Yeah, so I would say kind of that, both one, but also kind of these other kind of set of models that came around the same time, were kind of approaching were a big leap from, you know, kind of the previous kind of open source models, and, you know, kind of really kind of approaching the level of AlphaVault 3. But I would still say that, you know, even to this day, there are, you know, some... specific instances where AlphaVault 3 works better. I think one common example is antibody antigen prediction, where, you know, AlphaVault 3 still seems to have an edge in many situations. Obviously, these are somewhat different models. They are, you know, you run them, you obtain different results. So it's, it's not always the case that one model is better than the other, but kind of in aggregate, we still, especially at the time.Brandon [00:32:00]: So AlphaVault 3 is, you know, still having a bit of an edge. We should talk about this more when we talk about Boltzgen, but like, how do you know one is, one model is better than the other? Like you, so you, I make a prediction, you make a prediction, like, how do you know?Gabriel [00:32:11]: Yeah, so easily, you know, the, the great thing about kind of structural prediction and, you know, once we're going to go into the design space of designing new small molecule, new proteins, this becomes a lot more complex. But a great thing about structural prediction is that a bit like, you know, CASP was doing, basically the way that you can evaluate them is that, you know, you train... You know, you train a model on a structure that was, you know, released across the field up until a certain time. And, you know, one of the things that we didn't talk about that was really critical in all this development is the PDB, which is the Protein Data Bank. It's this common resources, basically common database where every biologist publishes their structures. And so we can, you know, train on, you know, all the structures that were put in the PDB until a certain date. And then... And then we basically look for recent structures, okay, which structures look pretty different from anything that was published before, because we really want to try to understand generalization.Brandon [00:33:13]: And then on this new structure, we evaluate all these different models. And so you just know when AlphaFold3 was trained, you know, when you're, you intentionally trained to the same date or something like that. Exactly. Right. Yeah.Gabriel [00:33:24]: And so this is kind of the way that you can somewhat easily kind of compare these models, obviously, that assumes that, you know, the training. You've always been very passionate about validation. I remember like DiffDoc, and then there was like DiffDocL and DocGen. You've thought very carefully about this in the past. Like, actually, I think DocGen is like a really funny story that I think, I don't know if you want to talk about that. It's an interesting like... Yeah, I think one of the amazing things about putting things open source is that we get a ton of feedback from the field. And, you know, sometimes we get kind of great feedback of people. Really like... But honestly, most of the times, you know, to be honest, that's also maybe the most useful feedback is, you know, people sharing about where it doesn't work. And so, you know, at the end of the day, it's critical. And this is also something, you know, across other fields of machine learning. It's always critical to set, to do progress in machine learning, set clear benchmarks. And as, you know, you start doing progress of certain benchmarks, then, you know, you need to improve the benchmarks and make them harder and harder. And this is kind of the progression of, you know, how the field operates. And so, you know, the example of DocGen was, you know, we published this initial model called DiffDoc in my first year of PhD, which was sort of like, you know, one of the early models to try to predict kind of interactions between proteins, small molecules, that we bought a year after AlphaFold2 was published. And now, on the one hand, you know, on these benchmarks that we were using at the time, DiffDoc was doing really well, kind of, you know, outperforming kind of some of the traditional physics-based methods. But on the other hand, you know, when we started, you know, kind of giving these tools to kind of many biologists, and one example was that we collaborated with was the group of Nick Polizzi at Harvard. We noticed, started noticing that there was this clear, pattern where four proteins that were very different from the ones that we're trained on, the models was, was struggling. And so, you know, that seemed clear that, you know, this is probably kind of where we should, you know, put our focus on. And so we first developed, you know, with Nick and his group, a new benchmark, and then, you know, went after and said, okay, what can we change? And kind of about the current architecture to improve this pattern and generalization. And this is the same that, you know, we're still doing today, you know, kind of, where does the model not work, you know, and then, you know, once we have that benchmark, you know, let's try to, through everything we, any ideas that we have of the problem.RJ [00:36:15]: And there's a lot of like healthy skepticism in the field, which I think, you know, is, is, is great. And I think, you know, it's very clear that there's a ton of things, the models don't really work well on, but I think one thing that's probably, you know, undeniable is just like the pace of, pace of progress, you know, and how, how much better we're getting, you know, every year. And so I think if you, you know, if you assume, you know, any constant, you know, rate of progress moving forward, I think things are going to look pretty cool at some point in the future.Gabriel [00:36:42]: ChatGPT was only three years ago. Yeah, I mean, it's wild, right?RJ [00:36:45]: Like, yeah, yeah, yeah, it's one of those things. Like, you've been doing this. Being in the field, you don't see it coming, you know? And like, I think, yeah, hopefully we'll, you know, we'll, we'll continue to have as much progress we've had the past few years.Brandon [00:36:55]: So this is maybe an aside, but I'm really curious, you get this great feedback from the, from the community, right? By being open source. My question is partly like, okay, yeah, if you open source and everyone can copy what you did, but it's also maybe balancing priorities, right? Where you, like all my customers are saying. I want this, there's all these problems with the model. Yeah, yeah. But my customers don't care, right? So like, how do you, how do you think about that? Yeah.Gabriel [00:37:26]: So I would say a couple of things. One is, you know, part of our goal with Bolts and, you know, this is also kind of established as kind of the mission of the public benefit company that we started is to democratize the access to these tools. But one of the reasons why we realized that Bolts needed to be a company, it couldn't just be an academic project is that putting a model on GitHub is definitely not enough to get, you know, chemists and biologists, you know, across, you know, both academia, biotech and pharma to use your model to, in their therapeutic programs. And so a lot of what we think about, you know, at Bolts beyond kind of the, just the models is thinking about all the layers. The layers that come on top of the models to get, you know, from, you know, those models to something that can really enable scientists in the industry. And so that goes, you know, into building kind of the right kind of workflows that take in kind of, for example, the data and try to answer kind of directly that those problems that, you know, the chemists and the biologists are asking, and then also kind of building the infrastructure. And so this to say that, you know, even with models fully open. You know, we see a ton of potential for, you know, products in the space and the critical part about a product is that even, you know, for example, with an open source model, you know, running the model is not free, you know, as we were saying, these are pretty expensive model and especially, and maybe we'll get into this, you know, these days we're seeing kind of pretty dramatic inference time scaling of these models where, you know, the more you run them, the better the results are. But there, you know, you see. You start getting into a point that compute and compute costs becomes a critical factor. And so putting a lot of work into building the right kind of infrastructure, building the optimizations and so on really allows us to provide, you know, a much better service potentially to the open source models. That to say, you know, even though, you know, with a product, we can provide a much better service. I do still think, and we will continue to put a lot of our models open source because the critical kind of role. I think of open source. Models is, you know, helping kind of the community progress on the research and, you know, from which we, we all benefit. And so, you know, we'll continue to on the one hand, you know, put some of our kind of base models open source so that the field can, can be on top of it. And, you know, as we discussed earlier, we learn a ton from, you know, the way that the field uses and builds on top of our models, but then, you know, try to build a product that gives the best experience possible to scientists. So that, you know, like a chemist or a biologist doesn't need to, you know, spin off a GPU and, you know, set up, you know, our open source model in a particular way, but can just, you know, a bit like, you know, I, even though I am a computer scientist, machine learning scientist, I don't necessarily, you know, take a open source LLM and try to kind of spin it off. But, you know, I just maybe open a GPT app or a cloud code and just use it as an amazing product. We kind of want to give the same experience. So this front world.Brandon [00:40:40]: I heard a good analogy yesterday that a surgeon doesn't want the hospital to design a scalpel, right?Brandon [00:40:48]: So just buy the scalpel.RJ [00:40:50]: You wouldn't believe like the number of people, even like in my short time, you know, between AlphaFold3 coming out and the end of the PhD, like the number of people that would like reach out just for like us to like run AlphaFold3 for them, you know, or things like that. Just because like, you know, bolts in our case, you know, just because it's like. It's like not that easy, you know, to do that, you know, if you're not a computational person. And I think like part of the goal here is also that, you know, we continue to obviously build the interface with computational folks, but that, you know, the models are also accessible to like a larger, broader audience. And then that comes from like, you know, good interfaces and stuff like that.Gabriel [00:41:27]: I think one like really interesting thing about bolts is that with the release of it, you didn't just release a model, but you created a community. Yeah. Did that community, it grew very quickly. Did that surprise you? And like, what is the evolution of that community and how is that fed into bolts?RJ [00:41:43]: If you look at its growth, it's like very much like when we release a new model, it's like, there's a big, big jump, but yeah, it's, I mean, it's been great. You know, we have a Slack community that has like thousands of people on it. And it's actually like self-sustaining now, which is like the really nice part because, you know, it's, it's almost overwhelming, I think, you know, to be able to like answer everyone's questions and help. It's really difficult, you know. The, the few people that we were, but it ended up that like, you know, people would answer each other's questions and like, sort of like, you know, help one another. And so the Slack, you know, has been like kind of, yeah, self, self-sustaining and that's been, it's been really cool to see.RJ [00:42:21]: And, you know, that's, that's for like the Slack part, but then also obviously on GitHub as well. We've had like a nice, nice community. You know, I think we also aspire to be even more active on it, you know, than we've been in the past six months, which has been like a bit challenging, you know, for us. But. Yeah, the community has been, has been really great and, you know, there's a lot of papers also that have come out with like new evolutions on top of bolts and it's surprised us to some degree because like there's a lot of models out there. And I think like, you know, sort of people converging on that was, was really cool. And, you know, I think it speaks also, I think, to the importance of like, you know, when, when you put code out, like to try to put a lot of emphasis and like making it like as easy to use as possible and something we thought a lot about when we released the code base. You know, it's far from perfect, but, you know.Brandon [00:43:07]: Do you think that that was one of the factors that caused your community to grow is just the focus on easy to use, make it accessible? I think so.RJ [00:43:14]: Yeah. And we've, we've heard it from a few people over the, over the, over the years now. And, you know, and some people still think it should be a lot nicer and they're, and they're right. And they're right. But yeah, I think it was, you know, at the time, maybe a little bit easier than, than other things.Gabriel [00:43:29]: The other thing part, I think led to, to the community and to some extent, I think, you know, like the somewhat the trust in the community. Kind of what we, what we put out is the fact that, you know, it's not really been kind of, you know, one model, but, and maybe we'll talk about it, you know, after Boltz 1, you know, there were maybe another couple of models kind of released, you know, or open source kind of soon after. We kind of continued kind of that open source journey or at least Boltz 2, where we are not only improving kind of structure prediction, but also starting to do affinity predictions, understanding kind of the strength of the interactions between these different models, which is this critical component. critical property that you often want to optimize in discovery programs. And then, you know, more recently also kind of protein design model. And so we've sort of been building this suite of, of models that come together, interact with one another, where, you know, kind of, there is almost an expectation that, you know, we, we take very at heart of, you know, always having kind of, you know, across kind of the entire suite of different tasks, the best or across the best. model out there so that it's sort of like our open source tool can be kind of the go-to model for everybody in the, in the industry. I really want to talk about Boltz 2, but before that, one last question in this direction, was there anything about the community which surprised you? Were there any, like, someone was doing something and you're like, why would you do that? That's crazy. Or that's actually genius. And I never would have thought about that.RJ [00:45:01]: I mean, we've had many contributions. I think like some of the. Interesting ones, like, I mean, we had, you know, this one individual who like wrote like a complex GPU kernel, you know, for part of the architecture on a piece of, the funny thing is like that piece of the architecture had been there since AlphaFold 2, and I don't know why it took Boltz for this, you know, for this person to, you know, to decide to do it, but that was like a really great contribution. We've had a bunch of others, like, you know, people figuring out like ways to, you know, hack the model to do something. They click peptides, like, you know, there's, I don't know if there's any other interesting ones come to mind.Gabriel [00:45:41]: One cool one, and this was, you know, something that initially was proposed as, you know, as a message in the Slack channel by Tim O'Donnell was basically, he was, you know, there are some cases, especially, for example, we discussed, you know, antibody-antigen interactions where the models don't necessarily kind of get the right answer. What he noticed is that, you know, the models were somewhat stuck into predicting kind of the antibodies. And so he basically ran the experiments in this model, you can condition, basically, you can give hints. And so he basically gave, you know, random hints to the model, basically, okay, you should bind to this residue, you should bind to the first residue, or you should bind to the 11th residue, or you should bind to the 21st residue, you know, basically every 10 residues scanning the entire antigen.Brandon [00:46:33]: Residues are the...Gabriel [00:46:34]: The amino acids. The amino acids, yeah. So the first amino acids. The 11 amino acids, and so on. So it's sort of like doing a scan, and then, you know, conditioning the model to predict all of them, and then looking at the confidence of the model in each of those cases and taking the top. And so it's sort of like a very somewhat crude way of doing kind of inference time search. But surprisingly, you know, for antibody-antigen prediction, it actually kind of helped quite a bit. And so there's some, you know, interesting ideas that, you know, obviously, as kind of developing the model, you say kind of, you know, wow. This is why would the model, you know, be so dumb. But, you know, it's very interesting. And that, you know, leads you to also kind of, you know, start thinking about, okay, how do I, can I do this, you know, not with this brute force, but, you know, in a smarter way.RJ [00:47:22]: And so we've also done a lot of work on that direction. And that speaks to, like, the, you know, the power of scoring. We're seeing that a lot. I'm sure we'll talk about it more when we talk about BullsGen. But, you know, our ability to, like, take a structure and determine that that structure is, like... Good. You know, like, somewhat accurate. Whether that's a single chain or, like, an interaction is a really powerful way of improving, you know, the models. Like, sort of like, you know, if you can sample a ton and you assume that, like, you know, if you sample enough, you're likely to have, like, you know, the good structure. Then it really just becomes a ranking problem. And, you know, now we're, you know, part of the inference time scaling that Gabby was talking about is very much that. It's like, you know, the more we sample, the more we, like, you know, the ranking model. The ranking model ends up finding something it really likes. And so I think our ability to get better at ranking, I think, is also what's going to enable sort of the next, you know, next big, big breakthroughs. Interesting.Brandon [00:48:17]: But I guess there's a, my understanding, there's a diffusion model and you generate some stuff and then you, I guess, it's just what you said, right? Then you rank it using a score and then you finally... And so, like, can you talk about those different parts? Yeah.Gabriel [00:48:34]: So, first of all, like, the... One of the critical kind of, you know, beliefs that we had, you know, also when we started working on Boltz 1 was sort of like the structure prediction models are somewhat, you know, our field version of some foundation models, you know, learning about kind of how proteins and other molecules interact. And then we can leverage that learning to do all sorts of other things. And so with Boltz 2, we leverage that learning to do affinity predictions. So understanding kind of, you know, if I give you this protein, this molecule. How tightly is that interaction? For Boltz 1, what we did was taking kind of that kind of foundation models and then fine tune it to predict kind of entire new proteins. And so the way basically that that works is sort of like instead of for the protein that you're designing, instead of fitting in an actual sequence, you fit in a set of blank tokens. And you train the models to, you know, predict both the structure of kind of that protein. The structure also, what the different amino acids of that proteins are. And so basically the way that Boltz 1 operates is that you feed a target protein that you may want to kind of bind to or, you know, another DNA, RNA. And then you feed the high level kind of design specification of, you know, what you want your new protein to be. For example, it could be like an antibody with a particular framework. It could be a peptide. It could be many other things. And that's with natural language or? And that's, you know, basically, you know, prompting. And we have kind of this sort of like spec that you specify. And, you know, you feed kind of this spec to the model. And then the model translates this into, you know, a set of, you know, tokens, a set of conditioning to the model, a set of, you know, blank tokens. And then, you know, basically the codes as part of the diffusion models, the codes. It's a new structure and a new sequence for your protein. And, you know, basically, then we take that. And as Jeremy was saying, we are trying to score it and, you know, how good of a binder it is to that original target.Brandon [00:50:51]: You're using basically Boltz to predict the folding and the affinity to that molecule. So and then that kind of gives you a score? Exactly.Gabriel [00:51:03]: So you use this model to predict the folding. And then you do two things. One is that you predict the structure and with something like Boltz2, and then you basically compare that structure with what the model predicted, what Boltz2 predicted. And this is sort of like in the field called consistency. It's basically you want to make sure that, you know, the structure that you're predicting is actually what you're trying to design. And that gives you a much better confidence that, you know, that's a good design. And so that's the first filtering. And the second filtering that we did as part of kind of the Boltz2 pipeline that was released is that we look at the confidence that the model has in the structure. Now, unfortunately, kind of going to your question of, you know, predicting affinity, unfortunately, confidence is not a very good predictor of affinity. And so one of the things that we've actually done a ton of progress, you know, since we released Boltz2.Brandon [00:52:03]: And kind of we have some new results that we are going to kind of announce soon is kind of, you know, the ability to get much better hit rates when instead of, you know, trying to rely on confidence of the model, we are actually directly trying to predict the affinity of that interaction. Okay. Just backing up a minute. So your diffusion model actually predicts not only the protein sequence, but also the folding of it. Exactly.Gabriel [00:52:32]: And actually, you can... One of the big different things that we did compared to other models in the space, and, you know, there were some papers that had already kind of done this before, but we really scaled it up was, you know, basically somewhat merging kind of the structure prediction and the sequence prediction into almost the same task. And so the way that Boltz2 works is that you are basically the only thing that you're doing is predicting the structure. So the only sort of... Supervision is we give you a supervision on the structure, but because the structure is atomic and, you know, the different amino acids have a different atomic composition, basically from the way that you place the atoms, we also understand not only kind of the structure that you wanted, but also the identity of the amino acid that, you know, the models believed was there. And so we've basically, instead of, you know, having these two supervision signals, you know, one discrete, one continuous. That somewhat, you know, don't interact well together. We sort of like build kind of like an encoding of, you know, sequences in structures that allows us to basically use exactly the same supervision signal that we were using to Boltz2 that, you know, you know, largely similar to what AlphaVol3 proposed, which is very scalable. And we can use that to design new proteins. Oh, interesting.RJ [00:53:58]: Maybe a quick shout out to Hannes Stark on our team who like did all this work. Yeah.Gabriel [00:54:04]: Yeah, that was a really cool idea. I mean, like looking at the paper and there's this is like encoding or you just add a bunch of, I guess, kind of atoms, which can be anything, and then they get sort of rearranged and then basically plopped on top of each other so that and then that encodes what the amino acid is. And there's sort of like a unique way of doing this. It was that was like such a really such a cool, fun idea.RJ [00:54:29]: I think that idea was had existed before. Yeah, there were a couple of papers.Gabriel [00:54:33]: Yeah, I had proposed this and and Hannes really took it to the large scale.Brandon [00:54:39]: In the paper, a lot of the paper for Boltz2Gen is dedicated to actually the validation of the model. In my opinion, all the people we basically talk about feel that this sort of like in the wet lab or whatever the appropriate, you know, sort of like in real world validation is the whole problem or not the whole problem, but a big giant part of the problem. So can you talk a little bit about the highlights? From there, that really because to me, the results are impressive, both from the perspective of the, you know, the model and also just the effort that went into the validation by a large team.Gabriel [00:55:18]: First of all, I think I should start saying is that both when we were at MIT and Thomas Yacolas and Regina Barzillai's lab, as well as at Boltz, you know, we are not a we're not a biolab and, you know, we are not a therapeutic company. And so to some extent, you know, we were first forced to, you know, look outside of, you know, our group, our team to do the experimental validation. One of the things that really, Hannes, in the team pioneer was the idea, OK, can we go not only to, you know, maybe a specific group and, you know, trying to find a specific system and, you know, maybe overfit a bit to that system and trying to validate. But how can we test this model? So. Across a very wide variety of different settings so that, you know, anyone in the field and, you know, printing design is, you know, such a kind of wide task with all sorts of different applications from therapeutic to, you know, biosensors and many others that, you know, so can we get a validation that is kind of goes across many different tasks? And so he basically put together, you know, I think it was something like, you know, 25 different. You know, academic and industry labs that committed to, you know, testing some of the designs from the model and some of this testing is still ongoing and, you know, giving results kind of back to us in exchange for, you know, hopefully getting some, you know, new great sequences for their task. And he was able to, you know, coordinate this, you know, very wide set of, you know, scientists and already in the paper, I think we. Shared results from, I think, eight to 10 different labs kind of showing results from, you know, designing peptides, designing to target, you know, ordered proteins, peptides targeting disordered proteins, which are results, you know, of designing proteins that bind to small molecules, which are results of, you know, designing nanobodies and across a wide variety of different targets. And so that's sort of like. That gave to the paper a lot of, you know, validation to the model, a lot of validation that was kind of wide.Brandon [00:57:39]: And so those would be therapeutics for those animals or are they relevant to humans as well? They're relevant to humans as well.Gabriel [00:57:45]: Obviously, you need to do some work into, quote unquote, humanizing them, making sure that, you know, they have the right characteristics to so they're not toxic to humans and so on.RJ [00:57:57]: There are some approved medicine in the market that are nanobodies. There's a general. General pattern, I think, in like in trying to design things that are smaller, you know, like it's easier to manufacture at the same time, like that comes with like potentially other challenges, like maybe a little bit less selectivity than like if you have something that has like more hands, you know, but the yeah, there's this big desire to, you know, try to design many proteins, nanobodies, small peptides, you know, that just are just great drug modalities.Brandon [00:58:27]: Okay. I think we were left off. We were talking about validation. Validation in the lab. And I was very excited about seeing like all the diverse validations that you've done. Can you go into some more detail about them? Yeah. Specific ones. Yeah.RJ [00:58:43]: The nanobody one. I think we did. What was it? 15 targets. Is that correct? 14. 14 targets. Testing. So we typically the way this works is like we make a lot of designs. All right. On the order of like tens of thousands. And then we like rank them and we pick like the top. And in this case, and was 15 right for each target and then we like measure sort of like the success rates, both like how many targets we were able to get a binder for and then also like more generally, like out of all of the binders that we designed, how many actually proved to be good binders. Some of the other ones I think involved like, yeah, like we had a cool one where there was a small molecule or design a protein that binds to it. That has a lot of like interesting applications, you know, for example. Like Gabri mentioned, like biosensing and things like that, which is pretty cool. We had a disordered protein, I think you mentioned also. And yeah, I think some of those were some of the highlights. Yeah.Gabriel [00:59:44]: So I would say that the way that we structure kind of some of those validations was on the one end, we have validations across a whole set of different problems that, you know, the biologists that we were working with came to us with. So we were trying to. For example, in some of the experiments, design peptides that would target the RACC, which is a target that is involved in metabolism. And we had, you know, a number of other applications where we were trying to design, you know, peptides or other modalities against some other therapeutic relevant targets. We designed some proteins to bind small molecules. And then some of the other testing that we did was really trying to get like a more broader sense. So how does the model work, especially when tested, you know, on somewhat generalization? So one of the things that, you know, we found with the field was that a lot of the validation, especially outside of the validation that was on specific problems, was done on targets that have a lot of, you know, known interactions in the training data. And so it's always a bit hard to understand, you know, how much are these models really just regurgitating kind of what they've seen or trying to imitate. What they've seen in the training data versus, you know, really be able to design new proteins. And so one of the experiments that we did was to take nine targets from the PDB, filtering to things where there is no known interaction in the PDB. So basically the model has never seen kind of this particular protein bound or a similar protein bound to another protein. So there is no way that. The model from its training set can sort of like say, okay, I'm just going to kind of tweak something and just imitate this particular kind of interaction. And so we took those nine proteins. We worked with adaptive CRO and basically tested, you know, 15 mini proteins and 15 nanobodies against each one of them. And the very cool thing that we saw was that on two thirds of those targets, we were able to, from this 15 design, get nanomolar binders, nanomolar, roughly speaking, just a measure of, you know, how strongly kind of the interaction is, roughly speaking, kind of like a nanomolar binder is approximately the kind of binding strength or binding that you need for a therapeutic. Yeah. So maybe switching directions a bit. Bolt's lab was just announced this week or was it last week? Yeah. This is like your. First, I guess, product, if that's if you want to call it that. Can you talk about what Bolt's lab is and yeah, you know, what you hope that people take away from this? Yeah.RJ [01:02:44]: You know, as we mentioned, like I think at the very beginning is the goal with the product has been to, you know, address what the models don't on their own. And there's largely sort of two categories there. I'll split it in three. The first one. It's one thing to predict, you know, a single interaction, for example, like a single structure. It's another to like, you know, very effectively search a space, a design space to produce something of value. What we found, like sort of building on this product is that there's a lot of steps involved, you know, in that there's certainly need to like, you know, accompany the user through, you know, one of those steps, for example, is like, you know, the creation of the target itself. You know, how do we make sure that the model has like a good enough understanding of the target? So we can like design something and there's all sorts of tricks, you know, that you can do to improve like a particular, you know, structure prediction. And so that's sort of like, you know, the first stage. And then there's like this stage of like, you know, designing and searching the space efficiently. You know, for something like BullsGen, for example, like you, you know, you design many things and then you rank them, for example, for small molecule process, a little bit more complicated. We actually need to also make sure that the molecules are synthesizable. And so the way we do that is that, you know, we have a generative model that learns. To use like appropriate building blocks such that, you know, it can design within a space that we know is like synthesizable. And so there's like, you know, this whole pipeline really of different models involved in being able to design a molecule. And so that's been sort of like the first thing we call them agents. We have a protein agent and we have a small molecule design agents. And that's really like at the core of like what powers, you know, the BullsLab platform.Brandon [01:04:22]: So these agents, are they like a language model wrapper or they're just like your models and you're just calling them agents? A lot. Yeah. Because they, they, they sort of perform a function on behalf of.RJ [01:04:33]: They're more of like a, you know, a recipe, if you wish. And I think we use that term sort of because of, you know, sort of the complex pipelining and automation, you know, that goes into like all this plumbing. So that's the first part of the product. The second part is the infrastructure. You know, we need to be able to do this at very large scale for any one, you know, group that's doing a design campaign. Let's say you're designing, you know, I'd say a hundred thousand possible candidates. Right. To find the good one that is, you know, a very large amount of compute, you know, for small molecules, it's on the order of like a few seconds per designs for proteins can be a bit longer. And so, you know, ideally you want to do that in parallel, otherwise it's going to take you weeks. And so, you know, we've put a lot of effort into like, you know, our ability to have a GPU fleet that allows any one user, you know, to be able to do this kind of like large parallel search.Brandon [01:05:23]: So you're amortizing the cost over your users. Exactly. Exactly.RJ [01:05:27]: And, you know, to some degree, like it's whether you. Use 10,000 GPUs for like, you know, a minute is the same cost as using, you know, one GPUs for God knows how long. Right. So you might as well try to parallelize if you can. So, you know, a lot of work has gone, has gone into that, making it very robust, you know, so that we can have like a lot of people on the platform doing that at the same time. And the third one is, is the interface and the interface comes in, in two shapes. One is in form of an API and that's, you know, really suited for companies that want to integrate, you know, these pipelines, these agents.RJ [01:06:01]: So we're already partnering with, you know, a few distributors, you know, that are gonna integrate our API. And then the second part is the user interface. And, you know, we, we've put a lot of thoughts also into that. And this is when I, I mentioned earlier, you know, this idea of like broadening the audience. That's kind of what the, the user interface is about. And we've built a lot of interesting features in it, you know, for example, for collaboration, you know, when you have like potentially multiple medicinal chemists or. We're going through the results and trying to pick out, okay, like what are the molecules that we're going to go and test in the lab? It's powerful for them to be able to, you know, for example, each provide their own ranking and then do consensus building. And so there's a lot of features around launching these large jobs, but also around like collaborating on analyzing the results that we try to solve, you know, with that part of the platform. So Bolt's lab is sort of a combination of these three objectives into like one, you know, sort of cohesive platform. Who is this accessible to? Everyone. You do need to request access today. We're still like, you know, sort of ramping up the usage, but anyone can request access. If you are an academic in particular, we, you know, we provide a fair amount of free credit so you can play with the platform. If you are a startup or biotech, you may also, you know, reach out and we'll typically like actually hop on a call just to like understand what you're trying to do and also provide a lot of free credit to get started. And of course, also with larger companies, we can deploy this platform in a more like secure environment. And so that's like more like customizing. You know, deals that we make, you know, with the partners, you know, and that's sort of the ethos of Bolt. I think this idea of like servicing everyone and not necessarily like going after just, you know, the really large enterprises. And that starts from the open source, but it's also, you know, a key design principle of the product itself.Gabriel [01:07:48]: One thing I was thinking about with regards to infrastructure, like in the LLM space, you know, the cost of a token has gone down by I think a factor of a thousand or so over the last three years, right? Yeah. And is it possible that like essentially you can exploit economies of scale and infrastructure that you can make it cheaper to run these things yourself than for any person to roll their own system? A hundred percent. Yeah.RJ [01:08:08]: I mean, we're already there, you know, like running Bolts on our platform, especially on a large screen is like considerably cheaper than it would probably take anyone to put the open source model out there and run it. And on top of the infrastructure, like one of the things that we've been working on is accelerating the models. So, you know. Our small molecule screening pipeline is 10x faster on Bolts Lab than it is in the open source, you know, and that's also part of like, you know, building a product, you know, of something that scales really well. And we really wanted to get to a point where like, you know, we could keep prices very low in a way that it would be a no-brainer, you know, to use Bolts through our platform.Gabriel [01:08:52]: How do you think about validation of your like agentic systems? Because, you know, as you were saying earlier. Like we're AlphaFold style models are really good at, let's say, monomeric, you know, proteins where you have, you know, co-evolution data. But now suddenly the whole point of this is to design something which doesn't have, you know, co-evolution data, something which is really novel. So now you're basically leaving the domain that you thought was, you know, that you know you are good at. So like, how do you validate that?RJ [01:09:22]: Yeah, I like every complete, but there's obviously, you know, a ton of computational metrics. That we rely on, but those are only take you so far. You really got to go to the lab, you know, and test, you know, okay, with this method A and this method B, how much better are we? You know, how much better is my, my hit rate? How stronger are my binders? Also, it's not just about hit rate. It's also about how good the binders are. And there's really like no way, nowhere around that. I think we're, you know, we've really ramped up the amount of experimental validation that we do so that we like really track progress, you know, as scientifically sound, you know. Yeah. As, as possible out of this, I think.Gabriel [01:10:00]: Yeah, no, I think, you know, one thing that is unique about us and maybe companies like us is that because we're not working on like maybe a couple of therapeutic pipelines where, you know, our validation would be focused on those. We, when we do an experimental validation, we try to test it across tens of targets. And so that on the one end, we can get a much more statistically significant result and, and really allows us to make progress. From the methodological side without being, you know, steered by, you know, overfitting on any one particular system. And of course we choose, you know, w

This episode is available in audio format on the Let's Talk Loyalty podcast and in video format on www.Loyalty.TV.In this episode we are delighted to interview Ben Stirling, an experienced commercial executive with a track record of scaling loyalty platforms, transforming sales organisation and delivering GTM strategies that drive acquisition and ARR growth. He has led commercial transformation at Expedia, Tenerity and Capillary, launched new solutions, expanded into international markets and delivered results across multiple sectors.He is currently a fractional CRO at TenX Strategy and supports PE-backed and enterprise firms in building predictable revenue systems and exit-ready growth. His impact includes scaling Tenerity's loyalty marketplace solution to acquisition in two years, providing loyalty solution to Santander, C&A, British Gas, TD Bank and Frontier, and growing commercial channels at Expedia that delivered $200M+ in new revenue.In this episode, Ben shares his proven insights on how to sell loyalty internally, from aligning feature sets to user needs, to securing C-suite backing with ROI models, and ultimately winning board-level buy-in by linking loyalty to long-term enterprise value. We'll also be learning about his favourite books and highlights and key learnings from the programmes he has worked on.Hosted by Charlie HillsShow Notes :1) Ben Stirling,2) TenX Strategy3) TenX Strategy - Budget Sign Off PDF4) Hooked- Book Recommendation5) The Road Less Stupid - Book Recommendation

For episode 676 of the BlockHash Podcast, host Brandon Zemp is joined by Lux Thiagarajah, CCO of OpenPayd. Lux Thiagarajah has over 17 years experience working for some of the largest and most innovative organizations in finance, including JP Morgan, HSBC, BCB and FalconX. He started his career as an FX trader at JP Morgan, before moving to the buy side to run a macro trading desk. More recently he has moved into senior roles in payments, becoming the CRO of BCB and now Chief Commercial Officer at OpenPayd. Lux joined OpenPayd with a track record for taking businesses to their next stage of development, and is responsible for driving revenue and growth from both new and existing clients, as well as and identifying strategic partnerships that can further OpenPayd's ambitions.

Can you scale customer support without burning out agents or frustrating customers?Ping Wu shares how Cresta combines AI and human intelligence into a single system that scales sustainably for companies like United Airlines and Porsche.In this episode, Ping also breaks down the three constraints that shape automation in the real world: conversation complexity, infrastructure debt, and customer demographics.Guest: Ping Wu, CEO of CrestaConnect with Ping WuX: https://x.com/ping_wuLinkedIn: https://www.linkedin.com/in/pingwu/Connect with JoubinX: https://x.com/JoubinmirLinkedIn: https://www.linkedin.com/in/joubin-mirzadegan-66186854/Email: grit@kleinerperkins.comFollow on LinkedIn:https://www.linkedin.com/company/kpgritFollow on X:https://x.com/KPGrit​Learn more about Kleiner Perkins: https://www.kleinerperkins.com/ 

We're really at a crisis point for a lot of marketers. It's not just that ads keep getting more expensive. It's that it just gets harder and harder to get and keep prospects' attention. And with everything being engineered and optimized by AI and CRO, stuff ends up looking more and more the same. And that only works against you. You know you need to stand out–but how? Well, the best way to get and keep attention is, and always has been, a story. But how long is a story? I mean, a Hero's Journey story can take hours. And even the type of compact tales I introduced in my book The Persuasion Story Code can take two to three minutes. That's not very long, but at a time of shrinking attention spans, it's still too long. Now, you can try using outrageous hooks. But in addition to shrinking attention spans, you're also fighting against rising levels of skepticism and outright distrust. If you say something that gets attention but just isn't believable, you're still sunk. So, what would be ideal to solve this problem? It would be a persuasion story you could tell in 15 or 20 words. Impossible, you say? That's what I thought until I really started working on it. One of my clients, Ari Nirsissian, helped me quite a lot in the development of my thinking and writing of these new kind of attention magnets, the one-sentence microstory. It really is a story. It really is persuasive. And it really is short! Just the right size for today's attention spans. Today I'm going to show you, step by step, how I developed three of them… and how I combined them into one electric three-sentence paragraph, which takes less than a minute to read out loud. Resources: To find out more about my book The Persuasion Story Code, check out this link to the Amazon page: https://www.amazon.com/dp/B0CFD2KXNQ And to find out more about my coaching for experienced copywriters and business owners, go to: https://garfinkelcoaching.com Download.

In this two-part series, James Hatfield, CRO for LiveSwitch, gives you some ways that AI is currently being used and strategies you can implement now and prepare for in the future. Free P&L Statement and Balance Sheet https://tinyurl.com/2rjd6wxu Ruth King Facebook - https://www.facebook.com/ruthking1650 LinkedIn - https://www.linkedin.com/in/ruthking1/   Podcast Produced by Nick Uttam https://www.linkedin.com/in/nick-uttam-4b33a1147