Podcasts about CRO

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

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:

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

Trideset i osmi ministar u Vladi Republike Hrvatske, oporba ne očekuje promjene na bolje. Stiže novih, gotovo milijardu i pol australskih dolara iz Europske unije za Hrvatsku. Izašla nova anketa o popularnosti stranaka i političara CRO demoskop. Hrvatska među najgorima u Europi po korupciji. Svako četvrto dijete u Hrvatskoj izložena neprimjerenom sadržaju, pa se i u Hrvatskoj razmišlja o ukidanju društvenih mreža za djecu. Kad vas na kvizu pitaju koji nogometni klub u Hrvatskoj ima operetu o sebi odgovorite Hajduk

Junto a Rafa Carbayeda repasamos todas las fases de la creación de campaña de Google Ads para Ecommerce, desde ver como clasificaremos los productos a montar las campañas e incluso pasando por la optimización. 4:06 Presentación de Rafa Carbayeda8:45 Estrategia Inicial para Clientes de E-commerce12:53 E-commerce Pequeños vs. Grandes15:34 Separación de Productos en Campañas24:50 Uso de Etiquetas Personalizadas26:37 Importancia de Rellenar Atributos del Feed31:51 Optimización de Imágenes en el Feed44:45 Comparativa entre PMAX y Shopping50:59 Estrategias de Segmentación en Campañas57:45 Introducción a los CSS1:06:33 Futuro de la Publicidad con ChatGPT1:08:24 Recursos y Errores Comunes en Google Ads1:13:17 Preguntas Rápidas y Consejos FinalesURL Episodio: https://ppccast.com/podcast/275-estructuracion-de-campanas-de-ecommerce-en-google-ads-con-rafa-carbayedaPPCFest: ppcfest.comPPCCast+: ppccast.com/plusPatrocinadoresRaiola Networks: ppccast.com/raiolaData Feed Watch: ppccast.com/datafeedConvertiam: ppccast.com/convertiam 

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.

Emily de Búrca, Croí na Gaeilge, tells Oliver about organising a Grá-lentines event in London.

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

Today's minisode features Chris Degnan, former CRO of Snowflake. In this clip, Chris explains what it really takes to grow with a company as it scales, and why earning your role does not stop once the title changes. He shares how treating every quarter like a 90-day contract, staying open to feedback, and knowing when to shift from grinding in the business to building leaders helped him navigate board pressure and scale through hypergrowth.If you're a sales leader navigating rapid growth, or questioning how to evolve without losing your edge, this is a perspective worth hearing.Chris Degnan is the former Chief Revenue Officer of Snowflake, where he helped build the company from zero to more than $1B in consumption revenue. He is known for his expertise in scaling go-to-market organizations through early-stage ambiguity, enterprise expansion, and consumption-based selling models.Connect with Chris:LinkedInFrom Zero to Billions: How Snowflake Scaled its Go-to-Market Organization by Denise Persson & Chris DegnanResources mentioned:Multiple Myeloma Research Foundation 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

Luigi Mallardo joined Woffu as an early angel investor and later became CRO, helping founder Miguel Fresneda shape a practical SaaS growth path. Based in Barcelona, Spain, Woffu has built a modern cloud-based time and attendance platform for SMEs and mid-market companies, replacing legacy tools and spreadsheets with a focused, mobile-first workforce solution. Starting from just €2K MRR, Luigi led growth first through inbound, then outbound, and partner channels, increasing average revenue per account five to seven times. By 2025, the company reached nearly €500K in monthly recurring revenue, or about €6M ARR, with more than 50 employees and profitable, efficient growth across Spain. Woffu sold to Visma in 2022 following a multi-year, proactive exit strategy, with a total reported value of €20–30M including the 3-year earnout. Luigi shares how early focus, diversified revenue, and optionality shaped every decision. His biggest lesson: clarity about your endgame determines your strategy early on, including your growth model and many other important decisions. Key Takeaways Strategic Focus - Choosing one clear use case and market unlocked faster growth than chasing horizontal HR suite ambitions across Europe. Optionality First - Designing for multiple future paths gave founders leverage rather than forcing a sale based solely on valuation. Revenue - Layers Inbound, outbound, and partners created resilience while steadily raising average contract value and predictability. Exit Readiness - Warming buyers years early turned selling into a strategic process rather than a rushed financial event. Customer Success - Investing deeply in retention created low churn and made Woffu more attractive to long-term acquirers. Builder Mindset - Great CROs zoom in and out, connecting go-to-market execution with strategy, culture, and long-term outcomes. Quote from Luigi Mallardo, Chief Revenue Officer at Woffu "We chose our focus of ICP and focus of use case, to reduce the space of market optionality to get more business optionality. You see what I mean?  "The advice I give most often is to focus, which doesn't mean to close off the option of having more verticals forever, but you need 75% or 80 % of your pipeline on where you are already monetizing and building traction. And then you leave that 20 % of pipeline to do experimentations in a new vertical. "It's one of the historical challenges, especially with young founders: the feeling of losing opportunities if they decide and don't do everything. But you are losing opportunities if you go too wide and you don't focus. Just be patient, postpone, and focus on what works." Links Luigi Mallardo on LinkedIn Woffu on LinkedIn Woffu website Podcast Sponsor – Lighter Capital This podcast is sponsored by Lighter Capital. In the last 15 years, Lighter Capital has helped over 600 software and SaaS founders secure simple, non-dilutive financing to grow a little faster—without giving up any precious equity or board seats to investors.  Simple debt funding from Lighter Capital can range from $50K to $10 million, with straightforward terms, no personal guarantees or covenants, and up to a 4-year payback period. Go to LighterCapital.com to apply and get a quick pre-qualification. Then talk with their experienced team to create a practical funding plan to achieve your goals.  The Practical Founders Podcast Tune into the Practical Founders Podcast for weekly in-depth interviews with founders who have built valuable software companies without big funding. Subscribe to the Practical Founders Podcast using your favorite podcast app or view on our YouTube channel. Get the weekly Practical Founders newsletter and podcast updates at practicalfounders.com. Practical Founders CEO Peer Groups Be part of a committed and confidential group of practical founders creating valuable software companies without big VC funding.  A Practical Founders Peer Group is a committed and confidential group of founders/CEOs who want to help you succeed on your terms. Each Practical Founders Peer Group is personally curated and moderated by Greg Head.

Subscribe to DTC Newsletter - https://dtcnews.link/signupJordan Gordon, VP of Retention and CRO at Pilothouse returns to the pod to break down a concept every retention marketer thinks they understand but rarely applies: Net Present Value (NPV). This isn't finance class — this is a tactical breakdown on how to treat your email list like an appreciating asset.For DTC retention leads and founders thinking beyond one-off campaigns.Why NPV thinking changes your entire email strategyHow to measure email value beyond open/click/purchaseWays to increase the long-term worth of every subscriberWhere CRO fits in to boost lifecycle valueEmail as a compounding channel: what most brands missWho this is for: Retention marketers, growth leads, lifecycle teamsWhat to steal:A framework to value your email list like a P&LHow to spot (and increase) high NPV customersWhy short-term ROI thinking stunts your retentionTimestamps00:00 Building engagement-first email habits02:10 Why email and CRO belong together at the bottom of the funnel04:05 How flat revenue businesses can increase valuation06:05 Using content emails to grow list size and engagement08:20 Pairing engagement email with high-converting site funnels10:15 Why a larger, more engaged list increases business value12:00 Finding the right engagement content for any brand14:05 Authentic vs produced content in engagement emails16:00 Email grows the audience, the website converts itHashtags#ecommerce #emailmarketing #retentionmarketing #netpresentvalue #dtc #cro #emailstrategy #customerlifetimevalue #digitalmarketing #shopify #ownedmedia #foundermarketing #growthstrategy #bottomfunnel #marketingstrategy Subscribe to DTC Newsletter - https://dtcnews.link/signupAdvertise on DTC - https://dtcnews.link/advertiseWork with Pilothouse - https://www.pilothouse.co/?utm_source=AKNF583Follow us on Instagram & Twitter - @dtcnewsletterWatch this interview on YouTube - https://dtcnews.link/video

Benefits have become the make-or-break factor in how people choose employers, stay engaged, and perform at their best. We sit down with Neil Ryland, CRO at Benifex, to unpack the Big Benefits Report 2025–2026 and translate its findings into practical plays HR teams can run now. From measuring ROI to closing the “value void,” this conversation maps the shift from perks to performance.We dig into why benefits surged in importance post-pandemic and how economic pressure, caregiving demands, and strained health systems put real support front and center. Neil explains how organizations are moving beyond compliance to strategy—using technology to simplify enrollment, improve access, and generate the data leaders need. When adoption is high and the experience is intuitive, HR can correlate benefits with engagement, sick days, claims, and attrition, proving impact and guiding smarter investments. We also cover sector contrasts—why tech, energy, and finance often see outsized gains—and how total reward visibility helps counter external offers without knee-jerk pay hikes.Annual enrollment alone no longer cuts it. We explore always-on education keyed to life events, personalization without complexity, and mobile-first delivery for deskless workforces. Real examples—from life-moment campaigns to holiday trading and healthcare clarity—show how the right message at the right time boosts utilization and retention. Neil closes with two priorities for 2026: make benefits a living expression of your EVP and bring benefits data into strategic workforce decisions. If you're ready to turn benefits into culture you can feel and outcomes you can measure, this one's for you.Enjoyed the conversation? Follow and subscribe, share with your HR team, and leave a quick review to help others discover the show.Support the showFeature Your Brand on the HRchat PodcastThe HRchat show has had 100,000s of downloads and is frequently listed as one of the most popular global podcasts for HR pros, Talent execs and leaders. It is ranked in the top ten in the world based on traffic, social media followers, domain authority & freshness. The podcast is also ranked as the Best Canadian HR Podcast by FeedSpot and one of the top 10% most popular shows by Listen Score. Want to share the story of how your business is helping to shape the world of work? We offer sponsored episodes, audio adverts, email campaigns, and a host of other options. Check out packages here. Follow us on LinkedIn Subscribe to our newsletter Check out our in-person events

Today on the show, we have Charles Rosenblatt, CEO of Butter Payments, providing ML AI-driven payment recovery for subscription merchants and recurring payments. Previously, Charles was CSO of Payoneer, CRO at Velo Payments, and ran treasury at D.E. Shaw during the late 90s—where managing a financial crisis taught him that outside factors can derail even the best strategies and smartest teams. In this episode, we uncover why the 14-day dunning period is an arbitrary standard that shouldn't exist—and how decoupling dunning strategy from product access unlocks smarter business decisions. Charles shares how Butter analyzes 128 variables across billions of transactions to predict payment recovery within 10 seconds, allowing companies to shut off high-cost AI users immediately when recovery is unlikely, while keeping loyal customers active when payments will clear. We explore why involuntary churn creates dangerous false signals: 30% of customers leaving might actually want to stay but have expired cards or temporary payment issues. This can lead product teams to catastrophic decisions—like Coca-Cola changing their formula when the real problem was payment infrastructure, not product-market fit. The lesson: understand what's within your control versus what's not before making strategic pivots. We also discuss how Capital One shifted their best people from acquisition to retention after realizing they were churning the equivalent of the 7th largest credit card company every year—because spending $20 to save a $500 NPV customer beats spending $300 to acquire a new one who might churn anyway. Finally, we dig into payment recovery ethics and strategy: why Butter refuses "forced payments" that drive customer accounts negative, how different card types (Amex vs. debit vs. prepaid) require completely different retry logic, and why competitors who inflate recovery promises by 100% damage trust across the industry.As always, I'd love to hear from you. You can email me directly at andrew@churn.fm, and don't forget to follow us on X.Churn FM is sponsored by Vitally, the all-in-one Customer Success Platform.

SEO expert Favour Obasi-ike, MBA, MS delivers an in-depth comparison of Shopify SEO and Squarespace SEO CMS platforms, focusing on their SEO and CRO capabilities and website development features. This discussion covers critical technical insights about theme management, URL structure optimization, metadata configuration, and platform-specific best practices.Favour shares actionable strategies for improving website visibility, including the importance of regular theme updates, proper sitemap configuration, and effective use of SEO metadata. The session also touches on comparisons with WordPress, Wix, and other CMS platforms, providing business owners with practical guidance for choosing and optimizing their e-commerce and content-driven websites in 2026.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 LinksEpisode Key Learning Topics1. Shopify Platform Deep DiveShopify as a closed-source e-commerce CMS platformTheme Liquid customization and custom code implementationImportance of regular theme updates for algorithm visibilityPre-installed sitemap functionality and automated SEO featuresApp ecosystem vs WordPress pluginsMulti-currency and multi-language capabilitiesSchema.org integration for product pages2. Squarespace Platform OverviewUser-friendly, content-driven platform positioningComparison with Shopify for product-based vs content-based websitesQuick setup and on-the-go management capabilitiesIntegration capabilities and limitationsBest use cases for small businesses and content creators3. SEO Metadata OptimizationProper configuration of SEO meta titles and descriptionsOpen Graph (OG) tags for social media sharingURL structure best practices and character optimizationThe importance of unique metadata vs duplicated contentHow to edit SEO metadata in Shopify product pages4. URL Structure StrategyStrategic URL naming conventions for productsUsing numbers strategically in URLs (e.g., "red-roses-12-piece" vs "12-piece-red-roses")Pattern disruption for user attention and click-through optimizationShorter, more concentrated URLs for better visual scanningPre-purchase click optimization through URL clarity5. Technical SEO FundamentalsSitemap management across different platformsGoogle Search Console setup and sitemap submissionThe difference between Google Analytics and Google Search ConsoleNAP (Name, Address, Phone) consistency for local SEORobots.txt configuration and indexing control6. Wix Platform InsightsHidden robots.txt settings affecting blog tag indexingHow to enable tag indexing in Wix SEO settings10-year evolution of the Wix platformCommon indexing issues and solutions7. WordPress vs Closed-Source PlatformsOpen-source flexibility vs closed-source constraintsPlugin management and sitemap conflictsThe analogy of "square footage" for platform capabilitiesWhen to choose WordPress over Shopify/Squarespace8. Content Strategy & Page ManagementThe power of compounding through content updatesUpdating old blog posts alongside publishing new onesFooter copyright year updates as ranking signalsOn-page SEO details that AI and search engines scanCreating and maintaining a content calendar9. Website Maintenance Best PracticesRegular theme updates and their impact on visibilityChecking and updating footer copyright yearsMonitoring broken links and slow page speedsPlatform-specific maintenance requirements (Shopify, Squarespace, WordPress, Webflow, Wix)10. Free Website Audit OfferFavour's offer for surface-level website auditsDeep dive capabilities for root problem identificationMulti-platform support (Shopify, Squarespace, WordPress, Webflow, Wix, Magento, Tilda, Duda)Email newsletter with SEO, marketing, and AI insightsEpisode Timestamps00:00 - Introduction: Shopify SEO vs Squarespace SEO comparison00:53 - Welcome and housekeeping (saving replays, accessing resources)02:36 - Shopify platform overview and e-commerce focus03:01 - Why Shopify stands out (price-friendly, brand-aware, aesthetically pleasing)03:43 - Shopify themes and purchasing considerations05:43 - Critical question: When did you last update your theme?06:40 - How theme updates affect algorithm visibility07:00 - Closed-source vs open-source platforms explained07:08 - Theme Liquid customization in Shopify08:00 - Shopify as your hosting platform08:10 - Apps in Shopify vs plugins in WordPress08:21 - Squarespace positioning and user-friendliness09:00 - Platform comparison analogy: Square footage (500 to 20,000 sq ft)09:33 - When aesthetics and ease-of-use matter most14:00 - Detailed Shopify theme management discussion18:00 - SEO metadata and URL structure fundamentals22:00 - The importance of page quantity and content strategy28:00 - Sitemap management and Google Search Console setup28:15 - Why Shopify pre-installs sitemaps (no conflicts)29:00 - WordPress sitemap conflicts and plugin management29:32 - The sitemap as "the brain of a website"30:00 - Content compounding strategy: updating old posts31:06 - Wix robots.txt issue: blog tags set to "no index" by default32:00 - How to fix Wix tag indexing in SEO settings33:00 - Tags as hashtags and their importance for visibility34:05 - Critical action item: Update your footer copyright year to 202635:00 - Why footer year matters for AI and search engine scanning36:01 - Shopify advantages for multi-language and multi-currency37:03 - Google Search Console vs Google Analytics confusion37:20 - The "reverse gear" moment in SEO audits42:00 - Deep dive into URL structure optimization45:00 - Strategic use of numbers in product URLs48:00 - Open Graph (OG) tags explained52:00 - Schema.org and structured data importance58:00 - Product page SEO metadata workflow in Shopify58:15 - How titles auto-generate URLs and the edit button59:00 - Example: "6-piece red rose bouquet" URL structure59:23 - Optimizing URL readability and pattern disruption60:00 - Pre-purchase click optimization through URL clarity61:00 - Character count optimization for URLs63:00 - Shopify vs Squarespace integration comparison63:16 - Schema.org as the "golden standard" for web documentation63:48 - NAP (Name, Address, Phone) consistency explained64:00 - "Dress how you want to be addressed" philosophy68:00 - Free website audit offer details70:00 - Platforms supported for audits72:00 - Newsletter signup for SEO, marketing, and AI insights74:00 - Surface-level vs deep-dive audit explanation75:00 - Closing remarks and call to actionFrequently Asked Questions (FAQs)Q1: What's the main difference between Shopify and Squarespace?A: Shopify is primarily an e-commerce platform optimized for product stores with extensive selling features (multi-currency, multi-language, robust app ecosystem), while Squarespace is more content-driven and user-friendly, ideal for portfolios, blogs, and smaller businesses that need quick setup without extensive product management.Q2: Why is updating my website theme important for SEO?A: Regular theme updates signal to search engine algorithms that your website has an updated setup and infrastructure. An outdated theme (e.g., last updated in August 2025 when we're in 2026) can cost you visibility because the algorithm may perceive your site as less maintained and current.Q3: What is Theme Liquid in Shopify?A: Theme Liquid is Shopify's templating language that allows you to customize code within the closed-source platform. It's where you would add custom elements like pop-ups, tracking codes, or other modifications that aren't available through standard theme settings.Q4: Do I need to create a sitemap for my Shopify store?A: No. Shopify automatically generates and maintains your sitemap as soon as you publish pages, products, collections, and posts. This is a major advantage over WordPress, where you need to install and configure sitemap plugins and ensure there are no conflicts.Q5: What's the difference between Google Search Console and Google Analytics?A: Google Search Console is for submitting your sitemap and monitoring how search engines crawl and index your site, while Google Analytics tracks visitor behavior and traffic sources. Both are important, but they serve different purposes. You must submit your sitemap to Search Console for proper SEO.Q6: How do I fix the Wix tag indexing problem?A: Go to your Wix dashboard, click Settings (bottom left corner), navigate to SEO Settings, find the Blog Tags section, and disable the "no index" robots.txt setting that's enabled by default. This allows your blog tags to be indexed by search engines.Q7: Why should I update my footer copyright year?A: The footer copyright year (e.g., "© 2026") is on-page text that AI and search engines scan. An outdated year (like "© 2023") signals that your site may not be actively maintained, even if you've updated content elsewhere. It's a simple but important ranking signal.Q8: How should I structure product URLs for better SEO?A: Use strategic placement of descriptive words and numbers. For example, "red-roses-12-piece" is better than "12-piece-red-roses" because users scanning search results will see "red roses" first, then the number variants (6, 12, 36), creating pattern disruption that draws attention and improves pre-purchase clicks.Q9: What is Open Graph (OG) and why does it matter?A: Open Graph tags control how your content appears when shared on social media, messaging apps, and other platforms. When you send a link via WhatsApp or iMessage and see a preview with title and image, that's Open Graph data. Properly configured OG tags ensure your content looks professional when shared.Q10: Should I choose Shopify, Squarespace, or WordPress for my business?A: Choose Shopify if you're running a product-based e-commerce store and need robust selling features. Choose Squarespace if you need a quick, aesthetically pleasing site for content, portfolios, or small-scale selling. Choose WordPress if you need maximum customization, flexibility, and control (open-source), but be prepared for more technical management.Q11: What is NAP and why is it important?A: NAP stands for Name, Address, Phone number. For websites, "address" includes your domain (www address). Consistent NAP information across your website and online directories is crucial for local SEO and helps search engines verify your business legitimacy.Q12: Can I get a free website audit from Favour?A: Yes! Favour offers surface-level website audits to help identify issues like broken links, slow pages, and basic SEO problems. The audit supports multiple platforms including Shopify, Squarespace, WordPress, Webflow, Wix, Magento, Tilda, and Duda. Links are available in the episode description or through the newsletter signup.About the Podcast HostFavour Obasi-ike, MBA, MS is an SEO and digital marketing expert who specializes in helping business owners optimize their websites for search visibility and conversion. Favour offers website audits, SEO consulting, and maintains a detailed email newsletter covering SEO, marketing, and AI insights. Visit our quick links above to get access.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of Mastering eCommerce Marketing, Eitan Koter welcomes Jordan Gordon, Head of Email Retention and CRO at Pilot House Digital, and the host of the World's Best Email Retention Podcast.Jordan shares how he found his way into email early on, why he stayed focused on it, and how that focus turned into a long-term advantage. The conversation centers on one simple idea. Brands cannot scale efficiently if retention is treated as an afterthought.They talk through what Jordan sees when auditing Shopify and Klaviyo accounts, how email revenue compares to paid media, and why the cheapest customer is often the one you already have. Jordan explains how email supports ads behind the scenes, improves return on spend, and creates room to grow without increasing costs.On the CRO side, Jordan walks through how he looks at funnels, where most sites lose momentum, and why homepage and product page paths matter more than most teams expect. He also explains why simplifying choices and highlighting hero products often works better than complex redesigns.They close with a look at the inbox today, why automation matters more than constant campaigns, and how brands can stay visible without wearing out their list.Website: https://www.vimmi.net Email us: info@vimmi.net Podcast website: https://vimmi.net/mastering-ecommerce-marketing/ Talk to us on Social:Eitan Koter's LinkedIn | Vimmi LinkedIn | YouTube Guest: Jordan Gordon, Head of Email, Retention and CRO at Pilothouse DigitalJordan Gordon's LinkedIn | Pilothouse DigitalWatch the full Youtube video here:https://youtu.be/-GQliosqjjkTakeaways:Scale, depth, and dedication are crucial in e-commerce.Email retention can significantly impact revenue.Your cheapest buyer is the buyer you already have.Email is a 50-year-old protocol that remains vital.

In this episode of RevOps Champions, host Brendon Dennewill talks with Hayden Stafford, President and Chief Revenue Officer at Seismic. Drawing on 25+years leading go-to market teams at Microsoft, Salesforce, IBM, and Pegasystems, Hayden explains why modern growth depends on a "well-plumbed" revenue system, where sales, success, support, partners, and service operate as one connected engine. Hayden reframes enablement as the strategic translation layer that turns boardroom strategy into frontline execution with the right context, content, and coaching inside the flow of work. The conversation also tackles market downturn readiness, the CFO/CRO tension, and the importance of leading indicators, and a pragmatic view of AI adoption. What You'll LearnHow revenue strategy and revenue systems work together to drive resultsWhy enablement is a cross-functional translation layer, not just trainingWhat it means for RevOps to move from reporting outcomes to surfacing signalsWhere AI delivers the most value when embedded in daily workflowsThe first alignment levers CROs should focus onHow to recognize when AI adoption stalls before impact shows upResources MentionedSeismicSatya Nadella Microsoft Dynamics 365 Salesforce AgentforceMicrosoft CopilotIs your business ready to scale? Take the Growth Readiness Score to find out. In 5 minutes, you'll see: Benchmark data showing how you stack up to other organizations A clear view of your operational maturity Whether your business is ready to scale (and what to do next if it's not) Let's Connect Subscribe to the RevOps Champions Newsletter LinkedIn YouTube Explore the show at revopschampions.com. Ready to unite your teams with RevOps strategies that eliminate costly silos and drive growth? Let's talk!

#CRO habla de todo y anticipa su show en Buenos Aires en esta #entrevista exclusiva de #tumusicahoy

In this episode of Why Did It Fail?, Stuart Taylor, CRO at The Lennox Academy, opens up about a failure most people only talk about in whispers: staying at a company too long after an acquisition… and being made redundant as a result. He talks candidly about what it felt like in the moment, the shock, the hit to his identity and much more.Stuart and Shivan dig into the uncomfortable truth about loyalty, timing your exit, and recognising when a role or company has stopped serving your growth. Stuart shares the mindset and practical steps that helped him rebuild, and how that experience now shapes the way he leads, hires and coaches revenue teams.

Send us a textReady for a frank reset after a year that tested every seam? I'm opening the curtain on the “year of shedding”: long-time team members leaving, an IP scare that cut deep, and the decision to step back into client work to rebuild standards and clarity. Out of that churn came a sharper model—paid acquisition as the engine, organic as the trust layer—and a repeatable funnel that books local services solid and sells property without an agent.We dig into the ad mix that actually works in 2026. Meta remains the scale lever, Google Ads is back for the right intent categories, and Pinterest Ads quietly outperform for lifestyle because users arrive in curation mode, ready to act. TikTok Ads still feel high-touch and budget-heavy, while organic TikTok can shine if your brand voice fits. We break down why halving ad spend can stabilise ROAS, how to craft offers people truly want, and why respectful, dignity-first messaging converts better—especially for women in skincare and wellness.Two case paths prove it. First, a simple but powerful local service funnel: targeted ads to a CRO-driven landing page plus a thoughtful introductory offer. It's been booking salons and clinics without the constant social posting treadmill. Second, a new venture—Proper—that uses the same digital spine to sell property privately: targeted ads, a compelling property page, and structured lead handling. We sold faster and cleaner than a traditional listing, and we're building case studies to help more owners take control.If you're weighing a niche shift, wrestling with team changes, or just tired of content grind without returns, this conversation will meet you where you are and hand you a simpler path forward. Subscribe, share this with a founder who needs a reality check and a roadmap, and tell me: should we lean harder into lifestyle or keep a foot in professional services? Your take might shape the next episode.Brands mentioned in this episode:https://www.propahomes.com.auTo work with us, book your client assessment call at https://www.birdcageangeladvisors.com/hire-an-angel/

Most enterprise AI pilots die in the sandbox trap. Miguel Milano, President & CRO at Salesforce with $40B in revenue operations, explains why 95% of AI implementations fail without enterprise-grade data infrastructure and deterministic workflows. He breaks down Salesforce's three-pillar strategy for scaling AI beyond proof-of-concept: data foundations with metadata context, agentic layers that combine probabilistic reasoning with deterministic execution, and apps that codify standard operating procedures into reusable automations.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Send me a text (I will personally respond)Are you navigating the challenge of landing enterprise deals before you even have market traction? Wondering how to build pipeline without SDRs and endless cold emails? Curious if targeting Fortune 500 clients from day one is smart, or suicidal? This episode dives into a truly unconventional path to cybersecurity startup success that defies industry norms.In this conversation we discuss:

The Best Dental Marketing Podcast, powered by Dentainment, delivers cutting-edge strategies to help dental practices attract more new patients and grow in today's competitive digital landscape. In this episode, we learn how a focused morning huddle can act as a powerful marketing engine by aligning your Dental Team to convert advertising-driven opportunities into real outcomes like new patients, case acceptance, and social proof. We also explore how integrating small daily habits—reviewing wins, priorities, specific patients, cultural focus, and marketing reminders—creates a consistent patient experience and Dental Practice culture that boosts referrals, profitability, and lifetime value per patient. To learn how call handling by Smith.AI can grow your Dental Practice faster, retain more patients and improve patient satisfaction, visit this link:  https://calendly.com/d/csry-6sv-5rd/dentainment-smith-ai-consultation 


If your revenue team takes three weeks to deliver a diagnostic report - and still misses the $21 million opportunity hiding in your pipeline - this episode reveals why manual analysis is costing you deals and how a RevOps CEO architected an AI agent that does it in 24 hours. Tara Kinney, Founder and CEO of Atomic Revenue, has led 18 CRM implementations across 76 companies. She knows what "normal" looks like in revenue operations - and she blew it up. Her agency used to spend weeks qualifying prospects and delivering diagnostics. Now her Digital Crew does it in 24-48 hours, cuts the sales cycle by 90%, and finds revenue opportunities her manual analysis missed. In this episode, Tara walks through the exact workflow: how the Revenue Reality Check agent integrates with HubSpot and Slack, how it auto-assigns prospects to the right strategist based on industry and pain point, and why the "Human-in-the-Loop" governance layer is non-negotiable. She also reveals the $21 million story - the moment her AI agent identified a conversion bottleneck she hadn't even tracked, proving that agents don't just save time, they see what humans miss. This isn't theory. This is a deployable blueprint for Revenue Leaders who need to stop drowning in Admin Drag and start architecting workflows that deliver measurable P&L impact. You'll Learn: Why manual revenue diagnostics are too slow (and too shallow) for 2026 B2B velocity The 24-hour diagnostic workflow that cut Atomic Revenue's sales cycle by 90% How AI found a $21M opportunity by modeling scenarios humans can't calculate at scale The HubSpot + Slack + Multi-LLM integration stack behind the Revenue Reality Check Why "Human-in-the-Loop" governance prevents hallucination risks and protects your brand The three revenue killers hiding in your pipeline: Sales handoffs, legal bottlenecks, and client onboarding gaps Guest: Tara Kinney, Founder & CEO, Atomic RevenueCredentials: 76 companies supported, 18 CRM implementations, RevOps practitioner with real P&L accountability If you're a VP of Sales, CRO, or RevOps Director tired of waiting weeks for answers while your competitors move faster, this episode gives you the audit framework and workflow architecture to deploy your first diagnostic agent. Resources: Try the Revenue Reality Check: atomicrevenue.com Download the Executive Guide to Shadow AI: theaihat.com/shadow-ai Chapters: 00:00 Introduction: The Impact of AI on Sales Processes 00:51 Meet Mike Allton and The AI Hat Podcast 02:00 Welcome to AI for Revenue Leaders 02:21 The Problem with Traditional Revenue Diagnostics 03:00 Introducing Tara Kinney and Atomic Revenue 03:55 The Evolution of Revenue Diagnostics with AI 06:07 How AI Transforms Revenue Reality Checks 08:45 The Role of AI in Enhancing Sales Strategies 17:00 The Human-AI Collaboration in Revenue Diagnostics 33:32 Key Metrics and Questions for Revenue Leaders 36:22 Conclusion and Next Steps Learn more about your ad choices. Visit megaphone.fm/adchoices

#766 What happens when a CRO expert ditches slides, skips keynotes, and bets big on human connection in a post-Zoom world? In this episode, host Brien Gearin welcomes back longtime friend and fellow Cincinnati native Sahil Patel, CEO of Spiralyze, for a conversation that goes beyond CRO and A/B testing. Sahil shares a behind-the-scenes look at why his team decided to launch their first-ever live event, Above the Fold, and what it really takes to plan an in-person experience that people are excited to travel for. From choosing the right location and audience to designing hands-on workshops, pricing tickets, and balancing sponsors without ruining the attendee experience, this episode is packed with practical insights for entrepreneurs and marketers considering hosting their own live event — whether for 10 people or 1,000! What we discuss with Sahil: + Why live events matter now + Craving human connection post-Zoom + Choosing the right event location + Designing workshop-only experiences + No slides, no keynotes philosophy + Attracting the right attendees + Inviting speakers with real reps + Pricing tickets strategically + Sponsors without killing the vibe + Creating a standout guest experience Thank you, Sahil! Check out Spiralyze at Spiralyze.com. Follow Sahil on LinkedIn and YouTube. To get access to our FREE Business Training course go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MillionaireUniversity.com/training⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ To get exclusive offers mentioned in this episode and to support the show, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠millionaireuniversity.com/sponsors⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

Scaling companies is hard. Exiting them successfully is even harder. Doing it repeatedly takes a different mindset. In this episode of Sales Lead Dog, host Chris sits down with Jeff Fleischer, a senior operating executive, capital advisor, and entrepreneur with more than 25 years of experience scaling technology and cybersecurity companies through hypergrowth, acquisitions, and strategic exits. Jeff has held CRO, SVP, and CEO roles across public and private markets, helping build and sell multiple businesses to acquirers, including McAfee, JPMorgan Chase, Raytheon, BlackRock/Pamplona, and Audax. Today, he is the Founder of ProScale Partners and is launching Grainview Capital, advising founders, private equity firms, family offices, and strategic investors during critical inflection points. This conversation dives deep into growth strategy, leadership alignment, go-to-market execution, capital formation, and what truly breaks companies during scale. Jeff shares real-world lessons from operating inside fast-moving environments where clarity, speed, and execution matter most. Whether you're a founder, operator, executive, or investor navigating growth or preparing for an exit, this episode delivers practical insight from someone who has done it repeatedly.

The Reset Podcast is Back! We're kicking off with our #29daysofmagic series. First guest is Erica Taylor Haskins, Co-founder/CRO at Tinsel Experiential. We talk about the power of connection & building what's next Follow us on IG, FB, Youtube: @theresetpodcast @lmigno Follow Erica here: @tinsel_experiential @ericataylorhaskins Thanks to our partner Realm Learn more about your ad choices. Visit megaphone.fm/adchoices

Meet Abdul Rastagar, Head of Marketing at Lexitas Pharma Services, a CRO specializing in ophthalmology clinical trials. Abdul shares his expertise on pharmaceutical marketing, emphasizing the importance of brand awareness and targeted content for lead generation. He also discusses the crucial role of LinkedIn and website development in client acquisition, highlighting the significance of website accessibility in the digital age.

According to Stripe Profitability Timeline, it often takes two to three years for a startup to become profitable. Fundraising Time Sink: About 25% of founders spend over half their week on fundraising, which directly impacts their ability to focus on revenue-generating activities.   Mark D. Gordon. Known by founders as the “Rebel CRO.” He's scaled companies from $1 million to over $30 million in annual revenue, rebuilt go-to-market engines across SaaS, services, and tech, and built a business around one big idea: most companies don't have a sales problem; they have a clarity and alignment problem. I help B2B founders turn scattered sales and marketing into one aligned, predictable revenue engine. As a fractional CRO, I lead 120-day GTM transformations across messaging, lead generation, sales process, and revenue tech. We build the systems that allow your team to do the best work of their lives. Mark is the founder of Integrated Go-To-Market Solutions, where he helps B2B founders transform from sales hopeful to sales-led by aligning their messaging, lead gen, sales process, and tech into a single system that actually closes. If you've ever felt like your team can't explain what you do, your outbound is getting ignored, or you're still the best salesperson in your company, this conversation is for you. For more information: https://igtms.com/ LinkedIn: @MarkD.Gordon Learn more about your ad choices. Visit megaphone.fm/adchoices

Missed calls, slow follow-up, and storm surges can quietly kill your revenue.In this episode of Restoration Pros Unplugged, Clinton James sits down with Nick D'Urbano, Co-Founder and CRO of Distance, to break down how restoration companies are using AI to capture more leads, handle overflow calls, and generate more jobs — without replacing their people.Nick walks through real-world use cases for:AI Voice to handle overflow and after-hours calls during storm surgesAI Chat to convert website visitors, Facebook leads, and form fills fasterAI Outbound to win back lost leads, collect reviews, and stay in front of commercial prospectsThey also discuss why AI works best as a backup and multiplier for your intake team — especially when phones are ringing nonstop during freezes, floods, and major weather events.Want to see how Distance works for your restoration company?Book a demo directly with Nick here:https://meetings.hubspot.com/nickdurbano/demoLooking to generate more high-quality leads that turn into onsite visits and jobs?Book a discovery call with the Water Restoration Marketing team:https://waterrestorationmarketing.com/discovery-call/

In this episode of the Breakfast Leadership Show, Michael sits down with Chris Kline, Co-Founder and CRO of BitcoinIRA, to unpack what it really takes to build long-term wealth in an economy where traditional retirement systems are no longer guaranteed. Chris shares the unfiltered origin story of BitcoinIRA, from leaving Colorado for Los Angeles, living in a motel, and grinding through long days, to scaling the company into a platform serving more than 200,000 users and managing over $12 billion in assets. Along the way, he reflects on the leadership tension between scaling complexity and the clarity of early-stage simplicity. The conversation expands into the broader retirement crisis facing North America. With pensions disappearing and nearly half of Americans not actively participating in retirement planning, Michael and Chris challenge listeners to rethink responsibility, ownership, and strategy when it comes to long-term financial security. They explore how Bitcoin fits into a modern, diversified portfolio, not as a speculative gamble, but as an alternative asset shaped by scarcity, adoption, and long-term macro trends. The discussion covers due diligence, dollar-cost averaging, portfolio diversification, and the persistent myth that Bitcoin is inaccessible due to price, despite the ability to invest fractionally. Beyond Bitcoin, the episode addresses the importance of diversified income streams. Michael and Chris share real-world examples of building resilience through multiple revenue channels, passive income strategies, and investments across traditional markets, AI, defense, and digital assets. The message is clear: in an uncertain economy, relying on a single paycheck is a structural risk. The episode closes with a reminder that leadership extends beyond business. Chris shares a personal story about his daughter's passion for helping others, reinforcing the idea that purpose, curiosity, and long-term thinking are skills worth teaching the next generation. Listeners also learn about a limited-time BitcoinIRA incentive, including a $1,000 reward for new accounts opened before April 15th, designed to encourage proactive retirement planning. This is a grounded, practical conversation for anyone thinking seriously about financial resilience, leadership, and building a retirement strategy that reflects today's economic reality, not yesterday's assumptions. http://bitcoinira.com/breakfastleadership

In this episode of Sales Is King, host Dan Sixsmith kicks off the show's 10th year and the launch of a brand new studio with a powerhouse guest: Andrew Brown, Senior Vice President and Chief Revenue Officer at Red Hat. Andrew shares how Red Hat is driving double‑digit growth with its hybrid platforms, automation, and AI capabilities—while staying anchored in long‑standing values like freedom, courage, commitment, and accountability. He also breaks down how AI is really changing sales, what separates top sellers from the middle of the pack, and why “happy customers” is his simple, non‑negotiable definition of success.​Red Hat's growth engines in 2025Three core platforms: Enterprise Linux, OpenShift (containerization/virtualization), and automation.​Why true hybrid (on‑prem, private cloud, hyperscalers) is resonating with customers globally.​The acquisition of Neural Magic and how Red Hat is playing in AI inference.​Values that customers actually feelHow Red Hat's long‑standing values—freedom, courage, commitment, accountability—show up through products and people, not posters.​Stories from customer visits (including India) where clients proactively praise the team, not just the tech.​The call to become CRO and first 90 daysHow Andrew was tapped from IBM by Rob Thomas to run “anything that touches revenue” at Red Hat.​Why he changed almost nothing at first: two ears, two eyes, one mouth—used in that ratio.​Moving the organization from “growing” to truly unlocking the next growth curve, with alignment on one vision and one belief.​What really separates top sellers from the middleActive listening as a true differentiator—probing pain, impact, and outcomes versus just hearing words.​Never settling: aiming beyond the renewal, operating on the “front foot,” and treating success and failure the same way.​A sports mindset: being ready for the clutch moments, orchestrating stakeholders, and failing at least 50% of the time but getting back up.​How AI is reshaping sales at Red HatBuilding and buying: Red Hat's own AI assistant embedded in sellers' workflow (Slack → CRM opportunity creation) plus tools like People.ai to free managers from data validation and focus them on coaching.​The big challenge: not building AI models, but getting them into production at scale with governance, cost control, and the right deployment (cloud vs. on‑prem).​Why only a small percentage of AI projects show real value today—and what needs to change.​Channel and ecosystem as revenue multipliersWhy a significant share of Red Hat's revenue runs through partners and how they're enabled pre‑ and post‑sales.​Technical certifications, revamped partner programs, and advisory boards to keep value and alignment high.​Customer success and value realizationConsolidating scattered customer success pockets into a central, technical CS team that engages the day after the contract is signed.​Focus on hands‑on deployment, embedding Red Hat tech in customer architectures, and rescuing under‑utilized hybrid commitments.​The direct link Andrew sees between CS, value realization, and recurring revenue uplift.​Andrew's personal journey and leadership lessonsFrom aspiring soccer player to IBM intern to CRO at Red Hat.​Doing an MBA nights/weekends to bridge technology and business outcomes in C‑level conversations.​Early “bad” first management role and learning from white‑space, door‑to‑door style selling.​Influences from Lou Gerstner and other mentors: keep it simple, communicate clearly, don't define your life only by work.​Andrew Brown is Senior Vice President and Chief Revenue Officer at Red Hat, where he leads all revenue‑touching functions globally across sales, services, and ecosystem partners. Prior to Red Hat, Andrew spent nearly three decades at IBM in a variety of technical, sales, and leadership roles, combining a deep technology background with a strong commercial track record.​

USDC closed the gap between software and law in modern finance.On Grit, Jeremy Allaire discusses how fully reserved, dollar backed digital currency became part of the financial system after more than a decade of work.He also shares why for him grit is about sustaining belief through deep uncertainty, even when Circle faced the threat of bankruptcy in 2019.Guest: Jeremy Allaire, Co-Founder, Chairman and CEO at Circle​Connect with Jeremy AllaireX: https://x.com/jerallaireLinkedIn: https://www.linkedin.com/in/jeremyallaire/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 argue that SEO isn't dying; bad SEO is. Fundamentals still drive wins while LLMs change how answers surface, making complete, original content and smart strategy more valuable than ever.• fundamentals over fads as LLMs shift discovery• why pages beyond top ten fuel LLM citations• customer journey, CRO, PR, and dev fluency as real leverage• global markets with lower competition and faster output• enterprise constraints, silos, and when to say no• partnerships over one‑stop shops to deliver depth• personalization pitfalls in tracking and sane KPI alignment• future of agents, multimodal search, and offline attentionGuest Contact Information: LinkedIn: linkedin.com/in/kresimir-corlukaWebsite: canonical.hrSummit: croatiaseosummit.comMore from EWR and Matthew:Leave us a review wherever you listen: Spotify, Apple Podcasts, or Amazon PodcastFree SEO Consultation: www.ewrdigital.com/discovery-callWith over 5 million downloads, The Best SEO Podcast has been the go-to show for digital marketers, business owners, and entrepreneurs wanting real-world strategies to grow online. Now, host Matthew Bertram — creator of LLM Visibility™ and the LLM Visibility Stack™, and Lead Strategist at EWR Digital — takes the conversation beyond traditional SEO into the AI era of discoverability. Each week, Matthew dives into the tactics, frameworks, and insights that matter most in a world where search engines, large language models, and answer engines are reshaping how people find, trust, and choose businesses. From SEO and AI-driven marketing to executive-level growth strategy, you'll hear expert interviews, deep-dive discussions, and actionable strategies to help you stay ahead of the curve. Find more episodes here: youtube.com/@BestSEOPodcastbestseopodcast.combestseopodcast.buzzsprout.comFollow us on:Facebook: @bestseopodcastInstagram: @thebestseopodcastTiktok: @bestseopodcastLinkedIn: @bestseopodcastConnect With Matthew Bertram: Website: www.matthewbertram.comInstagram: @matt_bertram_liveLinkedIn: @mattbertramlivePowered by: ewrdigital.comSupport the show

In this episode of the Grow A Small Business Podcast, host Troy Trewin interviews Matthew Stafford, founder of Build Grow Scale, shares his journey from running a commercial contracting business to generating over $15M in e-commerce sales. He explains how data, analytics, and user experience—not just CRO—drive predictable growth. Matthew opens up about cash flow stress, scaling teams, and hard lessons from rapid growth. He also dives into mindset, self-belief, and why the business owner is often the real bottleneck. A must-listen for entrepreneurs serious about sustainable, long-term success. Why would you wait any longer to start living the lifestyle you signed up for? Balance your health, wealth, relationships and business growth. And focus your time and energy and make the most of this year. Let's get into it by clicking here.   Troy delves into our guest's startup journey, their perception of success, industry reconsideration, and the pivotal stress point during business expansion. They discuss the joys of small business growth, vital entrepreneurial habits, and strategies for team building, encompassing wins, blunders, and invaluable advice.   And a snapshot of the final five Grow A Small Business Questions: What do you think is the hardest thing in growing a small business? Matthew Stafford shares that the hardest thing in growing a small business is staying resilient and persistent, as every stage of growth brings new challenges and the business owner often becomes the biggest bottleneck. What's your favorite business book that has helped you the most? Matthew Stafford shares that his favorite business book is The Slight Edge by Jeff Olson, which focuses on the power of small, consistent daily habits and long-term improvement. Are there any great podcasts or online learning resources you'd recommend to help grow a small business? Matthew Stafford shares that he recommends podcasts and learning resources like The Operators podcast and newsletter, where experienced entrepreneurs openly discuss real growth challenges, wins, and failures. What tool or resource would you recommend to grow a small business? Matthew Stafford shares that the most valuable tool for growing a small business is Google Analytics along with Google Tag Manager, as they provide clear insights into customer behavior and data-driven decision-making. What advice would you give yourself on day one of starting out in business? Matthew Stafford shares that the advice he would give himself on day one is to commit for the long term, stay patient, and not quit too early, because success often comes right after the hardest phase. Book a 20-minute Growth Chat with Troy Trewin to see if you qualify for our upcoming course. Don't miss out on this opportunity to take your small business to new heights! Enjoyed the podcast? Please leave a review on iTunes or your preferred platform. Your feedback helps more small business owners discover our podcast and embark on their business growth journey.     Quotable quotes from our special Grow A Small Business podcast guest: Most business problems aren't strategy issues—they're mindset issues hiding in plain sight — Matthew Stafford The entrepreneurs who win are rarely the smartest—they're the ones who don't quit — Matthew Stafford If your business is stuck, look in the mirror first—that's usually where the real work begins — Matthew Stafford      

⁠Watch Todd's Free Negotiation Masterclass⁠ ⁠Buy 'Four Levers Negotiating' Here⁠ Todd Caponi is a multi time CRO, award winning author, and one of the leading voices on transparency in sales. And these are the three negotiation moments that changed how he sells forever. From a used car purchase that went completely sideways, to a seven figure software deal saved at the last second, to a heated pricing argument with an angry marketer, Todd Caponi shares real stories that prove why honesty beats tactics in negotiation. No gimmicks. No fake leverage. Just clear, human conversations that build trust, reduce discounting, and turn negotiations into collaboration instead of combat. These Courses Will Get You to President's Club:

Kiera takes listeners through specific actions the most successful dentistry minds have incorporated into their day-to-day to stay elevated. She touches on: Planning out an ideal week Reviewing these numbers weekly Fostering problem-solvers And more! Episode resources: Subscribe to The Dental A-Team podcast Schedule a Practice Assessment Leave us a review Transcript:   The Dental A Team (00:00) Hello, Dental A Team listeners, this is Kiera and I hope that you guys are having an amazing day today. I hope you're loving dentistry. I hope you're loving your opportunities. I hope that you are remembering that we have one life and I hope that you're making it the absolute best life you can. There's a song that I recently heard called Time's a Ticken and it's like, so call your mom, love your babies, talk to your friends and...   I just think about it and another thing I saw were like, if your mom and dad are still able to call you, how blessed are we? And I know some people have strained relationships, but I think as much love that we can give and as much as we can foster great relationships in our lives and realize how much goodness we have, I think that's an amazing space for us. just hopefully you know how much I love and appreciate you and how much I'm rooting for you, whether I know you personally or whether...   you are someone in our Dental A Team family, or if you are in our podcast family, or if you're new to this, just know I'm rooting for you. Even if I don't know you personally, ⁓ you're doing better than you think you are today. Guys, it's going to be fun. I want to talk about CEO habits for next level, like what top practice leaders are doing and just some tips for you. As we're rolling into a 2026, I love looking at habits and not necessarily fads, but habits. And so what do things do? And I believe that habits, not just hustle,   are going to help you with success. ⁓ So many times it's like, well, what made the success successful? And it's like really consistency on doing the best things and the highest priority things consistently. And so giving a couple of three core habits that I think growth-minded leaders, practice owners have versus overwhelmed operators. And so really being able to give you that guidance and at Dental A Team we're obsessed with helping dentists become CEOs of their practices and having amazing teams thrive around them and.   ⁓ Giving you guys all of that today is really what it is and we want you guys to feel clarity, confidence and consistency. And I know sometimes when you're in the whirlwind of the day-to-day business, it can feel very hard to have this. But I will say, if you can build these as a building blocks, the noise can lessen. I'm not gonna promise it will go away, but it can definitely lessen and doing it over time. Just like with front office team members were like, I just don't have time, Kiera. And we're like, great, let's put in a power hour. And they're like, it's never enough time.   You're right. Today is not enough time, but if you do one hour a week blocked with no interruptions and you work on the highest level things, I've watched teams over and over and over again, be like, I actually don't need this hour anymore. And we get our recare calls done and we get our unscheduled treatment calls done and we block that and we do it. And office managers, they block that time and billers block the time to do insurance verification. It does not need to be a lot of time, but it does need to be consistent. So with that, you guys, this is going to be something that's a   a habit, ⁓ daily and weekly habits that you can create that you can really just put into your life now. So number one is, this sounds so silly and I do this often, it's creating and committing to an ideal week. ⁓ And so that's being able to have a rhythm and not reaction. so what I noticed and it's crazy because as my company evolves, my life and my business and my schedule needs to evolve as well.   When the business was smaller, I used to be able to run back to back to back to back meetings. There wasn't as much strategy that I needed to think about. There weren't as many hard decisions. There weren't as many like complex decisions that I used to be able to run a week like back to back to back. And then I realized like, I can't run like that anymore. I need to have like on time and off time, on time, off time. And then there's presenting like podcasts. Like you try to put meetings on a podcast day. You guys, am in podcast is creative land and I'm on presenting mode. And I'm like here hanging out with you guys and having a good time.   don't put meetings where I'm trying to like figure out a budget that is such a different mind than a creative mind. And so really being able to block this where we have it and color coding your calendar. What I really do believe is as a CEO of a practice, you're going to have clinician time, right? You're going to have being a dentist. Then you're going to have leader time where you're developing your leaders. And then you're going to have visionary CEO time. And if you can block this in there and you don't have to have it perfect. So do I have   leader time where I'm like developing my leaders and I'm spending time figuring out leadership pieces for them and investing in my leaders and coaching my leaders. Do I have that blocked in there? And then do I have this deep work visionary CEO time where I'm reviewing the financials and I'm answering questions from my office manager and doctors sometimes they even recommend you have another block of am I getting like all the busy work like the labs and the clin checks and the cases and looking at all the scheduling coming up. Do I have time to work on that? And   blocking this and it sounds like, gosh, there's so much and there is, this is why you feel overwhelmed and you feel radical. So having my doctor dentists in time, my leadership development time, my CEO time, and then if you need any other time, great. I also put in my personal time. So am I working out and taking care of my body? And we did this with our mastermind group where I learned a thing called rapid planning method from Tony Robbins and I really enjoyed it. And then I took it of course, ended Kiera spin to it.   But what I really loved is Tony actually had us rename our categories. So instead of saying workout time, it's my honoring my body time. And that was so much more fulfilling for me. And I also have buckets in there that are color coded of date time. Like I call it mine and Jason's forever love story. And what do I put into my calendar that's blocked specifically for that? And what's lovely is when you have colors around it, ⁓ you can actually make it to where you then are working on those specific areas.   and you're able to see them very, very easily. So when we look at this, I think about my colors and my favorite color is pink. So I always have my Kiera section where I'm honoring myself. It's in pink in my calendar. When I'm working on Dental A Team and I used to like call it just Dental A Team. Now it's my passion project and it's blue. Honoring my body is orange. I needed that like vibrant orange, like getting excited about it. And I have that in there.   my leadership visionary time, that's going to be a different color. For me, that's more of this like blue turquoise color. It's more serene, it's calm. So whatever that is for you, just having those color coordinated things and like I popped into my RPM planner. So I have my ⁓ ROASIS ⁓ is our home. And so working on my home, wealth, genius, fun, that's curious thing. And I always make sure I have fun built into my calendar. But I think like you can make it as complex or as simple as you want, but I would really recommend we've got our dentist time.   our leader time, so maybe that's like our give back time or our development time or our like my first team time and then my visionary, my exciting time. What does that look like and really blocking that in your calendar? And so then we audit our week at the end of the week and I remember I was taught like many times like the most productive thing is to go back and look where did I win my week? Where did I like lose the week and what do need to change for this? And   Even me going into a new year, actually have a new EA joining me pretty soon. So that's thrill. If any of you had a personal assistant EA that's been with you for a long time and you're getting a new one, let's ⁓ just say it's a thrill. And I'm really excited for Marissa to join as Shelbi's getting ready to have some life changes. And I'm so, so, so excited for her. ⁓ And going through that and being able to experience it, I realized I needed a different calendar.   What I've been doing is not going to get me to where I need to go. And so we've been working on it and I like built it. You guys, I like to like really mass and like if I'm in podcast mode, I'm in podcast mode. And if I'm in coaching call mode, I'm in coaching call mode. And if I'm in business mode, I'm in business mode. ⁓ but I realized what I was doing is I was business mode. I was coaching call AKA dentists thing that I was in heavy meetings and then I was in podcasting. And I think sometimes when we run that heavy, it's very hard to have like downtime. And so for you looking, you're working as a dentist all four days.   So could we block maybe Wednesday mornings where you have a catch up time or do we have a CEO day where it's a Friday and you actually have that block for four hours and you work on that. I have a dentist, he works Monday, Tuesday, Wednesday, Thursdays are always off and he works Friday. And I'm like, that is the weirdest schedule. He's like, Keira, I love it. I get all my admin stuff done when people are still there. I have time to think that's when I'm gonna work on my decisions. And then I go in and have a great Friday where I've got nothing on me and I produce my highest amount. And this doctor is a very high producing doctor but he's very regimented in how he does it.   And that's how he's been operating for the last like 30 years. So when you implement this and you commit, so I'm like, okay, let's break it down. guys know I like to make it easy. I like to make it tactical for you. You got to block these areas. What am I done to seeing? When am I leading? And when am I thinking about the greater big like CEOing of the company? And if I'm only going to do one, I'm going to block a two hour block every single week to work on high level of the business. Just like I recommended for our leaders blocking one hour minimum per week of deep work time.   and doing it at your prime optimal time. For me, it's early mornings. I operate so good from like 6 a.m. lately, it's been like 3 a.m. until about 11 and then like I'm out. I don't want to be thinking heavy. I don't like hard things. That's my operating. Just like I run on protein, Jason runs on carbs. Like it's just operating in how we function, but really making sure you do that. Again, this is a habit. It's a discipline. It's reviewing it. And I had a doctor who was really high level. We coached together for about a year and he said, Kiera, coaching with you was one of the most impactful years of my life.   because you taught me to prioritize my calendar, to review my calendar, to work on my family relationships, to work on my leadership, to delegate, to see what things were in my calendar that I could delegate. And this person has grown and added multi-multi-practices and I'm so proud of him. But truly, this is going to be your best thing. So action on this of getting this habit into place is block two hours as your CEO time, no operations, no calls. You are just fully focused on the business and commit to doing that.   for the next four months. Whoa, four months, can you imagine? Just try it. Test it out, tell me, Kiera, I'm trying the experiment. Email me, Hello@TheDentalATeam.com. I'm committed to it and I want you to not break that promise to yourself. You hold it strong. I had a doctor who did this. She put a like sign on her door and she said, do not interrupt me at all. Now you have to hold this strong because if someone's like, hey doc, I just have a quick question. Nope, right now is my time and I need you to respect my time. I'll be available at this time.   You call that one or two times and your team will not interrupt you again because they know you are dead serious on this. So review it. Now you're already doing that. I want you to take it one level further and I want you to add in your date time, your workout time, something that you are also adding in that needs to be blocked. And I want you to ramp it up one more. Okay, that's number one habit. Number two habit is reviewing your KPIs and your financials every single week. And you're making decisions based on data, not on feeling. So we all know that what we measure improves, right? All of that is there.   So what it is is KPIs, you gotta be looking at those, whether you're using dental Intel, we recommend Addit. Practice by numbers, I don't care. All of our clients do get Addit. So if you're like, hey, I'm thinking about consulting, but I'm not sure about cost or guess what, we cover that cost for you and it's free for you and we also have other perks for you. So ⁓ definitely cost savings that way. And we help you build a scorecard and a dashboard and we teach your team to look at this. But you as a CEO of your practice, this is how you become a CEO. CEOs make decisions based on numbers and metrics, not on feeling and gut.   but you have to take time to review the data to sift through the data. We have an amazing CRO on our team that's a chief revenue officer. didn't even know that was a position. And I have been begging our marketing team to go through our podcast data to figure out what did the listeners want? have, guys, oh my gosh, we're moving into, think our, we started in 2019. So this year, seven years on the pod, guys. I cannot believe that. Lucky seven over here. But thinking about it, I was like, go look at the data. want to,   not just what Kiera feels and what I think you guys, are 1,100 episodes in by now. Like we should be able to have great data of what you guys want. And you're gonna hear a change this year because we actually went through Paul kudos to him. He went through and he looked at all the data and he said, all right, Kiera, here are the episodes doing well here. The episodes not doing well. Here are the things that listeners want. Here's how we need to revamp it. And I was so proud of him and so grateful because now we're building content based on what the data is telling us. But you know how long that took him? It took him like three months to go through it all, sift through it all. And for you,   You've got data, you've got case acceptance data, you've got new patient call conversion data, you've got our billing, our AR data, you've got diagnosis of doctors, we've got hygiene period data. That is the stuff you need to be looking at to see how are we doing? You've got how long is it to our next appointment? We see how far out are we booking our new patients? We see how far out are we booking our six month appointments? Are we staying at six months? How much money are we losing? A doctor had me come in and I looked and saw it, you're booking your patients eight months out. It was about a million dollars worth of revenue that they were leaving on the table.   just by not having enough hygiene available. That is gold if you will take the time. So this is another step that we're gonna add in. So you've already got your CEO block. You can add this into it where we commit to reviewing our KPIs and our PNLs every single week and making adjustments to that. Now work in tandem with your office manager. Office managers, should be doing this as well. Every single week, where are we off and what do we need to do next? Every week. And we train our teams to use numbers, not feelings. And this is how we're going to lead.   So team members should be looking at the numbers. They should know their department. Are we on track? Are we off track? We have scorecards every single week. All of our departments are reporting. Where are we on? Where are we off of? Where do we need to pivot? We need lead measures and we need lag measures. We need to make sure we're looking at both of those. And you literally start looking at this. And I just told you like people who do this, I have an office and she was like, Garo, we need to increase. I want to increase it. And I was like, we are profit and production. That's all we're looking at, period. I cut out all the noise.   Profit production, what are the levers that are hitting that? How are we diagnosing? How are we block scheduling? How is our case acceptance? How are our new patients and how are we filling the schedule? Profit production, that's all we're hitting. And guess what? That doctor is the most profitable they have ever been. But it was because we had them laser focus. We focus on these numbers every single week. And this doctor was doing it, but they weren't optimizing and making decisions on where they really needed to go and focus on the most important thing. And I think even though you might look at the KPIs and data, are you focusing on the most important things that are gonna drive and move your practice forward?   So I want you profit and production are the number two that I go after. One and two, you've got to look at those two always. And then you use the other ones to boost those two up. And if you're struggling with that, hi, I'm Kiera. We work at Dental A Team. We're a consulting company committed to making you financially free, blissfully happy in your practice and getting the best life you want. So reach out, Hello@TheDentalATeam.com Okay, so let's have it number two. Habit number three is developing your people to solve problems instead of you always solving them. So.   This is something where it's like, what's leadership versus what's firefighting. And you guys, I'm not perfect at this. I do a lot of firefighting. I do a lot of problem solving for teams. And I'm like, my gosh, I'll just give you the answer. But the goal is we need to fix it. And we need to start asking the question. So I'm like, hey, here's a problem. Instead of being like, here's the answer. Then we train them that we're the person that they come to. Hey, what do you think is a solution? You can roll it out. It's a three solution company. If you've got a problem, you bring me three solutions, one of which does not cost money. We have one-on-ones that focus on development, not just updates.   I need to develop you as a leader. I need to work with you. I need to grow you. Where are we at? This is the things we need. Like, let's work through this. Is this really the best use of our time? Is this really the best KPI for us to be tracking? Is this really how we're gonna lead? You focusing and developing your leaders and coaching them, you don't wait for things to break. So like, let's look at the KPIs. All these, you can tell build upon each other. Let's look at the KPIs. Let's look at what you guys are needing. And then let's coach to that. But truly,   If you will coach your team, there's a practice that I have known for gosh, seven years. The doctors are working in there one day a week and their office manager is running the organization and they have leaders. They have people that are following up on issues. They have the team solving their own problems. They're a solution oriented organization rather than a problem like centric like, Hey, here's your problem. Go fix it. If you need a good book, ⁓ gosh, it's the monkey book. The one minute manager meets the monkey.   It's like a good little fable of don't let people put the monkey, like their monkey on your back and leave it. Another friend described it as a fridge with a magnet and like someone was like, here's this problem, here's this problem. We're like Post-it notes, right? Like they just put it all on you. Tiff and I did a video a long time ago where it's like Post-it notes all over you and you're just drowning in Post-it notes. Well, that's like draining your energy too. And if we can teach our team to solve problems and this is a habit, this is going to be, ⁓ this is going to be something that you work through.   So just letting you know, like, this is where it's at. This is how we do it. These are three habits for you. So how do we take action on this one of developing it is you're going to have monthly coaching one-on-one with each of your leaders and figuring out their gaps of where they need to grow and giving honest feedback to them. ⁓ There's some great things of, you guys know we run on EOS and we absolutely love EOS and there's quarterly conversations that you can have. it's like, how are they on core values? How are they on their position? How are they rating themselves?   ⁓ We are having the conversations and we're being direct with them and we're giving mutual reflection on things and how are we doing on our quarterly pieces and how's our team doing and what are the moving forward actions that we're doing and having these as consistent monthly and quarterly check-ins with our team, but growing them into leaders is going to be critical and pivotal for your team. So these are three, you guys, three quick habits that you can implement now.   If you need to read the book Atomic Habits, how do I stack things? How do I make this easy? Like, okay, I need to block CEO time. So CEO time sounds like C, I'm gonna C on Thursdays or C on Fridays. Like, I don't know, C, maybe at C2, I'm trying to think of like an alliteration for you. I need my CEO time, my power time. There's no P in the alphabet, in the Monday, Tuesday, So maybe it's like top time on Tuesday or Thursday. I'm gonna do my top time Tuesday or Thursday or like Focus Friday.   There you go, there's some alliterations for you, but I'm gonna block this and I'm gonna block my calendar. Then I'm also gonna commit to KPIs or numbers. So winning Wednesdays, that's when I'm always gonna look at my numbers. Or magic Mondays, I'm gonna look at my numbers. Or money Mondays, there you go. Money Mondays, I'm gonna look at my KPIs and I'm gonna make decisions and me and my OM are gonna meet on that. And then I'm going to have leaders that are solution oriented. So we roll that as a culture thing and I'm gonna set it to where once a month I meet with all of my leaders now.   Maybe we work on weekly in the future, ⁓ but I'm gonna make sure that I'm meeting with them once a month and that's where I'm putting my most important time. And I could add that as CEO time, that's fine, because you are working on leadership at that part, but you're gonna commit to one, two or three of these habits and you're gonna hold strong for at least four months and let me know how your life looks. Now, if you're like me, I have to have a gym trainer, otherwise I don't work out. I got all the workouts, I got all the things, I hear it, I see it, I see it on Instagram, I see how to make the good food.   But unless I have it booked, scheduled, and someone's holding me accountable to it, I don't do it. So if you're that person, hi, I'm Kiera. We have the Dental A Team and this is what I'm obsessed with. Second to sending you a carrier pigeon, we make sure that you stay accountable to this. Let's help you do that. Reach out Hello@TheDentalATeam.com because you deserve to be the CEO and sometimes just being redirected and getting a new habit and a new operating system is going to get you to where you want to be. So reach out Hello@TheDentalATeam.com and commit to this. I want you guys to act like the CEO of your practice.   and start with these three habits this week. Reach out, we're here to help. And as always, thanks for listening. I'll catch you next time on the Dental A Team Podcast.