Podcasts about Cros

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

This episode breaks down the foundations of academic clinical research, from how studies are conceived and funded to how protocols ensure rigor, consistency, and patient safety. Listeners learn about the roles of sponsors, CROs, IRBs, and research teams, as well as the advantages and challenges of conducting trials in academic settings. This independent medical education program is supported by Incyte.Please click here for a complete list of disclosures.

In the first official episode of Culture Over Quota, AJ Vaughan introduces a concept that sits right in the uncomfortable gap most high-growth organizations refuse to measure: People Profit.Every leadership team can tell you their CAC, EBITDA, unit economics, and revenue per employee. Those numbers are discussed, defended, and forecasted like gospel. But the most important operating system behind all of them — the lived reality of the workforce — often goes unmeasured until it breaks.This episode is a direct conversation to CHROs, CFOs, CROs, and private equity operators who are chasing scale without pretending the human layer will “figure itself out.”AJ breaks down the hidden margin crisis that shows up when companies optimize for short-term output while ignoring human capacity alignment: the quiet disengagement, the innovation drag, the internal hesitation, the missed handoffs, the cancelled collaboration meetings, the increase in “heroics,” and the fear-based grind that turns high performers into flight risks.You'll hear why a company can look “fine” on paper while internally bleeding speed — and why leaders often feel the month was “off,” even when dashboards don't explain it.AJ uses a simple but sharp sports analogy: teams that sprint too hard early burn out late. Businesses do the same thing — pushing intensity without building sustainable alignment — then act surprised when Q2 momentum fades, Q3 gets weird, and Q4 becomes a recovery plan.People Profit is AJ's push to change what we track:Not just financial outcomes, but the human signals that predict them alignment, psychological safety, workload strain, collaboration quality, and the invisible behaviors that either compound performance or quietly tax it.Because culture isn't a vibe.It's a performance system.And when you measure it honestly, it becomes a margin.This is Part One of a multi-part breakdown of the People Profit framework and the start of Culture Over Quota as a movement for leaders who want growth without burnout, speed without chaos, and profit without losing the people who create it.

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

Companies go upmarket, hit three slow months, and decide the strategy “doesn't work.” In this live episode, Yann (co-founder of Userled, former Salesforce enterprise seller) breaks down what enterprise deals actually look like up close: long stretches of silence that aren't rejection, stakeholders who shape the decision without ever joining a call, and why “activity” can feel busy while the deal goes nowhere. You'll also hear the less glamorous side: what founder life feels like when momentum disappears, why some teams survive the hard quarters (and others don't), and how hiring for energy changes everything. Yann shares how Userled changed their ICP, survived two brutal quarters — then closed more in October–November than the rest of the year combined. We enjoyed this conversation. Hope you will too.

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

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

In this week's episode of the Xtalks Life Science Podcast, host Ayesha Rashid, Senior Life Science Journalist at Xtalks, spoke with Joseph Sinkule, CEO, Founder and the Chairman of the Board of Directors at Klotho Neurosciences, a biogenetics company developing cell and gene therapies using a patented, secreted form of the “anti-aging” human α-Klotho gene for the treatment of neurodegenerative, age-related disorders such as Alzheimer's disease, amyotrophic lateral sclerosis (ALS), Parkinson's disease and others. Dr. Sinkule has over 40 years of drug, biologic and medical device R&D and commercialization experience. He has managed over eight drug and biotech products successfully through FDA approval to market, as well as five medical devices and eight in vitro diagnostics. After serving in academics and then in industry, Dr. Sinkule has evolved into a successful businessman and entrepreneur. He has led teams of all sizes, managed CROs and CDMOs, serves on two company boards and regularly advises venture capital firms, investment banks and pharmaceutical and biotech companies at all stages. Tune in to learn more about targeting pathways of aging in diseases like Alzheimer's and ALS. For more life science and medical device content, visit the Xtalks Vitals homepage. https://xtalks.com/vitals/ Follow Us on Social Media Twitter: https://twitter.com/Xtalks Instagram: https://www.instagram.com/xtalks/ Facebook: https://www.facebook.com/Xtalks.Webinars/ LinkedIn: https://www.linkedin.com/company/xtalks-webconferences YouTube: https://www.youtube.com/c/XtalksWebinars/featured

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

$1M in sales. $500K saved. Zero new headcount. Your sales team isn't ignoring dead leads because they're lazy - they're drowning in admin drag. While you're debating whether to hire two more SDRs, your competitors have stopped asking "Who do I hire next?" and started asking "What system do I build next?" Will Del Principe (Growth & Solutions Engineering Leader at Thoughtly) reveals how AI voice agents have crossed the quality threshold - handling objections, qualifying leads, and booking meetings at scale. Will personally drove close to $1M in sales in 6 months using voice agent workflows, and his clients are generating hundreds of thousands per month by reactivating dead pipeline. You'll learn: Why voice agents are fundamentally different from chatbots (and why that matters for stalled pipeline) How to design re-engagement campaigns that resurrect cold leads without damaging your brand Quality benchmarks for evaluating voice AI providers (70% of prospects don't detect it's AI) The mindset shift from headcount scaling to system architecture The low-risk pilot strategy for testing voice agents this quarter Who this is for: VPs of Sales, CROs, and RevOps Directors in mid-market to enterprise B2B who need workflow architecture that produces measurable P&L impact - not pilot purgatory. Download the free Executive Guide to Shadow AI at theaihat.com/shadow-ai CHAPTERS: 00:00 - Introduction: The Efficiency Crisis Killing Your Pipeline02:08 - Your Sales Team Isn't Lazy - They're Drowning in Admin Drag03:27 - Meet Will Del Principe: $1M in Sales Using AI Voice Agents03:58 - The Coffee Invitation: When AI Crossed the Uncanny Valley06:00 - What Is a Voice Agent? (And Why It's Not Just Another Chatbot)08:05 - The Podcast Guest Analogy: How AI Removes Bottlenecks Without Replacing People11:14 - Mixing Outbound and Inbound: The New Campaign Architecture13:20 - Why You Can't Cold Call with AI (And How to Get Consent)15:07 - The Handoff: When Does AI Transfer to Human Sales Reps?16:22 - Reactivating Dead Leads: 650 Meetings Booked in 90 Days18:04 - Mid-Roll: The Executive Guide to Shadow AI20:23 - Quality Benchmarks: What Makes a Voice Agent Sound Human?24:22 - The Thoughtly Roadmap: Omnichannel AI Personas Are Coming26:49 - From "Who Do I Hire?" to "What System Do I Build?"28:10 - How to Pilot a Voice Agent: The Low-Risk, High-ROI Use Case29:47 - The CRM Graveyard Audit: What Would 10% Reactivation Mean for Your Revenue?31:02 - Where to Learn More About Thoughtly31:46 - Outro: Download the Shadow AI Guide CONNECT:Will Del Principe - Thoughtly: thoughtly.co | thoughtly.co/demoMike Allton - The AI Hat: theaihat.com | theaihat.com/shadow-ai AI for Revenue Leaders is your operational playbook for the Agentic Era. Host Mike Allton deconstructs how practitioners are deploying sanctioned AI to hit quota and reclaim sales capacity lost to admin drag. Stop guessing. Start architecting. Learn more about your ad choices. Visit megaphone.fm/adchoices

Dennis looks back at the last few days and recaps the thrilling OT showdown in boy's hoop between Cros-Lex and Imlay City. Northern gets a nice revenge win over Roseville, and St. Clair girls continue to roll!

Hard-to-treat cancers like pancreatic ductal adenocarcinoma (PDAC) have long defied conventional therapies. Radiopharmaceuticals, combining targeted therapy with diagnostic power, are creating new opportunities in precision oncology.Host David Brühlmann speaks with Bryan Miller of Crown Bioscience, who explains how Crown's strategic partnerships, rigorous quality standards, and adaptive study design are shaping radiopharmaceutical development—delivering speed, safety, and real clinical impact.In this episode, you'll learn:The promise and practical implications of theranostics—agents used for both diagnosis and treatment (02:44)Definitions and distinctions between CDX (cell line-derived xenograft) and PDX (patient-derived xenograft) models, and why PDX models better recapitulate tumor heterogeneity (05:11)Strategies for building more predictive, clinically relevant research models (06:09)Balancing rapid innovation with rigorous quality standards—why robust QC systems enable speed without compromising safety (08:01)Key advice for scientists entering radiopharmaceutical development, including how to choose the right research partners (09:53)Why effective collaboration between biotech companies and CROs is akin to a well-chosen partnership (10:50)The future outlook for radiopharmaceuticals and their impact on hard-to-treat cancers (12:21)Strategic insight:Focusing on theranostic radiopharmaceuticals—agents that combine diagnostics and therapy—offers a high-impact strategy for hard-to-treat cancers like PDAC. By enabling simultaneous patient stratification and targeted treatment, theranostics can accelerate development, improve clinical outcomes, and create a competitive advantage in areas where traditional therapies are limited.Where do you see radiopharmaceuticals and advanced preclinical models making the biggest impact in oncology or beyond?Explore the full conversation to learn how Bryan Miller and Crown Bioscience are scaling innovation for the next generation of cancer therapies.Connect with Bryan Miller:LinkedIn: www.linkedin.com/in/bryan-miller-148344aaCrown Bioscience: www.crownbio.comNext step:Need fast CMC guidance? → Get rapid CMC decision support hereSupport the show

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!

Preview also available on our YouTube channelOur guest : HUGO GORDONIRELAND : 15 Jamie Osborne 14 Tommy O'Brien 13 Garry Ringrose 12 Stuart McCloskey 11 Jacob Stockdale 10 Sam Prendergast 9 Jamison Gibson‑Park  1 Jeremy Loughman 2 Dan Sheehan 3 Tom Clarkson 4 Joe McCarthy 5 Tadhg Beirne 6 Cian Prendergast 7 Josh van der Flier 8 Caelan Doris (c)  16 Ronan Kelleher 17 Andrew Milne 18 Finlay Bealham 19 James Ryan 20 Jack Conan 21 Nick Timoney 22 Craig Casey 23 Jack CrowleyFRANCE : 15 Thomas Ramos 14 Théo Attissogbe 13 Nicolas Depoortere 12 Yoram Moefana 11 Louis Bielle‑Biarrey 10 Matthieu Jalibert 9 Antoine Dupont  1 Jean‑Baptiste Gros 2 Julien Marchand 3 Dorian Aldegheri 4 Charles Ollivon 5 Mickaël Guillard 6 François Cros 7 Oscar Jegou 8 Anthony Jelonch  16 Peato Mauvaka 17 Rodrigue Neti 18 Régis Montagne 19 Hugo Auradou 20 Emmanuel Meafu 21 Leni Nouchi 22 Baptiste Serin 23 Kalvin GourguesGuinness Six Nations 2026 - Round 1Thursday, February 5, 2026Stade de FranceKO 8:10pmLive on : Virgin Media 1Referee: Karl Dickson (RFU)AR1: Angus Gardner (RA)AR2: Jordan Way (RA)TMO: Ian Tempest (RFU)FPRO: Richard Kelly (NZR)Where to find Harpin' On Rugbyhttps://linktr.ee/harpinonrugbyCOMMENT/SHARE/FOLLOW/SUBSCRIBE

Clinical trial complexity is rising with more procedures, endpoints, and technology, yet sites are turning these pressures into pathways for improvement. In this episode of WCG Talks Trials, host Jenna Goeller sits down with Trevor Cole to unpack practical ways research sites sustain readiness, build resilience, and spark innovation amid frequent protocol amendments and technology overload. Together, they explore what's changing on the ground and how sites are responding with stronger feasibility reviews, capacity planning, streamlined protocol advocacy, and risk‑proportionate oversight – all grounded in Quality by Design and the updated ICH E6(R3) guidance.Listeners will hear data‑driven insights on:The operational ripple effects of complexity, including resource strain, rework from amendments, and tech support burdens, and what's working to reduce them.Day‑to‑day applications of risk‑based quality management, data governance, and proportionality to protect participant safety and data integrity.Culture and maturity for empowering teams, mapping processes before SOPs, continuous training, Correction and Preventive Action (CAPA) discipline, and knowledge sharing across silos.How sponsors and CROs can better support sites through integrated technology, protocol simplification, early collaboration, and transparent communications.Turning complexity into growth by investing in people and processes, using fit‑for‑purpose tech (including selective AI use), and engaging local communities.Speakers:Jenna Goeller, Associate Director, Clinical Trial Insights & Analytics, WCGTrevor Cole, Program Director, Clinical Solutions & Partnering, WCG

Most revenue leaders chase numbers they can't actually control.The best ones build systems that compound.In this episode of Revenue Leaders, we break down why revenue is a lagging indicator — and how partnerships and ecosystems are becoming the most reliable way to drive predictable B2B growth.Our guest, Brian Williams, shares real-world lessons from building and scaling partner ecosystems, including what worked, what failed, and why most companies misunderstand partnerships completely.You'll learn:Why you can't control revenue — and what you can control insteadHow partner ecosystems drive pipeline, retention, and larger dealsWhy partnerships fail when treated like a short-term sales channelHow revenue leaders should think about 12–18 month growth strategiesHow founders, CROs, and sales leaders can build a partner-led GTM motionThis episode is for founders, CROs, revenue leaders, sales managers, and RevOps teams who want to stop chasing short-term wins and start building durable, scalable growth engines.If you sell complex or B2B deals, manage sales teams, or lead go-to-market strategy, this episode is for you.⭐ Unlock free resources (templates, frameworks & prompts):⁠https://coachpilot.beehiiv.com/⁠Join the community & access 157+ templates, frameworks and mega AI prompts used by top revenue teams.Watch Full Episode on YouTube:⁠⁠https://www.youtube.com/@revenueleaders⁠⁠Follow us:⁠⁠https://www.instagram.com/davidfastuca/⁠

My substack FREE: https://substack.com/@dansfera1?r=27gh4e&utm_medium=ios&utm_source=profileInato: https://go.inato.com/3VnSro6CRIO: http://www.clinicalresearch.ioMy PatientACE recruitment company: https://patientace.com/Join me at my conference! http://www.saveoursites.comText Me: (949) 415-6256Listen on Spotify: https://open.spotify.com/show/7JF6FNvoLnBpfIrLNCcg7aGET THE BOOK! https://www.amazon.com/Comprehensive-Guide-Clinical-Research-Practical/dp/1090349521/ref=sr_1_1?keywords=Dan+Sfera&qid=1691974540&s=audible&sr=1-1-catcorrText "guru" to 855-942-5288 to join VIP list!My blog: http://www.TheClinicalTrialsGuru.comMy CRO and Site Network: http://www.DSCScro.comMy CRA Academy: http://www.TheCRAacademy.comMy CRC Academy: http://www.TheCRCacademy.comLatinos In Clinical Research: http://www.LatinosinClinicalResearch.comThe University Of Clinical Research: https://www.theuniversityofclinicalresearch.com/My TikTok: DanSfera

You asked, we're answering. In this listener Q&A episode, Amber and Carolyn tackle the hard questions GTM leaders are wrestling with behind closed doors…from broken attribution models to navigating organizational resistance when you're trying to drive real change.In this episode:Real talk on entrepreneurship: the wins, the loneliness, and knowing when to walk awayNavigating organizational resistance when you're championing changeWhy being in the top 5% of GTM leaders means accepting you're always pushing uphillWhy first-touch and last-touch attribution keep haunting you (and how to finally escape)How to get executive buy-in when everyone's comfortable with the status quoWhy deals from different sources have wildly different ACVs and win ratesThe systematic reality of revenue generation, and why singular attribution models completely miss itThis isn't surface-level advice. Amber and Carolyn are in the trenches daily with CROs, CMOs and RevOps leaders, rearchitecting go-to-market strategies and challenging sacred cows. We're bringing real examples to this convo, honest reflections about entrepreneurship, and zero sugarcoating about what separates companies that evolve from those that don't.Keep sending your questions. We want to hear your hot takes, especially if you disagree with what we're saying.

Dennis looks back at an exciting boy's game between Cros-Lex and Yale that went to OT to dicide who was all alone in first place in the BWAC at the half-way point of the season! Weather has been messing with the MAC, but Northern, St. Clair, and Marine City all got important wins! That and more on this show!

Clinical trial budgeting remains one of the biggest bottlenecks in study startups, driving delays, rework, and frustration across sponsors, CROs, and research sites. In this episode of the Clinical Trial Podcast, recorded live at Research Revolution, a clinical research conference hosted by Florence Healthcare, we take a hard look at why clinical trial budget negotiations continue to break down—and what experienced operators are doing differently. This conversation brings together sponsor, site, and consultant perspectives to unpack the real drivers of delay, including slow escalation pathways, unclear or inconsistent budget justifications, misaligned expectations, and communication gaps between stakeholders. Rather than rehashing theory, this episode focuses on practical, experience-driven insights you can actually apply. In this episode, you'll learn: The most common causes of delays during clinical trial budget negotiations How sites can create clear, defensible budget justifications without triggering endless revision cycles What sponsors look for when approving higher-than-expected line items Best practices for internal rate cards, fee schedules, and budgeting templates How improved communication and transparency can reduce negotiation friction and speed study startup This episode features insights from: Kristen McKenna, Senior Manager and Investigator Contracts Lead at Pfizer Heidi Castle, Director of Business Development at Mercy Research Matt Lowery, CEO and Principal Consultant at The Pathways Group If you're involved in clinical trial budgeting, contracting, or study startup - whether at a sponsor, CRO, or research site - this episode offers practical insights to help you navigate negotiations more effectively and avoid common pitfalls. Listen to the episode to hear how sponsor, site, and consultant leaders approach clinical trial budgeting and study startup.

durée : 00:59:45 - Académie Charles Cros : Palmarès - par : Nicolas Pommaret - Nicolas Pommaret révèle le palmarès des Coups de Coeur jazz, blues et soul de l'Académie Charles Cros. Vous aimez ce podcast ? Pour écouter tous les autres épisodes sans limite, rendez-vous sur Radio France.

In this episode of Clinical Research Coach, host Leanne Woehlke sits down with Dr. Rohan Lall for a candid, thought-provoking conversation on what it truly takes to modernize clinical research—without losing sight of patients, providers, or purpose.Dr. Lall brings a rare dual perspective as a practicing physician and clinical research leader, offering firsthand insight into the structural, cultural, and operational challenges that slow progress across trials today. Together, Leanne and Dr. Lall explore how clinical research must evolve to better reflect real-world care, rebuild trust with patients, and support sites and investigators who are stretched thin.In this episode, you'll hear about:Why many clinical trials still feel disconnected from everyday clinical care—and how to close that gapThe growing tension between protocol complexity and patient participationWhat physicians actually need to stay engaged in research long-termHow trust, communication, and operational empathy impact enrollment and retentionWhere technology helps—and where human connection remains irreplaceableDr. Lall's vision for a more sustainable, clinician-friendly, patient-ready research ecosystemThis conversation is a must-listen for sponsors, CROs, sites, and innovators who believe the future of clinical research depends not just on better technology, but on better alignment between medicine, operations, and the lived experience of patients.Rohan Lall, MD M Health FarviewUniversity of Minnesota Medical Center  Spine Surgery / Neurosurgery/ Chief Medical Officer SynerFuse BIO:Dr Rohan R. Lall is a neurosurgeon in Edina, Minnesota and is affiliated withmultiple hospitals in the area, including M Health FairviewUniversity of Minnesota Medical Center and M Health FairviewSouthdale Hospital. He received his medical degree from University of Chicago Division of the Biological Sciences The Pritzker School of Medicine. He has expertise in treating spinalfusion, spinal stenosis and spondylosis, among other conditions  Dr Lall is a former investigator of the SynerFuse Proof of Concept trial and pioneered the e-TLIF procedure He performed the world's firstsolo SynerFuse e-TLIF procedure as well as the first 2-level procedure. SynerFuse®e-TLIF™ procedure, a ULE™ Therapy (Ultra Low Energy), is used to addresschronic low back and leg pain for spinal fusion patients. Dr Lall specializes in robotic and minimally invasive surgery, complex spinal surgery, brain for brain and spinal tumors and skullbase surgery/ pituitary tumor surgery.  He has been a leader in robotic spinal surgery and actively involved in the development of new technologies in spine surgery. The Innovative procedure integrates conventional spinal surgery techniques with targeted nerve stimulation to address chronic back pain at its source. These nerve stimulators fundamentallyalter the nerve's ability to transmit pain signals to the brain. The innovative implant allows patients to control nerve stimulation via smartphone, with early trial participants reporting significant pain reduction and improved quality of life without opioid dependence.  

Eddie Reynolds, CEO of UnionSquare Consulting, opens up about the often-fraught relationship between CFOs and CROs. Eddie shares insights from his unique journey—from banking and private equity to being an account executive at Salesforce which forecast within 5% accuracy despite 30%+ growth. The conversation tackles the critical disconnect between finance and go-to-market teams: Why do CFOs struggle to trust CRM pipelines? What breaks when companies hit $50-100M in revenue?  In this episode: How Salesforce was able to forecast with 5% accuracy, The role of FP&A and CROs in go to market strategy and efficiency The issues with LTV to CAC ratio in SaaS  Biggest challenges of the CFO/CRO relationship Bottoms up annual planning working with finance 

In this live episode of the CRO Spotlight Podcast, Warren sits down with Guy Rubin to discuss the recent acquisition of Ebsta by Fullcast and what it signals for the market. Guy explains the strategic reasoning behind the consolidation, highlighting how the current landscape of disparate point solutions is becoming unsustainable. He argues that the future belongs to unified revenue platforms that connect data across the entire lifecycle, moving from a fragmented tech stack to a cohesive "plan to pay" model that drives true efficiency.The conversation shifts to the broader Go-To-Market environment, where the cost of building tech is dropping while the cost of selling rises. Guy points out that while AI tools are exploding, they often create more noise than value when isolated. He predicts a massive consolidation where businesses move away from traditional CRMs requiring manual entry toward intelligent data lakes and AI agents. This shift requires leaders to learn how to ask the right questions of their data rather than simply managing administrative forms.Warren and Guy also explore the dramatic shift in buyer behavior, illustrated by how AI empowers customers to conduct deep research before ever speaking to a human. With buyers capable of building their own business cases, the role of the seller must evolve from a gatekeeper of information to a consultative partner. They discuss why this dynamic forces organizations to lean heavily into partner ecosystems and community validation, as buyers increasingly bypass traditional sales pitches to seek out trusted peer networks.Finally, they dig into strategies for CROs and Private Equity firms looking to audit their revenue engines. Guy introduces "Revenue Insights as a Service," a method of connecting to historical data to generate an immediate "X-ray" of the business before implementing new tech. This allows leaders to identify root causes of inefficiency—like bad data hygiene or territory imbalances—and present concrete, data-backed roadmaps to the C-suite for rapid improvement without waiting months for a new system implementation.

For 10 years, Belkins' founders never sat down on camera to talk honestly about what building this company has really cost them.Michael Maximoff and Vlad Podoliako have spent a decade building together. They took Belkins from zero to one of the world's leading B2B lead generation agencies, built Folderly, and managed a portfolio of joint investments.On paper, the story looks perfect. But a portfolio of companies doesn't show what it feels like to live through it.To close out one of the hardest years they've ever had, Michael and Vlad finally sit down — in person — to break the silence.This special episode isn't about “how we scaled an agency.” It's about what happens to two people when they spend a decade in hardcore mode: carrying a 300-person team, making painful decisions, holding impossible targets, and trying not to lose themselves (or each other) in the process.They talk about optimism turning into realism, when caring too much starts holding the company back, and why 2025 felt like a loss even with “good numbers.”If you've ever felt the weight of responsibility as a founder, co-founder, agency owner, or someone building ambitious things inside a B2B company, this conversation will hit home.In this episode, we unpack:

Welcome back to the Ultimate Guide to Partnering® Podcast. AI agents are your next customers. Subscribe to our Newsletter: https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ https://youtu.be/vEdq8rpBM3I In this data-rich keynote, Jay McBain deconstructs the tectonic shifts reshaping the $5.3 trillion global technology industry, arguing that we are entering a new 20-year cycle where traditional direct sales models are obsolete. McBain explains why 96% of the industry is now surrounded by partners and how successful companies must pivot from “flywheels and theory” to a granular strategy focused on the seven specific partners present in every deal. From the explosion of agentic AI and the $163 billion marketplace revolution to the specific mechanics of multiplier economics, this discussion provides a roadmap for navigating the “decade of the ecosystem” where influence, trust, and integration—not just product—determine winners and losers. Key Takeaways Half of today's Fortune 500 companies will likely vanish in the next 20 years due to the shift toward AI and ecosystem-led models. Every B2B deal now involves an average of seven trusted partners who influence the decision before a vendor even knows a deal exists. Microsoft has outpaced AWS growth for 26 consecutive quarters largely because of a superior partner-led geographic strategy. Marketplaces are projected to grow to $163 billion by 2030, with nearly 60% of deals involving partner funding or private offers. The “Multiplier Effect” is the new ROI, where partners can make up to $8.45 for every dollar of vendor product sold. Future dominance relies on five key pillars: Platform, Service Partnerships, Channel Partnerships, Alliances, and Go-to-Market orchestration. If you're ready to lead through change, elevate your business, and achieve extraordinary outcomes through the power of partnership—this is your community. At Ultimate Partner® we want leaders like you to join us in the Ultimate Partner Experience – where transformation begins. Keywords: Jay McBain, Canalys, partner ecosystem, channel chief, agentic AI, marketplace growth, multiplier economics, B2B sales trends, tech industry forecast, service partnerships, strategic alliances, Microsoft vs AWS, distribution transformation, managed services growth, SaaS platforms, customer journey mapping, 28 moments of truth, future of reselling, technology spending 2025, ecosystem orchestration, partner multipliers. T Transcript: Jay McBain WORKFILE FOR TRANSCRIPT [00:00:00] Vince Menzione: Just up from, did you Puerto Rico last night? Puerto Rico, yes. Puerto Rico. He dodged the hurricane. Um, you all know him. Uh, let him introduce himself for those of you who don’t, but just thrilled to have on the stage, again, somebody who knows more about what’s going on in, in the, and has the pulse on this industry probably than just about anybody I know personally. [00:00:21] Vince Menzione: J Jay McBain. Jay, great to see you my friend. Alright, thank you. We have to come all the way. We live, we live uh, about 20 minutes from each other. We have to come all the way to Reston, Virginia to see each other, right? That’s right. Very good. Well, uh, that’s all over to you, sir. Thank you. [00:00:35] Jay McBain: Alright, well thank you so much. [00:00:36] Jay McBain: I went from 85 degrees yesterday to 45 today, but I was able to dodge that, uh, that hurricane, uh, that we kind of had to fly through the northern edge of, uh, wanna talk today about our industry, about the ultimate partner. I’m gonna try to frame up the ultimate partner as I walk through the data and the latest research that, uh, that we’ve been doing in the market. [00:00:56] Jay McBain: But I wanted to start here ’cause our industry moves in 20 year cycles, and if you look at the Fortune 500 and dial back 20 years from today, 52% of them no longer exist. As we step into the next 20 year AI era, half of the companies that we know and love today are not gonna exist. So we look at this, and by the way, if you’re not in the Fortune 500 and you don’t have deep pockets to buy your way outta problems, 71% of tech companies fail over the course of 10 years. [00:01:30] Jay McBain: Those are statistics from the US government. So I start to look at our industry and you know, you may look at the, you know, mainframe era from the sixties and seventies, mini computers, August the 12th, 1981, that first IBM, PC with Microsoft dos, version one, you know, triggered. A new 20 year era of client server. [00:01:51] Jay McBain: It was the time and I worked at IBM for 17 years, but there was a time where Bill Gates flew into Boca Raton, Florida and met with the IBM team and did that, you know, fancy licensing agreement. But after, you know, 20 years of being the most valuable company in the world and 13 years of antitrust and getting broken up, almost like at and TIBM almost didn’t make payroll. [00:02:14] Jay McBain: 13 years after meeting Bill Gates. Yeah, that’s how quickly things change in these eras. In 1999, a small company outta San Francisco called salesforce.com got its start. About 10 years later, Jeff Bezos asked a question in a boardroom, could we rent out our excess capacity and would other companies buy it? [00:02:35] Jay McBain: Which, you know, most people in the room laughed at ’em at the time. But it created a 20 year cloud era when our friends, our neighbors, our family. Saw Chachi PT for the first time in March of 2023. They saw the deep fakes, they saw the poetry, they saw the music. They came to us as tech people and said, did we just light up Skynet? [00:02:58] Jay McBain: And that consumer trend has triggered this next 20 years. I could walk through the richest people in the world through those trends. I could walk through the most valuable companies. It all aligns. ’cause by the way, Apple’s no longer at the top. Nvidia is at the top, Microsoft. Second, things change really quickly. [00:03:17] Jay McBain: So in that course of time, you start to look at our industry and as people are talking about a six and a half or $7 trillion build out of ai, that’s open AI and Microsoft numbers, that is bigger than our industry that’s taken over 50 years to build. This year, we’re gonna finish the year at $5.3 trillion. [00:03:36] Jay McBain: That’s from the smallest flower shop to the biggest bank. Biggest governments that Caresoft would, uh, serve biggest customer in the world is actually the federal government of the us. But you look at this pie chart and you look at the changes that we’re gonna go through over the next 20 years, there’s about a trillion dollars in hardware. [00:03:54] Jay McBain: There’s about a trillion dollars in software. If you look forward through all of the merging trends, quantum computing, humanoid robots, all the things that are coming that dollar to dollar software to hardware will continue to exist all the way through. We see services making up almost two thirds of this pie. [00:04:13] Jay McBain: Yesterday I was in a telco conference with at and t and Verizon and T-Mobile and some of the biggest wireless players and IT services, which happen to be growing faster than products. At the moment, there is more work to be done wrapping around the deal than the actual products that the customer is buying. [00:04:32] Jay McBain: So in an industry that’s growing at 7%. On top of the world economy that’s grown at 2.2. This is the fastest growing industry, and it will be at least for the next 10 years, if not 2070 0.1% of this entire $5 trillion gets transacted through partners. While what we’re talking to today about the ultimate partner, 96% of this industry is surrounded by partners in one way or another. [00:05:01] Jay McBain: They’re there before the deal. They’re there at the deal. They’re there after the deal. Two thirds of our industry is now subscription consumption based. So every 30 days forever, and a customer for life becomes everything. So if every deal in medium, mid-market, and higher has seven partners, according to McKinsey, who are those seven people trying to get into the deal? [00:05:25] Jay McBain: While there’s millions of companies that have come into tech over the last 10 to 20 years. Digital agencies, accountants, legal firms, everybody’s come in. The 250,000 SaaS companies, a million emerging tech companies, there’s a big fight to be one of those seven trusted people at the table. So millions of companies and tens of millions of people our competing for these slots. [00:05:49] Jay McBain: So one of the pieces of research I’m most proud of, uh, in my analyst career is this. And this took over two years to build. It’s a lot of logos. Not this PowerPoint slide, but the actual data. Thousands of people hours. Because guess what? When you look at partners from the top down, the top 1000 partners, by capability and capacity, not by resale. [00:06:15] Jay McBain: It’s not a ranking of CDW and insight and resale numbers. It is the surrounding. Consulting, design, architecture, implementations, integrations, managed services, all the pieces that’s gonna make the next 20 years run. So when you start to look at this, 98% of these companies are private, so very difficult to get to those numbers and, uh, a ton of research and help from AI and other things to get this. [00:06:41] Jay McBain: But this is it. And if you look at this list, there’s a thousand logos out of the million companies. There’s a thousand logos that drive two thirds of all tech services in the world. $1.07 trillion gets delivered by a thousand companies, but here’s where it gets fun. Those companies in the middle, in blue, the 30 of them deliver more tech services than the next 970. [00:07:08] Jay McBain: Combined the 970 combined in white deliver more tech services. Then the next million combined. So if you think we live in an 80 20 rule or maybe a 99, a 95 5 rule, or a 99 1 rule, we actually live in a 99.9 0.1 parallel principle. These companies spread around the world evenly split across the uh, different regions. [00:07:35] Jay McBain: South Africa, Latin America, they’re all over. They split. They split among types. All of the Venn diagram I just showed from GSIs to VARs to MSPs, to agencies and other types of companies. But this is a really rich list and it’s public. So every company in the world now, if you’re looking at Transactable data, if you’re looking at quantifiable data that you can go put your revenue numbers against, it represents 70 to 80% of every company in this room’s Tam. [00:08:08] Jay McBain: In one piece of research. So what do you do below that? How do you cover a million companies that you can’t afford to put a channel account manager? You can’t afford to write programs directly for well after the top down analysis and all the wallet share and you know exactly where the lowest hanging fruit is for most of your tam. [00:08:28] Jay McBain: The available markets. The obtainable markets. You gotta start from the community level grassroots up. So you need to ask the question for the million companies and the maybe a hundred thousand companies out there, partner companies that are surrounding your customer. These are the seven partners that surround your customer. [00:08:48] Jay McBain: What do they read, where do they go, and who do they follow? Interestingly enough, our industry globally equates to only a thousand watering holes, a thousand companies at the top, a thousand places at the bottom. 35% of this audience we’re talking. Millions of people here love events and there’s 352 of them like this one that they love to go to. [00:09:13] Jay McBain: They love the hallway chats, they love the hotel lobby bar, you know, in a time reminded by the pandemic. They love to be in person. It’s the number one way they’re influenced. So if you don’t have a solid event strategy and you don’t have a community team out giving out socks every week, your competitors might beat you. [00:09:31] Jay McBain: 12% of this audience loves podcasts. It’s the Joe Rogan effect of our industry. And while you know, you may not think the 121 podcasts out there are important, well, you’re missing 12% of your audience. It’s over a million people. If you’re not on a weekly podcast in one of these podcasts in the world, there’s still people that read one of the 106 magazines in the world. [00:09:55] Jay McBain: There are people that love peer groups, associations, they wanna be part of this. There’s 15 different ways people are influenced. And a solid grassroots strategy is how you make this happen. In the last 10 years, we’ve created a number of billionaires. Bottom up. They never had to go talk to la large enterprise. [00:10:15] Jay McBain: They never had to go build out a mid-market strategy. They just went and give away socks and new community marketing. And this has created, I could rip through a bunch of names that became unicorns just in the last couple of years, bottoms up. You go back to your board walking into next year, top down, bottom up. [00:10:34] Jay McBain: You’ve covered a hundred percent of your tam, and now you’ve covered it with names, faces, and places. You haven’t covered it with a flywheel or a theory. And for 44 years, we have gone to our board every fourth quarter with flywheels and theory. Trust me, partners are important. The channel is key to us. [00:10:57] Jay McBain: Well, let’s talk at the point of this granularity, and now we’re getting supported by technology 261 entrepreneurs. Many of them in the room actually here that are driving this ability to succeed with seven partners in every deal to exchange data to be able to exchange telemetry of these prospects to be able to see twice or three times in terms of pipeline of your target addressable market. [00:11:26] Jay McBain: All these ai, um, technologies, agentic technologies are coming into this. It’s all about data. It’s all about quantifiable names, faces, and places. Now none of us should be walking around with flywheels, so let’s flip the flywheels. No. Uh, so we also look at, and I sold PCs for 17 years and that was in the high times of 40% margins for partners. [00:11:55] Jay McBain: But one interesting thing when you study the p and l for broad base of partners around the world, it’s changed pretty significantly in this last 20 year era. What the cloud era did is dropped hardware from what used to be 84% plus the break fix and things that wrap around it of the p and l to now 16% of every partner in the world. [00:12:16] Jay McBain: 84% of their p and l is now software and services. And if you look at profitability, it’s worse. It’s actually 87% is profitability wise. They’ve completely shifted in terms of where they go. Now we look at other parts of our market. I could go through every part of the pie of the slide, but we’re watching each of the companies, and if you can see here, this is what we want to talk about in terms of ultimate partner. [00:12:43] Jay McBain: Microsoft has outgrown AWS for 26 straight quarters. They don’t have a better product. They don’t have a better price, they don’t have better promotion. It’s all place. And I’ll explain why you guess here in the light green line. Exactly. The day that Google went a hundred percent all in partner, every deal, even if a deal didn’t have a partner, one of the 4% of deals that didn’t have a partner, they injected a partner. [00:13:09] Jay McBain: You can see on the left side exactly where they did it. They got to the point of a hundred percent partner driven. Rebuilt their programs, rebuilt their marketplace. Their marketplace is actually larger than Microsoft’s, and they grew faster than Microsoft. A couple of those quarters. It is a partner driven future, and now I have Oracle, which I just walked by as I walked from the hotel. [00:13:31] Jay McBain: Oracle with their RPOs will start to join. Maybe the list of three hyperscalers becomes the list of four in future slides, but that’s a growth slide. Market share is different. AWS early and commanding lead. And it plays out, uh, plays out this way. But we’re at an interesting moment and I stood up six years ago talking about the decade of the ecosystem after we went through a decade of sales starting in 1999 when we all thought we were born to be salespeople. [00:14:02] Jay McBain: We managed territories with our gut. The sales tech stack would have it different, that sales was a science, and we ended the decade 2009, looking at sales very differently in 2009. I remember being at cocktail parties where CMOs would be joking around that 50% of their marketing dollars were wasted. They just didn’t know which 50%. [00:14:23] Jay McBain: And I’ll tell you, that was really funny. In 2009 till every 58-year-old CMO got replaced by a 38-year-old growth hacker who walked in with 15,348 SaaS companies in their MarTech and ad tech stack to solve the problem, every nickel of marketing by 2019 was tracked. Marketo, Eloqua, Pardot, HubSpot, driving this industry. [00:14:50] Jay McBain: Now, we stood up and said the 28 moments that come before a sale are pretty much all partner driven. In the best case scenario, a vendor might see four of the moments. They might come to your website, maybe they read an ebook, maybe they have a salesperson or a demo that comes in. That’s four outta 28 moments. [00:15:10] Jay McBain: The other 24 are done by partners. Yeah, in the worst case scenario and the majority scenario, you don’t see any of the moments. All 28 happen and you lose a deal without knowing there ever was a deal. So this is it. We need to partner in these moments and we need to inject partners into sales and marketing, like no time before, and this was the time to do it. [00:15:33] Jay McBain: And we got some feedback in the Salesforce state of sales report, which doesn’t involve any partnerships or, or. Channel Chiefs or anything else. This is 5,500 of the biggest CROs in the world that obviously use Salesforce. 89% of salespeople today use partners every day. For the 11% who don’t, 58% plan two within a year. [00:15:57] Jay McBain: If you add those two numbers together, that’s magically the 96% number. They recognize that every deal has partners in it. In 2024, last year, half of the salespeople in the world, every industry, every country. Miss their numbers. For the minority who made their numbers, 84 point percent pointed to partners as the reason why they made their numbers. [00:16:21] Jay McBain: It was the cheat code for sales, so that modern salesperson that knows how to orchestrate a deal, orchestrate the 28 moments with the seven partners and get to that final spot is the winning formula. HubSpot’s number in separate research was 84% in marketing. So we’re starting to see partners in here. We don’t have to shout from the mountaintops. [00:16:44] Jay McBain: These communities like ultimate Partner are working and we’re getting this to the highest levels in the board. And I’ll say that, you know, when 20 years from now half of the companies we know and love fail after we’re done writing the book and blaming the CEO for inventing the thing that ended up killing them, blaming the board for fiduciary responsibility and letting it happen. [00:17:06] Jay McBain: What are the other chapters of the book? And I think it’s all in one slide. We are in this platform economy and the. [00:17:31] Jay McBain: So your battery’s fine. Check, check, check, check. Alright, I’ll, I’ll just hold this in case, but the companies that execute on all five of these areas, well. Not only today become the trillion dollar valued companies, but they become the companies of tomorrow. These will be the fastest growing companies at every level. [00:17:50] Jay McBain: Not only running a platform business, but participating in other platforms. So this is how it breaks out, and there are people at very senior levels, at very big companies that have this now posted in the office of the CEO winning on integrations is everything. We just went through a demographic shift this year where 51% of our buyers are born after 1982. [00:18:15] Jay McBain: Millennials are the number one buyer of the $5 trillion. Their number one buying criteria is not service. Support your price, your brand reputation, it’s integrations. The buy a product, 80% is good as the next one if it works better in their environment. 79% of us won’t buy a car unless it has CarPlay or Android Auto. [00:18:34] Jay McBain: This is an integration world. The company with the most integrations win. Second, there are seven partners that surround the customer. Highly trusted partners. We’re talking, coaching the customer’s, kids soccer team, having a cottage together up at the lake. You know, best men, bate of honors at weddings type of relationships. [00:18:57] Jay McBain: You can’t maybe have all seven, but how does Microsoft beat AWS? They might have had two, three, or four of them saying nice things about them instead of the competition. Winning in service partnerships and channel partnerships changes by category. If you’re selling MarTech, only 10% of it today is resold, so you build more on service partnerships. [00:19:18] Jay McBain: If you’re in cybersecurity today, 91.6% of it is resold. Transacted through partners. So you build a lot of channel partnerships, plus the service partnerships, whatever the mix is in your category, you have to have two or three of those seven people. Saying nice things about you at every stage of the customer journey. [00:19:38] Jay McBain: Now move over to alliances. We have already built the platforms at the hyperscale level. We’ve built the platforms within SaaS, Salesforce, ServiceNow, Workday, Marketo, NetSuite, HubSpot. Every buyer has a set of platforms that they buy. We’ve now built them in cybersecurity this year out of 6,500 as high as cyber companies, the top five are starting to separate. [00:20:02] Jay McBain: We built it in distribution, which I’ll show in a minute. We’re building it in Telco. This is a platform economy and alliances win and you have alliances with your competitors ’cause you compete in the morning, but you’re best friends by the afternoon. Winning in other platforms is just as important as driving your own. [00:20:20] Jay McBain: And probably the most important part of this is go to market. That sales, that marketing, the 28 moments, the every 30 days forever become all a partner strategy. So there’s still CEOs out there that believe platform is a UI or UX on a bunch of disparate products and things you’ve acquired. There’s still CFOs out there that Think platform is a pricing model, a bundle model of just getting everything under one, you know, subscription price or consumption price. [00:20:51] Jay McBain: And it’s not, platforms are synonymous with partnerships. This is the way forward and there’s no conversation around ai. That doesn’t involve Nvidia over there, an open AI over here and a hyperscaler over there and a SaaS company over here. The seven layer stack wins every single time, and the companies that get this will be the ones that survive this cycle. [00:21:16] Jay McBain: Now, flipping over to marketplaces. So we had written research that, um, about five years ago that marketplaces were going to grow at 82% compounded. Yeah, probably one of the most accurate predictions we ever made, because it happened, we, we predicted that, uh, we were gonna get up to about $85 billion. Well, now we’ve extended that to 2030, so we’re gonna get up to $163 billion, and the thing that we’re watching is in green. [00:21:46] Jay McBain: If 96% of these deals are partner assisted in some way, how is the economics of partnering going to work? We predicted that 50% of deals by 2027. Would be partner funded in some way. Private offers multi-partner offers distributor sellers of record, and now that extends to 59% by 2030, the most senior leader of the biggest marketplace AWS, just said to us they’re gonna probably make these numbers on their own. [00:22:14] Jay McBain: And he asked what their two competitors are doing. So he’s telling us that we under called this. Now when you look at each of the press releases, and this is the AWS Billion Dollar Club. Every one of the companies on the left have issued a press release that they’re in the billion dollar club. Some of them are in the multi-billions, but I want you to double click on this press release. [00:22:35] Jay McBain: I’m quoted in here somewhere, but as CrowdStrike is building the marketplace at 91% compounded, they’re almost doubling their revenue every single year. They’re growing the partner funding, in this case, distributor funding by 3548%. Almost triple digit growth in marketplace is translating into almost quadruple digit growth in funding. [00:23:01] Jay McBain: And you see that over and over again as, as Splunk hit three, uh, billion dollars. The same. Salesforce hit $2 billion on AWS in Ulti, 18 months. They joined in October 20, 23, and 18 months later, they’re already at $2 billion. But now you’re seeing at Salesforce, which by the way. Grew up to $40 billion in revenue direct, almost not a nickel in resell. [00:23:28] Jay McBain: Made it really difficult for VARs and managed service providers to work with Salesforce because they couldn’t understand how to add services to something they didn’t book the revenue for. While $40 billion companies now seeing 70% of their deals come through partners. So this is just the world that we’re in. [00:23:44] Jay McBain: It doesn’t matter who you are and what industry you’re in, this takes place. But now we’re starting to see for the first time. Partners join the billion dollar club. So you wonder about partnering and all this funding and everything that’s working through Now you’re seeing press releases and companies that are redoing their LinkedIn branding about joining this illustrious club without a product to sell and all the services that wrap around it. [00:24:10] Jay McBain: So the opening session on Microsoft was interesting because there’s been a number of changes that Microsoft has done just in the last 30 days. One is they cut distribution by two thirds going from 180 distributors to 62. They cut out any small partner lower than a thousand dollars, and that doesn’t sound like a lot, but that’s over a hundred thousand partners that get deed tightening the long tail. [00:24:38] Jay McBain: They we’re the first to really put a global point system in place three years ago. They went to the new commerce experience. If you remember, all kinds of changes being led by. The biggest company for the channel. And so when we’re studying marketplaces, we’re not just studying the three hyperscalers, we’re studying what TD Cynic is doing with Stream One Ingram’s doing with Advant Advantage Aerosphere. [00:25:01] Jay McBain: Also, we’re watching what PAX eight, who by the way, is the 365 bestseller for Microsoft in the world. They are the cybersecurity leader for Microsoft in the world and the copilot. Leader in the world for Microsoft and Partner of the Year for Microsoft. So we’re watching what the cloud platforms are doing, watching what the Telco are doing, which is 25 cents out of every dollar, if you remember that pie chart, watching what the biggest resellers are converting themselves into. [00:25:30] Jay McBain: Vince just mentioned, you know, SHI in the changes there watching the managed services market and the leaders there, what they’re doing in terms of how this industry’s moving forward. By the way, managed services at $608 billion this year. Is one and a half times larger than the SaaS industry overall. [00:25:48] Jay McBain: It’s also one and a half times larger than all the hyperscalers combined. Oracle, Alibaba, IBM, all the way down. This is a massive market and it makes up 15 to 20 cents of every dollar the customer spend. We’re watching that industry hit a trillion dollars by the end of the decade, and we’re watching 150 different marketplace development platforms, the distribution of our industry, which today is 70.1% indirect. [00:26:13] Jay McBain: We’re starting to see that number, uh, solidify in terms of marketplaces as well. Watching distributors go from that linear warehouse in a bank to this orchestration model, watching some of the biggest players as the world comes around, platforms, it tightens around the place. So Caresoft, uh, from from here is the sixth biggest distributor in the world. [00:26:40] Jay McBain: Just shows you how big the. You know, biggest client in the world is that they serve. But understand that we’re publishing the distributor 500 list, but it’ll be the same thing. That little group in blue in the middle today, you know, drives almost two thirds of the market. So what happens in all this next stage in terms of where the dollars change hands. [00:27:07] Jay McBain: And the economics of partnering themselves are going through the most radical shift that we’ve seen ever. So back to the nineties, and, and for those of you that have been channel chiefs and running programs, we went to work every day. You know, everything’s on fire. We’re trying to check hundred boxes, trying to make our program 10% better than our competitors. [00:27:30] Jay McBain: Hey, we gotta fix our deal registration program today, and our incentives are outta whack or training programs or. You know, not where they need to be. Our certification, you know, this was the life of, uh, of a channel chief. Everybody thought we were just out drinking in the Caribbean with our best partners, but we were under the weight of this. [00:27:49] Jay McBain: But something interesting has happened is that we turned around and put the customer at the middle of our programs to say that those 28 moments in green before the sale are really, really important. And the seven partners who participate are really important. Understanding. The customer’s gonna buy a seven layer stack. [00:28:09] Jay McBain: They’re gonna buy it With these seven partners, the procurement stage is much different. The growth of marketplaces, the growth of direct in some of these areas, and then long term every 30 days forever in a managed service, implementations, integrations, how you upsell, cross-sell, enrich a deal changes. So how would you build a program that’s wrapped around the customer instead of the vendor? [00:28:35] Jay McBain: And we’re starting to hear our partners shout back to us. These are global surveys, big numbers, but over half of our partners, regardless of type, are selling consulting to their customer. Over half are designing architecting deals. A third of them are trying to be system integrators showing up at those implementation integration moments. [00:28:55] Jay McBain: Two thirds of them are doing managed services, but the shocking one here is 44% of our partners, regardless of type, are coding. They’re building agents and they’re out helping their customer at that level. So this is the modern partner that says, don’t typecast me. You may have thought of me in your program. [00:29:14] Jay McBain: You might have me slotted as a var. Well, I do 3.2 things, and if I don’t get access to those resources, if you don’t walk me to that room, I’m not gonna do them with you. You may have me as a managed service provider that’s only in the morning. By the afternoon I’m coding, and by the next morning I’m implementing and consulting. [00:29:33] Jay McBain: So again, a partner’s not a partner. That Venn diagram is a very loose one now, as every partner on there is doing 3.2 different business models. And again, they’re telling us for 43 years, they said, I want more leads this year it changed. For the first time, I want to be recognized and incentivized as more than just a cash register for you. [00:29:57] Jay McBain: I want you to recognize when I’m consulting, when I’m designing, when you’re winning deals, because of my wonderful services, by the way, we asked the follow up question, well, where should we spend our money with you? And they overwhelmingly say, in the consulting stage, you win and lose deals. Not at moment 28. [00:30:18] Jay McBain: We’re not buying a pack of gum at the gas station. This is a considered purchase. You win deals from moment 12 through 16 and I’m gonna show you a picture of that later, and they say, you better be spending your money there, or you’re not gonna win your fair share or more than your fair share of deals. [00:30:36] Jay McBain: The shocking thing about this is that Microsoft, when they went to the point system, lifted two thirds of all the money, tens of billions of dollars, and put it post-sale, and we were all scratching our heads going. Well, if the partners are asking for it there, and it seems like to beat your biggest competitors, you want to win there. [00:30:54] Jay McBain: Why would you spend the money on renewal? Well, they went to Wall Street and Goldman Sachs and the people who lift trillions of dollars of pension funds and said, if we renew deals at 108%, we become a cash machine for you. And we think that’s more valuable than a company coming out with a new cell phone in September and selling a lot of them by Christmas every year. [00:31:18] Jay McBain: The industry. And by the way, wall Street responded, Microsoft has been more valuable than Apple since. So we talk in this now multiplier language, and these are reports that we write, uh, at AMIA at canals. But talking about the partner opportunity in that customer cycle, the $6 and 40 cents you can make for every dollar of consumption, or the $7 and 5 cents you can make the $8 and 45 cents you can make. [00:31:46] Jay McBain: There’s over 24 companies speaking at this level now, and guess what? It’s not just cloud or software companies. Hardware companies are starting to speak in this language, and on January 25th, Cisco, you know, probably second to Microsoft in terms of trust built with the channel globally is moving to a full point system. [00:32:09] Jay McBain: So these are the changes that happen fast. But your QBR with your partners now less about drinking beers at the hotel lobby bar and talking dollar by dollar where these opportunities are. So if you’re doing 3.2 of these things, let’s build out a, uh, a play where you can make $3 for every dollar that we make. [00:32:28] Jay McBain: And you make that profitably. You make it in sticky, highly retained business, and that’s the model. ’cause if you make $3 for every dollar. We make, you’re gonna win Partner of the year, and if you win partner of the year, that piece of glass that you win on stage, by the time you get back to your table, you’re gonna have three offers to buy your business. [00:32:51] Jay McBain: CDW just bought a w. S’s Partner of the Year. Insight bought Google’s eight time partner of the year. Presidio bought ServiceNow’s, partner of the year over and over and over again. So I’m at Octane, I’m at CrowdStrike, I’m at all these events in Vegas every week. I’m watching these partners of the year. [00:33:05] Jay McBain: And I’m watching as the big resellers. I’m watching as the GSIs and the m and a folks are surrounding their table after, and they’re selling their businesses for SaaS level valuations. Not the one-to-one service valuation. They’re getting multiples because this is the new future of our industry. This is platform economics. [00:33:25] Jay McBain: This is winning and platforms for partners. Now, like Vince, I spent 20 minutes without talking about ai, but we have to talk about ai. So the next 20 years as it plays out is gonna play out in phases. And the first thing you know to get it out of the way. The first two years since that March of 23, has been underwhelming, to say the least. [00:33:47] Jay McBain: It’s been disappointing. All the companies that should have won the biggest in AI have been the most disappointing. It’s underperformed the s and p by a considerable amount in terms of where we are. And it goes back to this. We always overestimate the first two years, but we underestimate the first 10. [00:34:07] Jay McBain: If you wanna be the point in time person and go look at that 1983 PC or the 1995 internet or that 2007 iPhone or that whatever point in time you wanna look at, or if you want to talk about hallucinations or where chat chip ET version five is version, as opposed to where it’s going to be as it improves every six months here on in. [00:34:30] Jay McBain: But the fact of the matter is, it’s been a consumer trend. Nvidia got to be the most valuable company in the world. OpenAI was the first company to 2 billion users, uh, in that amount of speed. It’s the fastest growing product ever in history, and it’s been a consumer win this trillions of dollars to get it thrown around in the press releases. [00:34:49] Jay McBain: They’re going out every day, you know, open ai, signing up somebody new or Nvidia, investing in somebody new almost every single day in hundreds of billions of dollars. It is all happening really on the consumer side. So we got a little bit worried and said, is that 96% of surround gonna work in ag agentic ai? [00:35:10] Jay McBain: So we went and asked, and the good news is 88% of end customers are using partners to work through their ag agentic strategy. Even though they’re moving slow, they’re actually using partners. But what’s interesting from a partner perspective, and this is new research that out till 2030. This is the number one services opportunity in the entire tech or telco industry. [00:35:34] Jay McBain: 35.3% compounded growth ending at $267 billion in services. Companies are rebuilding themselves, building out practices, and getting on this train and figuring out which vendors they should hook their caboose to as those trains leave the station. But it kind of plays out like this. So in the next three to five years, we’re in this generative, moving into agentic phase. [00:36:01] Jay McBain: Every partner thinks internally first, the sales and marketing. They’re thinking about their invoicing and billing. They’re thinking about their service tickets. They’re thinking about creating a business that’s 10% better than their competitors, taking that knowledge into their customers and drive in business. [00:36:17] Jay McBain: But we understand that ag agentic AI, as it’s going to play out is not a product. A couple of years ago, we thought maybe a copilot or an agent force or something was going to be the product that everybody needed to buy, and it’s not a product, it’s gonna show up as a feature. So you go back in the history of feature ads and it’s gonna show up in software. [00:36:38] Jay McBain: So if you’re calling in SMB, maybe you’re calling on a restaurant. The restaurant isn’t gonna call OpenAI or call Microsoft or call Nvidia directly. They’re running their restaurant. And they may have chosen a platform like Toast Square, Clover, whatever iPads people are running around with, runs on a platform that does everything in their business, does staffing, does food ordering, works with Uber Eats, does everything end to end? [00:37:08] Jay McBain: They’re gonna wait to one of those platforms, dries out agent AI for them, and can run the restaurant more effectively, less human capital and more consistently, but they wait for the SaaS platform as you get larger. A hundred, 150 people. You have vice presidents. Each of those vice presidents already have a SaaS stack. [00:37:28] Jay McBain: I talked about Salesforce, ServiceNow, Workday, et cetera. They’ve already built that seven layer model and in some cases it’s 70 layers. But the fact is, is they’re gonna wait for those SaaS layers to deliver ag agentic to them. So this is how it’s gonna play out for the next three and a half, three to five years. [00:37:45] Jay McBain: And partners are realizing that many of them were slow to pick up SaaS ’cause they didn’t resell it. Well now to win in this next three to half, three to five years, you’re gonna have to play in this environment. When you start looking out from here, the next generation, you know, kind of five through 15 years gets interesting in more of a physical sense. [00:38:06] Jay McBain: Where I was yesterday talking about every IOT device that now is internet access, starts to get access to large language models. Every little sensor, every camera, everything that’s out there starts to get smart. But there’s a point. The first trillionaire, I believe, will be created here. Elon’s already halfway there. [00:38:24] Jay McBain: Um, but when Bill Gates thought there was gonna be a PC in every home, and IBM thought they were gonna sell 10,000 to hobbyists, that created the richest person in the world for 20 years, there will be a humanoid in every home. There’s gonna be a point in time that you’re out having drinks with your friends, and somebody’s gonna say, the early adopter of your friends is gonna say. [00:38:46] Jay McBain: I haven’t done the dishes in six weeks. I haven’t done the laundry. I haven’t made my bed. I haven’t mowed the lawn. When they say that, you’re gonna say, well, how? And they’re gonna say, well, this year I didn’t buy a new car, but I went to the car dealership and I bought this. So we’re very close to the dexterity needed. [00:39:05] Jay McBain: We’ve got the large language models. Now. The chat, GPT version 10 by then is going to make an insane, and every house is gonna have one of the. [00:39:17] Jay McBain: This is the promise of ai. It’s not humanoid robots, it’s not agents. It’s this. 99% of the world’s business data has not been trained or tuned into models yet. Again, this is the slow moving business. If you want to think about the 99% of business data, every flight we’ve all taken in this room sits on a saber system that was put in place in 1964. [00:39:43] Jay McBain: Every banking transaction, we’ve all made, every withdrawal, every deposit sits on an IBM mainframe put in place in the sixties or seventies. 83% of this data sits in cold storage at the edge. It’s not ready to be moved. It’s not cleansed, it’s not, um, indexed. It’s not in any format or sitting on any infrastructure that a large language model will be able to gobble up the data. [00:40:10] Jay McBain: None of the workflows, none of the programming on top of that data is yet ready. So this is your 10 to 20 year arc of this era that chat bot today when they cancel your flight is cute. It’s empathetic, it feels bad for you, or at least it seems to, but it can’t do anything. It can’t book you the Marriott and get you an Uber and then a 5:00 AM flight the next morning. [00:40:34] Jay McBain: It can’t do any of that. But more importantly, it doesn’t know who you are. I’ve got 53 years of flights under my belt and they, I’m the person that get me within six hours of my kids and get me a one-way Hertz rental. You know, if there’s bad weather in Miami, get me to Tampa, get me a Hertz, I’m driving home, I’m gonna make it home. [00:40:56] Jay McBain: I’m not the 5:00 AM get me a hotel person. They would know that if they picked up the flights that I’ve taken in the past. Each of us are different. When you get access to the business data and you become ag agentic, everything changes. Every industry changes because of this around the customers. When you ask about this 35% growth, working on that data, working in traditional consulting and design and implementation, working in the $7 trillion of infrastructure, storage, compute, networking, that’s gonna be around, this is a massive opportunity. [00:41:30] Jay McBain: Services are gonna continue to outgrow products. Probably for the next five to 10 years because of this, and I’m gonna finish here. So we talked a lot about quantifying names, faces, places, and I think where we failed the most as ultimate partners is underneath the tam, which every one of our CEOs knows to the decimal point underneath the TAM that our board thinks they’re chasing. [00:41:59] Jay McBain: We’ve done a very poor job. Of talking about the available markets and obtainable markets underneath it, we, we’ve shown them theory. We’ve shown them a bunch of, you know, really smart stuff, and PowerPoint slides up the wazoo, but we’ve never quantified it for them. If they wanna win, if they want to get access, if they want to double their pipeline, triple their pipeline, if they wanna start winning more deals, if they wanna win deals that are three times larger, they close two times faster. [00:42:31] Jay McBain: And they renew 15% larger. They have to get into the available and obtainable markets. So just in the last couple weeks I spoke at Cribble, I spoke at Octane, I spoke at CrowdStrike Falcon. All three of those companies at the CEO level, main stage use those exact three numbers, three x, two x, 15%. That’s the language of platforms, and they’re investing millions and millions and millions of dollars on teams. [00:42:59] Jay McBain: To go build out the Sam Andal in name spaces and places. So you’ve heard me talk about these 28 moments a lot. They’re the ones that you spend when you buy a car. Some people spend one moment and they drive to the Cadillac dealership. ’cause Larry’s been, you know, taking care of the family for 50 years. [00:43:18] Jay McBain: Some people spend 50 moments like I do, watching every YouTube video and every, you know, thing on the internet. I clear the internet cover to cover. But the fact is, is every deal averages around these 28 moments. Your customer, there’s 13 members of the buying committee today. There’s seven partners and they’re buying seven things. [00:43:37] Jay McBain: There’s 27 things orchestrating inside these 28 moments. And where and how they all take place is a story of partnering. So a couple of years ago, canals. Latin for channel was acquired by amia, which is a part of Informa Tech Target, which is majority owned by Informa. All that being said, there’s hundreds of magazines that we have. [00:44:00] Jay McBain: There’s hundreds of events that we run. If somebody’s buying cybersecurity, they probably went to Black Hat or they probably went to GI Tech. One of these events we run, or one of the magazines. So we pick up these signals, these buyer intent signals as a company. Why did they wanna, um, buy a, uh, a Canals, which was a, you know, a small analyst firm around channels? [00:44:22] Jay McBain: They understood this as well. The 28 moments look a lot like this when marketers and salespeople are busy filling in the spots of every deal. And by the way, this is a real deal. AstraZeneca came in to spend millions of dollars on ASAP transformation, and you can start to see as the customer got smart. [00:44:45] Jay McBain: The eBooks, they read the podcasts, they listened to the events they went to. You start to see how this played out over the long term. But the thing we’ve never had in our industry is the light blue boxes. This deal was won and lost in December. In this particular case, NTT software won and Yash came in and sold the customer five projects. [00:45:07] Jay McBain: The millions of dollars that were going to be spent were solved here. The design and architecture work was all done here. A couple of ISVs You see in light blue came in right at the end, deal was closed in April. You see the six month cycle. But what if you could fill in every one of the 28 boxes in every single customer prospect that your sales and marketing team have? [00:45:30] Jay McBain: But here’s the brilliance of this. Those light blue boxes didn’t win the deals there. They won the deals months before that. So when NTT and Software one walked into this deal. They probably won the deal back in October and they had to go through the redlining. They had to go through the contracting, they had to go through all the stuff and the Gantt chart to get started. [00:45:54] Jay McBain: But while your CMO is getting all excited about somebody reading an ebook and triggering an MQL that the sales team doesn’t want, ’cause it’s not qualified, it’s not sales qualified, you walk in and say, no, no. This is a multimillion deal, dollar deal. It’s AstraZeneca. I know the five partners that are coming in in December to solidify the seven layers, and you’re walking in at the same time as the CMOs bragging about an ebook. [00:46:21] Jay McBain: This changes everything. If we could get to this level of data about every dollar of our tam, we not only outgrow our competitors, we become the platforms of the next generation. Partnering and ultimate partnering is all here. And this is what we’re doing in this room. This is what we’re doing over these couple of days, and this is what, uh, the mission that Vince is leading. [00:46:43] Jay McBain: Thank you so much. [00:46:47] Vince Menzione: Woo. Day in the house. Good to see you my friend. Good to see you. Oh, we’re gonna spend a couple minutes. Um, I’m put you in the second seat. We’re gonna put, we’re gonna make it sit fireside for a minute. Uh, that was intense. It was pretty incredible actually, Jay. And so I’m, I think I wanna open it up ’cause we only have a few minutes just to, any questions? [00:47:06] Vince Menzione: I’m sure people are just digesting. We already have one up here. See, [00:47:09] Question: Jay knows I’m [00:47:10] Vince Menzione: a question. I love it. We, I don’t think we have any I can grab a mic, a roving mic. I could be a roving mic person. Hold on. We can do this. This is not on. [00:47:25] Vince Menzione: Test, test. Yes it is. Yeah. [00:47:26] Question: Theresa Carriol dared me to ask a question and I say, you don’t have to dare me. You know, I’m going to Anyway. Um, so Jay, of the point of view that with all of the new AI players that strategic alliances is again having a moment, and I was curious your point of view on what you’re seeing around this emergence and trend of strategic alliances and strategic alliance management. [00:47:52] Question: As compared to channel management. And what are you seeing in terms of large vendors like AWS investing in that strategic alliance role versus that channel role training, enablement, measurement, all that good stuff? [00:48:06] Jay McBain: Yeah, it’s, it’s a great question. So when I told the story about toast at the restaurant or Square or Clover, they’re not call, they’re not gonna call open AI or Nvidia themselves either. [00:48:17] Jay McBain: When you look out at the 250,000 ISVs. That make up this AI stack, there is the layers that happen there. So the Alliance with AWS, the alliance they have with Microsoft or Google is going to be how they generate agent AI in their platforms. So when I talk about a seven layer stack, the average deal being seven layers, AI is gonna drive this to nine, and then 11, then probably 13. [00:48:44] Jay McBain: So in terms of how alliances work, I had it up there as one of the five core strategies, and I think it’s pretty even. You can have the best alliances in the world, but if the seven partners trusted by the customer don’t know what that alliance is and the benefits to the customer and never mention it, it’s all for Naugh. [00:49:00] Jay McBain: If you’re go-to market, you’re co-selling, your co-marketing strategies are not built around that alliance. It’s all for naught. If the integration and the co-innovation, the co-development, the all the co-creation work that’s done inside these alliances isn’t translated to customer outcomes, it’s all for naugh. [00:49:17] Jay McBain: These are all five parallel swim lanes. All five are absolutely critically needed. And I think they’re all five pretty equally weighted in terms of needing each other. Yes. To be successful in the era of platforms. Yeah. [00:49:32] Vince Menzione: And the problem is they’re all stove pipe today. If, if at all. Yeah. Maintained, right. [00:49:36] Vince Menzione: Alliances is an example. Channels and other example. They don’t talk to one another. Judge any, we’ve got a mic up here if anybody else has. Yep. We have some questions here, Jacqueline. [00:49:51] Question: So when we’re developing our channel programs, any advice on, you know, what’s the shift that we should make six months from now, a year from now? The historical has been bronze, silver, gold, right? And you’ve got your deal registration, but what’s the future look like? [00:50:05] Jay McBain: Yeah, so I mean, the programs are, are changing to, to the point where the customer should be in the middle and realizing the seven partners you need to win the deal. [00:50:15] Jay McBain: And depending on what category of product you’re in, security, how much you rely on resell, 91.6%. You know, the channel partners are gonna be critical where the customer spends the money. And if you’re adding friction to that process, you’re adding friction in terms of your growth. So you know, if you’re in cybersecurity, you have to have a pretty wide open reseller model. [00:50:39] Jay McBain: You have to have a wide open distribution model, and you have to make sure you’re there at that point of sale. While at the same time, considering the other six partners at moment 12 who are in either saying nice things about you or not, the customer might even be starting with you. ’cause there is actually one thing that I didn’t mention when I showed the 28 moments filled in. [00:51:00] Jay McBain: You’ll notice that the customer went to AWS twice direct. AWS lost the deal. Microsoft won the deal software. One is Microsoft’s biggest reseller in the world. They just acquired crayon. NTT who, who loves both had their Microsoft team go in. [00:51:18] Question: Mm. [00:51:19] Jay McBain: So I think that they went to AWS thinking it was A-W-S-S-A-P, you know, kind of starting this seven layer stack. [00:51:25] Jay McBain: I think they finished those, you know, critical moments in the middle looking at it. And then they went back to AWS kind of going probably WWTF. Yeah. What we thought was happening isn’t actually the outcome that was painted by our most trusted people. So, you know, to answer your question, listen to your partners. [00:51:43] Jay McBain: They want to be recognized for the other things they’re doing. You can’t be spending a hundred percent of the dollars at the point of sale. You gotta have a point of system that recognizes the point of sale, maybe even gold, silver, bronze, but recognizing that you’re paying for these other moments as well. [00:51:57] Jay McBain: Paying for alliances, paying for integrations and everything else, uh, in the cyber stack. And, um, you know, recognizing also the top 1000. So if I took your tam. And I overlaid those thousand logos. I would be walking into 2026 the best I could of showing my company logo by logo, where 80% of our TAM sits as wallet share, not by revenue. [00:52:25] Jay McBain: Remember, a million dollar partner is not a million dollar partner. One of them sells 1.2 million in our category. We should buy them a baseball cap and have ’em sit in the front row of our event. One of them sells $10 million and only sells our stuff if the customer asks. So my company should be looking at that $9 million opportunity and making sure my programs are writing the checks and my coverage. [00:52:48] Jay McBain: My capacity and capability planning is getting obsessed over that $9 million. My farmers can go over there, my hunters can go over here, and I should be submitting a list of a thousand sorted in descending order of opportunity. Of where my company can write program dollars into. [00:53:07] Vince Menzione: Great answer. All right. I, I do wanna be cognizant of time and the, all the other sessions we have. [00:53:14] Vince Menzione: So we’ll just take one other question if there are any here and if not, we’ll let I know. Jay, you’re gonna be mingling around for a little while before your flight. I’m [00:53:21] Jay McBain: here the whole day. [00:53:22] Vince Menzione: You, you’re the whole day. I see that Jay’s here the whole day. So if you have any other questions and, and, uh, sharing the deck is that. [00:53:29] Vince Menzione: Yep. Alright. We have permission to share the deck with the each of you as well. [00:53:34] Jay McBain: Alright, well thank you very much everyone. Jay. Great to have you.

In this episode, Dr. Steve Gard, editor-in-chief of the Journal of Prosthetics and Orthotics, sits down with Tiffany Graham, MSPO, CPO, LPO, FAAOP(D), to discuss her research on the effectiveness of 3D-printed cranial remolding orthoses (CROs) for infants. Tiffany walks through the evolution of CRO design—from early fabrication techniques to today's innovative 3D-printing approaches—and shares insights from a recent study conducted in Australia. Their conversation highlights key findings showing that 3D-printed CROs are a successful treatment option for cranial deformities, with some head shapes potentially requiring longer intervention. They also explore the practical benefits of 3D-printed devices, including improved ventilation, which may offer meaningful advantages for patients in warmer climates. Show notes JPO article: Efficacy of 3D-Printed Cranial Remolding Orthosis for Infants in Australia   O&P Research Insights is produced by Association Briefings.

In this episode of Data in Biotech, Ross Katz sits down with Callie Celichowski and Isa Kupke from Veloxity Labs to discuss how their CRO leverages speed, precision, and innovation to support drug development. Learn how they use mass spectrometry, cloud-based infrastructure, and hands-on client partnerships to drive rapid, high-quality bioanalytical insights that support everything from preclinical studies to FDA submissions. What you'll learn in this episode: >> Why "speed with purpose" is essential for bioanalytical CROs supporting biotech and pharma clients >> The benefits and challenges of working with peptides and GLP-1 receptor agonists >> How the SCIEX 8600 enhances detection of low-concentration analytes Meet our guests Isa Kupke is Scientist II at Veloxity Labs, where she specializes in mass spectrometry and method development for preclinical and regulated bioanalytical programs. She also co-founded Blyde Botanics, bridging plant-based science and product development. Callie Celichowski is Senior Director of Business Development at Veloxity Labs, with over two decades in the pharmaceutical and CRO space. She's recognized for building strategic client partnerships and driving rapid, data-driven decision-making. About the host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Sponsor: CorrDyn, a data consultancyConnect with Isa Kupke on LinkedIn Connect with Callie Celichowski on LinkedIn  Connect with Us: Follow the podcast for more insightful discussions on the latest in biotech and data science.Subscribe and leave a review if you enjoyed this episode!Connect with Ross Katz on LinkedIn Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn.

Revenue teams are dealing with more tools, more data, more automation - and more pressure - than ever before. Growth isn't just about selling better anymore. It's about how the entire revenue engine actually works.For the first time, RevOps isn't a background function — it's shaping how companies grow, scale, and make decisions.In this episode of the Belkins Podcast, Michael Maximoff sits down with Jen Igartua, Founder & CEO of Go Nimbly, to unpack what's really happening inside modern revenue organizations — and why RevOps is suddenly at the center of it all.Jen has helped architect revenue systems for some of the most respected SaaS companies in the world, including Twilio, Zendesk, Snowflake, Intercom, and Superhuman. But this conversation isn't about theory or frameworks. It's about what breaks when companies scale, where AI actually helps (and where it creates chaos), and why the future of sales, marketing, and RevOps looks very different than most teams expect.What you'll learn in this episode:Why RevOps is having a moment — and how rising complexity, AI adoption, and executive pressure have pushed RevOps into a strategic roleWhat Revenue Operations actually does beyond automation, reporting, and tooling — including enablement, strategy, and protecting the customer experienceHow AI is changing RevOps teams — from workflow automation and data architecture to the risks of agent and automation sprawlThe real future of sales roles — why junior SDR roles are disappearing, and why business development is becoming more senior, not automated awayHow to know when your company needs RevOps — including revenue thresholds, organizational signals, and common mistakes founders make too earlyWhat strong RevOps teams get right — clean data, shared definitions, cross-functional trust, and decision-making that actually sticksThroughout the episode, Jen and Michael go deep on the messy, human side of scaling revenue — misaligned incentives, broken handoffs, over-engineered stacks, and the uncomfortable truth that most companies don't actually have a single view of the customer.This isn't a hype conversation about tools. It's a grounded look at how modern revenue organizations are being rebuilt — and why RevOps is now one of the most critical functions inside growing B2B companies.Chapters:00:00- Intro: Who is Jen Igartua03:17- What is RevOps?09:25- AI Changed RevOps: AIOps, When to Hire RevOps, Build vs Outsource17:25- Workflow Automation Is Getting Out of Control24:02- What's Next: Platform Consolidation in RevOps27:46- Clay, HubSpot, and the Reality of the Modern RevOps Stack33:48- The Limits of AI in Sales & Marketing39:25- How SDRs, Marketing, and Social Selling Are Merging49:50- RevFest: Building Real RevOps Community01:00:49- Go Nibly's Evolution and Strategy01:12:44- Curated Dinners as Acquisition Strategies01:21:26- Creativity, Leadership, and “Follow the Fun”About the ShowWhat does it really take to grow a B2B business today? We ask the people doing it.The Belkins Podcast dives deep into the strategies, decisions, and behind-the-scenes insights driving real growth at top B2B companies. Each episode features candid conversations with industry heavyweights — CROs, CMOs, founders, and seasoned operators — who've navigated market downturns, scaled teams, and dealt with the realities of modern revenue growth.You'll hear hard truths, unfiltered insights, and actionable perspectives from leaders who've actually built and operated revenue engines at scale.

In this episode, Warren speaks with Dr. Grant Van Ulbrich, the world's first doctor of Sales Transformation. They discuss a critical gap in the modern revenue landscape: while other business functions have deep academic rigor, sales education has remained largely stagnant since the 1980s. Grant explains why traditional "tips and tricks" no longer work on informed buyers and why a shift from transactional interactions to genuine value alignment is necessary for business survival.The conversation dives into the psychology of change and Grant's "Scared So What" methodology. Most leaders manage operations, not emotions, yet sales is fundamentally an act of imposing change. Grant breaks down how understanding personal reactions to change allows revenue leaders to move from simply telling teams what to do to empowering them through transformational leadership. This approach helps teams navigate the fear of new strategies and structures.Grant shares insights from the global cruise industry, revealing how shifting focus from internal agendas to customer needs dramatically improved sales effectiveness. He highlights the dangers of "product dumping" and the importance of co-creation. By treating sales as a coaching opportunity rather than a coercion tactic, organizations can align internal culture with the external customer experience to ensure a consistent brand promise across all channels.Finally, the discussion offers actionable advice for CEOs and CROs on navigating organizational restructuring. They explore why high-performing individual contributors often struggle in leadership roles without the right psychological tools. This episode is essential for executives looking to modernize their go-to-market strategy, foster a culture of ownership among their teams, and utilize science-backed frameworks to drive sustainable revenue growth.

In this episode, we sit down with Simon Arkell, an experienced AI software company founder whose work sits at the intersection of artificial intelligence, biopharma, and cancer research. Simon is the co-founder of multiple venture-backed companies including Deep Lens, Predixion Software, and Versifi, which together raised over $100M and were all successfully acquired. Simon shares the story behind his latest company, Ryght, an agentic AI platform transforming how biopharma companies and CROs accelerate drug development. Ryght connects sponsors to a real-time, global network of AI-powered research sites known as AI Site Twins, enabling faster site selection, smarter feasibility, reduced costs, and compressed clinical timelines across all therapeutic areas and geographies. Beyond technology and startups, Simon's journey is remarkable. From arriving in the United States on a pole vaulting scholarship to becoming an Olympian representing Australia at two Olympic Games, three World Championships, and more, he explains how elite athletics shaped his mindset for entrepreneurship, leadership, and resilience. We also explore Simon's philanthropic work with Megan's Wings, supporting families of children diagnosed with cancer, and his initiative Olympians in Business, a growing community helping elite athletes transition into successful business careers. This conversation dives deep into AI in healthcare, venture-backed startups, drug development innovation, founder lessons, and how world-class performance in sports translates into building impactful companies. Host: Jake Aaron Villarreal, leads the top AI Recruitment Firm in Silicon Valley www.matchrelevant.com, uncovering stories of funded startups and goes behinds to scenes to tell their founders journey.  If you are growing AI Startup or have a great storytelling, email us at: jake.villarreal@matchrelevant.com

In this episode, I chat with Tom Stearns, a consultant to CEOs and CROs, about how mentorship helped him redefine and scale his business.   Tom shares how he gained clarity, focused on the right clients, shortened sales cycles, increased deal sizes, and walked away from boring work, all while achieving a four-day workweek. Whether you're a founder, sales leader, or consultant looking for practical strategies to grow your business and work smarter, Tom's story offers actionable insights and inspiration.   Contact team@principledselling.com Contact Tom https://www.linkedin.com/in/tomstearns/    

What happens when a GPCR bench scientist becomes a global connector in biotech? In this episode, Dr. Yamina Berchiche shares her inspiring journey from academia to industry, and how her passion for communication and community-building led her to found the Dr. GPCR ecosystem. With over 20 years in GPCR pharmacology across academia and biotech, Yamina blends deep scientific insight with entrepreneurial vision. Learn how she transitioned out of the lab, embraced marketing and sales, and now supports scientists and CROs through strategic consulting. You'll hear about: The origin of the Dr. GPCR podcast and how it grew into a global platform Yamina's leap from postdoc to sales, and why she left the bench How PhDs can reframe their skills to lead in industry settings Communication lessons from the world of scientific marketing Her thoughts on personal branding and building connections in biotech Whether you're exploring non-academic careers for PhDs or looking to amplify your science communication impact, this episode offers a grounded look at finding purpose beyond the pipette.  

Don't know what the hell CRO and experimentation is? Trying to understand the basic concepts and process? We gotchu. Rommil "The Thrill" Santiago and Shiva Manjunath went into all the common topics, and we can even help you explain this to others who might not fully understand what you do in CRO. Please passive aggressively share this episode to people who don't understand CRO. I will fall on my sword for you. We got into:- Why CRO is seen as a Hail Mary (and why it SHOULDN'T be....)- CROs shouldn't exist. Because everyone should be doing CRO in some way- What the hell MDE really even is? And do you even need it? (Yes....yes you do)Go follow Rommil on LinkedIn:⁠https://www.linkedin.com/in/rommil/Go check out his book:https://www.experimentnation.com/prove-it-or-lose-itSign up for Experiment Nation (I'm there... he's there too I guess): https://www.experimentnation.com/Also go follow Shiva Manjunath on LinkedIn:⁠⁠⁠https://www.linkedin.com/in/shiva-manjunath/⁠⁠⁠Subscribe to our newsletter for more memes, clips, and awesome content!⁠⁠https://fromatob.beehiiv.com/

2025-12-05 Hosts Dr. Amir Kalali, Craig Lipset, and Jane Myles were joined by Noelle Gaskill and Karla Polk from Tempus AI to unpack how DCTs are expanding access and accelerating research, especially in oncology. They explored how Tempus' data-connected site network, genomics capabilities, and operational model help match patients faster, streamline startup timelines, and support community practices in running complex trials.The conversation covers real-world strategies for patient activation, managing variability across sites, partnering with CROs, and enabling rural and community-based providers to participate in research. They also touched on the future: global expansion plans and how DCT approaches could extend beyond oncology into other therapeutic areas.You can join TGIF-DTRA Sessions live on LinkedIn Live on Friday's at 12:00 PM ET by checking out our LinkedIn. Follow the Decentralized Trials & Research Alliance (DTRA) on LinkedIn and X. Learn more about Membership options and our work at www.dtra.org.

We all know by now that plants grown in living, thriving, life-filled soil, give us living, thriving, life-filled food... but the steps to getting there in the face of a multinational industry devoted to toxic, nutritionally empty, addictive - and highly profitable - ultra-processed 'food-like substances' are harder to see.  This week's guest, Daphne du Cros, spends her life deep in the mycelial networks of food and farming systems, bringing both into genuinely regenerative balance. Daphne is a food policy researcher, educator, and farmer. She holds a PhD in Food Policy at the Centre for Food Policy at City St. George's University of London, and a Master's in Environmental Science and Management from Toronto Metropolitan University in Canada. She is Director and Coordinator at Shropshire Good Food Partnership; Director at Light Foot Enterprises; Project Lead at Food Forward BC (where BC stands for Bishop's Castle, not British Columbia or any of the other potential options) -   and she's co-owner of Little Woodbatch CIC, a farm just outside BC that hosts the Bishop's Castle Community Seed Bank. She is the author of the town's Community Food Resilience Strategy - the only such policy in Shropshire.Daphne and I are relatively near neighbours, we have swapped seeds - her more than me - and share ideas about systems thinking and how we might evolve our world. She's deeply involved at every level from actual growing up to governmental meetings trying to get those in power to find some wisdom when it comes to food resilience, food security and all the other things we say as we try to get them to move away from the corruption innate in our system towards something that actually works in service to life. Daphne on LinkedIn https://uk.linkedin.com/in/daphne-du-cros-743128332Instagram: https://www.instagram.com/littlewoodbatch/ Shropshire Good Food Partnership:  https://www.shropshiregoodfood.org/Instagram: https://www.instagram.com/shropshiregoodfood/ Soil Ed UK: https://www.instagram.com/soil_ed_uk/  Gaia Foundation Seed Sovereignty Network: https://www.seedsovereignty.info/Serving the Public https://uk.bookshop.org/p/books/serving-the-public-the-good-food-revolution-in-schools-hospitals-and-prisons-kevin-morgan/7657661?ean=9781526180469&next=tCivil Food Resilience Report: https://nationalpreparednesscommission.uk/publications/just-in-case-7-steps-to-narrow-the-uk-civil-food-resilience-gap/  Little Woodbatch Farm https://www.littlewoodbatch.co.uk/What we offer: Accidental Gods, Dreaming Awake and the Thrutopia Writing Masterclass If you'd like to join our next Open Gathering offered by our Accidental Gods Programme it's  'Dreaming Your Year Awake' (you don't have to be a member) on Sunday 4th January 2026 from 16:00 - 20:00 GMT - details are hereIf you'd like to join us at Accidental Gods, this is the membership where we endeavour to help you to connect fully with the living web of life. If you'd like to train more deeply in the contemporary shamanic work at Dreaming Awake, you'll find us here. If you'd like to explore the recordings from our last Thrutopia Writing Masterclass, the details are here

Send us a textA birth surprise. A scramble for answers. And a mother who refused to accept “good enough” when her daughter's hearing—and future—were on the line. We sit down with EarCommunity.org founder Melissa Tumblin to unpack microtia, aural atresia, and the real costs of unilateral hearing loss that too often go unseen: delayed speech, safety risks, and the daily strain of listening with one ear in a noisy world.We walk through the early months—ABR testing, confusing terminology, and the long wait to discover bone conduction hearing devices that bypass the outer and middle ear. Melissa shares the moment Ally's device switched on and the room changed, along with the aided audiograms that moved from loss to the normal range. From there we zoom out: how to practice at the top of scope as clinicians, when to refer, and what families need to know about candidacy for bone-anchored systems, CROS, and cochlear implants.The story widens into advocacy. Coverage denials are common for people with atresia and unilateral loss, even when a device is medically necessary. Melissa explains Ally's Act—a bipartisan, bicameral bill that would require private insurance coverage for bone-anchored systems and cochlear implants, including fittings, programming, surgery, post-op care, therapy options, and five-year upgrades for qualified patients up to age 64. We discuss the small but significant population at stake, the path in Congress, and how families and professionals can help: share your story, contact lawmakers, and close the loophole that keeps people from the hearing tech they need.If you're a parent new to microtia and atresia, you'll find reassurance and practical steps. If you're a clinician, you'll find a call to raise awareness and make the right referrals. And if you care about access, you'll hear how a single family's journey became a movement for equity in hearing health. Subscribe, share with someone who needs this conversation, and leave a review to help more listeners find it. Connect with the Hearing Matters Podcast TeamEmail: hearingmatterspodcast@gmail.com Instagram: @hearing_matters_podcast Facebook: Hearing Matters Podcast

If your 2026 budget is starting to feel like a no-win puzzle—flat headcount, higher growth expectations, fewer resources—this episode is for you. Craig Moore of Forrester joins Drew to reveal the budgeting mistakes too many B2B CMOs are still making—and what to do instead.  From rethinking budget architecture to organizing around business outcomes, Craig shares the frameworks that enable CMOs to go beyond justifying their spend—and start leading the strategic conversation with CEOs, CFOs, and CROs.  Get ready to challenge your assumptions, realign your org, and turn your budget into a true lever for growth.  In this episode:  The big 3 budgeting mistakes CMOs make  Why campaign-based budgeting unlocks strategy  Areas of volatility in 2026  AI's Role in Budget Planning  This is just the first half of one of CMO Huddles monthly Bonus Huddles with B2B marketing strategists. To hear the rest of the conversation with Craig, visit CMO Huddles Hub on YouTube.  For full show notes and transcripts, visit https://renegademarketing.com/podcasts/ To learn more about CMO Huddles, visit https://cmohuddles.com/

Behind every well-known tech company is a leader navigating pressure, uncertainty, and constant change. That's what makes this conversation with Wade Foster — Co-Founder and CEO of Zapier — stand out.Wade doesn't show up with clichés or polished CEO lines. He talks about the decisions that actually shape a $5B company: how he manages his time, how he thinks about hiring and org structure, what AI really means for Zapier, and why leadership gets harder — not easier — as the company scales.This is one of those episodes you watch if you care about real leadership, decision-making under pressure, and what it takes to build a product people depend on every day. It's grounded, tactical, and surprisingly honest.

Joel's LinkedIn: https://www.linkedin.com/in/joelwhite01?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=ios_appQ3 Recap Slides: https://www.linkedin.com/posts/joelwhite01_q3-2025-recap-activity-7396874466405933056-BvuR?utm_medium=ios_app&rcm=ACoAAAMXVmgBnc7UoIJwJXEE-js1R9q6H1Qczc8&utm_source=social_share_send&utm_campaign=copy_linkThe worst acquisition article: https://www.linkedin.com/pulse/worst-acquisition-joel-white-ytawc?utm_source=share&utm_medium=member_ios&utm_campaign=share_viaInato: https://go.inato.com/3VnSro6CRIO: http://www.clinicalresearch.ioMy PatientACE recruitment company: https://patientace.com/Join me at my conference! http://www.saveoursites.comText Me: (949) 415-6256Listen on Spotify: https://open.spotify.com/show/7JF6FNvoLnBpfIrLNCcg7aGET THE BOOK! https://www.amazon.com/Comprehensive-Guide-Clinical-Research-Practical/dp/1090349521/ref=sr_1_1?keywords=Dan+Sfera&qid=1691974540&s=audible&sr=1-1-catcorrText "guru" to 855-942-5288 to join VIP list!My blog: http://www.TheClinicalTrialsGuru.comMy CRO and Site Network: http://www.DSCScro.comMy CRA Academy: http://www.TheCRAacademy.comMy CRC Academy: http://www.TheCRCacademy.comLatinos In Clinical Research: http://www.LatinosinClinicalResearch.comThe University Of Clinical Research: https://www.theuniversityofclinicalresearch.com/My TikTok: DanSfera

In this episode of Molecule to Market, your host Raman Sehgal discusses the pharmaceutical and biotechnology supply chain with Jean-François Brepson, Chief Executive Officer at PathoQuest. The conversation covers: Navigating 20 years of global leadership roles at Ipsen before moving from the corporate world into an investor-led entrepreneurial adventure The tough decision to refocus PathoQuest from diagnostics into a pure play CRO and pharma services business How a major strategic partnership transformed the company's trajectory and why Jean sees partnerships as a competitive weapon Riding the tailwind of the FDA's move away from animal testing and offering something game-changing in the CMC and GMP space The opportunities ahead for CROs and CDMOs in helping unlock the next wave of innovation Jean-François Brepson is a dedicated leader with deep experience in biotechnology and pharmaceuticals. Since becoming CEO of PathoQuest in 2015, he has built the company into a leading global CRO specializing in quality control of biological drugs using Next Generation Sequencing (NGS). Over his career, he has advanced innovative technologies and solutions that bridge scientific progress with real-world application. Prior to joining PathoQuest, Jean was Senior Vice President at Ipsen, where he led the global GI-Oncology and Endocrinology franchise.   Molecule to Market is sponsored by Bora Pharma, Charles River, and Lead Candidate. Please subscribe, tell your industry colleagues, and help us celebrate the value of the global life science outsourcing space. We'd also appreciate a positive rating!

To get more insights about clinical research technology from a vendor's perspective, I invited Mike Wenger on the Clinical Trial Podcast. Mike Wenger is a software developer with over 15 years of experience creating innovative solutions in clinical research.  At the Michael J. Fox Foundation for Parkinson's Research, he worked to connect Parkinson's patients with clinical studies.  He later developed Citeline Connect, bridging patient recruitment companies with pharmaceutical organizations, and founded VersaTrial to streamline clinical trial site workflows.  Mike is currently the Chief Innovation Officer at CRIO, an intuitive eSource solution that collects data directly at the point of patient interaction to lighten site burden while driving protocol compliance. Please join me in welcoming Mike on the Clinical Trial Podcast. This podcast is brought to you by Florence Healthcare. Florence eliminates chaotic workflows in clinical research operations with remote access and digital workflow platforms. More than 37,000 study sites, sponsors, and CROs in 90 countries trust them to accelerate their operations. To learn more, visit https://florencehc.com  This podcast is brought to you by Calyx. Calyx is a trusted name in medical imaging, having delivered imaging services to meet the needs of global biopharmaceutical sponsors and clinical research organizations for over 25 years. To learn more, visit https://www.calyx.ai/  

Recorded live at American Society of Nephrology's Kidney Week 2025, this episode of Moving Medicine Forward features Courtney Cordaro, Director of Therapeutic Strategy at CTI, and Morgan Terry, Site Director at Eastern Nephrology Associates. Together, they discuss what drives a healthy and productive partnership between clinical research sites and CROs, the critical role of communication, and how both sides can adapt to the evolving landscape of nephrology trials.  01:31 What defines a strong site-CRO relationship 01:49 Key elements of a successful partnership 02:32 The importance of consistency and communication in nephrology research 03:33 How CRO staff changes affect timelines and site efficiency 04:09 Common misconceptions CROs have about sites 05:23 Behaviors and practices that show a CRO's understanding and build trust 06:31 How an effective CRO response can shape stronger partnerships 07:35 Why minimal turnover and familiarity with site processes are key to building long-term trust 09:05 Adapting to decentralized trials by leveraging strategies for remote monitoring and precision medicine 10:37 Highlighting collaboration and adaptability as keys to success 

In this episode of Medsider Radio, we sat down with Somer Baburek, CEO and co-founder of Hera Biotech. Hera is developing AI-driven tissue diagnostics for conditions that disproportionately affect women, including endometriosis and cervical cancer.Before launching Hera, Somer spent nearly a decade in venture capital, where she evaluated early-stage medtech startups and learned what separates the survivors from the rest.In this conversation, Somer explains how Hera designed global clinical pathways that balance cost and credibility, why boutique CROs can outperform big names, and how a pre-commercial startup completed three strategic acquisitions using equity and brand trust rather than cash.Before we dive into the discussion, I wanted to mention a few things:First, if you're into learning from medical device and health technology founders and CEOs, and want to know when new interviews are live, head over to Medsider.com and sign up for our free newsletter.Second, if you want to peek behind the curtain of the world's most successful startups, you should consider a Medsider premium membership. You'll learn the strategies and tactics that founders and CEOs use to build and grow companies like Silk Road Medical, AliveCor, Shockwave Medical, and hundreds more!We recently introduced some fantastic additions exclusively for Medsider premium members, including playbooks, which are curated collections of our top Medsider interviews on key topics like capital fundraising and risk mitigation, and 3 packages that will help you make use of our database of 750+ life science investors more efficiently for your fundraise and help you discover your next medical device or health technology investor!In addition to the entire back catalog of Medsider interviews over the past decade, premium members also get a copy of every volume of Medsider Mentors at no additional cost, including the latest Medsider Mentors Volume VII. If you're interested, go to medsider.com/subscribe to learn more.Lastly, if you'd rather read than listen, here's a link to the full interview with Somer Baburek.

Chad Peets is one of the greatest sales leaders and recruiters of the last 25 years. From 2018 to 2023, Chad was a Managing Director at Sutter Hill Ventures. Chad has worked with the world's best CEOs and CROs to build world-class go-to-market organizations. Chad is currently a member of the Board of Directors for Lacework and Luminary Cloud and on the boards of Clumio and Sigma Computing. He previously served as a board member for Astronomer, Transposit, and others. He was an early-stage investor at Snowflake, Sigma, Observe, Lacework, and Clumio. In Today's Discussion with Chad Peet's We Discuss: 1. You Need a CRO Pre-Product: Why does Chad believe that SaaS companies need a CRO pre-product? Should the founder not be the right person to create the sales playbook? What should the founder look for in their first CRO hire? Does any great CRO really want to go back to an early startup and do it again? 2. What Everyone Gets Wrong in Building Sales Teams: Why are most sales reps not performing? How long does it take for sales teams to ramp? How does this change with PLG and enterprise? What are the benchmarks of good vs great for average sales reps? How do founders and VCs most often hurt their sales teams and performance? 3. How to Build a Hiring Machine: What are the single biggest mistakes people make when hiring sales reps and teams? Are sales people money motivated? How to create comp plans that incentivise and align? Why does Chad believe that any sales rep that does not want to be in the office, is not putting their career and development first? Why is it harder than ever to recruit great sales leaders today? 4. Lessons from Scaling Sales at Snowflake: What are the single biggest lessons of what worked from scaling Snowflake's sales team? What did not work? What would he do differently with the team again? What did Snowflake teach Chad about success and culture and how they interplay together?  

Cell and gene therapies are transforming modern medicine, but their path to market is fast and complex. They often jump from small trials to global launch at record speed, putting pressure on analytics, supply chains, and partnerships. Success depends on making smart choices about what to build in-house and what to entrust to expert partners.Daniel Galbraith knows these challenges intimately. With decades of hands-on experience and as Chief Scientific Officer at Solvias, Daniel has witnessed firsthand the seismic shifts in analytical development for advanced therapies. He's been on every side of the table: troubleshooting manufacturing snags, scaling up from a single batch to hundreds per month, and guiding companies as they choose between in-house development and relying on a CRO's muscle.In this episode:How evolving cell and gene therapy timelines are driving the need for true CRO-drug developer partnerships (00:00)The unique challenges of scaling CMC analytics from early trials to global commercialization (02:51)Key pitfalls to avoid when outsourcing to CROs—especially around communication, scheduling, and troubleshooting (06:26)Deciding whether to build capabilities in-house or outsource to a CRO, and how to find the right balance for your team (08:41)The critical importance of strong project management for juggling relationships between developer, CRO, and CDMO (09:51)Daniel's perspective on the future of combination therapies and what the analytical landscape will demand of CROs (13:33)Practical advice for building transparent, open CRO partnerships that support your goals (15:21)Facing scale-up challenges or a first CGT launch? This conversation shares practical strategies to advance therapies efficiently.Tune in for actionable insights on CMC, outsourcing, and analytical development.Connect with Daniel Galbraith:LinkedIn: www.linkedin.com/in/daniel-galbraith-26a6138Solvias website: www.solvias.comEmail: daniel.galbraith@solvias.comNext step:Book a 20-minute call to help you get started on any questions you may have about bioprocessing analytics: https://bruehlmann-consulting.com/callPreparing for your IND? Grab our Startup Founder CMC Dashboard in Notion to help you track tasks, timelines, and risks in one place at https://stan.store/SmartBiotech/p/discovertoind-cmc-dashboard-for-startup-founders

The Biggest GTM Mistake (Spoiler Alert: Stop Chasing CAC!!!)Mark Roberge shares how AI is transforming sales, customer success, and go-to-market strategy. The former HubSpot CRO, now co-founder of Stage 2 Capital and senior lecturer at Harvard Business School, Mark Roberge breaks down the 4 phases of AI evolution that will redefine how companies sell, serve, and scale. From agentic AI to LTV-driven growth, this is a masterclass on what the next era of go-to-market looks like.Mark Roberge helped take HubSpot from $0 to $100M and literally wrote The Sales Acceleration Formula. Now, he's turning his attention to the AI transformation sweeping every GTM function. In this episode, Mark explains why it's time to stop obsessing over CAC and start optimizing for LTV—the customers who actually succeed—and how AI can make that possible at scale.He also shares bold predictions about the future of work, the death of departments, and why capitalism itself may need to evolve for the AI era.Timestamps0:00 – Preview & Introduction1:19 – Meet Mark Roberge: Co-Founder, Stage 2 Capital2:45 – The Early Days of AI in GTM6:33 – What's Slowing Down AI Adoption8:00 – Why Most AI Startups Are Still Too Iterative12:00 – The "Agentic" Shift: From Co-Pilots to Autonomous Agents14:15 – The 4 Phases of AI Go-to-Market Evolution20:35 – Managing Your Agents: The New CRO Skillset26:00 – Deciding the ICP: It's Not CAC29:35 – How AI Breaks Down Department Silos35:40 – Can Capitalism Survive the AI Era?46:00 – The Science of Scaling: Mark's Next Big Book---What You'll Learn* Why CAC is the wrong north star metric for GTM leaders* How to use AI to identify and retain high-LTV customers* The 4 phases of AI transformation in go-to-market* How agentic AI will redefine the roles of CROs, CSMs, and RevOps* Why AI will blur departmental boundaries and change the structure of business* How capitalism and work culture must evolve in the AI era---Check out the Key Takeaways & Transcripts: ⁠https://www.gainsight.com/presents/series/unchurned/⁠---Where to Find Mark:LinkedIn: ⁠https://www.linkedin.com/in/markroberge/Where to Find Josh: LinkedIn: ⁠https://www.linkedin.com/in/jschachter/⁠---Resources mentioned:* Stage 2 Capital Blog – Go-to-Market AI Case Studies: https://www.stage2.capital * The Sales Acceleration Formula by Mark Roberge