Podcasts about LMS

In this Part 2 episode Eric interviews Chris Hakala from Springfield College in Springfield, MA. Chris discusses his evolving role as executive director of the Center for Excellence in Teaching, Learning and Scholarship, which provides faculty development (workshops, consultations, observations) and also manages online courses, instructional design, LMS/ed tech support, and growing online graduate programs. He discusses gaining tenure and retreat rights, pandemic-driven support for reluctant online instructors, and institutional pressures such as enrollment declines, faculty reductions, and "right-sizing," including pausing a PhD program amid capacity and political challenges. He describes teaching multiple psychology courses, heavy service and leadership work, and deep involvement in POD, STP, and NITOP. Much of the conversation centers on AI, cognitive offloading, and the need to redesign assessments to emphasize clear expectations and learning processes rather than polished products, while acknowledging faculty and student resistance and workload strain. [Note. Portions of the show notes were generated by Descript AI.]

Step inside the courtroom of Learning & Development, where long-held beliefs are called to the stand and only evidence, experience, and impact will determine the verdict. This high-energy, live episode brings together three of L&D's most respected voices to challenge the myths holding our profession back. David James will play the role of courtroom judge, inviting the audience to be the jury - and witnesses - in a fast-paced, interactive trial of truth versus tradition. Each expert panellist takes on a widely accepted L&D myth - presenting their “case,” examining the evidence and defending the reality based on years of frontline experience: Becky Willis (CLO & Founder, Tractus Learning)  Jess Almlie (Learning & Performance Strategist)  Kevin M. Yates (The L&D Detective®)  Audience members become the jury - voting “Guilty” or “Not Guilty” after each myth and ultimately deciding which challenged belief brought the biggest insight. This unmissable episode blends drama, debate and practical truth - leaving the audience with grounded strategies to modernise their L&D approach, reject outdated assumptions and focus on what truly drives performance and impact. Take your L&D to the next level Take advantage of 1000s of hours of analysis and conversations with industry innovators and 25+ years of hands-on L&D leadership - here - https://360learning.com/maturity-model KEY TAKEAWAYS Today's challenges need integrated, AI-driven, business-aligned ecosystems, not legacy, course-catalogue LMS “dinosaurs with wheels.” L&D's value does not lie in delivering exactly what leaders request. Our value lies in helping the organisation achieve its goals through strategic performance improvement and capability development. Completions, surveys and smile sheets have value, but they will never show true performance impact. Focus measurement on workplace performance outcomes. BEST MOMENTS “It might look slick, but in reality, it won´t do the job.” “Training the business to think differently about how they approach us with requests ... happens one person at a time.” “We need to remove the myth that training alone can fix it, ghat L&D has magic dust, magic wands and secret formulas.” Becky Willis Becky Willis is founder and chief learning officer at Tractus Learning, helping customers build successful digital learning strategies. She also founded WillLearn Consulting. Previously, she was VP of Engagement at EdCast and led learning innovation at Hewlett Packard. https://www.linkedin.com/in/beckywillis/ https://tractuslearning.com/ Jess Almlie Jess Almlie is an L&D leader, consultant, speaker, and author of L&D Order Taker No More. With 20+ years of experience, she has led enterprise learning strategies across healthcare, education, and financial services. Most recently, she was VP of Learning Experience at WEX. https://www.linkedin.com/in/jessalmlie/ L&D Must Change Podcast: https://www.jessalmlie.com/podcast L&D Order Taker No More: https://www.amazon.co.uk/Order-Taker-No-More-Strategic-ebook/dp/B0F83XBBCV Kevin M. Yates Kevin M. Yates, the L&D Detective®, is a global expert in measuring learning's impact on workplace performance. With 25+ years of experience at organisations including McDonald's, Meta, and Grant Thornton, he helps L&D teams use data to demonstrate business impact. He also founded the nonprofit Meals in the Meantime. https://www.linkedin.com/in/kevinmyates/ https://kevinmyates.com/detective-kit https://kevinmyates.com/ RESOURCES L&D Master Class Series: https://360learning.com/blog/l-and-d-masterclass-home THE HOST David  James has been a People Development professional for more than 20 years, most notably as Director of Talent, Learning & OD for The Walt Disney Company across Europe, the Middle East & Africa.  As well as being the Chief Learning Officer at 360Learning, David is a prominent writer and speaker on topics around modern and digital L&D. https://twitter.com/davidinlearning https://www.linkedin.com/in/davidjameslinkedin This Podcast has been brought to you by Disruptive Media. https://disruptivemedia.co.uk/

L'IA est désormais partout. Elle a envahit notre quotidien.Et si le vrai enjeu derrière tout cela était aussi d'apprendre à penser ? Retrouver le vide, reconnecter avec le silence et penser à nouveau. Ce n'est pas la première fois que nous parlions d'IA avec Lucie Dhorne. Il y a quelques années, elle nous avait expliqué comment l'utiliser comme un véritable assistant pédagogique. Mais aujourd'hui, en 2026, on va plus loin.On parle de cognition, d'apprentissage, de créativité, et surtout d'un risque encore trop peu adressé : celui de déléguer notre capacité à réfléchir. Parce que oui, utiliser l'IA, ce n'est pas neutre. Est-ce qu'on s'augmente vraiment ou est-ce qu'on s'affaiblit sans s'en rendre compte ? Jusqu'où déléguer sans perdre son esprit critique ? Et surtout : comment continuer à penser dans un monde où tout devient instantané ? Elle nous explique tout !Bonne écoute !À très vite,Prenez soin de vous !Plus d'info :Pour suivre Lucie sur LinkedIn : https://www.linkedin.com/in/luciedhorne/Pour écouter le premier épisode enregistré ensemble : #84 - Lucie Dhorne - Faire de l'IA son assistant pédagogiqueEt pour en savoir plus sur son podcast Lady WhistleDhorne : https://podcast.ausha.co/lady-whistledhorneSon site web : https://cogniscore.fr/Pour recevoir gratuitement notre sélection hebdo de conseils pratiques pour animer votre équipe, rendez-vous ici : https://newsletter.teambakery.com/subscribeEt n'oubliez pas de laisser 5 étoiles et un gentil commentaire sur Apple Podcast et Spotify si l'épisode vous a plu.CHAPITRAGE00:00:00 — Intro00:01:06 — Présentation de Lucie et de son parcours00:05:32 — Quel est son rapport à l'IA en 2026 ?00:08:40 — Qu'est-ce qui différencie un “bon” usage de l'IA d'un usage passif ?00:11:28 — Que faut-il déléguer et ne surtout pas déléguer à l'IA ?00:15:17 — Quels sont les signes d'un usage toxique de l'IA en équipe ?00:19:24 — Son analogie du “nutri-score” appliqué à l'IA00:26:10 — Comment aider les équipes à mieux utiliser leur esprit critique ?00:33:29 — Qu'est-ce que le “vide fertile” ?00:39:31 — Comment créer du vide dans le travail en équipe ?Vous aimerez cet épisode si vous aimez : Outils du Manager • Happy Work • HBR on Leadership • Le Podcast de la Formation • MANAGEMENT & LEADERSHIP • Learn & EnjoyHébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Learning leaders are often asked to solve performance challenges with more training but that may not be the answer. Kathryn Murrow, Director of Leadership Development and eLearning at the restaurant chain Bojangles, explains why the most effective learning organizations spend more time asking questions than building courses. She shares lessons on collaboration, leadership development, AI, and creating learning experiences that people truly own.Show Notes:Kathryn Murrow of Bojangles talks about the importance of co-creation with learners, diagnosing root causes, and using AI to elevate the role of L&D. Her key takeaways include: Start with the root cause—not the training request. Before building another course, learning leaders should ask what behavior needs to change, why the problem exists, and whether training is actually the right solution.Build learning with your audience, not for them. Involving leaders and frontline employees in designing learning experiences creates stronger buy-in, better content, and more sustainable adoption.Learning succeeds when the conditions are right. Organizations need alignment, clear expectations, leadership support, and the right performance environment before learning initiatives can drive meaningful change.An LMS should be more than a compliance checklist. Kathryn shares how Bojangles is transforming its LMS into a personalized learning hub that connects development opportunities with business outcomes and measurable ROI.AI should create space for more human work. Rather than replacing people, AI can automate administrative tasks so learning professionals can spend more time on coaching, strategy, performance consulting, and measuring impact.Powered by Learning earned Awards of Distinction in the Podcast/Audio and Business Podcast categories from The Communicator Awards and a Gold and Silver Davey Award. The podcast is also named to Feedspot's Top 40 L&D podcasts and Training Industry's Ultimate L&D Podcast Guide. Learn more about d'Vinci at www.dvinci.com. Follow us on LinkedInLike us on Facebook

Why do enterprise skills programs always look great on paper, but break down the moment real work happens?In the most recent HR Leaders Podcast episode, I had an inspiring conversation with Josh Newman, VP of Skills and Talent Readiness at ServiceNow.He explains why organisations must shift away from perfectionist data collection and embrace good-enough skills capture paired with real-world talent readiness.By moving training out of LMS silos and leveraging ambient data signals alongside AI simulation playgrounds, ServiceNow tracks true human capabilities in real time while keeping human judgment at the centre of workforce strategy.5 things you'll learn from this episode:Why traditional skills-based models fail and how to move past perfectionist data gatheringHow Talent Signature captures ambient data signals from daily work to track capability growthHow SimStudio simulation playgrounds replace course completion certificates with real-time practiceThe key difference between basic AI adoption and true behaviour absorption that drives business ROIHow building psychological safety around AI tools directly boosts employee performance and sales quota attainmentSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Starting a new school year can be challenging enough without a dozen new platforms, devices, and passwords waiting for you on day one. In this episode of Art Ed Radio, host Tim Bogatz talks with Jen Leban about helping new art teachers move through technology anxiety with practical, manageable strategies. Jen explains why your LMS is the best place to start, how to think about some technology as tools rather than separate problems, and why digital portfolios can wait until later in the school year.   Tim and Jen also discuss how to reframe technology from a "have to" into a "get to," when creative digital tools can add something meaningful to student artwork, and why teachers should feel comfortable saying, "I don't know--let's figure it out together." With ideas about exploring technology and support for those struggling with devices and expectations, this conversation offers encouragement and concrete advice for all of the art teachers who are feeling overwhelmed by classroom technology. Resources and Links Come join the Art of Ed Community! Finding and Utilizing Creative Digital Tools Utilizing Digital Portfolios

Craig Patterson, global channel chief at Exabeam For years, SIEM has been one of those technologies that looked good in theory but was genuinely hard to build a profitable managed service around. Deal-by-deal discount negotiations, licensing structures built for enterprise resale rather than recurring managed services revenue, and no predictable floor on margin. For many MSPs, the math just never worked. Exabeam – the combined company formed from the merger of the original Exabeam and LogRhythm – is making a direct play to change that. Global channel chief Craig Patterson and senior director of service provider alliances Peter Stratis join In The Channel to walk through the new MSSP commercial framework inside the recently launched APEX Partner Program. Two new licensing pathways: a single-pool capacity model for high-volume, multi-tenant environments serving SMB and mid-market clients, and a federated subscription model that isolates customer environments for compliance and data sovereignty requirements. For Canadian MSSPs navigating PIPEDA, OSFI E-21, or Protected B, that second model is the one to pay close attention to. Peter Stratis, senior directof of server provider alliances at Exabeam The conversation also covers Sherpa, Exabeam’s new AI-powered partner enablement platform – a move away from the traditional LMS toward an always-on coaching tool that can join partner sales calls in real time – and Agent Behavior Analytics, Exabeam’s new capability for detecting malfunctioning, misaligned, and subverted AI agents inside customer environments, included at no additional cost. The standout line from Peter Stratis – who called this his first-ever podcast appearance – is the one worth writing down: “We treated our service providers like resellers, unfortunately.” The new framework is a direct acknowledgment of that history, and an attempt to rebuild the commercial relationship from the ground up. Read Full Transcript Robert Dutt: Hello and welcome to In The Channel from ChannelBuzz.ca, bringing news and information to the Canadian IT channel community for the last 16 years. I’m Robert Dutt, editor of ChannelBuzz.ca and your host for the show. If you’ve been in the channel for any length of time, you know that SIEM has always been one of those technologies that seems great in theory but has been genuinely hard to build a profitable managed service around. Licensing models that weren’t built for multi-tenancy, unpredictable costs, discount structures that made margin planning more of a guessing game than a business model. A lot of MSPs have looked at the security operations space and quietly backed away for exactly those reasons. Exabeam, the combined company that emerged out of the merger of Exabeam and LogRhythm, is making a direct play to change that. They have overhauled their channel program into what they’re calling the APEX Partner Program and at the centre of it is a new commercial framework built specifically for managed security service providers. Two distinct pathways: one for high-volume multi-tenant environments and one built with compliance and data sovereignty in mind. For Canadian MSPs navigating PIPEDA, OSFI E-21 and Protected B requirements, that second lane is worth paying close attention to. I’ve got two Exabeam executives here to walk us through it. Craig Patterson is Exabeam’s global channel chief and Peter Stratis is the senior director of service provider alliances, the person who’s been working directly with MSSPs to build this out from the ground up. Let’s get right into it. My chat with Craig Patterson and Peter Stratis. Gentlemen, thank you for taking the time. Craig Patterson: Thank you, Robert. Super excited to be on here with you today, my friend. Peter Stratis: Thank you. Robert Dutt: Craig, can you just kick us off with a quick version of where Exabeam sits right now? You know, you guys went through a significant merger with LogRhythm not that long ago. Now you’re pushing an updated partner program. For solution providers who maybe haven’t been following closely, what does the combined company look like from a channel perspective? Craig Patterson: The short answer, my friend, is that we’re sitting in an amazing place. We’re absolutely in a good place positioning to really drive value to our partner community. And so to give you a little more context around that, like you asked, we’ve spent the last 12 months really kind of rethinking, reimagining the whole partner ecosystem in a way to create value for all of our partners globally. And so there was a number of things we went through over the last 12 months. We spent a lot of time really going to this assessment loop, understanding everybody’s perspective. So we did that by having very strategic conversations with our top-tier partners. We did some survey work. We looked at the broad landscape in terms of the trends that the partners are really looking for in these modern channel programs. So all of that really became this assessment loop. The output of that is that really became the foundation for what we built here with APEX. And so with APEX, the Exabeam APEX Partner Program, what you have here is you have a program that’s really centered on value that’s really focused on solving a problem that exists in our market today around enablement. And so when you think about enablement today, I’ve written a lot of articles on this. Most enablement programs really don’t drive to the level of outcome that companies are looking to have. Outcomes like conversion rates, outcomes like time to first deal, outcome rates like retention rates, all these things. And so what we’ve done is we’ve really focused on enablement as the key catalyst to really drive value to our partners. And so with that, we’ve launched new enablement programs really with a focus on increasing their competency level so we can align to those outcomes we’re looking to have with our company’s operating plan. And so there’s a lot of thought that’s got into this. The short answer is we have a program that’s built on value. It aligns to where the market is going and what partners are really asking for. Robert Dutt: Peter, your title as senior director of service provider alliances is a pretty specific role. Can you tell us a little bit about what that looks like sort of on a day-to-day basis and the big problems that you’re focused on? Peter Stratis: Sure thing. Thanks, Robert. Well, I’ve been with Exabeam for about eight years now and service providers have always been a key component of not only our channel strategy, but our go-to-market and just from our net new revenue perspective. After our merger with LogRhythm, that actually continues and if anything, it’s only been more emphasized because both from an on-prem and from a cloud perspective, we see the MSSPs being a strong driver of that strategy of our go-to-market. So over the last eight years, we’ve seen that trend of not only on net new revenue, net new logos being a major part of our business, but then how do, to Craig’s point, how do we support them? To be quite honest, in the past, it was quite difficult. We really didn’t have any kind of structured pricing for these partners. It was, to say the least, it was more of a resale program that had some discounts tied to it. So through Craig’s efforts, through our whole surveys and our intent to really go after this market and treat them the way they should be treated, he mentioned that we did these surveys. We asked internally, what do you look for in a service provider partner? We asked externally what these partners were looking for from us. And that’s when in building the APEX Partner Program here at Exabeam, we also took into account what service providers would look for in a new partner program. So that’s everything from pricing to support. Craig mentioned enablement. Enablement is a huge part of that, where they felt in the past they were just lumped up as just a regular partner. Now we have supported APIs, documented APIs that most, if not all, of our partners are using as part of their foundation for their services. So we’ve really come a long way and continue actually to build upon that, as you’ll see throughout 2026 and beyond. Robert Dutt: Okay, let’s get into the framework itself. You guys positioned it at launch as solving commercial and operational friction for MSSPs. Curious, what did you hear that friction looked like in practice? What were MSSPs telling you was broken or was a big challenge? Craig Patterson: Yeah, so I’ll take a stab at this and I’ll let Peter give more context. So a lot of this came out during that assessment phase. Robert, we’re talking to the MSSPs globally. I’m like, what’s working? What’s not working? What would they like to see incorporated into the MSSP program 2.0? So a lot of the feedback we heard was really around the flexibility. Being able to have a license that is catering to all the customer demand they have beneath. So it’s really giving them the flexibility to buy that one license and carve it up as they see fit. And giving them more flexibility on the commercial terms. That was a lot of the commentary we heard. The other thing we heard was really they wanted more value as it leads to the enablement side. So obviously getting them enabled on the pre-sales side, but more importantly on the post-sales side. So they could actually drive those implementations, drive the management and really help those customers create a lot of value. And so I think those were kind of the big levers that I heard from those assessments. And then in practice, Peter can give you some more context in terms of how we’re putting all this together. Peter Stratis: Yeah, thanks Craig. A lot of what we heard from the service provider community in the past was friction. So when they’re trying to price out their services and our product and etc., they were seeing friction at onboarding. They were seeing friction in trying to predict their margin on deals. As mentioned, not airing any dirty laundry here. It was more like a resale program. So we gave discounts and there were very opportunistic discounts on a deal-by-deal basis. So they didn’t build predictable service models around it in the past. And then you always hear the buzzword, multi-tenancy. We kept on getting asked about our multi-tenant roadmaps, etc. We’re looking at this framework as a way of solving for that. We continue to make feature enhancements into the platform that will strive for that multi-tenancy. But the way we’re solving for it is by these two pathways. One is that single license, pooled capacity, data segregation model. And the other is that federated workflow that we announced where it’s more for, whether you’re within data sovereignty, if in different regions or just different use cases from a compliance perspective, whether it’s healthcare or finance, and you have to keep these environments isolated. We have a plan and we worked with our MSSPs specifically to have these kind of pathways. So we heard from our MSSPs and we actually developed these two pathways with them in mind. So they were in the design phase and in the rollout phase for both federated and the single pool capacity. Robert Dutt: The federated model is such an interesting one, I think, for the Canadian market, specifically data sovereignty, huge topic. And there are specific compliance requirements, PIPEDA, OSFI E-21, Protected B status. It means that a lot of Canadian MSSPs can’t just kind of throw everything into one pool. Was that the sort of thing that was explicitly on the radar when you built this out or a happy coincidence of the architecture and the feedback that you heard along the way? Peter Stratis: It’s actually a little of both, right? So it just so happened to be the maturity of our platform. Even from our Exabeam New Scale platform, we went from an on-prem hardware appliance way back in 2012, to our version 1.0 was a SaaS product, to our native cloud. It was always a single-tenant solution. So it worked well for certain service providers that had the capacity. They had their APIs and their own platforms that could manage this solution. As you heard more and more about multi-tenancy and the need for data sovereignty and all that, we still had a big part of our MSSPs were asking for this single license pooled capacity. So we structured it in a way where for midsize organizations or even some small, medium business, you still have that single pool capacity using data segregation. You lose some of the customization, but you could actually solve for a lot of those customers in that model. And then you have another plan with the federated. So the more mature MSSPs are running both models in some capacity. They could still run that single license for their SMB play. And then for either large enterprise or very compliance-driven customers that want those isolated environments, they have that flexibility. And that’s what we built a framework around. Obviously, that’s one point of feedback that sort of directly informed the framework. Robert Dutt: You guys have said that this whole thing was built, as you said, with direct collaboration with your MSSP partners rather than kind of coming down on high. I’m curious along with what you’ve touched on already, what actually changed as a result of going through that process? What did you go in thinking you’d build and how did it come out differently because of what partners told you along the way to building it? Craig Patterson: Yeah. So I think there’s a lot of things that have been addressed. Obviously, the packaging and the commercial aspects as Peter was describing, but think about some of the fundamental problems in terms of partners want this path to profitability, right? Really understanding how they can create margin. That was one thing. Another path is like, how do I become enabled with Exabeam? And how do I stay informed in terms of where you’re going? Another problem we wanted to solve. So I think it’s a lot around the financial aspects of doing business with us. A lot of it’s around becoming enabled, becoming more knowledgeable on all the new features and releases that we’re dropping. And so those were some of the big fundamentals that we wanted to solve in the APEX framework. And then beneath that, obviously, is the whole MSSP play. And that’s what Peter’s been talking about. So you can probably give a little more context on that. Peter Stratis: Yeah. As mentioned, there is no one-size-fits-all. So the feedback we were getting was obviously their security platform was important to them. Some of them had an in-house platform they built on their own. And there’s ways of differentiating. So basic SIEMs are just going after alert monitoring. So how can I differentiate my service if I’m a service provider? Well, there’s ways of going to market, but also there were things we needed to do in the back office from a platform perspective to make those possible. So making our behavioral analytics available in these models so they can actually differentiate their services. As I said, we have a history of actually adding features quarter-over-quarter, month-over-month. So that’s not stopping. We didn’t announce necessarily multi-tenancy to the world. We announced a commercial framework for that. So you’ll continue to see on a month-to-month, quarter-over-quarter basis, features added to support not only the commercial framework, but the underlying platform to make it easier for service providers to add that operational efficiency, to add those differentiators from a product portfolio as well. Robert Dutt: Let’s talk about the economics underneath there. You use the term predictable margins as a phrase that shows up in the messaging. SIEM has historically been a tough service to make money on. Licensing models that didn’t fit the managed services motion, unpredictable costs on data ingestion, those sorts of things. What specifically changes for an MSSP’s P&L under the framework? Craig Patterson: Yeah. So I think there’s really two components here. The first is the whole financial package associated to the MSSP partners. And the second is the discounting framework. And so let’s maybe start with the discounting framework. One of the observations that we made during this whole assessment phase was the vast majority, Robert, of all of our deals were flowing through this non-standard process, which means the discounts that were aligned to the traditional framework were not putting the MSSP partners in a position to actually transact. And so what we did is we went through and we re-looked at the discounting framework and sort of realigned it based upon our actual data points. We looked at the last 12, 24 months, the discounts that were being derived to actually transact. And we sort of rebuilt the entire discounting framework for our company in a way that really empowers the MSSP partners now to have enough discount to actually transact without going to this non-standard queue. So what does it mean? Well, we really kind of flipped the script. Instead of 80% being non-standard, we believe 80% will flow through the standard process now because we’ve built the discounts in a way to align with what the market is looking for. That’s kind of the key component number one. And then as it relates to the discounting side, we reimagined how those discounts are calculated. And so now you kind of have your standard program discount. So that’s based upon your tier. So top-tier MSSP partners get the highest level discount. The second is deal registration. Obviously, they put the deal reg in that ties to a discount. Those are both standard common things. But what’s new, which is what you care about. What is new? Well, we’ve aligned the third discount based to their competency level. And so we measure that based upon certifications. And so if you think back to those choose-your-own-adventure books as a kid, we’re really giving the partners their own choose-your-own-adventure. And if they want to drive to the highest level discount, well, simply, MSSP partners got to go take all of our certifications, pre-sales and post-sales, so they have the highest level of competency to drive our services in the market. And our thesis around that is partners that have higher certifications, they’re going to be more active, they’re going to be more interested, they’re going to drive more pipeline. And if we do this the right way, Robert, they’re actually going to convert at a higher percentage, we’re going to see shortened sales cycles, all of which align to the operating plan of our company. So it’s kind of those two fundamental things that were addressed through that process. And then I’m sure Peter can fill in the detail for you. Peter Stratis: Yeah, if I can actually elaborate on that. Thanks for that, Craig. And just some historical context, Robert, as mentioned in the past, we treated our service providers like resellers, unfortunately, so it was very deal-specific in terms of what they were getting on a deal-by-deal basis from a discount. So the economics of it was they really couldn’t rationalize their margin predictability on an overall services basis. And you know, different regions go to market different ways. In Europe, Asia, Latin America, predominantly, it’s all SIEM as a service and MSSP owns the license. In the Americas, both US and Canada, we saw a lot of proliferation in the past of customer-owned licenses. So the MSSP would resell the license, and consequently, just provide managed services wrap on top of that. Not only do we see more of that MSSP-owned model now where it’s SIEM as a service in the US and Canada. So it’s proliferated itself throughout all the regions. Now with these frameworks, we actually are able to build these economics, the margin predictability, as Craig mentioned, because now they know as a standard, what they’re going to be selling for. So especially as we do this federated model, and even the single license, you know what your price is across the board, you know what license you’re buying, you know what price you’re buying it for, you know, the more customers you add to these models, the more your profitability will increase as well. So it continues to grow from a pure profit play. Partners want to know what their margin would be as their customer licenses grow. And this is exactly what the framework did. Robert Dutt: This is sort of a broader question around MSP/MSSP distinctions as opposed to directly about the framework. But there’s a distinction worth drawing between an MSP trying to bolt a security practice onto the existing managed services business and the established MSSP who’s been at this for a year or who has built it up. Are those two different conversations for you? And if so, what are the different entry points and care-abouts? Peter Stratis: So it’s interesting, not only because of this announcement, even prior to it, the announcement of the APEX Partner Program here at Exabeam caused a lot of interest from partners and different kinds of partners. The traditional MSP, when inquiring, it was kind of hard when we were vetting them that they had no security practice of their own. So oftentimes they would actually outsource that security to an MSSP, to a classic MSSP, or maybe just resell services from those other organizations. We see that, we see a lot of interest from MSPs with that. And we see VARs or resellers come to us that want to build managed service practices as well. So we look at both of these in two different ways. One, how can we take care of these partner inquiries now, and then how can we grow with these organizations? So both MSPs and resellers that are interested in managed services now, our first inkling is to try to introduce them to our current managed service base. These people have the experience, they have the certifications, they have the technical knowledge. We’ve seen that move from a lot of MSP partners actually having channels of their own. So they actually sell their MDR or MSSP services through a channel of resellers or MSPs. But then if that’s our first step with these type of partnerships, then it’s like, how can we grow within your organization? How can we help you get the technical skills required? Because for a true MSP to have success, not only in SIEM, but just security as a service, you can’t just train one or two people, you need the 24-by-7 support, you need the tier one and tier two level of support services as well. So you have to grow your organization or outsource it to people that are already prepared to handle that. So that MSP play, we actually see it more and more going towards our current managed security service providers and getting that as a resource. Craig Patterson: Just to add a little more context to that too. So this actually becomes a very interesting point for the distributors worldwide as well. Because a lot of what they provide in terms of value is helping those MSPs in terms of deployment and management of the services. And so we’ve gone through the vetting process globally, looking at all of our distributors and we’ve handpicked our strategic distributors around the world. So if we have MSPs that want to come into the program, but they’re not ready on that post-sale side, well, guess what? That can become the role of the distributor. And secondarily, this is where the enablement really comes into play as well. And so that’s why we’ve built very specific paths on enablement, pre-sales and post-sales, where partners can choose their own adventure. “Hey, if I want to get going on the pre-sale side, well, guess what? I can simply resell.” Or, “Hey, I want to really start focusing on the post-sales services implementation.” I can start to take the enablement around those courses to become more of an expert to really give me those new capabilities. And so there’s a whole conversation around what we’re doing on enablement with our brand new Sherpa that’s really given a lot of these partners those capabilities. Robert Dutt: On the note of Sherpa, an AI-powered tool for partners, it’s essentially a virtual channel account manager in terms of enablement, onboarding, that sort of thing, especially for an MSSP who’s new to SIEM. How does it change the friction of getting started with Exabeam as their platform? Craig Patterson: You’re going to love this. You’re going to love this. So we’ve sort of reimagined all of the enablement. Again, when you look at traditional enablement, it’s like most enablement is built in these LMS platforms. Like, “Hey, partner, go log on to this LMS platform, get your certification, and then we expect you to actually know what the hell you’re doing.” Reality is that’s not what happens. They log on to the LMS platforms. They fast-forward as quickly as they can to the end. They turn the volume down. And then when the quiz comes, they use AI to answer the questions. And so they just find a way to get the certification. The reality is none of that helps them be better in life or actually raise their competency. And so that’s a problem we took on head-on with Sherpa. And so Sherpa was built in a way to really change the way partners learn with the whole goal of raising their competency level so they can be better on the market. And there was really like three use cases we were trying to solve with the emergence of Sherpa. The first is like you think about this global ecosystem that Peter and I have. We have 3000 partners. The partner ecosystem looks different. We have VARs. We have MSPs. We have MSSPs. We have distributors. We have the trusted advisor market as well. All of them have different needs in terms of where they are from a learning perspective. And so the first use case, Robert, is simply like a tool to be able to ask questions. What are the use cases? How do I position this? Why is SIEM or UEBA better than the competition? Just an always-on tool for partners to ask questions. And so that was kind of use case one. And then the cool thing around that is you think about the ecosystem being very global in nature. The other problem with LMS platforms is I’ve got partners in Japan. Well, that means the LMS platform they log on to needs to be able to talk to them in Japanese. And so the beauty with Sherpa, it does all the translation for us. And we’ve got 15 plus languages that are now live in Sherpa. Partners in Japan are talking to it. We got partners in India and all over the world really asking questions in terms of how we position our services. And that integration can be done by just logging on to our portal. You’ll see a bot pop up. They can just simply ask a question. It integrates in Teams, integrates in Slack. So that was use case number one. Use case number two was we reimagined the whole enablement certification platform. And so it’s a very dynamic learning experience. And so the way it happens is you log on, there’s a topic that you like, you click on that, you start learning, it asks you questions, it asks you to position services, and then you record your answer to how you’re actually positioning those services or the features. And it gives you feedback like, “Robert, you did really good on this aspect, but next time you should use this and this.” Or, “Robert, if you’re talking to a customer that’s in this vertical, you should talk about this use case because that’ll help resonate.” And so the whole certification process has been rebuilt and that’s the second use case. The third use case, this is a game changer. And this really goes to your question. And it’s an always-on coach. And so partners are now able to invite Sherpa to calls. And so as they’re having those conversations with customers, and the customer may say something or give them an objection, well, in the background, Sherpa will give them the answer to that objection and say, “Customer said this, talk to them about this.” Or, “Have you shared this new feature that was just released in the quarterly launch?” So it’s like this always-on coach, always-on assistant to really give them what they need. And then we’re putting it on this innovation roadmap. And so every single quarter, we’re launching new innovation in Sherpa. As an example, we’re now launching our LinkedIn integration. So if you’re an MSSP partner, you log on to Sherpa, you’re connected to LinkedIn, it’s going to ask you, if Sherpa can look through your network to find customers that may be a good fit for our services. And then it’ll say, “Okay, great. We found these contacts. Should we go ahead and write the campaign? Should we write a campaign that you can use to send to those customers in your ecosystem on LinkedIn?” And so quite honestly, I think we’re bleeding edge in terms of really being able to use AI and adopt AI in a way to drive good outcomes, well beyond where most companies are with their simple ChatGPT things like that. We’re actually driving outcomes. Robert Dutt: The rise of AI baked into the partner program and partner tools is a fascinating space for me to watch. And that certainly, you make a compelling case for the role of Sherpa there. That sounds really interesting. A quick one on the product side, not directly related here, but just out of curiosity, Exabeam just dropped Agent Behavior Analytics in your April release, sort of extending behavioral detection to AI agents, ChatGPT usage, Copilot activity, those kinds of things. For an MSSP looking to take this to market as a service, is it a new revenue line? Is it an upsell? Or is this sort of becoming table stakes that clients expect to see bundled into what you’re doing for them? Craig Patterson: I’m glad you asked. It was just recently at RSA, the conference, obviously AI is the buzzword, but what do you do with that? When we presented the agentic behavior analytics to a lot of our partners or potential new customers, the question that was often asked was, “Well, how much is this extra?” And that’s not how we license our product. So the behavior analytics has been part of our solution since our inception from our analytics model. So specific to AI, this is going to be, you could differentiate your service from other service providers by using this behavior analytics, but by no means is it an extra cost on the MSSP’s behalf. So they’re going into an organization that has a thousand users, human entities, and overnight they now have 10,000 non-human entities. We look at and model all of them using our analytics. So now you actually have at least a basis of what’s normal from a behavior standpoint for both non-human and human entities. So we really change the game, but haven’t changed the pricing along with it. So it comes naturally within our platform. So no change for me as a partner, but if I can find a way to upsell based on it, all the better. If not, I add additional features. Hopefully my customer is more happy. Peter Stratis: I was just going to say, if you look at the macro trends we’re seeing, this is the number one conversation that’s being had right now, especially like you look at the financial sector. Every single company is facing this problem. And so this really, not only does it give them a new use case to go after, I think it just makes the overall security services of Exabeam more relevant based upon what’s happening in the overall market, which all that makes the revenue stickier, makes those conversations more impactful that those MSSP partners are having. Craig Patterson: Yeah. Well, what I’m going to mention is operational efficiency and service differentiation is what’s key to our MSSPs and their success. So the license is foundational. And now that we’ve actually solved for being predictable from a margin perspective, how can they differentiate themselves, making them operationally efficient using automation, using our threat detection, and then also the service differentiation. And the other thing too, just thinking through this a little bit, I mean, there’s different AI agents that exist out there that are doing different things. You think about the malfunctioning agent, the one that’s just off base and it’s doing things that are just incorrect based upon the fundamentals or foundation of the AI agent. That’s one thing that gets addressed by looking at the abnormal behavior. The second is the misaligned agent, the ones that are pursuing goals in a way that could negatively impact the company. And that gets a little bit more scary. But really what gets scary is those subverted agents, the ones that have been hijacked that are actually causing harm. And so you think about all those different use cases that are happening, and that’s the beauty of what we just released is our new ABA, sort of creating this new category in the market. That’s really what our ABA is looking for, is all those different things that are happening, whether it’s misused, misaligned, or subverted. All that can be detected through this new agent behavior. Robert Dutt: Okay, last question for me. If I’m an MSP who’s been sitting on the sidelines, I’ve been thinking about them or are upgrading my security operations practice. What’s one thing that you wish I understood about the opportunity and the economics, but I probably don’t at this point? Peter Stratis: It’s all about how they actually start off. They’re interested in selling managed security, but they don’t know that they have to standardize their delivery model. They can’t make it where every customer is custom, because that’s when that price predictability goes away. So everything from onboarding to customizing your offering has to go away. You might be able to do it for a certain amount of customers, but you have to build a model that’s repeatable. Automation is going to be very important to that. And then finally, you could add optional add-ons, but you have to resist the temptation to over-customize everything. The great thing about what Craig has done with the APEX Partner Program and the way we built it out here at Exabeam is it supports all of this through all the enablement efforts. So Craig mentioned all the enablement built into the program, but then we have certification tracks. So we’ll help you along in that process. And we have everything from APIs and the use case and the scripts to help you automate that track for you to make it easier, but just don’t jump in and try to do a custom solution for each customer. Robert Dutt: Gentlemen, I thank you very much for your time. Once again, I appreciate your walking us through the commercial framework. Craig Patterson: Thank you, Robert. Appreciate it. Peter Stratis: Thank you, Robert. Robert Dutt: There you have it. Craig Patterson and Peter Stratis from Exabeam. I’d like to thank Craig and Peter for their time today. And a special note, this was Peter’s first podcast appearance. You never would have known it. A few things I’ll leave you with. First, if Peter’s candid admission landed for you — that Exabeam used to treat service providers like resellers with opportunistic deal-by-deal discounts that made it impossible to build a predictable margin — sit with that for a moment. Not unique to Exabeam. That was the industry. And it goes a long way to explaining why so many MSPs have struggled to make managed SIEM work as a business. The new framework is a direct attempt to fix that math. Two pathways: a single-pool capacity model that works well for SMB and mid-market clients, and a federated model that isolates environments for compliance-heavy customers. The discounting structure has been rebuilt from the data up with the goal of moving 80% of deals through a standard process. Up from what Craig described as the opposite of that. The Sherpa AI tool is worth watching closely, not just as a training platform replacement, but as an always-on coach that can actually sit in on partner sales calls and surface real-time objection handling. The LinkedIn integration is coming next, and it starts looking less like an LMS and more like a business development tool. And the closing advice I’ll leave you with is Peter’s. If you’re an MSP thinking about entering the security space, standardize your delivery model before you take on your first customer. Resist the urge to customize every environment. That’s exactly where price predictability and profitability goes away. Thanks as always for listening. In The Channel is available on Apple Podcasts, Spotify, YouTube, and all the major podcast directories. If you’re finding value in the show, leave a rating or review. It goes a long way to helping other folks in the channel find us. Until next time, I’m Robert Dutt for ChannelBuzz.ca, and I’ll see you in the channel.

Recently funded and aiming for sustainable retention? Intro chat (no sales pitch): professorgame.com/chat Episode Summary Travis Clapp, co-founder and CEO of Coursebox AI, joins Rob to explain how AI is reshaping course creation and delivery for a platform used in 180 countries with over 300,000 courses built on it. He walks through Coursebox's "Break My Brain" feature, an AI mode built for personalized, adversarial learning rather than points and leaderboards, and explains why the platform now generates in 10 minutes what used to take him days to compile by hand. Travis also shares two gamification experiments gone wrong, an overbuilt golf training module and a crossword in an endangered Aboriginal language, and where he draws the line between fun and function. Listeners come away with a clearer read on why surface-level game mechanics rarely move the needle, and what deep personalization through AI looks like in practice. About the Host Rob Alvarez is Head of Engagement Strategy, Europe at The Octalysis Group (TOG), a leading gamification and behavioral design consultancy. A globally recognized gamification strategist and TEDx speaker, he founded and hosts Professor Game, the #1 gamification podcast, and has interviewed hundreds of global experts. He designs evidence-based engagement systems that drive motivation, loyalty, and results, and teaches LEGO® SERIOUS PLAY® and gamification at top institutions including IE Business School, EFMD, and EBS University across Europe, the Americas, and Asia. Key Takeaways Coursebox AI now generates the five-document compliance package Travis used to build by hand for RTOs in about 10 minutes, work that once took him days per client. Coursebox's "Break My Brain" feature lets a learner, or Travis himself testing it on his own sales calls, get challenged and questioned by AI instead of clicking through a linear course, engaging Core Drive 3 (Empowerment of Creativity & Feedback) and Core Drive 4 (Ownership & Possession). Travis's own team told him not to talk about leaderboards, points, certificates, or badges going into this episode, because platform-level leaderboards rarely produce deep engagement on their own, just a carrot on a stick. A German trucking client turned Coursebox training into a fully hands-free, podcast-style experience so drivers could complete quizzes and assignments by voice while on the road. Coursebox is used across 180 countries, has generated over 300,000 courses, and one flagship edX MOOC built on the platform reached more than 20,000 learners. An early gamified golf training module Travis built in 2011 nailed the learner experience but got flagged internally as the most expensive, overpriced piece of work the company had shipped, a reminder that engagement design still has to answer to budget. Topics Covered 0:00 — Opening hook: "don't talk about leaderboards" 1:18 — Travis Clapp's path to Coursebox AI 2:42 — New fatherhood and running a global startup from Australia 6:03 — Favorite fail: an over-engineered golf training module 9:03 — A crossword in an endangered language, gamification taken too far 10:57 — Why leaderboards and points rarely drive real engagement 12:57 — "Break My Brain": AI's personalized deep-learning mode 16:24 — Killing the step-by-step method for open, prompt-based design 18:02 — Tesla's Grok, Porsche, and voice-driven learning everywhere 20:55 — German truck drivers turning training into a podcast 21:41 — AI risk, specialization, and what never depreciates 27:20 — Book picks, favorite game, and where to find Coursebox AI Recently funded and aiming for sustainable retention? Intro chat (no sales pitch): professorgame.com/chat About Travis Clapp Travis Clapp is the co-founder and CEO of Coursebox AI, an AI course creation and training delivery platform used across 180 countries with over 300,000 courses created on the platform. He spent 15 years as an instructional designer before building Coursebox, working with companies like Johnson & Johnson, Electrolux, Michael Hill, and SA Health. At Open Colleges, he built the five-document compliance packages by hand that every RTO needs; Coursebox now generates the same packages in 10 minutes. One of his courses reached over 20,000 learners as a flagship edX MOOC. He knows what good learning takes because he spent years doing it the slow way. Before Coursebox, he founded and ran a social-based LMS for seven years, which became the foundation for what Coursebox AI is today. Four years ago he launched Coursebox AI, not to automate course creation, but to get to the part that matters: delivery. The AI tutor, the grading, the 24/7 coaching, the outcomes. Course creation, in his words, is about 1% of what the platform does. Travis still checks customer support messages at 4am. Find Travis Clapp Online Website: coursebox.ai LinkedIn (Travis): linkedin.com/in/travisclapp LinkedIn (Coursebox AI): linkedin.com/company/courseboxai Instagram: instagram.com/coursebox.ai TikTok: tiktok.com/@coursebox.ai Facebook: facebook.com/courseboxai X/Twitter: x.com/Coursebox_ai YouTube: youtube.com/@CourseboxAI Mentioned in This Episode Some links below are affiliate links. As an Amazon Associate, I earn from qualifying purchases. TOG / P&G gamification case study The Octalysis Framework Book: I, Robot by Isaac Asimov Book: The Millionaire Mind by Thomas J. Stanley Book: The Expectant Father by Armin A. Brott & Jennifer Ash Rudick Favorite game: SimCity 2000 (1993) Free Resources and Get in Touch Core Drives in the Wild: Professor Game Free Guide Get Daily Value on Your Email Let's chat about your gamification project YouTube LinkedIn Instagram Facebook Start Your Community on Skool for Free Ask a question

This episode is sponsored by SiteGround's new AI Agent for Wordpress → In an age of AI overwhelm, noise, distraction and behavioral change, I know we all wonder, can courses survive?What will online education look like in 5, 10, shoot, 2 years?!?Well, in times like these, I need a voice of reason. A calming voice of optimisim and hope. And for me, that's Chris Badgett. CEO of Lifter LMS. Hands down the best LMS for WordPress.And I'm pumped to have him back on the podcast today to talk all things landscape change (and future) with courses and online education. Yes I'm an online course creator but I'm also an online course taker and so many online courses have changed my life so I'm very protective of the A-Z, results-based experience but I'm also a realist and I know learning behavior is changing with AI.But as always, after a chat with Chris, I feel better. I hope you will too

“Le travail, c'est génial, mais ça ne doit pas résumer toute sa vie.”C'est avec cette conviction que Fabienne Broucaret, journaliste et directrice éditoriale du groupe CDI Médias (Courrier Cadres, Rebondir, My Happy Job…), a co-créé avec Aurélie Durand le podcast Les Petits Cailloux, puis co-écrit un livre : Le SAV des Managers. Alors autant dire que les situations managériales un peu délicates, ça la connaît !Au programme de ce nouvel épisode de Learning by Doing, Fabienne nous explique donc quelles sont les grandes tendances actuelles du monde du travail (recul du télétravail, épuisement, quête de sens), pourquoi la santé mentale et la déconnexion sont les fondations d'un management durable et comment un podcast peut devenir une trousse de secours pour managers.Bonne écoute !À très vite,Prenez soin de vous !Plus d'info :Pour suivre Fabienne sur LinkedIn : https://www.linkedin.com/in/fabienne-broucaret-4743634b/Pour retrouver leur livre : Le SAV des managers - 30 situations décryptées pour alléger votre quotidienEt pour écouter leur podcast Les Petits Cailloux : https://courriercadres.com/podcasts/les-petits-cailloux/Pour recevoir gratuitement notre sélection hebdo de conseils pratiques pour animer votre équipe, rendez-vous ici : https://teambakery.com/nlEt n'oubliez pas de laisser 5 étoiles et un gentil commentaire sur Apple Podcast et Spotify si l'épisode vous a plu.CHAPITRAGE00:00:00 - Intro00:01:28 - Présentation de Fabienne et de son parcours00:04:30 - Pourquoi avoir choisi de se spécialiser sur le monde du travail ?00:05:58 - Les Petits Cailloux en quelques chiffres00:06:53 - Quelles sont les grandes tendances actuelles du monde du travail ?00:09:15 - Comment ont-elles pensé leur livre ?00:15:59 - À qui s‘adresse ce livre ?00:18:27 - Quels retours ont-elles sur leur ouvrage ?00:20:41 - Et ses autres activités ?00:24:34 - Comment applique-t-elle les sujets de bien-être dans son quotidien ?00:30:37 - Comment manager une IA ?00:33:01 - Comment apprend-t-elle ?Vous aimerez cet épisode si vous aimez : Outils du Manager • Happy Work • HBR on Leadership • Le Podcast de la Formation • MANAGEMENT & LEADERSHIP • Learn & EnjoyHébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

According to Chris Badgett, effective LMS and eLearning companies gain valuable clients by serving as strategic business partners rather than just creating websites. Agencies should charge for a systematic discovery process that produces a clear blueprint before any work starts, rather than diving right into development or offering planning for free. He likens it to […] The post The Agency Sales Process Behind 5-6 Figure eLearning Website Clients appeared first on LMScast.

LMS is officially back. Let's welcome our first guest Alex. she recently finished with her masters degree from Florida State University. Before joining the FSU mariachi Alex had no experience with mariachi even still she was a part of the first ever FSU mariachi recital. Find out more about her story. email us at losmariachisagas@gmail.comor instagram @losmariachisagasSupport the show

Comment faire grandir une organisation sans perdre en efficacité ni en engagement ?C'est le défi qu'a relevé Fabrice Bernhard, le cofondateur de Theodo. Partis de l'Agile, lui et son équipe ont vite dû trouver une nouvelle voie face à la croissance rapide de l'entreprise : des projets plus gros, des équipes support à embarquer, des recrutements massifs… Et leur réponse face à tout ça : le Lean. Un modèle qui transforme chaque problème en opportunité d'apprentissage et qui a permis, par exemple, de réussir 120 recrutements en un an sans sacrifier la qualité.Dans ce nouvel épisode de Learning by Doing, Fabrice partage donc les limites de l'Agile quand l'entreprise grandit, mais aussi comment appliquer le Lean au-delà de la tech (RH, recrutement, commercial). Et bien sûr, tout cela illustré par des pratiques concrètes : feedback en continu, visualisation des flux…Bonne écoute !À très vite,Prenez soin de vous !Plus d'info :Pour suivre Fabrice sur LinkedIn :Pour découvrir son livre : The Lean Tech Manifesto: Learn the Secrets of Tech Leaders to Grasp the Full Benefits of Agile at ScalePour recevoir gratuitement notre sélection hebdo de conseils pratiques pour animer votre équipe, rendez-vous ici : https://teambakery.com/nlEt n'oubliez pas de laisser 5 étoiles et un gentil commentaire sur Apple Podcast et Spotify si l'épisode vous a plu.CHAPITRAGE00:00:00 – Intro00:04:50 – De la méthode Agile au Lean00:13:14 – Qu'est-ce que le Lean exactement ?00:17:10 – Comment ont-ils su qu'ils allaient dans la bonne direction ?00:19:06 – Quel a été le résultat de tout ce qu'ils ont mis en place ?00:22:22 – Et qu'est-ce que cela a apporté à leurs autres équipes ?00:25:09 – Comment se matérialise l'approche de « flux » ?00:31:28 – Comment se matérialise l'organisation de travail apprenante ?00:36:11 – Quels autres rituels de partage ont-ils mis en place ?00:38:24 – Quelles sont les conséquences heureuses de ce livre ?00:41:37 – Et dans son organisation personnelle, qu'utilise-t-il du Lean ?00:43:44 – S'il ne fallait retenir qu'une chose du Lean ?Vous aimerez cet épisode si vous aimez : Outils du Manager • Happy Work • HBR on Leadership • Le Podcast de la Formation • MANAGEMENT & LEADERSHIP • Learn & EnjoyHébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

There are roughly 100x more people who use code than who can write code. As code that “just works” becomes easier to generate, this group may be the biggest prize of all — if you can get the agentic interface right.A key trend we have been tracking over at AINews is the absolute explosion in Codex usage this year, with MAU now up >10x from Jan 2026. Less than two weeks after their July 9th launch, OpenAI said ChatGPT Work and Codex had reached 10M users combined (as we cover in the pod, Codex now powers ChatGPT Work, so all ChatGPT Work users are now users of the Codex harness, even if they aren't traditional engineers) — showing the early innings of what happens when you graduate from coding agents to knowledge work agents:We've been calling out how coding agents are “breaking containment” to do everything else this year to power every other part of knowledge work - and it started with the org chart, with a major reorg last month that amounted to two of Codex's most prominent leaders, Greg and Tibo, taking responsibility over product and ChatGPT specifically, completing a “Superapp” consolidation cycle first discussed in March.With these updates Codex is no longer just a coding tool. In June, OpenAI said knowledge workers already accounting for roughly 20% of Codex's user base and growing more than 3x as quickly as developers. A product dedicated for knowledge workers was being pulled out of the Codex team.However, knowledge work has a different set of problems and environments than coding. For decades, knowledge work has been scattered across different primitives like documents for writing, spreadsheets for analysis, slide decks for communication, and specialized applications for everything else. ChatGPT Work now enables users to work across every primitive with agents. Instead of opening an application and manually operating its features, the user can describe an outcome and collaborates with an agent that can assemble the tools, context, and artifact needed to reach it.From building no-code products at Airtable to leading Productivity Engineering at OpenAI, Akshay Nathan has spent much of his career trying to make the power of software accessible to people who do not write code. In this episode, Akshay joins swyx and Vibhu to unpack the launch of ChatGPT Work, why Codex unexpectedly took off among non-developers inside OpenAI, and the company's broader plan to bring useful agents from software engineers to knowledge workers and eventually everyone.We go deep on the shared agent harness behind Codex and ChatGPT Work, why OpenAI brought the experiences together without making them identical, and how persistent computers, artifacts, Sites, plugins, memory, and sub-agents are changing what people can delegate to AI. Akshay explains why some teams are replacing decks and spreadsheets with interactive websites, how agents can gather context across code, Slack, documents, and local files, and what OpenAI learned from personal-agent products like OpenClaw.Side note: also don't miss Abhihek's sandbox track keynote at AIE, which now powers a lot of the sandboxing for ChatGPT Work… and yes was also broken by an unreleased OpenAI model in the recent HuggingFace incident.Akshay also reflects on how AI is transforming product development itself: why more people will become generalists with a specialty, why ideas and taste become the bottlenecks when almost anyone can build, why LLMs still struggle to generate genuinely grounded new ideas, and why teams must distinguish increased motion from actual progress.We discuss:* Why Codex unexpectedly took off among non-developers inside OpenAI* Why employees felt like using Codex gave them a new superpower* The product insight that led OpenAI to build ChatGPT Work* Why Codex and ChatGPT Work share the same underlying agent harness* How their UX, Git visibility, artifacts, and sandboxing defaults differ* Why OpenAI merged its agent experiences instead of building separate products* How AI is blurring the boundaries between engineering, design, strategy, and operations* Why OpenAI wants the default model configuration to work for most users* When power users should use deeper reasoning, Ultra, or multi-agent modes* Artifacts, agentic spreadsheets, and creating high-fidelity work products* Why interactive Sites may replace decks and spreadsheets* The challenge of designing a simple interface for an agent that can build almost anything* Why users should retry tasks that models could not handle three or six months ago* How AI can gather context for performance reviews without replacing human judgment* The OpenAI automation that turns internal Slack and document activity into memes* What reaching ten million ChatGPT Work and Codex users means for the product* How OpenClaw inspired persistent environments, scheduled tasks, and personal agents* Using ChatGPT for financial planning, budgeting, workouts, meals, and household management* The design tradeoffs behind sub-agents and how much of their work users should see* ChatGPT memory, Chronicle, and long-term context* Why AI may make more people generalists with deep specialties* Why ideas and taste become more important when almost anyone can build* Why LLMs still struggle with the instruction “bring me new ideas”* Measuring productivity through quality at-bats instead of commits, tokens, or pull requests* The critical difference between AI-generated motion and meaningful progressAkshay Nathan* LinkedIn: https://www.linkedin.com/in/akshaynathan/* X: https://x.com/akshaynathan_Timestamps00:00:00 Introduction and Bringing the Power of Code to Everyone00:01:33 Joining OpenAI and Preserving a Startup Culture00:02:40 What OpenAI Learned from Enterprise AI Adoption00:05:28 Why OpenAI Built ChatGPT Work00:07:17 Codex vs. ChatGPT Work and the Shared Agent Harness00:12:07 Why OpenAI Merged Its Agent Experiences00:16:24 Models, Reasoning Levels, and Choosing the Right Default00:20:26 Artifacts, Agentic Spreadsheets, and Model–Product Collaboration00:24:22 Why Sites Could Replace Decks and Spreadsheets00:30:08 Designing an Agent That Can Build Almost Anything00:34:28 From Developer Agents to Knowledge Work—and Everyone00:36:07 Power-User Advice and AI-Assisted Performance Reviews00:40:41 OpenAI's Internal AI Memes and the Ten-Million-User Launch00:44:39 OpenClaw, Personal Agents, and ChatGPT as an Operating System00:50:24 Sub-Agents, Ultra Mode, and How Much Control Users Need00:54:39 ChatGPT Memory, Personalization, and Chronicle01:00:19 How AI Is Reshaping Product Development and Tech Roles01:03:15 Ideas, Taste, and Why LLMs Struggle to Generate New Ideas01:04:42 Measuring Productivity, Quality At-Bats, and Motion vs. ProgressTranscriptIntroduction: Akshay Nathan, ChatGPT Work, and the No-Code ArcSwyx [00:00:00]: We're here in the studio with Akshay from OpenAI. Welcome.Akshay Nathan [00:00:07]: Thank you.Swyx [00:00:08]: And with our trusty co-host, Vibhu. So you recently launched ChatGPT Work. You lead Core Product Engineering. It's been a long journey, into all this. I find it very interesting that you started with no code or low code, with Walrus and Airtable. And to some extent, ChatGPT Work is like the super app of super apps of, well, here is the ultimate no code. You just write a prompt.Akshay Nathan [00:00:32]: Yeah. It's funny how things come, full circle. I think for a long time in my career, I started my career working consumer fintech, but then after that, like, there's this hypothesis that, the things that we were able to do with code, like, as engineers, like, if we could bring that to many more people in a more, accessible way, then that would be truly magical. We were working on a startup. It's funny, like, before LLMs, before vision LLMs, on how to do automated testing with AI. It was just kinda jank, back then, but doing what we can, and then worked at Airtable for a while on the same thesis that, like, if we can bring a database or the primitives behind a database to people, that'd be really useful to them. But once LLMs came onto the scene, it became clear that, this was the missing piece, like, the missing technology required to, like, bring the magic of code to everyone without them having to know what's going on underneath the hood. And so, like, I think this launch and a lot of the stuff that we've been up to is, like, the manifestation of that.From Walrus and Airtable to OpenAIVibhu [00:01:33]: How was stuff when you joined? So you joined OpenAI 2023. Now we've got, so much more stuff, so ChatGPT, Codex app, ChatGPT Work. Have things changed?Joining OpenAI and What Hasn't ChangedAkshay Nathan [00:01:44]: I think the more interesting thing is how things haven't changed. Like, one, I joined I remember when I joined, it was, like, five hundred people. One thing I was worried about was, like, I was looking for something, more early stage and, like, was it gonna feel startup enough? And I joined, and I was like, “This feels even more startup-y than I could ever imagine.” And, like, that really hasn't changed even till now. I think the, like, level of, like, bottoms-up ambition and, like, the ability of anyone to, like, do anything or have an idea and ship it is really cool. But on the, like, mission side, I think what was really compelling to me is this mission of, bringing frontier intelligence to everyone. Like, building AGI and then bringing it to everyone. And, I think acknowledging back then that, like, that vision is gonna, not be a linear progression. Like, we're probably gonna, like, try different products and have different things that succeed and don't. But the vision has stayed the same, and the mission has stayed the same, and we're starting to see the pieces, fall together, and that's really cool.Enterprise Lessons: No One-Size-Fits-All AISwyx [00:02:40]: You worked on Enterprise. What A lot of people never touch ChatGPT Enterprise. What is something that you learned from there that you're bringing into your work now?Akshay Nathan [00:02:52]: I think how there's no one-size-fits-all solution in Enterprise. I remember in the early days of ChatGPT Enterprise, like, when we talked to customers and, like, everyone. That was, like, when I think it was a year after ChatGPT was released, and everyone was so excited to bring, AI into their enterprise. And, there were all these teams being stood up. It was, like, the AI deployment team with, like, these enormous budgets. And if you asked anyone, like, what were they excited about? Like, what were they excited about solving? Like, at first, you'd get, like, kinda like the baseline answers of, like, “Yeah, we have all this context and data and all this stuff.” But then if you ask them, like, “What was, like, a discrete use case that, like, they want AI to enable in their workplace?” You get such a different, like, variance, like, explosion of, different types of answers. And it's interesting, like, you using, like, these models and these products, you have this box, and you can say anything to it, which is the magic. But it'on the flip side, it also means that, like, you don't know what to do with it. And in Enterprise, I think a big part of that is, like, meeting the users where they are, like, what use case were they trying to solve, and then teaching them how they can use AI to, like, gain leverage there.Swyx [00:03:56]: Do you meaningfully differentiate that from forward-deployed engineering?Akshay Nathan [00:04:01]: I think there is the go-to-market side of it and then there is the product side of it. I think you need someone on the product side. And I think, like, however good we get at FDE motion, like, I think at the end of the day, if we have a user who's, like, looking at their computer or looking at their phone, like, it's our job in the product to, like, be enabling them and showing them where to go. So we're really excited about that.Vibhu [00:04:24]: Do you think there's been changes, over the past three years of adoption? So there have been, step function changes. You have reasoning models and whatnot. Is there still the same problems of Enterprise has black box, don't know what to do with it, or have things changed?Adoption, Agents, and the Next 10x MarketAkshay Nathan [00:04:39]: We're seeing now that, like, there's this huge uptake, right? Everyone is extremely excited about it. It feels like, many people are, millions, hundreds of millions of people are using ChatGPT. They understand, like, how generally to work with AI. But then, like, every time, like, a new capability gets unlocked, so now, like, we're seeing with agents, like, there is probably a contingent of, like, early adopters still who, truly get it, who are like, “ we you can do anything. You just have to make sure the right context is there, it's connected to the right tools, and that you are supervising it, but, like, anything is possible.” But then there's, like, this, like, 10x or 100x bigger market where, like, they don't yet get that, or they don't yet see that. And so I think that's the next stage here. So to answer your question, like, I think the adoption is there and growing fast, but I think the opportunity is, like, far bigger than that. That's where we wanna play, especially with ChatGPT Work.ChatGPT Work, Codex, and the Super App MergeSwyx [00:05:27]: Yeah. well, let's, let's skip ahead to ChatGPT Work. only, like, a month ago or so, announced. what was the decision process that led into it? there was this, overall merging of the super app. Is that what we're officially calling it? you deprecated the browser as well. Just, summarize your last, like, couple months of working on this thing.Akshay Nathan [00:05:50]: Yeah. It feels like forever now, but it's only been a few months. I think maybe the one, impetus that, like- Is most salient is when we release Codex, or even internally had Codex, like, it was really surprising to us, I think we recently put out some stats on this, that there was this, like, real inflection of, like, adoption among non-developers at OpenAI. And, I, through this product development process, like, would go to, like, these UXR sessions to talk to people internally. And the thing that stuck out to me is, like, one, like, you go talk to, like, strategic finance or marketing or whatever, and they're all using Codex for, their use cases. That part's cool, but the thing that really stuck out to me is how proud people were that they were using Codex. Like, how, likeSwyx [00:06:34]: It's like, “I'm not supposed to be using it, but I am.”Akshay Nathan [00:06:36]: It was that. It was, like, that they were, early to this, like, new thing, but it was also this thing of, like, they felt like they had a superpower, right? And, what we recognized then is that, like, the power of Codex, the power of agents, like, we already had this massive distribution base of people who have, come to know and love ChatGPT. Like, how do we show that to them? Like, how do we bring it to them? Which is, like, a hard product problem, and it's, like, a tricky thing, right? There's many ways you can go about it. And so that's what we called the Merge and the Super App over time, and ultimately launched it in ChatGPT Work, is how do we do that? But it came from that initial realization that, like, the power was not only for developers, like, much earlier than probably even we thought. Like, it could be extended to everyone.Swyx [00:07:17]: How do you see the products differently? So, like, who is it for, right? So Codex started out even CLI, then app. Now there's a merge of ChatGPT Codex and ChatGPT Work, so is it the opening for the average user, for enterprise, for work? How do you position it?Akshay Nathan [00:07:36]: I think we want to get it to position it for if you're doing work-related things, for lack of a better word, right?Who ChatGPT Work Is ForAkshay Nathan [00:07:42]: I think productivity is what, like, the pillar that I support. Like, that's the name of the team. And the reason for that, the reason we call it productivity and not, like, enterprise or, like, work or something like that, is because there's also personal productivity, right? And, like, I think ChatGPT Work is I've seen people do things in their personal lives that you wouldn't classify as, like, work technically, but, like, these agents are, super capable for. Like, one recent example that someone posted about, on our Slack is, like, someone had, like, a missed package, like they didn't receive it, and then they got, like, the picture of it, from Amazon or whoever the courier was, and they, like, asked ChatGPT Work to, like, find out where that package is. And, like, the agent, is extremely tenacious and, like, took the image and, like, looked at a bunch of, like, listings around their neighborhood and figured out exactly the apartment complex in which the package was, like, gave them some information. And so, like, I think there's all these things that, like, you, work-related or productivity-related things, I think that's what we want the product to be. You asked about Codex. I think we think Codex is, a durable brand, but we have a principle that, like, the user we don't want a user to get stuck in a tab or an experience where they don't get the power of the product. And so, like, everything that you can do, in the Codex portion of the product on desktop, you can do in ChatGPT Work and vice versa. But we made some opinionated product decisions on, like, how much of the Git state, if you're in a Git repo, do we wanna expose to the end user? Or how much do we wanna make the experience of seeing the agents thinking, like, diff forward so that you get exposed to the diffs out of the box. And then, like, on the safety side, like, how do we wanna think about, like, sandboxing and making sure that we have the right defaults in one state versus the other? So, there's, like, some opinions that go behind that, but we do want We don't want the user to need to choose which experience they're in.Swyx [00:09:26]: That is a good goal for AGI, right? Like, people don't want, like, to hide to choose what version of AGI they want. They just want the AGI to decide for them. can I get an answer or, like It's not super clear to me. Is the Codex harness and the ChatGPT Work harness the same? Is it just UI affordances, or are there prompt level or even deeper differences?Shared Harness, Different UX: Codex vs. WorkAkshay Nathan [00:09:49]: So the harness is the same. The harness is shared. on In both of the products, we made improvements to the harness to make it good for knowledge work, especially as it relates to plug-ins or computer use or artifacts. You get that power regardless of which experience you're in. On the UX side, there's opinionated takes that we have when you're in Codex mode, what the UX should be how the UX should behave, and some stuff around the sandbox like I mentioned, but the underlying harness and capabilities should be the same.Swyx [00:10:16]: I'm just kinda curious. Maybe we can, -- Is there a query that we can run that would look different in the two modes?Akshay Nathan [00:10:23]: Yeah. I tried to create, like ask it to create, like, a retirement calculator spreadsheet or something, in both modes. And then in Codex mode, you might have to be in a repo for this, but you'll see, like, the diffs of, like, the sheet that it's creating and stuff like that, and the file edits. But in Work you won't be able to see that.Swyx [00:10:42]: I think that's, that's super clear. And then also the other thing I wanted to dive into was your, the productivity team. what else is there? first of all, what are the top-level teams other than productivity? Isn't productivity everything?Productivity Teams and Core ChatAkshay Nathan [00:10:55]: SoSwyx [00:10:55]: Science?Akshay Nathan [00:10:55]: We have a team focused on ChatGPT. Like, the core chat experience, for consumer, which is like, not, I think all productivity. Like, there'People are using ChatGPT every day for search to, figure out how to write messages to loved ones, to think about, how to, like, learn a new topic, et cetera. And so there's so much more inside to create images. And there's so much more in chat that, the hundreds of millions of users are using that warrants, like, a very dedicated effort. And there's teams focused on enterprise and infrastructure and API and stuff like that, so.Swyx [00:11:33]: I will bring it up.Retirement Calculator Demo and Git-First UXSwyx [00:11:34]: Yeah. So I have them both running. This is ChatGPT Work. There's a Codex version here. I picked “Five Little Ducks” song, so this will take a while.Akshay Nathan [00:11:43]: Huh.Swyx [00:11:43]: I think we'll just keep it in the background and, as they finish, we'll look into some of the differences.Akshay Nathan [00:11:48]: Yeah. But immediately, I think if you flip back to the Codex version you'll see that,Swyx [00:11:53]: That it assumesAkshay Nathan [00:11:54]: Like theSwyx [00:11:54]: It assumes Git. Yeah. Yeah.Akshay Nathan [00:11:56]: The, like, dynamic island assumes that you're in a Git repo. And you might miss some stuff because some of it is, like, in the actual chain of thought with those changes and how we display that, but yeah.Swyx [00:12:07]: Is there an unintuitive like, is there a thing that you wanted to ship and then you got feedback, and you were like, “No, let's not do it?” Like, what's the thinking behind that?Why Merge the ExperiencesAkshay Nathan [00:12:14]: In, ChatGPT Work?Akshay Nathan [00:12:17]: I think one direction we could have gone with this is, like, keeping the experiences, like, completely separate. So it's like, whySwyx [00:12:22]: Different apps.Akshay Nathan [00:12:23]: Exactly, like different apps or even in the same app, like different, completely different experiences. Like, why merge it all? Like, what is. Codex, people love. Like, why bring these products together? And I think the intuition here is that, like, all of our jobs are, like, changing dramatically with AI. Like, for, like, every few months, like, I feel like I wake up, and I'm, like, doing a completely different thing than I was doing a few months ago. And my hypothesis here is that, or I should say our hypothesis is that, like, part of what we're, we're building, this technology is giving people leverage. Like, the things, maybe it's the more mundane parts of your job or parts that, like, if you were able to automate, you'd be able to share more ideas faster or whatever, like, you're able to do now. And because of that, like, that might blur the lines between someone who's, like, only writing code or creating strategy docs or, planning events or, helping with marketing or doing podcasts or whatever, right? And so, like, these things are gonna get blurred over time. And so, like, trying to draw a hard boundary based on, like, the who you are is gonna be, is gonna be tough. And, like, we should enable users to choose, but we shouldn't box them in. And so a lot of the work that went in here, like, keeping the primitives the same, like for example, plugins are, like, unified across, this product and ChatGPT and the cloud, was because of that. It's this thesis that, like, eventually things are gonna come together and we don't wanna be Like, we wanna be prescriptive about when to be in either experience, but we don't want to box anyone in.Swyx [00:13:45]: I wonder if there's users who are very tuned to the old ChatGPT harness that is effectively now replaced by the Codex harness. I can't imagine what that was, but maybe they're more the more conversational side. Can you compare and contrast the two harnesses? ‘Cause only you've seen it.Akshay Nathan [00:14:02]: Yeah. I think ChatGPT, the existing harness, like, still exists today. Like, it exists in this app,Harness Engineering: ChatGPT vs. CodexSwyx [00:14:08]: The classic, right?Akshay Nathan [00:14:09]: TheVibhu [00:14:09]: You just start a new chat, and you don't go under Work, right?Akshay Nathan [00:14:13]: Yeah. If you startVibhu [00:14:13]: SoAkshay Nathan [00:14:14]: A new chat and go to chat, then you're, you're talking to ChatGPT with the instant model.Vibhu [00:14:16]: Oh, we can technically do another. But on instant.Swyx [00:14:21]: Yeah. So this one's not gonna code or it's gonna be in line. It's on a in line in a sandbox.Akshay Nathan [00:14:26]: It'llVibhu [00:14:27]: Oh, that's coolAkshay Nathan [00:14:27]: We try to push you to go to Work if you're creating a spreadsheet. Yeah, but this isSwyx [00:14:30]: And this is a router decision? Sorry. Is it a router decision?Akshay Nathan [00:14:34]: This is the decision that, the model is making, and then, like it sees that you're able to. or you're trying to do something that would be better served in Work mode. But I think your question was like, what are the advantages of, like, the chat, like ChatGPT chat harness?Swyx [00:14:48]: It's more broadly, like, I wanna, do an oral history of harness engineering. Right? the ChatGPT harness lasted us from, let's call it the ‘01 era, until now, and now it's being replaced by the Codex harness effectively. And they're, they're overlapping somewhat, but I'm curious what changed if there is.Akshay Nathan [00:15:10]: My perspective on this is, like, there's, there's, there's there's like a constant process of, like, divergence, convergence, divergence, convergence. And in chat, like, many of the use cases I was talking about before, like, search or learning, I think we're, we're really optimizing for latency and optimizing for personality and, like, different things that, over time, like the product The reason people love ChatGPT is because we've been optimizing for those things and working on them for so long. Codex, what we learned was that, like, if you give the agent access to this infinitely flexible environment as a computer, it can do really powerful things. And so when we think about, like, okay, well, for knowledge work, like, what is which mode should we choose? It was like it felt more natural to us to bring that to this, like, computer environment and, maybe abstract some of the details of this computer away from users who might not be used to that, but, like, give them that same power. But ultimately, I think that we want the power in all places, right? We wanna meet people where they are. So I'm sure there'll be work down the road in order to get things to be, equivalently capable in all scenarios. But it's just a question of, like, what we've been focusing on the product on historically and what we're focusing on now.Models, Defaults, and the Reasoning SliderVibhu [00:16:24]: I think alongside that, outside of just harness and when to use Codex, ChatGPT, or Work, there's also the new models you've released, right? any guidance there? So people love to min-max what to use, like only use Terra on high reasoning versus, for this, you wanna use Sol here, ignore all theseAkshay Nathan [00:16:44]: There's 32 options.Vibhu [00:16:46]: But, that being said, for people that are expanding, so, productivity trying stuff for work that don't have the breakdown of what all this is what's, what's the advice, right?Akshay Nathan [00:16:59]: Well, I think before the advice, like the first thing is, like, none of this would be possible without these models. Like, the, I think you asked earlier, like, what was, like, the inspiration for work and, like, early on, like I mentioned, like, what we were seeing with Codex, but that was also because the models were getting infinitely more capable. That's happening again. I think it's like another step function jump now. And to answer the question on advice, like we want this default to be the best possible. Like, we wanna be opinionated about the default, and so we've we've chosen a default that we think is gonna be the best for everyone. And, we have for power users options under the hood. We could One could argue that there might be too many right now, and we're, working on simplifying it. But you can extend, the reasoning level, and you can change between the different model classes if you need to, but the default should be the best for most use cases. So my advice to most people would be to stick to that. And then, if you reach a situation in which you think that you could, you wanna try, a different configuration, if you're not seeing either the efficiency on the cost side or the quality on the intelligence side, then you can change the defaults and see if you can get something better. But we think that the default should be good enough.Swyx [00:18:09]: I have, I'm just gonna run something by you since you have way more experience than me. I've recently been doing Sol Lite but with goal, with the idea that the goal augments the reasoning effort, but with more terminations and turns.Swyx [00:18:24]: Is that a good way to think about it as opposed to Sol Ultra or Sol, Extra High?Akshay Nathan [00:18:29]: Yeah. It's hard to say becauseSwyx [00:18:31]: Yeah. It's like an interaction effect.Akshay Nathan [00:18:33]: exactly. It's like there's a preference on, for you as an individual, like how do you like to collaborate with the models? Like how many of those like terminations, as you call them, do you want where, you can steer or make sure that it's doing the right thing?Akshay Nathan [00:18:46]: I think generally people should try whatever works for them. I think that like using Ultra or the like multi-agent setups are best for like when you have like tasks that are either incredibly complicated, like open explorations or very paralyzable. I think even for tasks using goal, I think is best for tasks that you'll be able to make consistent progress in a way that's verifiable over time. But I think for most tasks, they don't fall into either of those buckets. And so like at least when they're starting, and so that's why I think the best first step is like trying it with the default configuration and then seeing like where you wanna go from there.Swyx [00:19:29]: Right. You guys worked on a slider, which is super helpful for reducing the amount of panic.Vibhu [00:19:36]: It's nice on mobile at least. There's a nice slider there.Swyx [00:19:38]: It's nicer.Vibhu [00:19:39]: I haven't tried it.Swyx [00:19:40]: So you have the advanced view there, but if you click advanced view. Yeah.Vibhu [00:19:44]: Ooh, it's just a nice slider. Yeah.Swyx [00:19:46]: Very pretty, very colorful.Akshay Nathan [00:19:48]: Yeah. The idea was here was like reduce it to like one dimension even though there's multiple dimensions, right? Try to project it onto a single dimension for the user. Like, something from that represents like, speed and efficiency on one side and then like quality and thoroughness on the other side.Artifacts, Spreadsheets, and the Work LaunchSwyx [00:20:04]: I am just puzzled that it uses Sol so much, like the lowerVibhu [00:20:07]: NoSwyx [00:20:07]: Grounds I would've usedVibhu [00:20:08]: I think the slider, if I'm not mistaken, isSwyx [00:20:09]: Terra.Vibhu [00:20:10]: Oh, it is.Swyx [00:20:11]: Yeah. See? So they preset Terra to only be the light one. But like I think a lot of people would more people should use Terra. One, because Sol keeps running out of capacity.Vibhu [00:20:22]: I'm the reason. Here's ten minutes of ourSwyx [00:20:24]: There you goVibhu [00:20:25]: Retirement calculator.Swyx [00:20:26]: Oh, that's the Excel thing working for you.Vibhu [00:20:28]: This is,Swyx [00:20:28]: Oh my God. Look at thatVibhu [00:20:28]: This is work, and then Codex is still cooking, so we'll get back into it. I think it'll be interesting to see the thought process, the reasoning, and also, this is eight minutes on work. Codex is still cooking.Swyx [00:20:41]: Yeah. And by the way, so I've, do Gabriel Chua? He's part of the OpenAI Singapore team. He showed me this, and I was like pretty shocked that this looks like Excel. It edits Excel files. You never paid an Excel license, right? Like, but somehow this is like workable and it's agentic Excel.Akshay Nathan [00:21:01]: Yeah. one of the big like pushes that we made for this launch was like artifacts, right?Akshay Nathan [00:21:05]: Like both on the model side, like I think if you compare this with GPT-5.5 and GPT-5.4 before that, you'll see that there's been pretty dramatic improvements in the quality of these artifacts and then also on the product side.Vibhu [00:21:16]: The UX side is also crazy, like hosted sites and whatnot. No longer needing to host your own little webpage, like itSwyx [00:21:23]: Oh, I have a story about that. I can do, a separate thing. I'll need to take the visuals here, but we-we'll, we'll cut to that later. Was there co-training, because you were moving making this big move and you launched GPT-5.6 on the same day as ChatGPT Work? Was there influence between the model training teams and the harness teams, or did they did the launch dates just happen to line up the same day?Akshay Nathan [00:21:46]: I think the we collaborate heavily with the research teams, and I think that's like one of the most magical parts of the job, like the most fun parts of the job. But yeah, just using artifacts as an example. Like, a lot of what you're seeing, like underneath the hood, there's a lot of work that went into making sure that like, we had the right infra to be able to train the models to get better at this. And then on the product side, like had the right experience for users to be able to collaborate with the model on an artifact like this. In fact, like this whole viewer, like the intuition here is that like, it's not necessarily that you wouldn't need an Excel license. This is stage one, right? Like, this is probably not what you meant when you're like making a retirement calculator.Vibhu [00:22:24]: Yeah, you can iterate very easily. Yeah.Akshay Nathan [00:22:24]: You wanna iterate and like when you're seeing it, and if this thing is high fidelity to like what you would see in or what your coworkers would see if you were to send this to Sean, like that I think makes it so easier and makes you trust the product in terms of iteration.Vibhu [00:22:39]: When you say coworkers would see, do you see a multiplayer, multi-team collaboration with artifacts? Any things you guys think about that?Multiplayer Artifacts and CollaborationSwyx [00:22:46]: You can already share it, right?Akshay Nathan [00:22:48]: Yeah. It's inter It's something that, we're actively thinking about. one thing that, we've noticed internally without talking too much about the roadmap is that like there's many times when someone will ping me about something, and I will ask ChatGPT Work the question, and then I'll ping them back the answer.Akshay Nathan [00:23:04]: And then I'll be thinking likeVibhu [00:23:04]: Like the simplest would be, the three of us are just all on one hosted.Akshay Nathan [00:23:07]: Exactly. And I'll think about like was I required in this loop or and then maybe it was, rephrase like what they were asking or pulled from certain context or whatever. But like, when I gave them back the answer, that process was also lossy, right? Like I gave them just like my interpretation of what ChatGPT Work cooked up. But like underneath the hood, there's so much context like in the rollout and stuff that could be interesting.Vibhu [00:23:28]: Yeah, it'sSwyx [00:23:28]: So like the answer was preemptively respond to every inbound request?Akshay Nathan [00:23:33]: No, it was just like literally like this is what I do sometimes as my job.Swyx [00:23:36]: I know you copy-paste and then you're just a message forwarding serviceAkshay Nathan [00:23:39]: Yeah. Yeah, exactlySwyx [00:23:39]: From AI to AI.Vibhu [00:23:40]: But I think it's interesting, right? It helps people understand the capability of what you can ask and delegate that oftentimes people don't realize until they try or someone shows you, and then you're like, “Oh, okay. Okay, I see.”Swyx [00:23:52]: I think it's als there's also like a, light security issue, where like you're the permissions layer. Like yes, I could query everything that you query, and I could get an automated response, but maybe I'm not supposed to see it. And that there's no way I would know because I'm not supposed to know what I don't know.Akshay Nathan [00:24:07]: Especially as like, with ChatGPT Work, we're, we're asking you to connect your plug-ins and, it's pulling from your local files and stuff like that. Like the amount of context that the agent has access to is like- Deeply personal and like that's something I think we need to preserve, so that'll be definitely a challenge.Swyx [00:24:22]: There's Excel, there's PowerPoint, there's Docs, the, grand trio of work. What other formats of work do you think about? like you worked on Airtable. Is there a future where there's like OpenAI Airtable? Like what does that look like if you ever ended up doing it?Akshay Nathan [00:24:41]: It's a really good question. I think,Formats of Work: Sites as Knowledge ArtifactsAkshay Nathan [00:24:43]: one that you didn't bring up was Sites, and I think that wasSwyx [00:24:46]: SitesAkshay Nathan [00:24:46]: A core part of this launch. There's one side of Sites that I think people commonly talk about, especially on Twitter and stuff or X, of like, this like prototyping tool. And like we saw that happen with this launch even. The model slider that you guys were referencing earlier, like that was developed almost fully in a Site. Like, the collaboration between design and engineering and product on that was like on a site where we play with, the affordance and figure out how it feels and all of that. But the other aspect that I think is a little bit less talked about is like Sites as like an artifact for knowledge work. I was talking to someone the other day who's on like our corporate finance team, and like we were mentioning how like now when they have these reports that they're, they're working on as a team month to month, historically those things were in slide decks and in spreadsheets, and now they're just in Sites. And like Sites is the mechanism that they collaborate across the team. And the reason is ‘cause it's like, it's like somewhat higher bandwidth. Like, at these tools like PowerPoint and Excel are like infinitely flexible, but at some point you reach the boundary of like either as a human you may not know how to use some feature or something, or the product itself doesn't support it. But with a site you can do anything. You ask for anything and you can get that. once people see that magic, I think it's been really valuable.Swyx [00:26:02]: Yeah, let me show you my case study. this involves all the hot topics including ChatGPT Work, but also GPT-5.6 token billionaires and token maxing and Sites and auto research. I'm a fan of this game called Strata. It's, it's like a little board game that youSites, Auto Research, and Research DashboardsSwyx [00:26:17]: That you play with, physical blocks, that come on top of it like that. So over the weekend I took like thirty photos and just threw into ChatGPT. one point seven billion tokens later, out comes this site with a fully playable thingAkshay Nathan [00:26:32]: WowSwyx [00:26:32]: With 3D, block placement and everything. Because it requires physical blocks and I needed friends to train on it so they can get better, so I can play against them. But also, I could also, do things like train an AI on it and that's, thatAkshay Nathan [00:26:45]: That's your auto researchSwyx [00:26:46]: That gets into auto research. So, you want to train your own AIs, and then make sure they self-play against, each other. I need to set both AIs. So this is AI versus AI, and they're, they're gonna self-play. the AIs start out bad and then you want to define a loss function and get good. I wasn't gonna supervise all this. I was at, I was down in San Mateo, attending a conference. What I ended up doing was, auto researching and on this and creating benchmarks and that there was just way too many parameters for me to read. So I started asking it for a site, and it's created this lab, panel. Where is there a, is there a shortcut for a site that is created?Akshay Nathan [00:27:28]: You should be able to go in the sidebar to Sites, top of the sidebar. The left sidebar.Swyx [00:27:33]: This one? Oh, left?Akshay Nathan [00:27:35]: Yeah. Just scroll all the way to the top.Swyx [00:27:36]: Oh. Oh, it says Sites. Oh, there you go. Yeah.Akshay Nathan [00:27:39]: Ooh.Swyx [00:27:40]: So it create, it creates the sites. I don't, I don't think this is, it is exactly what I wanted, but let me show you what it popped up, right? Like I think as a research artifact, it is very important to communicate, exactly, what is being done. Outputs this thing which I eventually started publishing. So I moved it off of Sites because I wanted more, database and infrastructure than Sites afforded me. But this is like a research output that you can start to mess with and like try to think about like what hyperparameters are you tuning for training AIs. And like I was trying to make like scaling laws and everything and doing all sorts of like game optimization stuff. And the fact that you can just throw this up as a research artifact, like I no longer need to read ChatGPT output. I read Site output. But then there's also a huge sprawl. Like look at how long this thing is. There's so many numbers. It is pretty overwhelming, so then I have to start pruning it from there. But, it's an interesting transition from Markdown effectively that you're putting out to, you're putting out a whole functional site.Akshay Nathan [00:28:41]: I think Markdown just isn't that optimal for people to read, right? Might as well just write HTML website and I don't know. I think you can do a lot with customizing this, right? You have your skills that explain what you want. Like I noticed they're quite verbose. I don't need a lot of this information.Swyx [00:28:57]: It's very verbose.Akshay Nathan [00:28:58]: So and then the nice thing of having a site side by side is, you just iterate on what you want and what you don't, right?Swyx [00:29:05]: Yeah. I don't know if, any that triggers any stories for you of how it's run internally. Am I doing this right?Akshay Nathan [00:29:11]: Yeah. I think that this is like a workflow that we're seeing like all different types of teams use, where like the canonical artifact that was previously a deck or something is now becoming a site. And like with a site you, because it's just HTML, you can like. It's infinitely flexible. And so, if you want to give more prominence to a certain thing that like in a slide deck would, feel like it was buried, like you can do that. You can have it be like the hero image, right? And so I think that like, people are starting to see that. There's more work to be done to make these things like much more easier, easy to collaborate on. You mentioned that they're very, they're long and verbose, could be broken up. I'm sure that there's still something to do there.Swyx [00:29:53]: They're super long. Yeah.Akshay Nathan [00:29:54]: Yeah. But I think we're starting to see that like there is this aspect of this is a really interesting, format, for people to use, that's like much more flexible than what they ever had before.Swyx [00:30:07]: I think your job also comes becomes meta. You're not designing the products. You're designing a product to make products, and I'm curious how you manage that.Designing a Product That Makes ProductsAkshay Nathan [00:30:18]: I think one thing that we've been Like when we look at the UX, like that we've been thinking a lot about is how can we balance like simplicity with capability? Like if we're designing a product, like you said, that like is made to make up build other things, right? You can build so many different things. But we can't put that all in front of you because you'll get overwhelmed.Vibhu [00:30:41]: Yes.Akshay Nathan [00:30:41]: And so we had similar problem or similar challenges even Chat-with ChatGPT, but especially now, like when there's so much that can be done, I think the balance that we're constantly trying to strike is like, how can we give the user enough of a UI surface where, they can be expressive, they can tell the agent what they need, they can verify that it's using the right tools, it's pulling from the right sources, et cetera, but then it gets out of the way. And then how can we build the right system such that we can show them instead of telling them what can be done? Because so much of this is gonna be like, how do they discover the next use case and the next one after that if they really want to be super powered by the AI.Games, Private Evals, and Show-Don'TellVibhu [00:31:19]: Yeah. It's interesting. I feel like everyone also just has a different way to do it, right? I made a similar version of this same game. I didn't take any pictures of board or rule game. I threw in at goal eighteen minutes, fifty-three seconds later, a lot of tokens later, I've got a similar version. not with all the auto research and whatnot, butAkshay Nathan [00:31:39]: You gotta do all the latest trends.Vibhu [00:31:40]: And yeah, I did it with, did it with Codex, not Work, but it's interesting, right?Akshay Nathan [00:31:45]: Yeah. And this is GPT Image generating the pro avatars. Very good for game design. LikeVibhu [00:31:51]: AndAkshay Nathan [00:31:52]: A lot of game designers were like really into GPT Image for assets.Vibhu [00:31:54]: I will say like the broader takeaway probably is the reason that we do this is more so just to test the tools, right? Like, this was also a test for GPT-5.6 came out. I had done the game on GPT-5.5, right? The ability for me to no longer need it to. I had to feed it the rules. It's, it's a pretty niche game. It couldn't find how to do this on its own.Akshay Nathan [00:32:15]: Oh, yeah.Vibhu [00:32:15]: GPT-5.6Akshay Nathan [00:32:16]: It is out-of-distribution, which is why I was also very keen on testing the GPT-5.6 capability.Vibhu [00:32:21]: But, this is just as work comes out, as new things come out, these are just our side ways to test things, right?Akshay Nathan [00:32:27]: Yeah. It's some private eval. That is not this private.Vibhu [00:32:31]: But also valuable because now you can send this to your friends and I learned about this game through seeing this.Akshay Nathan [00:32:36]: It's a hard game. He's very good.Vibhu [00:32:39]: It's good to when no one is competing with you. But yes, it's a classic RL problem of like self-play, bootstrapping your game AI. yeah, you see how easily work becomes personal and personal becomes work because the thing I do for personal, it directly informs people I work with because I showed it to them. They were like, “Oh, you can do that with GPT?” Which like I imagine is the growth strategy.Akshay Nathan [00:33:02]: Yeah. The show not tell is a big piece that, I think we've we're not still not fully cracked of like, showing people all the things that they can do with the product versus like trying to teach that to them through like, articles or onboarding or whatever.Akshay Nathan [00:33:18]: So meeting them in the moment.Vibhu [00:33:19]: It's a career risk for me, because I used to be in developer relations, right? Where your job is to show, and then you're like, “What do you mean? You don't, you don't need.” your job is to tell. And then. But the product people are like, “Well, we don't need you if our product is intuitive enough.” SoAkshay Nathan [00:33:37]: Yeah. that's the magic of the models. So you can tailor the telling or the showing to like specifically what the user needs, like what they care about, what they've done in the past, exactly where they are on the adoption journey. So I think that's like gonna be a super big opportunity.Vibhu [00:33:50]: Seems easier and easier now to tailor custom showing, right? People have different use cases. As much as you said you don't wanna segment different people into different buckets, right? It's also not that hard to for people that are in different categories. But the question, is you said your team is more broadly on. What was the term you used? Productivity?From Developers to Knowledge Work to EveryoneAkshay Nathan [00:34:12]: Productivity.Vibhu [00:34:12]: Productivity. So howAkshay Nathan [00:34:12]: Which is now work.Vibhu [00:34:14]: Is it work? Is there another distribution that we're not hitting? Is there a group of people that will have something different than ChatGPT, Codex or Work? Is there more that the mass isn't targeting?Akshay Nathan [00:34:28]: I see it as like a sequencing, like. The vision is like bring useful agents to everyone. We started with like developers. Like developers historically are like early adopters that are willing to put up with more friction, set things up, et cetera. Like that's where, Codex started. I think the next opportunity is like what we call general knowledge work, all the other functions around developers. I think when you go from developers to this segment, like there's inherent challenges with like, this show not tell thing that we're talking about, making the product more understandable, bringing in new capabilities that matter more for this cohort than matter for developers, things like artifacts, things like computer use, et cetera. And then I think like the same learnings, like similarly how we took the learnings from developers and brought it to, general knowledge work, the next stage will be like taking the learnings from general knowledge work and bringing it to everyone no matter what they're doing in their lives. And we're already seeing that a little bit. Like this game example that you have is, something that's like on the border of like fun and personal life to, your professional life. I use ChatGPT Work full-time at home for everything, like for whatever I'm doing. I used it the other day to come up with a meal plan and like, save that on the like computer environment that it has and something that I can continue going back to. Like is everyone doing that yet? Probably not because the thing says work on it, but eventually, we wanna get people there.Vibhu [00:35:51]: ChatGPT life.Akshay Nathan [00:35:52]: Yeah, exactly. ChatGPT cooking. But I think there's a lot of, there's a lot of opportunity there, but I see it as like, we're, we're built we built a foundation in software engineering, and we're gonna take the same learnings that we take from software engineering to knowledge work to everyone.Vibhu [00:36:07]: Do you have any power user advice? I feel like, there's a group of people that will live it, use it for everything, stay on it twenty four-seven. And then there's a bit of a gap between that crew and people that, okay, I use it for work. I use it occasionally. Sometimes I type questions. any advice, any learnings, anything you recommend or just, takeaways that you've found that help bridge that gap?Power User Advice: Push the Frontier of ImaginationAkshay Nathan [00:36:30]: I think a couple things that I've seen is like, one, that it really helps to broaden your imagination of what's possible, and this has been a learning even for me. Like, the technology has progressed so fast that, something that, like, even three months ago, like, no way the models can do this. Like, now it's like, wow, it's like it can. Like,Swyx [00:36:52]: Give an exampleAkshay Nathan [00:36:52]: We're going through right now our, like, review cycle internally, and, people always talked about this as, like, a thing that the models are good at and like, there's a cliché of like: Okay, like, no one wants to be writing reviews and, like, we just use AI to do it. But in all seriousnessSwyx [00:37:09]: And it can evaluate it as well.Akshay Nathan [00:37:10]: Yeah, exactly. In all seriousness, before it was, like, just, like, slop and, like, I think it was helpful, but, not super productive. Now I've found that, like, the model can do a much better job than me, especially in this environment of, like, pulling context on, like, what people are up to, how they've like the things that they've done to make a difference, highlighting like, wins that they've had that, like, I might may not even have seen. It has access to, like, everything, right? Like the code, like, things that they've caught, reviews, Slack, everything. And so it's, like, incredibly powerful in that domain and, like, just like six months ago, the last time we did this cycle, like, I didn't even I tried using it, but it was not at all helpful. And this time it's been, like, incredibly helpful and, like, so I think continuing to push the frontier of imagination of what's possible, even if you tried something before, I think is maybe the my biggest piece of advice. The other, thing is, like, the more you put in, especially in this environment where, like, the model has access to everything on your computer or in ChatGPT Work, like you can create, artifacts over time and save them in your library and, like, the model will continue having access to those. Like, the more information you give it about whatever domain you're in, whether it's your life or your work, the more valuable it becomes, and it'll become valuable in, like, ways that might surprise you. Like, it might pull from context in a way that, may be proactive and that you might not even have thought about. But it needs to have access to those, to that those tools or that context first.Reviews, Agentic Search, and Context GatheringSwyx [00:38:27]: One thing I just wanna talk about the review stuff because I'm still that's a very sensitive thing and you're, you're a founder, you've managed people, you've hired people. As manager myself, I'm very reticent to put out any LLM-generated things especially when it comes to people, ‘cause it feels like you don't care.Swyx [00:38:46]: Presumably at OpenAI, people are more open to being eval rated by GPT. But are there any unofficial rules around this? Like, what's the etiquette?Akshay Nathan [00:38:57]: Oh, I think the etiquette is that, like, I would never write something via, like, well, solely via AI and, like, present it as, like, a review for someone. What I was talking about is more, like, gathering context. That's the place where it's incredibly helpful.Swyx [00:39:08]: So it's just search.Akshay Nathan [00:39:09]: Yeah, exactly.Swyx [00:39:09]: It's agentic search. Yeah.Akshay Nathan [00:39:10]: It's like agentic search, but, that you can tailor and steer much more capably than you could before, ‘cause, like, the thing is it's all there's a flywheel happening, right? Because of Codex, people are able to do, and because of ChatGPT, people are able to do so much more now than ever before. And if you're able to do so much more, it's easy to miss things as well. And so, like, I think we need to use these same tools to keep up with all the impact that people are having and understand, where we can be helpful.Swyx [00:39:39]: I think the thing, like, I run a small company, so easy to search, but at the scale of OpenAI with the amount of messages that you guys put in Slack, do you think that it misses things?Remembering What Humans MissAkshay Nathan [00:39:50]: Probably, but I think that I also miss things.Swyx [00:39:52]: Like, it doesn't matter, right?Vibhu [00:39:53]: I think sometimes it'sSwyx [00:39:53]: Like it's, as it needs to be human-levelAkshay Nathan [00:39:54]: It's all relative, right? Yeah.Vibhu [00:39:56]: Sometimes it's nice when it finds things you wouldn't, right? Like right now, my Codex system prompts, they're set up in such a way that every project I have has a secret- separate, notes MD, and it just writes learnings to there. And then the global one can pull from all these. So sometimes it'll be like: Oh, there's this project you did like four months ago. Here's a note that we had, and it randomly pulls it back into context that I would never do, I haven't thought about.Vibhu [00:40:20]: And I'm like, okay, this is quite superhuman, right? Like, stuff that would. And, it'll save like hours on chunking of stuff or find something that's already been done. I'm like, as much as it might miss stuff, I would too, but it's very useful when it finds stuff. And I have like a very, non-super engineered solution to this. It's just marked down files that get pulled whenever they want.Akshay Nathan [00:40:41]: Yeah. I have a funny anecdote about this. Like, recently gearing up to this launch, the team has been, really cooking on it for a couple months, and over that time, like there's so much conversation and chatter going on in Slack and Docs and elsewhere. And, one of the members of the team set up this, scheduled tasks, like automation to like look at everything that's going on and, like, come up with the best memes and then post it in one of our shared channels. And like, there are two cool things about this. Like, the first is, like, I think the models are, over time, like starting to become like funny.Swyx [00:41:13]: Funny. Nice.Akshay Nathan [00:41:13]: Whereas like, a year ago, like that was not at all the case. The second is, it was what you were saying, like they find things that in surprising ways that you may not have thought of and like create connections that you may not have thought of. And that really helps with like the meme generation because then you can see something that, genuinely surprises you and, is funny in that way. So yeah, that's like not like the most productive, use of this the technology, but it does it does uncover this, like this capability that's emerging, which is just like to find information that you otherwise would not know of.Launch Momentum and the 10 Million User MilestoneSwyx [00:41:43]: Talking about the launch, I think, I have pretty much said this is the most successful launch in a long time. I think even more successful personally than 5.0, and they're announcing ten million users. Does it feel different? You've been through a lot of launches.Akshay Nathan [00:41:58]: I think it feels like a culmination. Well, I think two things. One, it feels like a culmination, like I was mentioning earlier, like this like vision mission that we've been on for a long time. Like I said, we saw the magic of Codex internally, and then we're like extremely excited to bring this to many more people and to see it working, to like see us reach, the distribution goal, numbers that you mentioned, like I think that's like huge and super exciting. The flip side of that is like, there's so much more to do too. Like, that's also really exciting. Like, ChatGPT as a whole, like the this product that, everyone almost equates to AI and like loves, has hundreds of millions of users. And so like ten million is really cool, but like we need to get this to everyone. Like, we need everyone to feel this magic. And so that's the next step from here. But yeah, I think extremely pumped about how it's going so far and the opportunities.Swyx [00:42:46]: Awesome. I did want to also Because I've, I've, I've been tracking the number closely, it transitioned at some point from just Codex users to Codex plus ChatGPT Work, because they're same harness. The whole point is that you don't, you can't, count them separately. Do you have roughly a billion, ChatGPT users? Why did it just jump to one billion right away? Like, isn't that the default on ChatGPT or no?Codex, ChatGPT Work, and the Developer BrandAkshay Nathan [00:43:11]: We don't default you into ChatGPT Work if you're on ChatGPTSwyx [00:43:14]: If you're free. YeahAkshay Nathan [00:43:15]: It's also only available to paid users right now. And I think there's like a process of, educating users of what is the value of this product, having them try it, learning from their feedback, and making it better over time. But the goal is to, get as many of the people who love ChatGPT today to like feel the power of ChatGPT Work. But I think it'll be a journey.Swyx [00:43:36]: Yeah. And Codex will still be alive as a brand for the foreseeable future. And we'll just toggle between them as needed for UI stuff.Akshay Nathan [00:43:44]: Yeah, I think it's even stronger point than that. Like, I think we fully intend to like, treat developer. Like, developers have been, a core market for us for so long, and like there's, there's so much more that we can do to make Codex great specifically for, software development, and we'll continue to do that. This doesn't take away from that at all. If anything, it should increase the utility of something like Codex, because now you can move seamlessly between writing a diff to creating an artifact or, doing a search over your factor.Swyx [00:44:11]: I do wonder how much this terminology leaks to the non-technical user. Like, do they have to learn to say artifact if I want artifact? Or.Akshay Nathan [00:44:20]: It's funny, like we call it artifacts internally ‘cause that's what the teams call it.Swyx [00:44:23]: It's nice. Yeah.Akshay Nathan [00:44:23]: But like externally, like no one says that, no one calls it an artifact. But I think that people like often, like describe things, whatever they're used to, right? So if, ChatGPT Work is good at creating slides, they'll say ChatGPT Work is good at creating slides, and that's what we want.OpenClaw, Personal OS, and Persistent ComputersSwyx [00:44:38]: One big Another, it's July of twenty-six. One big thing that also happens in, for OpenAI was OpenClaw, and that's I think a lot of people's first time really maxing a agent for personal stuff, but also crossing over to work in essence same way. As far as I understand, OpenClaw is still independent, but did you go through your own OpenClaw moments? Were there any lessons you took from OpenClaw to Codex or back? Whatever.Akshay Nathan [00:45:06]: I think there's a lot of inspiration. I did go through my own OpenClaw moment. I,Swyx [00:45:10]: Yeah, tell the storyAkshay Nathan [00:45:10]: Me and my wife like set up an OpenClaw to like try to manage everything in our house. Not that there's like a ton, but it was like quite useful. We gave it a calendar. It started, creating events for us and stuff. At some point, the laptop that we were running on, it died and never got a chance to pick it back up. But there was a lot of inspiration there, like, in ChatGPT Work, in web and mobile, like you get access to this like persistent computer environment where, you can store files, and those files stay around between sessions. And the idea is to be able to enable use cases like this. one of the members of our team uses ChatGPT Work for what they used OpenClaw from before, and then feel like it has like completely transitioned, which is like, workout planning and like meal tracking. which again, it's like a work-related thing, right? It's like not work necessarily, but it's like in personal productivity space. But it has all the same primitives. So it has scheduled tasks. It has the ability to store files on a file system. It has the ability to like reference those things over time. And so you start to see the same types of use cases emerge, which has been really cool.Swyx [00:46:14]: Is there a point that ChatGPT Work completely replaces OpenClaw? they're independent, so.Akshay Nathan [00:46:20]: Yeah, I'm, I'm not close to it, so I can't speak to the OpenClaw roadmap, but I don't think so. I think that there's gonna be, there's always a need for like this like incredible, like open source technology that team has built. And I think that we can draw inspiration, in the product and, ChatGPT, I think many more people have like heard about and used ChatGPT than have used OpenClaw. And if we can take the magic from OpenClaw and bring it to them, I think that'll be a success. I think that like one thing on the ChatGPT Work side that we feel strongly about is that like the core experience is that you come to this product and you have a conversation, start a session, whatever you wanna call it, with this agent. And the magic of the product is that you can do anything in that moment. And we would like to create a product where you don't have to click a button or to go to a different place, whatever, and you can get whatever functionality exists in, your finances app or where or any other product like in this one place. And so that's the goal. It's like it we want an extensible system with plugins where you can connect to the tools that you need in order to be able to accomplish like a financial task, where you can, if you're doing like science work, like we have an ability to like extend the system in such that you can like write the tech and it performs well. There'll always be like products that we support that are best in class at those things, but we want as much of the magic as possible in that core experience.Swyx [00:47:45]: Yeah. Do you think that you can do everything you used to do with Wealthfront in ChatGPT Finance?Finance, Data Access, and Centralized ContextAkshay Nathan [00:47:50]: I tried it. like ChatGPT doesn't yet custody, cash and assets for me. So that part, no, not yet. But I, there was like a whole component of like retirement planning and, like financial planning and budgeting and stuff that, we were looking into when I was there. And like with the finances plugin, like that's all possible with ChatGPT today. So, I feel

“Je rêve d'un monde où les dirigeants assument dormir 9h par nuit et avoir besoin de week-end où ils ne travaillent pas.”Gaetan de Lavilleon est docteur en neurosciences et cofondateur de Cog'X. Il y a 3 ans, en 2022, nous discutions déjà de la surcharge mentale en entreprise. L'objectif : permettre à tou·te·s les collaborateur·rice·s de travailler dans de meilleures conditions. Alors où en sommes nous quelques années plus tard ?Quelles évolutions a-t-il vu ces dernières années ? Les dirigeant·es commencent-ils à s'emparer du sujet ? Dans ce nouvel épisode de Learning by Doing, on fait le point ! Et Gaetan nous rappelle aussi comment la fatigue mentale entraîne de mauvaises décisions au CODIR, et pourquoi il est plus qu'urgent de penser en termes d'hygiène de travail, comme on pense déjà à l'hygiène de vie.Bonne écoute !À très vite,Prenez soin de vous !Plus d'info :Pour retrouver le premier épisode enregistré ensemble : #28 - Gaetan de Lavilleon - Surcharge mentale : et si notre cerveau avait la solution ?Pour suivre Gaëtan sur LinkedIn : https://www.linkedin.com/in/gaetan-de-lavilleon-18583541/Pour recevoir gratuitement notre sélection hebdo de conseils pratiques pour animer votre équipe, rendez-vous ici : https://teambakery.com/nlEt n'oubliez pas de laisser 5 étoiles et un gentil commentaire sur Apple Podcast et Spotify si l'épisode vous a plu.CHAPITRAGE00:00:00 - Intro00:01:32 - Présentation de Gaetan de Lavilleon et de son parcours00:04:05 - Quels sont les éléments déclencheurs qui poussent des entreprises à le contacter ?00:11:02 - Comment faire en sorte que les dirigeants s'emparent du sujet ?00:15:16 - Quelques chiffres00:17:00 - L'IA peut-elle nous aider à réduire notre charge mentale ?00:18:26 - Quelles solutions mettre en place ?00:24:57 - Comment mesurer l'impact des mesures prises ?00:27:32 - Comment faire de l'hygiène de travail un vrai sujet de société ?00:33:14 - Est-ce corrélé à la performance des entreprises ? Comment le mesurer ?00:36:20 - Un dernier conseilVous aimerez cet épisode si vous aimez : Outils du Manager • Happy Work • HBR on Leadership • Le Podcast de la Formation • MANAGEMENT & LEADERSHIP • Learn & EnjoyHébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Is everything in your LMS complete, accurate, and up-to-date? Have you lost track? Find out how an AI-driven learning content audit helps on this episode of the Talented Learning Show! The post Podcast 117: Inside an AI-Powered Learning Content Audit appeared first on Talented Learning.

A shiny new tool can feel like instant progress, but what if it quietly makes your course harder to learn? Jackie is tackling one of the most stubborn myths in instructional design and education: the idea that technology automatically improves learning. Whether you're rolling out a new LMS, testing an AI content creation tool, or adding interactive features to eLearning, the real question isn't “Is it modern?” It's “Does it help learners do something meaningful?”Jackie breaks down why this belief sticks around, especially when organizations are pressured to look innovative. Technology is easy to point to, while the deeper work of alignment, practice, feedback, accessibility, and transfer is less visible. Jackie also brings in Richard Mayer's multimedia learning principles to ground the conversation in what we know about cognitive load and attention. When media and interactivity add noise, learners pay the price, even if the experience looks impressive on paper.You'll leave with a purpose-first design mindset and a set of practical questions to audit any platform, app, simulation, or AI tool before it becomes “required.” Jackie also shares a weekly myth reset challenge to review one technology-supported lesson and make one intentional adjustment, like simplifying the flow, removing an unnecessary tool, or adding clearer instructions and feedback.If you want learning technology that actually supports performance, press play, then share this with a colleague and subscribe for more myth resets. If it helps, leave a review so more instructional designers and educators can find the show.

What happens when AI makes creating learning content easier than ever? According to 7taps co-founder Ezra Charm, that's when the real work of learning and development begins. In this episode, Ezra joins the Powered by Learning team to discuss why the future of L&D is about more than creating content—it's about driving engagement, improving performance, and measuring impact.Show Notes:From the rise of the "LMS-less learner" to the role of AI in modern learning strategies, 7tap's Ezra Charm shares why learning professionals have an opportunity to become more valuable than ever. Here are some of Ezra's top takeaways.AI is shifting L&D's role from content creation to performance improvement. As authoring becomes easier, learning professionals can focus on driving behavior change and demonstrating business value. Organizations don't have a content problem—they have an engagement problem. Success depends on delivering learning when and where employees need it, not simply creating more courses. Learning should meet employees where they work. Today's "LMS-less learner" expects learning to be accessible on any device, in the flow of work, rather than requiring a trip back into the LMS. Microlearning works because it's grounded in learning science. By respecting cognitive load, spaced repetition, and mobile-first behaviors, organizations can improve retention and application. The future of L&D is measuring impact. Learning teams that can connect training to business outcomes will move from being viewed as cost centers to strategic business partners.Learn more about 7tapsRead more about d'Vinci's partnership with 7tapsAbout Ezra Charm:Ezra Charm is Co-Founder and COO of 7taps, where he's spent years building the brand and go-to-market engine for the microlearning platform. A marketing leader with deep startup experience, he's known for challenging conventional L&D thinking and pushing teams to focus on business outcomes over completion metrics.Powered by Learning earned Awards of Distinction in the Podcast/Audio and Business Podcast categories from The Communicator Awards and a Gold and Silver Davey Award. The podcast is also named to Feedspot's Top 40 L&D podcasts and Training Industry's Ultimate L&D Podcast Guide. Learn more about d'Vinci at www.dvinci.com. Follow us on LinkedInLike us on Facebook

This week we're pleased to speak with Dr. Cristi Ford, Chief Learning Officer at D2L, about the evolving role of learning management systems in an AI-driven world. They explore how the LMS has grown from a simple repository for content into a broader learning ecosystem, and why institutions must rethink not just the technology they use, but how learning is designed, assessed, and supported. Guest Name: Dr. Cristi Ford - Chief Learning Officer at D2L Guest Social: LinkedIn Guest Bio: Dr. Cristi Ford is the Chief Learning Officer for D2L. She is an education leader and learning technology strategist with 20+ years of experience advancing high-quality, equitable digital learning across higher education and secondary education. Her work sits at the intersection of pedagogy, technology innovation, and institutional change—supporting organizations as they adopt AI-enabled approaches to teaching, learning, and assessment with clarity, integrity, and measurable impact. She has built and supported digital education initiatives across the U.S., Africa, and the Asia-Pacific region (including Singapore, the Philippines, and Australia), specializing in faculty development, instructional design, and advancing digital learning strategies. Recognized as an ASU+GSV Leading Woman in AI (2025), a 2022 OLC Fellow, and a recipient of the 2024 Mildred B. & Charles A. Wedemeyer Award for Outstanding Practitioner in Distance Education, Cristi is known for translating complex learning and technology challenges into actionable institutional practice. Her research focus includes guiding and supporting efficacy-based studies that evaluate learning interventions—including emerging technology-enabled and AI-supported practices—and their impact on learner outcomes. She hosts the Teach and Learn podcast and earned her Ph.D. in Educational Leadership from the University of Missouri–Columbia. - - - -Connect With Our Host:Dustin Ramsdellhttps://www.linkedin.com/in/dustinramsdell/About The Enrollify Podcast Network:The Higher Ed Geek is a part of the Enrollify Podcast Network. If you like this podcast, chances are you'll like other Enrollify shows too!Enrollify is made possible by Element451 — The AI Workforce Platform for Higher Ed. Learn more at element451.com. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Most companies think of education as a support function, the real shift is treating it as a go-to-market engine.In this episode of the UnChurned Podcast, Josh Schachter and Samantha Murray sit down with Yash Tekriwal, Head of Go-to-Market Engineering Ecosystem at Clay, to unpack how he became the company's first-ever GTM Engineer, why Clay rebranded its entire education team, and what it actually takes to build a learning ecosystem people don't just consume, but live inside of.Yash shares the origin story of the GTM Engineer role, why he believes "education" has become a dirty word in tech, and how Clay is rethinking everything from LMS platforms to certification to attribution.They also dive into:- The origin story behind the first-ever "GTM Engineer" title- Why Clay killed its education team and rebranded to a GTM engineering ecosystem- Why traditional LMS platforms get learning fundamentally wrong- The "control the environment, not the process" philosophy of learning- Why brand affinity and "vibes" matter more than marketing attribution- Rethinking certification and skill assessment in the age of AIIf you're building a GTM team, leading customer education, or trying to figure out how learning and community actually drive growth, this episode is a masterclass in building an ecosystem instead of a content library.Want the playbook, not just the conversation? Subscribe for deep-dive, actionable breakdowns from every episode at unchurned.substack.com.Chapters00:00 – Intro & Backstory01:03 – Yash's Pre-Clay Career and Failed Startups05:30 – Why He's More Of A Founder Than When He Was One06:32 – Becoming Clay's First-Ever GTM Engineer09:41 – Category Creation and Market Sizing13:42 – "We Killed The Education Team At Clay"15:36 – Why Education Became A Dirty Word In Tech21:08 – Yash's Path From Teacher To GTM Engineer24:54 – The Build vs. Buy Problem With LMS Platforms28:09 – Control The Environment, Not The Process32:37 – Why Attribution Doesn't Matter — It's All Vibes36:18 – Inside Clay's Six-Pod GTM Ecosystem42:16 – Yash's One-Year Goal: Rebuilding CertificationJosh is writing a book on building customer relationships. Follow his journey and insights at www.joshschachter.comWhere to Find the Guest:Yash's LinkedIn: https://www.linkedin.com/in/yashtekriwalWhere to Find the Hosts:Josh's LinkedIn: https://www.linkedin.com/in/jschachter/Unchurned Substack: https://unchurned.substack.com/Samantha's LinkedIn: https://www.linkedin.com/in/samantha-murray613/

We've been running a bit of an Agent Cloud series surveying all the top inference/compute/cloud providers, from Databricks to Daytona to Railway and, even further back, E2B, but we're excited to conclude this series returning to Modal, which has just raised a monster $355M Series C.The cloud was built for developers. But agents are now changing that.The old infra stack was designed for a human who could read docs, reason through YAML, and understand dashboards to figure out what they need when something broke. While this was painful for developers, it worked since they could fill in missing context in their heads.However, agents don't have that luxury. Now in this new era of agents, everything has to be tighter.They need a place to write code, run it, inspect the output, change the environment, debug failures, and try again. Fast iteration and feedback loops with all the necessary context are crucial for agents to operate properly. Furthermore, sandboxes are a clear representation of this shift as agents can easily spin up isolated environments. This programmatic infra even extends to research:Two years ago, we were one of the first to cover Modal with CEO Erik Bernhardsson and Alessio designed our favorite LS thumbnail of all time:At the time, Modal was just a teeny little company with a $17M Series A.Today, fresh off their $355M Series C, Modal is one of the clearest examples of the agent cloud future being built in real time: a cloud platform moving past traditional web app assumptions toward the workloads AI actually creates such as elastic inference, sandboxes, GPU burst, post-training, background agents, and infrastructure that agents themselves can operate.In this episode, Modal CTO Akshat Bubna joins swyx and Vibhu to unpack why AI applications don't fit traditional cloud assumptions, why Kubernetes was never designed for bursty compute-heavy workloads, and why Modal is now shifting from developer experience to agent experience.We go deep on Modal's AI infra stack: serverless functions, decorator-based infrastructure, elastic inference for custom models, GPU snapshotting, DeFlash, speculative decoding, Auto Endpoints, sandboxes, persistent storage, networked containers, private IPv6, RDMA, multi-node training, and Modal's capacity pool across 17 cloud providers. Akshat also explains why RL rollouts can require 100,000 sandboxes, why production agents need hard guardrails, why observability may matter more than reading code, and why AI has made infrastructure exciting again.We discuss:* Why Kubernetes wasn't built for bursty AI workloads* How Modal started as a better runtime before becoming an AI cloud* Why Modal added GPUs before ChatGPT* The shift from developer experience to agent experience* Why observability matters when agents are writing the code* Elastic inference for custom models across audio, video, robotics, and comp bio* GPU snapshotting, cold starts, and why inference workloads are so bursty* Why RL rollouts can require 100,000 sandboxes* DeFlash, speculative decoding, and frontier-level inference performance* Auto Endpoints and making optimized inference easier to deploy* What Modal adds beyond vLLM, SGLang, and raw GPU rental* Modal's 17-cloud capacity pool and supercloud strategy* Networked sandboxes, sidecars, private IPv6, and RDMA* Serverless multi-node training for post-training and research workloads* Auto-research, model-guided sweeps, and agents launching GPU experiments* Compute strategy, capacity planning, and batch tiers* Why production agents need specialized sandboxes and hard guardrails* Modal's take on managed agents, CI, Gitpod/Ona, Python, TypeScript, and Modal BenchAkshat Bubna* LinkedIn: https://www.linkedin.com/in/akshat-bubna-188885103* X: https://x.com/akshat_bModal* Website: https://modal.comTimestamps00:00:00 Introduction00:00:39 Modal's origin and why Kubernetes wasn't enough00:04:32 Developer Experience → Agent Experience00:06:21 Modal's AI cloud primitives00:09:14 Sandboxes, agent loops, and proto-Cognition00:12:12 Elastic inference, GPU snapshotting, and 100,000 sandboxes00:15:24 DeFlash, speculative decoding, and Auto Endpoints00:19:59 Production-grade inference beyond raw GPUs00:22:00 Background agents, Ramp Inspect, and the agent lifecycle00:24:08 Modal's 17-cloud supercloud strategy00:26:40 Networked sandboxes, private IPv6, and RDMA00:32:48 Multi-node training, post-training, and auto research00:37:36 Compute strategy, capacity planning, and batch tiers00:40:55 Open models, real-time AI, and production agent infra00:43:06 Hard guardrails, managed agents, and specialized sandboxes00:46:06 Why AI made infrastructure exciting again00:48:30 Model APIs, differentiated products, and agentic video00:51:50 CI, coding-agent infra, SDKs, and Modal Bench00:57:28 Closing ThoughtsTranscriptIntroduction: Modal, Series C, and the Art PartySwyx [00:00:00]: We're here with Akshat, CTO of Modal, together with Vibhu. Congrats on your Series C.Akshat [00:00:10]: Thank you.Swyx [00:00:11]: Your party yesterday was amazing.Akshat [00:00:15]: Yeah.Swyx [00:00:15]: From all the photos and all the swag.Akshat [00:00:17]: We had a bunch of art installations, which was fun, seeing, like, our products on pedestals next to, like, Rodin.Swyx [00:00:25]: Very nice. Very nice. When you started, it was not the GPU inference company. Maybe it was in your mind. Take us back to the origin story.Modal's Origin: A New Runtime Beyond KubernetesAkshat [00:00:39]: I first met Eric, who's the CEO, through an investor. Back then Eric was already thinking about building, a new runtime, and he got there thinking through why are workflow orchestration products so hard to use. It's because you have to run them on Kubernetes. Kubernetes is hard to manage. It's not built for burstiness and, custom images,Swyx [00:01:03]: YeahAkshat [00:01:03]: It has a terrible developer experience.Swyx [00:01:05]: And I'll, I'll interjectAkshat [00:01:06]: YeahSwyx [00:01:07]: For listeners, who are new, we interviewed Eric two years ago, and there's a bit more of the story there from Spotify and all those things.Swyx [00:01:14]: And I came across Eric through Data Council because he did that talk on the serverless container stack that you guys did, which was like, that was my first like, “Okay, I need to take Modal very seriously” moment.Akshat [00:01:26]: Yeah.Swyx [00:01:26]: But it was still very unclear, like, do I need all this for just my data pipelines?Akshat [00:01:33]: Yeah. initially what we were thinking about was if we build a better runtime, it's a very useful primitive in itself. It's There's a lot of things that, get solved by serverless functions, like you can do, ETL stuff, you can do job queues, you can do all this, like, bursty processing, which it turns out every company had needs for. but then we also were thinking about this as like, this is a primitive that we can build a whole collection of products on, which are very verticalized. So perhaps data engineering would've been the first one, but we were thinking about inference. Back then it was more classical inference, like computer vision stuff and running XGBoosts and whatnot. But we added GPUs to the product a year before ChatGPT came out.From Serverless Containers to GPU WorkloadsSwyx [00:02:19]: Nice.Akshat [00:02:19]: We just didn't think it would be that big of a deal.Swyx [00:02:22]: Yeah, just like add A100.Vibhu [00:02:23]: Was there any, like, early key problem that really sparked off why you built it?Akshat [00:02:28]: Yeah. Primarily it's just, none of the tooling that was out there was built for, one, a really great developer experience, and also there's a general trend of, a lot of the workloads that we were seeing were very. I wish there was a better word for it, but compute-heavy. Like, they need, one, like, need a lot more resources, so you need to burst up and down a lot, versus like Kubernetes designed for, like, slow scaling and, more for, like, web server use cases. And also there's just a lot more specialization in, like, what kinds of environments these workloads run in. Like, we had sometimes they need accelerators, sometimes they need different kinds of images, and this is just like a consistent thing that we saw across a lot of companies. That would be the next step.Software-Defined Infrastructure and Decorator-Based DXSwyx [00:03:13]: Yeah. Yeah. Be nice. I don't know how much this factored into the early story, but I wrote a post when I was at Temporal about infrastructure, software-defined infrastructure or something like that.Akshat [00:03:22]: Yeah, the self-provisioningSwyx [00:03:23]: Self-provisioning.Akshat [00:03:24]: Yeah.Swyx [00:03:24]: Yeah. I can't even remember my own post.Swyx [00:03:26]: And then you put me on the landing page.Akshat [00:03:28]: Yeah. We really like, the term and so we stole it.Swyx [00:03:32]: Because you had the insight that everything can just be in decorators co-located with the code, right?Akshat [00:03:37]: Yeah.Swyx [00:03:37]: Was that a big part of the originalAkshat [00:03:39]: YesSwyx [00:03:39]: Story or it was just like a DX layer?Akshat [00:03:41]: That was, really important because we really didn't want people to spend, so much time, writing YAML, and it seemed like you could really condense the surface area of what you're doing, put it in code so you can operate on it just like you operate on other code, and like build stuff that's more expressive and dynamic. and so yeah, that was always a very important part.Swyx [00:04:04]: Then the pushback is this is a DSL.Akshat [00:04:07]: Yeah.Swyx [00:04:07]: It's you're closed source. I am locked into Modal.Akshat [00:04:11]: Yeah. We never really got pushback for that because the nice thing about Modal is you can bring whatever code you have, and sure, the DSL is at the configuration layer for, what hardware you're using, how you're scaling things up, but you still own the code.Akshat [00:04:27]: And that's, that's been an important, part of our story, even as we do inference now.Swyx [00:04:32]: Yeah.Vibhu [00:04:32]: How much of do you think still stays the same today? Like if you were to build something today, DevX very important, but I feel like, a lot of this has been changed with just hook it up to an agent, have Claude Code, have Codex implement a tool. there's very agent native primitives that are different than if I'm doing this myself, right?Developer Experience → Agent ExperienceAkshat [00:04:54]: We've changed our SDK team to think about agent experience instead of, developer experience and we think that the same benefits that apply for DX also apply for AX, which is why would you have an agent read through hundreds of Kubernetes files and like write YAML that's not even typed when it can make a couple of changes in a decorator and it gets this self-provisioning runtime of, being able to see its changes live in action? yeah, it just seems from the customers we talk to, they find Modal is much faster for agents to use versus operating on a different substrate.Swyx [00:05:34]: Yeah, because like you, again, you co-locate the infrastructure requirements to the code that runs it.Akshat [00:05:38]: Yeah.Swyx [00:05:38]: Well, the negative thesis now is that nobody's looking at their code anymore, so there's no point.Akshat [00:05:44]: Yeah, people aren't looking at code. one thing we still see is really important is observability.Swyx [00:05:51]: Yeah.Akshat [00:05:51]: Like how good is your dashboard? And of course, like we have, we push a lot of it to the CLI so the agents can do their own investigation, but you still need humans to go interpret what's going on and, make judgment calls and whatnot. and that's I feel like, Maybe more important now than looking at the code itself.Swyx [00:06:11]: Yes, because like, you can try to treat the code as a black box and then use, see the observable action that comes out of it, and then just prompt a change.What Modal Is For: AI Cloud PrimitivesAkshat [00:06:21]: Yeah.Swyx [00:06:22]: So I think it takes a bit of restraint to not specialize, to say, “I want to ship a new primitive,” and then just be general purpose.Swyx [00:06:31]: People ask you, “What are you for?” You're like, “ I don't know. We can do this, we can do that.”Vibhu [00:06:36]: Well, I'd be curious to see, like, okay, if we were to ask you, like, what is Modal for even at a high level? There's a lot you guys do, sandboxes, GPUs, everything. How do you answer?Akshat [00:06:46]: Modal is a cloud platform that's built for, where we've built the primitives from scratch for AI applications. and right now it covers, inference, training, batch processing, and sandbox workloads.Akshat [00:07:00]: But we're building a lot moreSwyx [00:07:02]: I noticed you didn't say web server, so there is still a role for, like, the always-on large-scale Kubernetes type things.Akshat [00:07:09]: Yeah, absolutely. We're, we're not trying to compete with the renders of the world, because yeah, we think the differentiator for us is the, are the workloads that need specialized compute, need to scale up and down a lot. yeah, they're, they're, they're just shaped differently.Working Alongside Frontier StartupsVibhu [00:07:26]: I think you're building a lot of it alongside the startups, right? They're innovating quite a bit, even in your, like, latest blog post. Like, even in the series C, the customers that you mention here, the cognitions, technical ones, ramps and whatnot, they're, they're innovating with you, right? And that's not something AWS is doing directly with.Akshat [00:07:45]: Yeah, absolutely. I think, this is again classic. We're a small team. We can move really fast. our engineers are working with our customers and figuring it out. Yeah.Swyx [00:07:54]: So my first week at Cognition, I walked in, there was someone wearing a Modal shirt. I was like, “What are you doing here?” They're like, “Yeah, I just. I am embedded inside of Cog.”Akshat [00:08:05]: Yeah, I think that was Peyton. We sent him overSwyx [00:08:07]: Yeah.Akshat [00:08:07]: Because, the latency of communication was too high otherwise.Swyx [00:08:12]: Yeah, distributed node, you have to - you have to place one and collocate.Vibhu [00:08:16]: Yeah.Swyx [00:08:16]: So I had a, I had direct personal experience, right? So I worked on smol developer three years ago. it was inspired by Claude 1. I think you onboarded me at some point, like, just before, and I was like, “Oh, like, I need some bursty compute. Like, I was just gonna try using Modal.” And it was a, it was a pretty pleasant experience. apparently, I showed up in the board meeting, like the analytics.smol developer, Sandboxes, and Proto-CognitionAkshat [00:08:39]: Yeah, you blew up on Hacker News and,Swyx [00:08:41]: YeahAkshat [00:08:41]: We got a big traffic spike. I. I think the way you used smol developer was Modal functions for running stuff, which was. Like, the, that was a good use case. but then, yeah.Swyx [00:08:53]: Yeah. That - So to me, that was proto-cognition.Akshat [00:08:55]: Right.Swyx [00:08:56]: If only I had, like, stuck to it.Swyx [00:08:58]: Like, that was like, if - did you say draw the tech treeAkshat [00:09:00]: AbsolutelySwyx [00:09:00]: You're just like, “Yeah, like, probably this will happen.”Akshat [00:09:02]: Yeah. Like, he was so close. You were just rebuilding upon usSwyx [00:09:04]: I just didn't realize.Akshat [00:09:05]: But the funny story there is at the same time, we were talking to a bunch of customers who needed something like sandboxing.Swyx [00:09:14]: Yeah.Akshat [00:09:14]: This is like twenty-three.Swyx [00:09:15]: Yeah.Akshat [00:09:16]: So we builtSwyx [00:09:17]: You introduced a new API right after that.Akshat [00:09:18]: Yeah.Swyx [00:09:19]: Yes.Akshat [00:09:19]: Like, we built sandboxes in May of twenty-three before anyone was even knew this was gonna be a thing. And the first example we published was, we took smol developerSwyx [00:09:28]: Smol developerAkshat [00:09:28]: And put it in a loop, so the agent can iterate on itself.Swyx [00:09:33]: Loops are hot these days.Vibhu [00:09:34]: It's the looper.Akshat [00:09:34]: Yeah.Vibhu [00:09:35]: Loops in. When was this, twenty-three?Akshat [00:09:38]: Yeah.Vibhu [00:09:39]: A small check.Akshat [00:09:39]: Yeah.Swyx [00:09:39]: It's like twenty-three. so the. the, those for listeners, like, the problem was the models are not built for any of this, right?Swyx [00:09:46]: Like, you're just trying to like. They're not post-training to understand, like, looping and, like, self-correction and tool calling was there, but, like, also not that great.Akshat [00:09:55]: Yeah.Akshat [00:09:55]: I don't remember if you used tool calling in this one, but yeah, the models would just diverge after like ten iterations and not produce anything meaningful.Swyx [00:10:03]: Yeah. But like, then. So okay, like now talking to myself three years ago, the answerVibhu [00:10:08]: Of course they will get betterSwyx [00:10:09]: Collect all the failures, build benchmark, and then collect all the, examples, build the RL environmentAkshat [00:10:15]: RightSwyx [00:10:15]: Sell it for like ten billion dollars to Meta.Swyx [00:10:17]: And then also train a model and then sell that for sixty billion dollars to Elon. And this isAkshat [00:10:23]: Yeah, of courseSwyx [00:10:23]: The funny machine. Like, it's like, it's about the hardware.Akshat [00:10:28]: It's hard to have that inherent conviction that the stuff will get that much better.Swyx [00:10:33]: In retrospect, it's so f*****g obvious.Akshat [00:10:36]: Fair enough.Swyx [00:10:37]: Like, what else were we doing back then? I don't know. anyway. Yeah. So this. That was the start of your sandboxing journey, right? I feel like it didn't blow up until, like, last year.Akshat [00:10:49]: Yeah.Swyx [00:10:50]: So there was like a couple years of quietness.Akshat [00:10:52]: Exactly, yeah. We wereVibhu [00:10:53]: I think very underrated product value. Like, my experience with Modal, Charles, before he had joined Modal, met this guy at a hackathon, and he really insisted we wanted to run some small model, not hosted anywhere, and he's like, “ there's this cool company, Modal. They'll like spin up a GPU sandbox, we can throw it on there. They'll take a Hugging Face link.” And like there's so much value just right there, right? Like instant hosting, spin it up, spin it down. It'll stay cold, but we run the demo a few days later, it'll come back up and like all this stuff in retrospect, like it's still what we needed like today.Akshat [00:11:27]: Yeah, it's still needed today. workload shapes have changed a lot as, we run stuff for people with really massive production scale and, there it's it's not about scaling from zero to one, but it's how do we scale really elastically, from like thousand to fifteen hundred GPUs very quickly in a given region. It's the same shape problem.Elastic Inference, GPU Autoscaling, and Custom ModelsVibhu [00:11:50]: Okay. So you look at, say, Cursor Composer, right?Akshat [00:11:53]: Yeah.Vibhu [00:11:53]: They had a. “We'll do RL on a model every couple hours.” you guys have a whole version of RL inference gym and whatnot.Vibhu [00:12:01]: When you look at workloads like that, you're doing train runs where you need to scale up, scale down every hour thousands of GPUs, right? That's the example for we do need it, right?Akshat [00:12:12]: Yeah. Well, so I'll, I'll take a step back and, maybe talk about like how people use Modal today. because our biggest use case is, elastic inference. And the thing we first found product market fit, with was inference for custom models. So we stayed away from the LLM space, and we were serving companies like Suno for audio, Runway for video, robotics, comp bio companies that train their own model elsewhere. But Modal is the best black box that for deployment, scaling to however many GPUs you need as your traffic pattern changes. And we saw all of them like have a very unpredict- predict- predictable, traffic pattern. it's like diurnal. It's Some days, like the company will do a launch and, they'll need like, way more. And it's not just one model that they deploy. They-- all these companies deploy, lots of different models in different regions, and so the autoscaling problem becomes even harder because then you have to scale within a certain region, and those cycles are offset. So different times you scale up in different regions.Akshat [00:13:20]: So that's like our sortVibhu [00:13:22]: And thatAkshat [00:13:22]: YeahVibhu [00:13:22]: That in and of itself is a huge category. There's a bunch of inference providers which, provide this fireworks, does this as a service together, whatnot, Base10. that's carved into its own niche for language models, at least right now.Akshat [00:13:36]: Yeah. the thing that we have specialized in is the autoscaling aspect.Vibhu [00:13:41]: Yeah.Akshat [00:13:41]: Because we found that it's not universally true that everyone else can autoscale, and we've gone deeper into it on the tech side by, we've incorporated GPU snapshotting into the product so we can take the GPU state, like your torch.compile model, snapshot it, and the next cold start is way faster. And so going back to your question, it's That's why you need a lot of burstiness for inference. But then people also do a lot of demand training, like for RL stuff, your rollouts are bursty, as you said. People also do a lot of batch jobs. So we'll see, a lot of companies, before they have a training run, they'll need thousands of GPUs to run encoding or something like that. And I think those things are much more bursty than. I agree that agents are not that bursty. sandboxes are, except when you're doing RL. RL is justRL, Batch Jobs, and 100,000 SandboxesVibhu [00:14:28]: Or commerceAkshat [00:14:28]: Insanely bursty.Vibhu [00:14:29]: Yeah.Akshat [00:14:30]: Yeah. Like when you're doing, rollouts, you sometimes need a hundred thousand sandboxes in your sandboxes.Vibhu [00:14:37]: Yeah. I'm curious if you've seen early sparks of continual learning. There are some people, like our friends, ngram, recently announced thisAkshat [00:14:45]: YeahVibhu [00:14:45]: They're, they're trying to do training. That also seems like a different workload, right? If you're doing training twenty-four/seven per se, there's a very weird dynamic of how you're using GPUs between people and whatnot, but seems like something you guys would work for.Akshat [00:15:00]: As you said, we're, we're fortunate to work with a number of, customers at the frontier and grab some of our customers. and they are taking the primitives we have, and trying to use them in very interesting ways, like continual learning. It's possible as the stuff gets better, some of that will be part of, our offering as well if, more people need it. but we're, we're just waiting to seeVibhu [00:15:23]: YeahAkshat [00:15:23]: How it shakes out.Vibhu [00:15:24]: Is there a primitive that you added after sandboxing that was the next step in the story?LLM Inference, DeFlash, and Speculative DecodingAkshat [00:15:32]: I guess we've been going much deeper into LLM inferenceVibhu [00:15:35]: YeahAkshat [00:15:35]: Because we realized that some of the advantages we have with like autoscaling, again, especially in different regions and whatnot, are, not present elsewhere. and the place where we had a gap was we weren't, working on the model layer itself. Like we were a black box. And, we realized that, we can get to frontier-level model performance, with, by having great people who work on this. And, we've been open sourcing a lot of our work, in terms of, Recently, we, shared our work on DeFlash, which is a block-based, speculator, and we've open sourced, all of it. So, you can - By using open source DeFlash, you can get the same performance as you would with one of the proprietary providers. And the next thing we're thinking about hereVibhu [00:16:23]: I thought this wasAkshat [00:16:24]: YeahVibhu [00:16:24]: An interesting blog post as well, right? Like, I think in here you make a claim that. Not a claim, just that how effective speculative deco-decoding really just get to.Akshat [00:16:33]: Yeah.Vibhu [00:16:33]: Anything you wanna point out from this around, what people should know?Akshat [00:16:39]: Yeah, absolutely. the high-level summary is, it would help to describe what speculative decoding is.Vibhu [00:16:44]: Yes.Akshat [00:16:44]: I will, yes.Vibhu [00:16:45]: I think, likeAkshat [00:16:46]: YeahVibhu [00:16:46]: So we've covered like Eagle and all thisAkshat [00:16:47]: YeahVibhu [00:16:47]: Like Hydra and all those things, but it was like two years ago.Akshat [00:16:51]: Yeah.Vibhu [00:16:51]: I think it doesn't hurt, right?Akshat [00:16:52]: Yeah. Speculative decoding is you have a smaller model, called a draft model, predict tokens ahead of the bigger model, and then you have the bigger model, verify all of this, all the tokens are predicted. And the reason it's faster is if you're predicting, one token at once, you're bound by memory bandwidth. But if you can batch the verification of, the draft model, then you're much more efficient using compute, and it's faster, and as long as your draft model is producing a lot of tokens that can get accepted, which is called the accept length, you can get a speed up that's, multiple times of, the original model speed. and well, that's what we highlight here. It's Like people talk a lot about we made these kernels faster and whatnot, but improving kernel will only give you like few percentage points of improvement, and, increasing accept length, literally is a multiplicative decreaseVibhu [00:17:47]: Like two to four X.Akshat [00:17:48]: Yeah, exactly.Vibhu [00:17:48]: Without much head-on performance.Akshat [00:17:50]: Yeah. I think it may - you are running a second model, right? So it may be something more expensive in the compute,Vibhu [00:17:57]: I meant quality performanceAkshat [00:17:58]: Probably not by muchVibhu [00:17:58]: But yeah. I thinkAkshat [00:17:59]: So there's no drop in quality performanceVibhu [00:18:01]: YeahAkshat [00:18:01]: Because you're always. You're never accepting a token that the big modelVibhu [00:18:04]: It's strictly betterAkshat [00:18:05]: YeahVibhu [00:18:05]: Or it's same.Akshat [00:18:06]: Exactly.Vibhu [00:18:07]: Right. Yeah.Akshat [00:18:08]: And so we've been working a bunch on DeFlash, which is a block-based speculator. so it's instead of predicting, one token at a time, it's predicting a block. And we've been open sourcing our work with it. The next thing for us here is for helping people train speculators and custom models. it's it's something that traditionally is very forward-deployed engineering driven, support deployed, engineer driven, like you work with customers and help them do that. And our vision for. This is why we launched Auto Endpoints, is we want to make frontier-level performance available to everyone. And so, we mentioned this in the announcement, we teased it. The next thing we're, we're launching is, as you run an auto endpoint, we shadow trafficAuto Endpoints and Frontier-Level PerformanceVibhu [00:18:54]: Do you want to explain what auto endpoints are?Akshat [00:18:57]: Yeah.Vibhu [00:18:57]: I lovely, yeah.Akshat [00:18:58]: Yeah. So, this is, I guess, going back to your Modal is you touch the code, but, sometimes people don't wanna touch the code, and they wanna get started with an endpoint that works and has all the great performance and, scalability that Modal has. So we've made that easier with, a way to create an endpoint from our UI, from the CLI, that has all of our optimizations that we talked about, like the DeFlash stuff already baked in, and there's full transparency. So we give you the code, you can go run it yourself, and if you want, you can eject out into the full Modal experience, which we see as people get sophisticated, they do wanna tweak the models, they wanna, fine-tune stuff. You can still do all of that. It's it's not a black box. And yeah, the next thing, as we teased later in the post, is how do we give you value even beyond this in terms of having your draft models evolve as your data distribution evolves, again, without having to talk to a person and, yeah.Vibhu [00:19:59]: I guess just to understand it directly, you have the GPUs, you have an endpoint that's compatible, you serve open model. If someone was to do this themselves, what's the delta that you guys provide? So you do a lot of open source great work on effective inference. how does it compare to, say, I take the same model, 5.2 FP8, take shelf inference engine, vLLM, SGLang, get compute of similar capacity, similar cost. What's the delta that plugging into something this, like this offers outside of the benefit of, scaling?Production Inference Beyond Raw GPUsAkshat [00:20:34]: It's interesting because we've taken the approach of open sourcing our contributions and upstreaming them. we work closely with the SGLang team. We want the improvements that our team, comes up with to be, there in open source for others to use, even outside of Modal. The benefit to us is we have a team that has significant expertise in terms of if you do have something that is not there, our team can help you get that performance, first. the other thing is with these endpoints, we are way more elastic, as you said, than, anyone else, and you have true scaling to zero. you have true, burstiness, and in practice, that matters a lot more to people than just finding, the GPU and, running Modal code on something.Vibhu [00:21:20]: Yeah. And I will say it's not that straightforward to just. like what I said is easier said than done, right?Akshat [00:21:26]: Yeah.Vibhu [00:21:27]: It's I think still for the average person, still hard to just gut check using different. There's, there's quite a bit of combinations you can make there. the trade-offs aren't really known at face value.Akshat [00:21:40]: Yeah. it's it's not just that. I think it's it's that running production-grade inference is a hard infer problem.Vibhu [00:21:49]: YeahAkshat [00:21:49]: Even if you subtract out the autoscalingVibhu [00:21:50]: YeahAkshat [00:21:51]: Is controlling things like tail latency and, making sure every, request is delivered at least once and whatnot.The Model and Agent LifecycleVibhu [00:22:00]: There's a lot of innovation that you can do here. I think, it's very interesting that you're starting to encroach on, like as you become a full cloud, you're starting to encroach on other people's turf.Vibhu [00:22:09]: What will you not do?Akshat [00:22:13]: Well, we wanna follow our users and, make sure they get like a platform that has everything that works well together. so right now we're focused on the model lifecycle and the agent, lifecycle. so both like going from data prep to training to inference, and then also if I want to deploy a background agent, let's say, sandbox, do persistent storage, a whole bunch of other stuff.Vibhu [00:22:38]: We talked to Cole, who did, OpenInspect. Yeah.Akshat [00:22:42]: Yeah.Vibhu [00:22:42]: And RealInspect also is on Modal.Akshat [00:22:44]: Yeah. So Ramp Inspect was a great example of a background agent that was really successful because they, were able to use some of the primitives like snapshotting and fast scaling to just have something that feels really reactive and works well.Ramp Inspect and Background AgentsVibhu [00:23:02]: Yeah. That's the new CTO of, Ramp right there.Akshat [00:23:05]: Yeah, Rahul.Vibhu [00:23:08]: It was really fun. yeah, okay, I think, all very bullish. Like, one of my reflections was also I did not originally. So when I met you guysThe Inference Inflection: CPU, GPU, and Co-LocationVibhu [00:23:19]: You weren't that much in the GPU game, and now you're all about, inference. And one of the points that I hinged on for Jensen's keynote at GTC this year was, what we're calling like the inference inflection, right? That let's say in AI workloads or machine learning workloads, it used to be like, let's call it eight to one GPU to CPU, and now it's more like one to one, which is like a interesting. Like, - because of how much agents are blocked or call out to this, to CPU heavy stuff the actual, like, limiting factor, like, swings back and forth from GPU to CPU a lot more than it used to be all GPU and then occasional CPU.Akshat [00:24:01]: Yeah.Vibhu [00:24:02]: GPU, CPU. And now it's like just constantly, and you just have to locate everything.Seventeen Clouds and the Supercloud StrategyAkshat [00:24:08]: Yeah. And that's one of the things that, again, we see as, something appealing about Modal, which is we've built this capacity pool that spans, 17 cloud providers, so we're, we're very good at Running on various kinds of cloud capacity across the worldSwyx [00:24:24]: You don't have your own data centers?Akshat [00:24:25]: We don't have our own data centers. We just run across a lot of neo cloudsSwyx [00:24:29]: Yeah. AreAkshat [00:24:30]: Metal providers.Swyx [00:24:30]: Yeah. Question mark.Swyx [00:24:31]: Yeah. You're, you're running the math, and you're like, “What's the cutover point where you're like.”Akshat [00:24:36]: Yeah, it's a good question. part of it is we see our differentiator in the software layer, and, being capital light and focusing on the software helps us move really fast. so far it's worked out well because there are so many other people building data centers that we're able to work effectively with them, and again, focus on what makes us, special.Swyx [00:24:55]: Yeah.Swyx [00:24:56]: 17 gets you into, like, the local providers sometimes. LikeAkshat [00:25:00]: The,Swyx [00:25:01]: Which was the most interesting one?Akshat [00:25:02]: There are a lot more neo clouds than you expect, and they all have various degrees of, various levels of reliability. And, that's why it's something we've invested a lot of time in, is building our own reliability layer on top. so if the GPU falls off the bus or something happens, we user workloads are not affected, and that lets us use a lot more capacity than,Swyx [00:25:30]: YeahAkshat [00:25:30]: You as a user would be able to.Swyx [00:25:32]: It's a useful thing to have because like now everyone knows, like, what layer you are and, like, you optimize for being the super cloud of all clouds.Akshat [00:25:41]: Yeah. That's, that's, that's the idea. and so I guess when you mentioned colocation, that's, that's another interesting thing where, one thing we've seen is people come to us when they want, very specifically located, CPUs or GPUs, like they wantSwyx [00:25:57]: Oh, they pin it in likeAkshat [00:25:58]: YeahSwyx [00:25:58]: EU?Akshat [00:25:59]: Exactly. Or EU, US.Swyx [00:26:01]: Right. Data resiliencyAkshat [00:26:02]: AustraliaSwyx [00:26:02]: Locality thing or performance or what?Akshat [00:26:04]: It's either data locality or latency, yeah.Swyx [00:26:07]: Yeah.Akshat [00:26:07]: Like, you want your. They're running sandboxes and model. They want them to be right next to aSwyx [00:26:10]: Yeah, it's easy thenAkshat [00:26:11]: YeahSwyx [00:26:12]: To. That is important in all those things. and so, like, you've accidentally, I don't know if it's accident, but, like, you've built the perfect primitive for agents to express themselves. And then, like, it's almost very funny how every extra development just involves more file system, just involves more CPU.Akshat [00:26:30]: Yeah.Swyx [00:26:31]: Just like the things that you already have. I don't know much about, if there's any, like, networking usages that are interesting, but you've also done some good work on networking.Networking, Sidecars, Private IPv6, and SandboxesAkshat [00:26:40]: Yeah, that's exactly right. Like, we're just taking compute storage and networking and building stuff on that layer, for, again, the stuff people need.Swyx [00:26:49]: YeahAkshat [00:26:50]: We see a few interesting networking things coming up. one is people want networked sandboxes. so we haveSwyx [00:26:57]: For like a Docker cluster type thing.Akshat [00:26:59]: Yeah.Swyx [00:26:59]: Sorry, Docker Swarm. Oh, f**k. What is it called?Akshat [00:27:02]: Compose.Swyx [00:27:03]: Compose type thing.Akshat [00:27:04]: Yeah. So if you want Docker Compose, our sandboxes now support, this thing called sidecars. So you can. A sandbox is a pod of containers, and you can run multiple containers in, a sandbox. also useful because, going back to networking, people want a lot of control over, outbound networking from a sandbox.Swyx [00:27:23]: Yeah.Akshat [00:27:23]: Like, they might wanna run a middle proxy for, like, maybe logging stuff for RL or, controlling how egress can happen to a domain, injecting credentials. and yeah. So we've, we've had to build a lot of that stuff ourselves.Swyx [00:27:38]: Yeah.Akshat [00:27:39]: But then also sometimes people want, sandboxes spanning multiple nodes to talk to each other, which is an emerging thing we're seeing. We have support for that for a different reason, and yeah, we'll see if that becomes stable.Swyx [00:27:52]: Like, just an open socket. It's a. This is directly like mTLS.Akshat [00:27:56]: We do support that, which is you can, expose a tunnel inside a sandbox.Swyx [00:28:01]: Yeah.Akshat [00:28:01]: And then you can either expose it to public internet or it can be, you can add like a HTTP, auth layer above it. But we have this thing called I6PN, which we haven't talked about, which is this, like, overlay network using IPv6 addresses. so if Modal containers, within the same workspace, when this is enabled, can address each other using this private IPv6 address, and no one else can.Akshat [00:28:28]: So it's like private networking, for containers. We built it because we needed it as a primitive for our distributed training product. so we have this other feature, which is you can add a decorator to a function, and you get a cluster of GPUs. and they have RDMA networking. so you can run a distributed training job, that's truly serverless. and we did the overlay network for that. But then we've seen that people are using it for other reasons, and, I'm intrigued to yeah, what would people do with it.Swyx [00:28:59]: Build primitives and let people figure it out, right?Akshat [00:29:01]: Yeah, exactly.Swyx [00:29:02]: You put out a pretty interestingAkshat [00:29:03]: They're like, they read the docs webpage. Let me use thatSwyx [00:29:06]: YeahAkshat [00:29:06]: Something they never intended to work. This is literally not even in our docs page. People somehow found it, and they're using it.RDMA, Memory Movement, and Distributed TrainingSwyx [00:29:12]: Huh.Swyx [00:29:14]: The way you portrayed it with, like, RDMA versus TCP, like, very well laid out, but just the transfer speed change at scale for RL, like yeah, you have it, you have it built in. I'm sure someone found it. It's found it to be a lot more efficient before you made a thing out of it, right?Akshat [00:29:32]: Yeah. And not to split hairs, I guess the overlay network is the TCP overlay network.Akshat [00:29:39]: The reason we have that is you need that to do the key exchange for RDMA before you set up the RDMA network on top of that. but then people found the TCP part.Swyx [00:29:48]: Can I tell you, this is like a big aha moment for me becauseAkshat [00:29:51]: YeahSwyx [00:29:51]: So I review 2,200 submissions for the World's Fair.Akshat [00:29:56]: Yeah.Swyx [00:29:57]: And then I got this from John OsterhoutAkshat [00:29:58]: HuhSwyx [00:29:59]: Who I don't know if. Do John Osterhout by name?Akshat [00:30:01]: The name sounds familiar.Swyx [00:30:02]: He published a. He's a well-known professor, published a lot of interesting software design books, and this is the talk he chose to submit, is on RDMA at Inference. And I'm like, you wouldn't think that this guy, who is like operating systems guy, would care about RDMA.Akshat [00:30:20]: I, it makes sense to me because I,Swyx [00:30:24]: This is the cloud, right? YeahAkshat [00:30:25]: Like, the way you move around your KV cache and how efficiently you can do it, how efficiently you move, your weights from your training GPUs to your inference GPUs in RL is there's a lot of degrees of freedom, and it is a systems problemSwyx [00:30:41]: YeahAkshat [00:30:41]: Moving memory aroundSwyx [00:30:42]: YeahAkshat [00:30:43]: Scheduling.Swyx [00:30:44]: This shows you how primitive my understanding of networking stuff is.Swyx [00:30:46]: Is this like the domain of WireGuard as well?Akshat [00:30:50]: Not quite.Swyx [00:30:51]: It's adjacent?Swyx [00:30:53]: Explain everything.Akshat [00:30:54]: Sure.Swyx [00:30:56]: How do we move memory around GPUs?Akshat [00:30:58]: Well, so sorry. Yeah, that is memory. Sorry, I was talking more, and maybe I was talking like five minutes back, about the private IPv6, addressing that you've set up.Swyx [00:31:09]: Yeah.Akshat [00:31:09]: Is it like it's a VPN?Swyx [00:31:10]: Yeah, it is like a VPN, and yeah, WireGuard is, yeah, you're right. It is,Akshat [00:31:16]: Right. Yeah, you already moved on to new topicsSwyx [00:31:17]: A similarAkshat [00:31:18]: OkaySwyx [00:31:19]: In the same space, WireGuard is, encrypted and this is,Akshat [00:31:23]: And you don't need encryption.Swyx [00:31:23]: Yeah.Akshat [00:31:24]: Yeah.Swyx [00:31:24]: This is not encrypted. that's the main difference. This is TCP and we have eBPF programs that will reject or allow the TCP connection based on whether you're allowed to do it.Akshat [00:31:35]: Used to involve a full sidecar, but now you have eBPF in the Linux kernel.Swyx [00:31:39]: Yeah.Akshat [00:31:40]: Yeah. I don't know if this is a natural follow-on to the topic of like my skepticism on distributed training is that while, like, people spend a lot of money on, like, cables to hook up GPUs, and even that is not, like, fast enough, and that's the bottleneck, is your networking fast enough?Swyx [00:31:59]: Yeah. So I guess you're talking about fully distributed training like, Dialog or something which is like cross data centerAkshat [00:32:06]: That would be, yes.Swyx [00:32:07]: That's the extreme.Akshat [00:32:08]: Yeah.Swyx [00:32:08]: You're in the middle, and then other people would have like the Mellanox cables up in, like, their actual data center.Akshat [00:32:14]: When you run multi-node training on Modal, RDMA, I think Mellanox, is, or InfiniBand is like a, is all seen as RDMA. but it's a way to bypass the TCP networking stack and, transfer, stuff much faster, between one node, to the other. And we have I think like 3 terabit per second, internal networkingSwyx [00:32:40]: OkayAkshat [00:32:40]: Which is the standard that's needed.Swyx [00:32:42]: Okay. So I misunderstood whatAkshat [00:32:43]: 50Swyx [00:32:43]: What part of the stack you wereAkshat [00:32:44]: 50 gigs overSwyx [00:32:45]: YeahAkshat [00:32:45]: If you wentSwyx [00:32:45]: YeahAkshat [00:32:46]: RDMA.Swyx [00:32:46]: Okay.Swyx [00:32:48]: Yeah. I, very impressive work.Multi-Node Training, Post-Training, and Auto ResearchSwyx [00:32:52]: So effectively you're extending like the model philosophy to the training cluster, like, yeah.Akshat [00:32:59]: Yeah. And we're, we're not going for like large scale training runs. the thing that we've built multi-node training for is, we see a lot of, smaller scale post-training. like, people are post-training like medium sized fund models, so they can, get higher quality on inference. this is a perfect fit, for something like that.Swyx [00:33:21]: Yeah. That is my impression of how a lot of these labs explore branches in post-training and then eventually merge whatever they find in.Akshat [00:33:31]: Yeah. The other use case we've seen for multi-node training is even if you have a big cluster, your researchers are still doing small runsSwyx [00:33:38]: YesAkshat [00:33:39]: Having elasticity thereSwyx [00:33:40]: Right, sureAkshat [00:33:40]: Matters a lot more.Swyx [00:33:41]: Yeah. the, like, this is like the current limiting factor for auto research, which is like you need to give your model some GPUs in order for it to completely run.Akshat [00:33:51]: We have a blog post on auto resource and model is,Swyx [00:33:55]: YeahAkshat [00:33:56]: Yeah, like, turns out to be pretty good substrate for that.Swyx [00:33:59]: So my impression is auto research means many things, likeAkshat [00:34:01]: YeahSwyx [00:34:01]: Anything that Andrej coins. Right now it's still science fair, right? Like not like, I don't know how many people are doing this.Akshat [00:34:08]: We're having a golf.Swyx [00:34:08]: Yeah.Akshat [00:34:09]: I thought the same thing.Swyx [00:34:11]: Yeah, you would know.Akshat [00:34:12]: We, like, our internal both training and inference teams use this the general shape of this quite a bit. like we have this one internal repo called auto inference, which essentially we've automated our own forward-deployed engineering efforts using, this harness, which is, the agent will just spin up a sweep of different things. It'll even run like, NVIDIA inside profiler and it'll like tweak configs and it'll arrive the right thing. it'll change your GPUs both from H200 to B200, and works really well.Swyx [00:34:47]: Nice.Akshat [00:34:47]: So yeah.Swyx [00:34:48]: By the way, I enjoy that your forward-deployed engineering is so technical that you have to do these things.Swyx [00:34:52]: It's very different from forward-deployed engineering from other people.Akshat [00:34:54]: Yeah. For our forward-deployed engineering team is, essentially they're like applied inference researchers or applied training researchers.Swyx [00:35:02]: Someone told me like they have to be able to build, but they also have to be able to sell. do they have to sell or are they like they're good, they're just like post-sale type of thing?Akshat [00:35:09]: It does, being able to talk to a customer and engage effectively with themSwyx [00:35:13]: YeahAkshat [00:35:13]: Matters a lot.Swyx [00:35:14]: They want the same thing.Akshat [00:35:15]: Yeah.Swyx [00:35:15]: ?Akshat [00:35:15]: But it's it's not really a sales, thing. We pair them with-- We have solution architects as well that are more on the sales side.Swyx [00:35:23]: Okay. Let's spend a bit more time on auto research. This is a big focus for for this year. Where does this go? like, have people explored enough? Like, there's all these beautiful charts of like improve and then level off a bit and then you find the next thing. Is this one abstraction up from normal training? Is that how we think about it, or do you think about it differently? Like model level training versus high, like driven hyperparameter search.Auto Inference and Modal BenchAkshat [00:35:51]: Yeah, like,Swyx [00:35:51]: Someone, some people call it like neural architecture search or whatever, right? Like.Akshat [00:35:54]: Yeah, - So the stuff I've seen people do with it is nowhere on the architecture level. It's pretty much tweaking parameters, but it's it's a hyperparameter sweep that's guided by some model intuition, so it's like much more efficient than, whatever other, sweep you would have.Swyx [00:36:12]: Yeah, it's just, it's just a question of where you want to spend your compute?Akshat [00:36:16]: Right.Swyx [00:36:16]: ‘Cause yeah, you can just throw infinite amounts of money on this and somehow you'll bang out Shakespeare?Akshat [00:36:22]: Yeah, infinite monkey.Swyx [00:36:24]: Yeah, so like the very good for model. and I think it's also very important that agents can spin up other agents, can spin up their infrastructure. Like very good for you. how good is our LLMs at generating model code? Like the benefit of existing LLMs is that you are in the data.Akshat [00:36:42]: Yeah. They're, they're surprisingly good. I think like pre Cloud 4 they were not, and then now they're able to shot, stuff out of the box. But we're playing around with releasing like a Modal Bench for like the harderSwyx [00:36:55]: YeahAkshat [00:36:55]: Things, that the LLMs cannot do yet and maybeSwyx [00:36:59]: What's an example of that?Akshat [00:37:01]: I think the things that- Sometimes agents struggle with, without right guidance and a skill is, how to, use the rest of our observability. Like how to. Something is failing, like how do you look at the logs and then update the right thing? It's reasoning about that. But they're able to shot, likeSwyx [00:37:23]: Yeah. You can just add a skill to it?Compute Strategy and Capacity PlanningAkshat [00:37:26]: Yeah. So we have a Modal skill now that. Which is why we built this Modal Bench. It's to find things like that, so we can address them in our tool.Swyx [00:37:35]: Tune a skill. Yeah.Akshat [00:37:36]: Yeah.Swyx [00:37:36]: No. it's it's good. are you facing any shortages? like we talk a lot about GPU shortages, but also CPU, also memory.Swyx [00:37:44]: Yeah.Akshat [00:37:45]: We have had a lot of growth, which means that, there's - we've had to be much better aboutSwyx [00:37:53]: PlanningAkshat [00:37:54]: Proactive capacity planning.Swyx [00:37:55]: Yeah.Akshat [00:37:55]: So we have,Swyx [00:37:57]: Which by the way, like it's like a MBA's like dreamAkshat [00:38:00]: YesSwyx [00:38:00]: Is like just planning this stuff. I think last time you and I talked about something maybe about this.Akshat [00:38:03]: Yeah. we have a really competent team of people that we call, The role is called compute strategy. so yeah, if anyone listening here or wants to work on thatSwyx [00:38:13]: Compute strategy?Akshat [00:38:13]: Yeah.Swyx [00:38:14]: I think,Akshat [00:38:14]: I feel like,Swyx [00:38:15]: I think the normies call it FP&A or something.Akshat [00:38:18]: Well, it's more It's it's not FP&A. It's it's There's a lot of interesting financial questions of like what is the blend between one year and three-year reservations? how do we forecast our own capacity? how do we. especially since our capacity is very fungible across different GPU types and different regions, like you have to model a lot of it. and you also have to have an opinion on how the supply chain is gonna evolve, and then you have to like, take bets,Swyx [00:38:49]: YeahAkshat [00:38:49]: Based on that.Swyx [00:38:50]: Tokenomics.Akshat [00:38:50]: Yeah.Swyx [00:38:51]: This is like probably a not a real point, but, I was trying to think about like what other industries. I was trying to think about like, we cannot be first to like these kinds of problems.Akshat [00:38:59]: Yeah.Swyx [00:39:00]: And what other industries have had this? And I was like, airlines with fuel and like they have to hedge their fuel and like, I think for a long time Southwest because they made like a hero fuel bet, they like were like super low cost becauseAkshat [00:39:12]: OhSwyx [00:39:12]: Compared to everyone else.Akshat [00:39:14]: Yeah. I hadn't thought about that.Vibhu [00:39:16]: We're at a fun time too?Akshat [00:39:18]: Yeah. It's. A lot of the compute business in general, for us is also about being very good about capacity management. That is how you have great unit, economics. but also over time it's how you can unlock more value for customers. Like, one of the things we're building now is like a way for customers to get, If they don't care about latency, like get much cheaper pricing and they'll get results back in like next 24 hours or something, like a batch tier essentially.Batch Tiers and Latency-Insensitive WorkloadsSwyx [00:39:47]: Yeah.Akshat [00:39:47]: And those are levers we have because we control the whole stack and scheduling and whatnot to give people a sufficientSwyx [00:39:53]: Yeah. I feel like they're not as popular. Like those, like the Frontier Labs have all those APIs. They're not as popular as they should be.Akshat [00:40:00]: The demand that we see for something like that is not for LLMs. although sometimes people wanna run evals andSwyx [00:40:08]: OkayAkshat [00:40:08]: Synthetic data prep and there it makes sense.Swyx [00:40:10]: Okay.Akshat [00:40:11]: But it's from a lot of LLM companies, like people who are doing computational bio, like they have to run really big batch jobs and they don't care about when they get it back.Swyx [00:40:22]: Yeah. And like they have a reasonable. It's it's also like a cousin to the stopping problem of like, will this finish in time?Akshat [00:40:30]: Yeah. You can bound it.Swyx [00:40:33]: Yeah.Akshat [00:40:33]: Like you can give peopleSwyx [00:40:34]: YeahAkshat [00:40:34]: SLAs on it.Swyx [00:40:35]: Yeah. I think what's, what's interesting is like the next phase of model.Swyx [00:40:38]: Like what, do people expect from you, now that you're established and you're like well-known compute player among all these leading companies. You had an inference launch week, and we talked a little bit about the launches. like what else? Like what else should people know?What Modal Builds NextAkshat [00:40:55]: We are building primitives that make our users' lives much easier. So, I think for example, with LLM inference, thousands more companies are gonna post-train their own models and, deploy open source models for inference. so we're thinking a lot about what is the best product shape for that. And, that involves everything from our training gym to, then, endpoints that get frontier-level performance. again, but I haven't talked to anyone. It looks somewhat different on other verticals. Like, we're also seeing a lot of real-time, audio-video stuff in there, which is why like, we're working on things like regional routing, with fallbacks. So you can get GPUs that are as close to users as possible. so you get like low latency for video streaming and whatnot. And then on the agent side, it's,Akshat [00:41:52]: We're still working very closely with our customers because stuff is changing so fast in terms of what they need. And, I think beyond sandboxes and persistent file systems, there's a lot of other things people will need from this agent stack as they build production agents. So yeah, we're thinking about those other things that fit in there.Swyx [00:42:13]: I want to ask what the other things are.Akshat [00:42:15]: Yeah. I probably should share right now.Swyx [00:42:17]: I think-- I think, okay, so, I do think a lot about the principal components of cloud, and you do talk about compute storage networking.Akshat [00:42:25]: Yeah.Swyx [00:42:25]: Because so far for me, it's fine. so far for the. the first couple generations of cloud, it's fine. What's different, qualitatively different about agents that you need some new permission level? Like a lot of people, okay, and I'll just kinda spew tokens at you until it like hopefully sparks something.Akshat [00:42:43]: Yeah.Swyx [00:42:44]: Like the new level now is whatever Claude Code does, which is dangerously scope permissions or like allow list by command or like whatever, right? And sometimes they're like, “Well, okay, we have like this adaptive thinking mode where like, just trust me, bro. I will make the calls for you.” Is that it? like mediated permissions.Hard Guardrails vs. LLM-Mediated PermissionsVibhu [00:43:03]: Now you're looping it with a goal and letting it roll.Akshat [00:43:06]: Yeah, I'm, I'm skeptical of LLM media permission for stuff that is at the sandbox level because you do want hard boundaries.Swyx [00:43:16]: Yeah.Akshat [00:43:16]: Otherwise, someone can exfiltrate stuff.Swyx [00:43:20]: But likeAkshat [00:43:20]: YeahSwyx [00:43:20]: Maybe that's old school thinking. Maybe we're the dinosaurs.Swyx [00:43:23]: Maybe the AI OS or the LLM OS is really the kernel is a goddamn LLM.Swyx [00:43:30]: Like it makes you feel uncomfortable.Akshat [00:43:31]: Yeah, I'm, I'm toldSwyx [00:43:32]: But that's what trusting the LLM is. Like imagine a spherical cow perfect LLM.Akshat [00:43:36]: Right.Swyx [00:43:37]: That it.Akshat [00:43:39]: Maybe.Swyx [00:43:41]: I wanna test the boundaries, right?Akshat [00:43:42]: Yeah.Swyx [00:43:42]: Like, and I don't believe that, but I wanna see where I'm wrong ‘cause that's, that's the consensus.Akshat [00:43:49]: Yeah. I think you always need hard guardrails when you want, And you can pair those with softer guardrails, right? And that's gonna be a lot of mediated.Managed Agents and Specialized SandboxesSwyx [00:44:00]: There. I'll also get you a end with a couple of your commentary on like the ecosystem outside of Modal. Manage agents. Everyone has one. Gemini, OpenAI, Claude, very useful for you, but also like it is their way of starting to edge into your space.Akshat [00:44:17]: Yeah.Swyx [00:44:17]: What's going on?Akshat [00:44:19]: Yeah, we're, very excited to partner with Anthropic and some of the other foundation labs, will not name who we're also working with. the way we see it is the manage agent thing is a great place to start if you're starting out building an agent and, But then when you get to, building something more production grade, like you're a company that's like Ramp that's building their own, Ramp also runs their accounting agent on us, so their external-facing agent. You need a lot more control over, your compute primitive on things like, what sort - how do you persist different files that the agent has access to, and how do you snapshot and restore? How do you control the networking? maybe you want GPUs. When you get to that point, you kinda want, a specialized sandbox provider, that gives you those things, and that's the role that we are trying to play.Swyx [00:45:15]: YeahAkshat [00:45:16]: We don't really have an opinion on the harness, whether it runs - it's a cloud-managed agent, and you hook it up to Model Sandbox, or you run the harness in Model Sandbox. We'll see where people converge with that.Swyx [00:45:26]: Yeah. Do you any opinions on like the meta harnesses, or just another layer on top of these things?Akshat [00:45:31]: You mean like the OpenPipeSwyx [00:45:33]: OpenPipe is one. I think Vercel had one, which I can't remember the name of right now. Fredshot had one. and then, to me, most recently was Data Databricks that had Omnigen. All these are meta harness. Like it's kinda pseudo agent cloud type things.Akshat [00:45:50]: I personally have not played around with them.Swyx [00:45:53]: Yeah.Akshat [00:45:53]: Build agents with them.Swyx [00:45:54]: Everything's bullish Modal, as long as it consumes more infra.Akshat [00:45:57]: That's why we're focusing on the infra layer. It's somewhere where our, relative competence is and, also it's a hard problem to solve.Swyx [00:46:06]: Yeah. I will say like just generally reflecting on that, I don't know if - if there's other topics on Modal, but like just generally reflecting as an infra person, not as intense as you, but in that field, this has like been the most exciting time in infra. Like it was boring for a while, and you couldn't really get people excited about data infrastructure. Like Eric would get on Data Console, everyone just watched the video and like say, “Look at how many sandboxes I can spin up,” and no one gave a crap.Why Infrastructure Became Exciting AgainAkshat [00:46:39]: Yeah.Swyx [00:46:40]: And like now everyone gives a crap.Akshat [00:46:42]: That's true. It is a very exciting time, and I think a lot of that's driven by just the amount of scale all of this stuff needs.Swyx [00:46:50]: I think the, like a lot of your initiatives or a lot of your like product directions make sense in retrospect, which is like the best kind, but I wouldn't necessarily have thought about it myself, which.Akshat [00:47:00]: We need the predictions.Swyx [00:47:02]: I think there's a lot that you just don't even see, right? Like you have the batch, you have the voice, you have the multimodal, but what else?Akshat [00:47:10]: What else is coming up for usSwyx [00:47:11]: Yeah. Where do you see things going?Akshat [00:47:13]: Yeah. I, in generalBiotech, Robotics, and Non-LLM AI WorkloadsAkshat [00:47:15]: It's it's clear that there's there's a huge shift happening. I think one thing that's not as obvious to people because LLM inference gets talked about so much and is also we work a lot of companies that are, doing things like drug discovery and computational bio, like the Chai Discoveries of the world. Big things are probably gonna happen there. we work a lot of robotics companies that are putting robots in like active deployments and getting good results out of them.Swyx [00:47:45]: Is there Air Gap Modal? Is there a version that is like prem air gapped whatever?Akshat [00:47:50]: No. We,Swyx [00:47:51]: You should cloud only.Akshat [00:47:51]: Yeah.Swyx [00:47:52]: Yeah. Okay. But yeah, so what you're saying is like because you're focused on primitives and they're good primitives, you find use cases in all these kinds of things.Akshat [00:48:01]: Yeah.Swyx [00:48:01]: Probably diversifies you a little bit away from LMS all the time.Akshat [00:48:05]: Yeah, absolutely. We're, we'- our goal isn't to only serve the LLM inference market.Swyx [00:48:10]: There are a lot just on the website, the audio,Akshat [00:48:12]: Yeah. We said both onSwyx [00:48:14]: Computational bio images. Yeah, there's a lot here. There's QTA TTS, customizing. Oh, Chatterbox. there was customizing Whisper.Akshat [00:48:24]: Okay. Yeah.Swyx [00:48:25]: This screen reminds me of a fallen competitor, which Replicate.Model APIs vs. Differentiated AI ProductsSwyx [00:48:31]: What's your postmortem on what happened?Akshat [00:48:34]: This is one thing we've stayed away from is providing an API for models because I think providing model APIs is some of it ends up serving like a really hobbyist market, which is much less sticky.Swyx [00:48:50]: Yeah.Akshat [00:48:50]: And we've always wanted to build for companies that are building products and need more flexibility that's not just an API.Swyx [00:48:57]: Which you can build an API for a model and this is clearly what it is. But you - but what you're saying, you can wrap it into a more fully functioning back end that you run.Akshat [00:49:06]: Yeah. So all of our examples, it's not that spin up this model, here's an API token, use it. They're all code.Swyx [00:49:13]: Okay.Akshat [00:49:13]: And so the point is that this is just an example.Swyx [00:49:16]: Starter code.Akshat [00:49:17]: Yeah. But you can tweak it however you want.Swyx [00:49:20]: Yeah.Akshat [00:49:21]: And if you're like a company building a product, like, computational bio whatnot, yeah.Swyx [00:49:26]: I guess I'm trying to tease out for listenersAkshat [00:49:28]: YeahSwyx [00:49:28]: When does it stop becoming, oh, you're just an API call and you're just a wrapper on API to becoming what you call a product, right?Swyx [00:49:36]: Like, what is that layer? Like what-- Like, more lines of code, but like beyond that, what is the substance that people add that qualifies it to be something more?Akshat [00:49:46]: I think there's a little bit of like a selection effect of like a lot of the companies who do wanna get deeper into that level are probably building something that's more differentiated. And, I think, an example is like - with LLM inference, originally we, worked with companies that were building their own post-training frameworks or they were, - Ramp early in the day was training their own tokenizer and like swapping out the tokenizer in Llama and whatnot. I'm not saying that's, that successful, in that case. But a better example is like, let's say Suno. because Suno, does not use Modal for training.Swyx [00:50:26]: Mikey on the pod. Yeah.Akshat [00:50:27]: But they use Modal for all their inference and that's because they have like a custom-- They have completely custom model architecture and that means that they have to be at the code level and tweak things that are not, just an API.Swyx [00:50:41]: It's interesting as well, like we had, Ethan, most recently on the xAI Groq team make a prediction that like the next tier in video gen is not a better video model, it's a better model or agent that orchestrates video models.Video Agents and Production WorkflowsAkshat [00:50:56]: Oh, interesting.Vibhu [00:50:56]: Language model backbone that can use toolsAkshat [00:50:58]: RightVibhu [00:50:59]: And write code.Akshat [00:51:00]: Like, yes, I can make my second video or my second video from Groq, but I want my minute video.Akshat [00:51:06]: And I'm not going there through normal video gen.Swyx [00:51:10]: Yeah, that's interesting. I - So we have GPU sandboxes and recently have seen a few companies doing agents that do video manipulation or,Akshat [00:51:22]: Yeah. Give it FFmpeg and just do it.Swyx [00:51:23]: Run FFmpeg. But likeAkshat [00:51:25]: That's not enough.Swyx [00:51:25]: Yeah.Akshat [00:51:26]: You need to give it Adobe.Swyx [00:51:27]: Yeah, I hadn't put it together with like it would be a video production thing. in my mind these things were going more towards editingAkshat [00:51:36]: Yeah.Vibhu [00:51:36]: Well, shout out Mantis.Akshat [00:51:37]: I think about this a lot.Swyx [00:51:38]: .Akshat [00:51:41]: Yeah. Sorry.Vibhu [00:51:41]: Luma. Luma Agent is a version of this for video production, but it's a off.Swyx [00:51:46]: I was gonna get your quick takes, on some other stuff that happensGitpod/Ona, CI, and Runtime SandboxesSwyx [00:51:50]: In recent news and just-just see if you have anything interesting. Gitpod, very li

Cancer Trials Ireland and the Beatrice Pembroke Walsh Foundation have announced a new partnership that will drive research into sarcoma in Ireland. The Beatrice Pembroke Walsh Foundation was established by Cordal native David Walsh, to fund research and create awareness of leiomyosarcoma (LMS), an extremely rare and aggressive cancer. David’s wife, Beatrice Pembroke Walsh lost her life to LMS at 53 years of age in October, 2022. Jerry spoke to David and to Angela Clayton Lee who’s CEO of Cancer Trials Ireland.

On pense souvent que l'autonomie appartient aux petites structures. Et si la plus vaste transformation du management se jouait aujourd'hui avec 60 000 facteurs sur le terrain.Dans cet épisode, Delphine Zanelli reçoit Nathalie Anton, Directrice de la transformation et du système d'excellence au sein du Groupe La Poste.Donner les clés de l'organisation à ceux qui distribuent le courrier bouscule les certitudes historiques. Le contrôle rassure traditionnellement l'entreprise. Le lâcher prise inquiète les échelons hiérarchiques. Les managers craignent de perdre leur rôle essentiel face à cette transformation du management. Nathalie Anton observe cette tension humaine sur le terrain. Elle explique comment aider un chef de proximité à comprendre que sa place ne disparaît aucunement. Sa position se déplace. Il arrête de résoudre tous les problèmes à la place des autres. Il devient un véritable libérateur d'énergie au sein de son équipe.L'échange s'ancre profondément dans les routines partagées chaque matin par les facteurs. On comprend exactement comment l'équipe bâtit sa propre organisation pour s'adapter à une rue barrée, un chien menaçant ou un imprévu logistique. L'institution comprend que l'organisation fonctionne beaucoup mieux quand ceux qui arpentent les rues décident des itinéraires.Cette dynamique collective engendre des résultats économiques tangibles et une adhésion très forte. L'absentéisme et les accidents du travail baissent de 30 %. L'engagement des collaborateurs progresse de façon notable chaque année. Les initiatives commerciales des facteurs doublent au contact direct des usagers.Nathalie Anton pilote cette transformation du management depuis près de 10 ans. Elle s'inspire directement du vécu des facteurs et des responsables locaux. Elle refuse catégoriquement les modèles théoriques conçus loin de la réalité des opérations. Elle accompagne la révolution culturelle d'une institution historique complexe.Cette pratique managériale inédite génère une performance extrêmement solide. Le leadership se redéfinit pour valoriser ceux qui agissent. Le futur du travail s'invente ici avec un immense pragmatisme. Les facteurs retrouvent du plaisir au travail grâce à cette confiance sincère qui leur est accordée.Cet épisode donne des éléments concrets pour définir une journée réussie en équipe, animer des espaces de discussion réguliers sur le travail et encourager la solidarité face aux difficultés.CHAPITRAGE :(00:00) Une rencontre née grâce aux auditeurs (05:18) Gérer 100 000 collaborateurs face au changement (11:54) Pourquoi l'autonomie exige la présence des managers (19:19) Définir collectivement une journée réussie (27:30) L'impact direct sur l'absentéisme des facteurs (31:46) Changer de posture pour libérer l'énergie (51:08) Préserver le discernement humain face à l'IA

Grab the Secondary Teacher Systems Toolkit here: https://khristenmassic.thrivecart.com/systemstoolkit/?ref=pod Too many preps and not enough time? Let's make your planning period actually work for you. Reserve your spot in the Unit Planning Lab here: https://khristenmassic.thrivecart.com/unit/?ref=podcastPlanning for the next school year? If your day is organized by class period, your planning calendar should be too. Grab my Editable Class Period Calendar here: https://khristenmassic.com/secondarycalendarpodGet the Planning Period Reset Toolkit—a free set of quick-start tools to help you protect your time, focus faster, and finally finish something… even during chaotic school days. https://khristenmassic.com/resetShop my Teachers Pay Teachers store: https://www.teacherspayteachers.com/Store/Khristen-Massic-Cte-Teacher-CoachIf you're a middle or high school teacher who's tired of answering, “Did we do anything yesterday?” before you even have your coffee, this one's for you. The latest episode of The Secondary Teacher Podcast is all about student accountability routines that actually teach independence instead of demanding compliance. Host Khristen Massic is drilling into the practical moves that make your students more responsible and keep you from being the classroom help desk every single period. This episode doesn't just preach accountability—it hands you teacher tips for setting up classroom routines that free up your brain and restore a little bit of your work life balance in the secondary classroom.Let's get honest: too many of us lose precious minutes of every class period explaining what students missed, repeating directions, and hunting down lost copies. Far too often, especially for teachers with multiple preps, our so-called “systems” (Post-it calendars, folders for every kid, forms to track late work) become monuments to overthinking—nobody uses them, least of all the students. Host Khristen Massic shares the raw classroom reality of spending an entire summer crafting an absent work policy that flopped in actual practice. The folders sat untouched, while a never-ending line of students still needed explanations.Here's the better way: the core routine for student accountability in this episode is stripped down to what works—students are taught to check the LMS (learning management system) first, then a crate with extra copies organized by period and day, then (and only then) ask you. Why this order matters is simple: it builds self-advocacy, prevents you from becoming the information-retrieval machine, and gives your students the gift of real independence, not just compliance. This method is especially tuned for the complex demands of multi-prep secondary teaching, where you don't have the bandwidth for six different calendars and fleets of note-takers.Every teacher knows the temptation to over-engineer routines in the summer: color-coded folders, elaborate binders, policies for every contingency. On the podcast, Khristen calls this out. When you design systems in July for a classroom that doesn't exist yet, you're solving problems you might never have. The real-world classroom is messy. Students need simple systems that they'll actually use. Host Khristen Massic shows how the minimalist approach—one clear routine, taught and practiced—beats complexity every time.The discussion zeroes in on four key student independence routines every secondary teacher should have down: hall pass procedures, tardy routines, returning absent work, and how to turn in assignments. For example, instead of guilt-tripping students about lateness or absences, Khristen emphasizes avoiding the punitive mindset—roots of these routines are about minimizing classroom disruption and teaching students to handle the basics themselves. That gives you more energy for what really matters.One vivid anecdote brings it all home: despite setting up the intricate absent work system with folders and binders, not a single student used it—and students kept returning for answers anyway. The real breakthrough came when the routine got cut to the bone: LMS, crate, then ask. Host Khristen Massic walks through exactly how to teach and practice this in class, including the overlooked but crucial steps—like simulating an absence so students actually rehearse the routine, not just hear about it. If you're tired of routines that fall apart the first time they're tested, this episode is your new playbook.The message for secondary classroom teachers is clear. If your systems only work when you micromanage every step, you're not building accountability—you're just setting up another rod for your own back. Routines should remove repetitive conversations, not multiply your paperwork. This episode is a must-listen for any teacher who wants to set up independence routines that students can follow with or without you standing at the front. Science teachers, CTE and elective teachers—get ready for even more tailored tips in the upcoming episode focused on lab and tech procedures.If you ever catch yourself scrambling to chase down work, answer the same old absent questions, or feel your patience fraying at questions about what kids missed, this practical, wry, student-centered take is your call to action. In Khristen's words, every time a student navigates the routine without your help, you get a bit of your mental energy back—and that's worth its weight in sanity for secondary teachers.Check yourself: if a student in your class was absent tomorrow, would they know exactly what to do—without asking you? If the answer is “no,” it's time to choose and teach your next routine. Finish something today that makes tomorrow lighter.Stop overbuilding—start teaching for real student accountability. That's how you get your brain (and your lunch break) back. So go raise a little productive hell, teacher.

Master the new Microsoft Marketplace ecosystem. Subscribe to our Newsletter:https://theultimatepartner.com/ebook-subscribe/ Check Out UPX:https://theultimatepartner.com/experience/ Discover the tectonic shifts happening within the Microsoft ecosystem as Cyril Belikoff and Jon Yoo dive deep into the unification of the Microsoft Marketplace and the explosive rise of AI-driven commerce. This comprehensive discussion explores how the marketplace is transitioning from an incubation island to the mainland of Microsoft’s go-to-market strategy, allowing partners to tap into massive Azure consumption commitments. Learn how product-led growth, AI agents, and optimized digital flows are replacing traditional sales motions, making cloud marketplaces the default engine for scaling revenue in 2026 and beyond. https://youtu.be/cAeSIEXbnNo Key Takeaways Microsoft unified its various marketplaces into a single digital flywheel for customers to discover, try, and buy applications. Applications, such as Copilot certified agents, are contextually surfaced directly within Microsoft products to meet users in their flow of work. Customers are making massive Azure commitments, and purchasing full software stacks through the marketplace retires those commitments entirely. Cloud marketplaces have evolved from a secondary channel into the default go-to-market engine with triple-digit revenue growth. Partners must shift from deal-led transactions to product-led growth by optimizing their digital marketplace listings for AI and search engines. The future of software procurement will increasingly involve AI agents acting on behalf of organizations to seamlessly integrate multiple smaller applications. 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. Key Tags: Microsoft Marketplace, Azure commitments, AI agents, Frontier transformation, M365 Copilot, Foundry, digital flywheel, co-selling, product-led growth, ecosystem shift, SaaS distribution, REO, resale enabled offer, listing optimization, search engine optimization, agentic commerce, cloud go-to-market, revenue recognition, multi-party private offers, Hyperscalers Transcript: Cyril Belikoff and Jon Yoo Audio Podcast [00:00:00] Cyril Belikoff: Why do you have to outsource this or create this vi? Just edit the video right there yourself. Like why do you have to just go do the, as a marketer you want to create a beautiful piece of content, just go and create it. ’cause it can create it for you. Now [00:00:13] Jon Yoo: you can feel it happening. The ecosystem is shifting beneath us, the way Hyperscalers are partnering. [00:00:19] Jon Yoo: How AI is remaking the channel and what it means to win in 2026. [00:00:25] Vince Menzione: Welcome to The Ultimate Partner Podcast. I’m Vince Manzione, your host. [00:00:30] Jon Yoo: We just wrapped up two days in Bellevue with some of the sharpest partner leaders in the business, and what we heard wasn’t incremental, it was tectonic. In this series, we’re going deeper into these conversations, the insights, the frameworks, the real movement we’re seeing in this market right now, because being in the room changes everything and we’re bringing that room to you. [00:00:55] Vince Menzione: And I am thrilled actually for this next one. Uh, I’ve had this opportunity to spend a little bit of time with this gentleman before, and welcoming him back is a pleasure and an honor. Cyril Beov, the vice president. I’m gonna botch up your title ’cause I always say marketplaces, but it’s much more than that. [00:01:14] Vince Menzione: So come on up, zero. And we’re gonna have a conversation and Cyril is amongst other things at Microsoft. Good to see you, sir. Yeah, [00:01:24] Cyril Belikoff: you too, [00:01:25] Vince Menzione: uh, is responsible for the, the Microsoft marketplace. [00:01:29] Cyril Belikoff: Are these your notes here? [00:01:30] Vince Menzione: These are, um, what is that? Yeah, these is gonna be our, our questions, so we, yeah, I, I need help sometimes so prompting, but, uh, so great to have you. [00:01:38] Vince Menzione: So just for purposes of title and context, ’cause your role is much bigger than just marketplace. Yeah. And we’re, we’re gonna sit down and John, is this me? Yes. And then John’s gonna join us. [00:01:47] Cyril Belikoff: Okay. Great. [00:01:48] Joe, [00:01:48] Vince Menzione: well, we’re gonna get started and start having a conversation. And we’ve, we’ve done some of these things before. [00:01:53] Vince Menzione: I’ve, I’ve had, you had me on your stage Yes. In your event at Alyssa Taylor’s event. [00:01:57] Cyril Belikoff: Yes. [00:01:58] Vince Menzione: And then, uh, we’ve, we’ve done some nice things together on stage, both at, at our event in Redmond last year. Yes. Then at our big, uh, ignite breakfast back [00:02:07] Cyril Belikoff: and forth, we had Vince come to our wider org and sort of, uh, I got to do the reverse. [00:02:13] Cyril Belikoff: And so interview him in front of a bunch of, uh, 500 marketers on what do we have to think about for partners. And so he gave us sort of the what’s going on in the partner ecosystem, how to think about it as we think about it, our marketing. [00:02:26] Vince Menzione: And I tried to be candid and represent this group. [00:02:28] Cyril Belikoff: Yeah, that’s great. [00:02:29] Yeah. [00:02:30] Vince Menzione: Was wonderful. Thank you for doing that. [00:02:31] Cyril Belikoff: Yeah. [00:02:32] Vince Menzione: You, you work, you work in an amazing organization. Um, I’ve known Alyssa for many years as well, and you’re an incredible leader. And I just, I wanna frame this maybe with a conversation about, ’cause we’re gonna talk about what’s changed, but, but I think it’s still important for everybody in the room to understand what you did and what your team orchestrated around market. [00:02:52] Cyril Belikoff: Yeah, [00:02:52] Vince Menzione: because it was fragmented, it was in different organizations, it felt very dis disorganized, I guess. Yeah. For lack of a better word. [00:02:59] Cyril Belikoff: Yeah. Thank you. Um, essentially we took it from incubation Island to the mainland Microsoft. I love it. Uh, GTM [00:03:07] Vince Menzione: Yeah. [00:03:07] Cyril Belikoff: Is the simplest way to think about it. Um, and, uh, come September last year now, uh, we unified the, the, the, the many marketplaces. [00:03:19] Cyril Belikoff: Uh, whether it was an Azure marketplace or AppSource and others, and we created the new Microsoft marketplace. Yeah. So one single place for customers to come, discover, try, buy, and for partners, software companies and other NSIs to put their wares up. And, uh, it was the first step in a vision for us to create this digital flywheel for us to bring our customers and our partners together in a more, you know, automated way. [00:03:47] Vince Menzione: Which it, it sounds crazy when you think about it, right? Microsoft has always been like the partnership leader, the leader in the technology, and to have fragmented marketplaces before, right? Yeah. And so what clarity to bring that all together. [00:04:00] Cyril Belikoff: Yeah. It was a big, it was a big step for us and I think what we realized is that customers and partners were saying, Hey, uh, it’s all one place. [00:04:07] Cyril Belikoff: If I’m looking for a SaaS application or an agent or, and plug into teams or whatever it is. I just want to get it all in one spot. Uh, and, uh, and then we need you to connect us to your channel. [00:04:21] Vince Menzione: Yes. [00:04:21] Cyril Belikoff: Uh, and your partners and, uh, can you build out, you know, partner capabilities for that Connects channel to software companies, to, to customers in a digital flywheel way. [00:04:30] Cyril Belikoff: Um, one of the things we actually also announced at that time was this concept of what we call. The marketplace framework. So it’s not just the fact that we have this digital experience or web experience, but that, um, applications that go into the marketplace, depending on the type of applications they get contextually surfaced within Microsoft products. [00:04:53] Vince Menzione: Okay. [00:04:53] Cyril Belikoff: And so if you’re, [00:04:54] Vince Menzione: explain that for this Yeah. For me and for this crap. [00:04:57] Cyril Belikoff: Yeah. So if you are a, um, if you’re a co-pilot certified agent. M 365 copilot agent, you’ll be in the marketplace, but you’ll also be automatically surfaced inside the M 365 copilot, um, product. [00:05:11] Vince Menzione: Nice. [00:05:12] Cyril Belikoff: Same for, uh, large language models in foundry, add-ins in teams, those sort of things. [00:05:18] Cyril Belikoff: ’cause it’s one thing to be where people go to discover Tribu, but users also go into these stores, whether it’s a developer user or an end user. They go in the flow of their work and they want to move quickly. Yes. And so, uh, so they have access. We think that’s very attractive. And the feedback was, Hey, that’s quite differentiated. [00:05:35] Cyril Belikoff: ’cause we have, uh, hundreds of millions of customers in these products every day. [00:05:39] Vince Menzione: Yes. [00:05:39] Cyril Belikoff: And so giving our customers access, our partners access to it and improving their customer experience, um, has worked out well. [00:05:47] Vince Menzione: And something else you did too, because at one point, you know, we were talking about co-selling and single-threaded. [00:05:53] Vince Menzione: And Microsoft has this incredible ecosystem and channel. [00:05:57] Cyril Belikoff: Yes. [00:05:58] Vince Menzione: And it, it was totally disconnected from the whole marketplace strategy, right? Yes. Yeah. [00:06:03] Cyril Belikoff: Yeah. So we, uh, um, we launched, I think in, uh, sorry. At that time in September, we launched five of our largest distributors who were also federating the Microsoft marketplace into their marketplaces. [00:06:16] Cyril Belikoff: And then in the November timeframe at Microsoft Ignite, we launched resale enabled offer [00:06:23] Vince Menzione: RO, [00:06:23] Cyril Belikoff: which is REO, which is a new capability that is proven extremely popular, that connects the software company and the reseller, you know, um, to go and do more deals at scale faster. So [00:06:36] Vince Menzione: we have, we have four of the Es in the room, [00:06:38] Cyril Belikoff: right? [00:06:38] Vince Menzione: Some of the four at the top. Five or six. And then also one of your largest resellers software, one is here as well. [00:06:45] Cyril Belikoff: Yeah. Great. [00:06:46] Vince Menzione: Yeah. [00:06:47] Cyril Belikoff: Great. So it’s, uh, we’ve been busy. [00:06:48] Vince Menzione: Yeah. [00:06:49] Cyril Belikoff: Yeah. Like I said, um, uh, much more work to do. But we are moving from like this incubation project to mainland get it integrated into our core customer, go to market, uh, so that our, uh, software companies and partners have access to those customers. [00:07:03] Cyril Belikoff: And then integration, uh, with the channel. So [00:07:06] Vince Menzione: it’s been a lot happening these last 12 months. Uh. Then Frontier, let’s talk about Frontier. That’s another piece of this. [00:07:14] Cyril Belikoff: Yeah. I’m sure Steven touched on, uh, frontier Transformational or becoming Frontier or Frontier Firm. So probably not helpful to me rehash that. [00:07:23] Cyril Belikoff: I think in general. If you go to a Microsoft discussion or session and you don’t hear about frontier or Frontier transformation, please like, raise your hand and give feedback because that is, um, [00:07:34] Vince Menzione: I kept away from the word frontier with Steve. We were talking about it, but we weren’t using the term frontier. [00:07:38] Vince Menzione: Yeah. ’cause I feel like it gets overused. It’s, [00:07:40] Cyril Belikoff: yeah, it does. It’s, it’s sort of, um, what we try to do with it is articulate it in a way that it’s not just about AI for AI’s sake. [00:07:48] Vince Menzione: Yeah. [00:07:48] Cyril Belikoff: But it’s AI based on what the customer outcome is trying to, what the customer is trying to achieve on their outcome. Um, and so as part of that, of course, you know, agents, AI applications is a big part of what’s driving customers’ capability of, uh, to become frontier. [00:08:05] Cyril Belikoff: And, uh, the marketplace is part of that. ’cause they can either custom build that. Uh, or they can, you know, buy off the shelf, right? Uh, or, and actually in Combin they do mostly they do both, right? And so, um, in order to accelerate their ability to become more frontier, we have these, uh, partners that build, uh, third party solutions through a marketplace. [00:08:27] Cyril Belikoff: And then, uh, uh, those same partners or others that build bespoke solutions around that. So, um, a lot of momentum around, uh, AI apps and agencies, as you can imagine. Um, we have, I think, 5,000 plus AI apps and agents. [00:08:43] Vince Menzione: I was gonna ask you what you’re focused on now, but you, I think you’re tying into this now already. [00:08:47] Cyril Belikoff: Yeah. Um, so of course that’s important. [00:08:50] Vince Menzione: Yeah. [00:08:50] Cyril Belikoff: But really for us it’s about doubling down on driving customer, customer demand. How do we merchandise the right things that customers are looking for? How do we, uh, accelerate any of our flows? Like we will spend hours just looking at like the flow of one scenario. [00:09:07] Cyril Belikoff: Where is it getting stuck? How do we improve it? Um, and then how do we connect it to the channel? And, and what, what more things can we do like EO or multi-party private offers and the like, and we have. Probably an announcement a month in the next three or four months. [00:09:24] Vince Menzione: Oh, come on. Let’s, [00:09:24] Cyril Belikoff: that will, [00:09:25] Vince Menzione: I know, I know it’s early. [00:09:26] Vince Menzione: I know it’s early, but you, you’ve got some, I know you’ve got some things. Think [00:09:29] Cyril Belikoff: about the customer experience. Think about the channel integration and dream about [00:09:33] Vince Menzione: what maybe more of a global scale with some of the offerings, maybe, maybe. Um, so, you know, I, so I, the earnings, we talked about the earnings with Steven, but I thought that there was a very compelling number around the commitment number. [00:09:46] Vince Menzione: Yes. You and you run your Azure as part of your remit. We didn’t go through your entire remit. [00:09:50] Cyril Belikoff: Yep. [00:09:51] Vince Menzione: It’s not just marketplace. You also, you also own the Azure number. [00:09:53] Cyril Belikoff: Yep. [00:09:54] Vince Menzione: Let’s talk about that. [00:09:55] Cyril Belikoff: Yeah. Um. You know, it’s, it’s exciting times for customers. They want to do things not only with us, but with everyone in the room. [00:10:04] Cyril Belikoff: Um, and they are making very large commitments, huge commitments over the next two to three years to spend on Azure. Um, and so it’s now our joint jobs to go and help them identify the right business outcome and go and, you know, consume that commitment. It is just a commitment. It’s not actual. Consumption. [00:10:27] Cyril Belikoff: Yes. And so it’s all of our jobs to take advantage of that. The, the customers are saying, Hey, we have line of sight to the types of things we want to go do over the next two to three years. Uh, probably not everything is, you know, i’s dotted and t’s across, but they have line of sight to most of it. Um, and how do we go help them drive that? [00:10:46] Cyril Belikoff: Um, and so from an Azure perspective, obviously that’s very encouraging for us. It, it allows us to. Invest in more data centers and more capacity that we are doing as fast as we can. Um, and then, um, and then of course on marketplace, the marketplace can retire. [00:11:04] Vince Menzione: That’s [00:11:04] Cyril Belikoff: that Azure commitment. That’s, [00:11:05] Vince Menzione: I wanted to make sure [00:11:06] Cyril Belikoff: people understand that the, in fact, not only the Azure component from the marketplace, but the full software stack from the partner, uh, the software company retires the Azure commitment. [00:11:17] Cyril Belikoff: So if it’s. Uh, I’ll make it up if it’s, uh, a hundred bucks and it’s 50 50, it’s not 50 50, but, um, I won’t disclose any percentages, but let’s say it’s 50 50, it’s much more for the software company, by the way. Um, if it’s 50 50, it’s not just like the $50 for Azure that gets retired. It’s the entire a hundred dollars that gets retired on the customer commitment, commitment, which is great for the software company. [00:11:39] Cyril Belikoff: It’s also great for the customer that they can, you know. Bring that through, uh, to, uh, to their Azure commitment. And we do the same thing with our sellers. So the marketplace sales retire our sellers compensation. So we’re like checking every box so that there’s no friction in the system. So that, so the partner, the customer, our sellers, they’re all juiced to go and. [00:12:03] Cyril Belikoff: Deals with marketplace. [00:12:04] Vince Menzione: I, I hope everybody un understand. I mean, I understand this. I hope everybody else understand this too. ’cause I, I was, watch, you know, I, I, I watched LinkedIn and I see people post things and somebody made a comment about how difficult it was to use Microsoft Portal. And I was thinking to myself, do you realize that there’s, I’ll say 150 billion, but it’s probably a bigger number than that. [00:12:22] Vince Menzione: That’s a total addressable market available to you if you’re a Microsoft partner. You can access these customer commitments. [00:12:30] Cyril Belikoff: Oh yes. [00:12:30] Vince Menzione: If you bring your product on Mark over 400 now [00:12:32] Cyril Belikoff: Yeah. [00:12:33] Vince Menzione: You bring your product into the marketplace, you have access and the customer can retire that commitment. [00:12:38] Cyril Belikoff: Yeah. [00:12:39] Vince Menzione: Without having to justify a new cost justification. [00:12:41] Cyril Belikoff: Yeah. They don’t have to go to procurement. They don’t have to. Yeah. [00:12:44] Vince Menzione: Yeah. I mean, it’s a huge opportunity. [00:12:46] Cyril Belikoff: Yeah. [00:12:46] Vince Menzione: So, um, so you’ve been focused on quite a bit. We wanted to invite John on stage. [00:12:53] Cyril Belikoff: Great. [00:12:53] Vince Menzione: John, you from Sugar is here. Where’s John? Is John in the house? Where’s John? [00:12:57] Cyril Belikoff: There he is. [00:12:58] Vince Menzione: Who’s also an expert in Marketplace. [00:13:00] Vince Menzione: A great friend of Ultimate Partner. Great. Hey, how’s it going? He’s a great partner of Ultimate Partner. Great to see you, sir. All the way from San Fran. Oh, actually we’re on your side of the coast, so, uh, it’s long. He looks [00:13:12] Jon Yoo: cooler than us [00:13:12] Vince Menzione: though. He, he always looks cool. I said that about time. [00:13:15] Jon Yoo: You know, I gotta be comfortable. [00:13:16] Jon Yoo: I gotta be comfortable. [00:13:18] Vince Menzione: So John, good to, good to have you. You’re thanks for having us. You guys are like, every time I see a post from you, you’re moving into a new office space ’cause you’ve outgrown your office space. [00:13:26] Jon Yoo: Yeah, we’re, we’re really excited about the new office. We have hvac, which is a, a big, big, uh, it’s a big thing. [00:13:31] Jon Yoo: Improvements, high ceilings, you know, the whole works h help [00:13:36] Cyril Belikoff: sometimes. Yeah, [00:13:37] Jon Yoo: yeah, yeah, yeah. We, we have our, uh, office opening party if anyone’s an SF on Ally first. [00:13:41] Vince Menzione: Nice. Nice. Yeah. May, may, May 21st. May 21st. That’s right. Well, so, so you’ve been, you’ve had like a front row seat to all of this. I would love to get your perspective on what you’re seeing across partners and what’s changed over the last 12 months. [00:13:55] Jon Yoo: Yeah, so, uh, for those that don’t know, um, sugar is a, uh, a revenue platform for cloud marketplaces and co-selling. Um, so we partner very closely with Microsoft as well as either hyperscalers and other marketplaces like Snowflake, Alibaba, et cetera. And what we, what, what I’m seeing is a couple things. One marketplace is becoming a default to go to market engine. [00:14:17] Jon Yoo: So, you know, I think a lot of people see the stats about how the, the GMV, so, you know, the, the throughput through these marketplaces have been doubling. We’re seeing that in our data as well. So we’re seeing triple digit revenue growth from marketplaces. We’re seeing companies who, you know, maybe the earlier end of marketplaces were infra platform layer of software that used to really adopt it. [00:14:37] Jon Yoo: Now you’re seeing. You know, explosion in a business application layer of companies as well. And so that’s super exciting. I’d say the second piece is channel players are getting more and more involved. Um, I think, you know, I’m the Silicon Valley SaaS bubble, uh, or the AI bubble, so to speak. And I didn’t know as much about the channel world and even these big AI companies. [00:14:59] Jon Yoo: I mean, you’re seeing unprecedented, unprecedented demand, uh, for these, you know, LMS or these AI biz apps. And despite that, they are really working with a partner ecosystem because you’re realizing that most of the world do not really know how to adopt ai. Yeah. And they’re really leaning on expertise. [00:15:18] Jon Yoo: And these AI companies, AI native companies, are looking to channel partners who have these. You know, relationships with their end buyers on how to deliver change managements, how to deliver enablement, not just a system integration site. [00:15:32] Vince Menzione: And they’re also looking to the platform or platforms in the case, Microsoft here also. [00:15:36] Vince Menzione: Right. Which, because like what, where am I gonna do just go out and buy philanthropic or Claude or whatever? I need that to be integrated into my enterprise as well. Right, exactly. [00:15:46] Jon Yoo: So we’re definitely seeing like more adoption of Microsoft Foundry, for example, as people think about security and governance and whatnot. [00:15:52] Vince Menzione: Yeah. Very cool. Any comments on, on? [00:15:55] Cyril Belikoff: Yeah, that makes absolute sense. It’s, uh, um, you know, lots of layers to AI from data. The a, the AI layer itself, the application layer, uh, and innovations happening at all of those pieces of the stack. Um, and so when a software company is trying to, you know, uh, modernize their thought process or build new. [00:16:19] Cyril Belikoff: They ha they have to think about all of those components. Foundry obviously is the AI layer and it provides them with capability to be agile and move quickly and do compliance and, and snap into an organization’s, um, architecture. But it’s the same applies to data, like how it’s fine to have AI but doesn’t, doesn’t do anything without data. [00:16:40] Vince Menzione: Right. [00:16:40] Cyril Belikoff: And so then how do they get data in the cloud? Um, uh, [00:16:44] Vince Menzione: and then how do you security [00:16:45] Cyril Belikoff: govern and govern, right? And how you secure govern, you know, all those sort of things. So obviously we have first party experiences, but they have partners with, um, their own solutions. They’re built on top of, [00:16:53] Vince Menzione: yeah. [00:16:53] Cyril Belikoff: Those Microsoft layers. And, uh, you know, like John says, lots of momentum. [00:16:58] Vince Menzione: So let’s talk about the maturity curve. Like walk us through it. Where do you see partners in this room sitting today? And what does it take to move for them to move to the next stage? Like, what would be your guidance for, for this group? [00:17:11] Jon Yoo: So, you know, we, we work across the entire spectrum of companies. You know, we work with the, the largest enterprises who’ve done billions through these marketplaces like Snowflake, workday, to leading AI companies like Glean or OpenAI, uh, to earlier stage startups who are completely new to marketplaces and really look for, for guidance around what do I do in my first 90 days? [00:17:33] Jon Yoo: How do I get the attention of Microsoft sellers, or how do I. Optimize my marketplace operations so that we can be discovered, uh, really easily on Microsoft Marketplace and others. And the, the way that I think about it is companies first come on, because it is buyer driven oftentimes. So, I mean, that’s just the truth of the nature of you have these big enterprises that want to, you know, burn down their Mac agreements, for example, and that’s how they get started. [00:17:59] Jon Yoo: And or a, a as like this whole space maturing, you’re seeing. A new CRO come in and they’ve done this at X, Y, Z companies and they want to bring that playbook over. But then as they start to do a couple deals through these marketplaces, they think, and this is start of the the flywheel, right? Hey, what do I need for co-selling? [00:18:19] Jon Yoo: And there are systems, you know, integrations and playbooks that need to be done well. Once you do have co-selling figured out, how do I now know which opportunities to co-sell? So we have things like intense signals to be able to. Help them, you know, help overlay a cloud, go to market lens over your existing pipeline. [00:18:36] Jon Yoo: And then now it becomes less of a partnership initiative and actually elevates up to the CRO initiative. And across each of those, um, layers, uh, you have different automation needs because once you actually start to get the flywheel going, it becomes. Holy crap. Now I’m doing 10, 20, 30, 50% of my revenue through these market, you know, through, through marketplace. [00:18:58] Jon Yoo: And it’s creating different data pipelines of work to be done. And now I have to figure out my finance angle of how do I do revenue recognition through these indirect channels. And so that, that, that is kind of the maturity covers. I think about it when you try to retrofit like, uh, all the automation up front, it doesn’t go as well. [00:19:16] Jon Yoo: You have to do a crawl, walk, run approach so that you can also build and bring the rest of the organization with you. So if I look at ’em to the, you know, there’s some familiar faces, some folks that probably are wondering some new faces [00:19:28] Vince Menzione: as well. [00:19:29] Jon Yoo: Yeah. What, what Microsoft marketplace or what marketplace even is. [00:19:33] Jon Yoo: I’d probably say people are in that transformation bucket of, Hey, I’ve done a couple of deals, we’re in this early stages of co-selling. Now I, now I gotta figure out how to supercharge it because this is kind of the future, you know, we can talk more about agenta commerce and whatnot, but, um, I think a lot of people are figuring it out than looking for. [00:19:51] Jon Yoo: For guidance here, [00:19:53] Vince Menzione: zero. [00:19:55] Cyril Belikoff: Yeah. You know, I would say, um, I’ll get what I call tactical yet strategic. [00:20:01] Vince Menzione: Okay. [00:20:01] Cyril Belikoff: Which just think about product-led growth. Yeah. Just think about, uh, similar to the consumer world, if you wanna sell something, you need search engine optimization. If you are thinking about these marketplaces and Microsoft marketplace being one, how are you optimizing your listing so that when a user goes into like the search bar, it’s optimized to bring back the results that make sense to you? [00:20:29] Cyril Belikoff: I think historically we’ve had scenarios where some partners have used the marketplace primarily as like a transaction thing. They’ve done the deal, but then they’re transacted on the marketplace ’cause they wanna retire the the Azure commitment. And that is changing to be sort of product led and marketplace led, uh, versus deal led. [00:20:49] Cyril Belikoff: Uh, but you cannot have a generic one line sentence in your listing. You will not be surfaced unless the customer literally knows your name and will search for you. You’ll not be surfaced if they search for a particular category healthcare app that does something right. And if that’s your thing, you should have the right keywords, you have the right images, have the right videos, and we believed in it so much. [00:21:14] Cyril Belikoff: We actually built a listing optimization AI tool that will auto look at your listing. And based on our best practices, and we know what our search engine is doing, we will make recommendations to you on how to improve. Listing. And so it’s not like a read a document as a best practice. It actually will be an ai, uh, customized tool for your particular listing. [00:21:37] Cyril Belikoff: So lean into those types of things, you know, to, as John says, says, think about the digital flow. This over time will become much more of your, of your business. So make sure you’re thinking about, you know, if someone’s on the marketplace and they decide to trial something for you, how, how are you following up? [00:21:56] Cyril Belikoff: Like, how do you take the next steps? Um, maybe you, maybe you working with a reseller and you don’t have your own sellers, but how do you wiring that into your resellers so your resellers are following up? Or if you have your own sellers, you know, your own sellers are doing it. So you have to sort of digitize your thinking on sales and marketing in this new market, commercial marketplace world, versus in the same way we would’ve done in like the consumer marketplace on Amazon or, you know, um, as you search something on Google. [00:22:26] Cyril Belikoff: Maybe bing. Um, so, um, yeah, yeah. This [00:22:29] Vince Menzione: size. B Come on, come on. [00:22:31] Cyril Belikoff: I [00:22:31] Vince Menzione: had bing. [00:22:32] Cyril Belikoff: Um, [00:22:33] Jon Yoo: I do wanna double click on that, which is when, when we, you know, I talk about like, hey, a lot of it’s buyer driven. A lot of people think about private offers, but then the marketplace offers. But the reality actually, when we look at the data is that there’s a lot more self-service [00:22:46] Vince Menzione: correct [00:22:47] Jon Yoo: offers than there are what they call private offers. [00:22:50] Jon Yoo: So where there’s deal led and that, that, that, that shift. It’s super exciting to see where now these marketplaces are becoming where buyers or users go to discover new products. Let alone, you know, in the future where let’s say there’s an AI agent that has some reward seeking function, and in order to do its job, it needs to go purchase a tool marketplace might be the channel where this happens, which is all the reason why that your listing does need to be optimized. [00:23:16] Jon Yoo: So that one, the agent knows exactly what your tool does and it can match against. Reward. And then second, you have to win the a EO race, right? Yeah. Of, of how you show up in these AI engines. So just wanted to double click on how important that is. Yeah. [00:23:31] Cyril Belikoff: Makes, makes total sense. And we’re, we’re seeing that shift from private office to having a public listed price and a, a public offer as well. [00:23:39] Cyril Belikoff: And so we’re, we’re ourselves investing in our own marketing demand generation. Just in the last three to four months because we’ve seen that taking off and as soon as we saw the signal, we’re like, oh, we should pour fuel on that fire. Because if, if we see organically customers don’t do it, we should, you know, we should go after that and help them understand that we’re here. [00:23:58] Cyril Belikoff: And, uh, if it’s working for some that are doing it by themselves, it’ll work for others and we’re seeing really, really good results. Um, so yeah, get your listing optimized. Think about your digital flows. As John says, the flows of the future are probably agentic wise, maybe not even a human coming to the Microsoft marketplace. [00:24:17] Cyril Belikoff: So you gotta be thinking, not now, it’s okay. You have time. Um, but in the future, you know, six months from now, that quite easily is a scenario. So you really have to start thinking about those, uh, those, uh, digital flows. [00:24:29] Vince Menzione: Yeah. It’s so insightful. Well, it’s what you call product led growth, basically. Yes. And agent led growth. [00:24:35] Vince Menzione: Yes. Right. In some respects. So this is where like we need to think about that. The future model where the agent comes in and actually does the purchasing. We’re not there yet, are we? [00:24:45] Jon Yoo: No. With some products, you know, um, especially, especially if it’s like developer tools. We’re starting to see some of that, but, uh, no one’s gonna buy a cybersecurity solution. [00:24:55] Jon Yoo: Um, that’s seven figures or an agent’s not gonna do that. Right. But the eng the, the search functions are a little bit changing. [00:25:03] Vince Menzione: Yeah. So a lot of ai, you know, we had Steven Boyle on earlier, we were talking about ai, AI natives, you know, that’s Jason Grey’s organization does a lot of work in that area. How do these organizations need to think and act and how do they leverage the cloud marketplaces? [00:25:17] Vince Menzione: Or how do le how do marketplaces fit into the equation? ’cause most of them are coming from a different like paradigm or mindset. [00:25:24] Jon Yoo: I mean, uh, it’s like the number one question, you know, I’ll give a little personal story of like, I get a lot of parking tickets, uh, and I don’t know how to pay off my late fees. [00:25:34] Jon Yoo: So we set up a little open claw that will actually go and ask me questions, and it actually pays off my parking tickets on my behalf. Um, so, so, so, you know, on the other hand, like there, there’s layers to it, right? It could be like layer one, I ask ai, Hey, how do I pay off my parking tickets? Layer two could be. [00:25:52] Jon Yoo: Hey, you know, what’s the best way for me to structure my cadence to pay off the parking tickets? And then layer three is like, Hey, AI, proactively check if I have parking tickets and go pay it on my behalf. Here’s my credit card information, you know, stored in a secure vault. So, uh, what, what I mean by a native as a company bring that analogy is, uh, bringing it at its core. [00:26:12] Jon Yoo: So like a little for, for sugar. We’ve spent the past six months optimizing around our entire, like company brain where we are storing all the context. To a singular, singular place that is queryable, that there’s no coordination tax between teams. Our entire product development process actually is automated where we have multiple agents debating one another, PRDs to code generation, code review, uh, you know, security, qa, et cetera, et cetera. [00:26:40] Jon Yoo: And then there’s humans in the loop across the process. So I would actually think about when we, when we fit that into, into marketplaces, it’s not just thinking about who’s going to send the offers or who’s gonna do the co-sell, or who’s gonna X, Y, Z, but how do you bring all that together so that your sales reps and your partnership person and Microsoft all has to share context in a given deal and it’s done automatically without, you know, back office folks having to update what partner led or partner influence means and reporting things in a, in an old way. [00:27:11] Jon Yoo: So it’s almost re-imagining. Entire workflow, given everyone should have open context of what’s going on in a given deal. So let’s say maybe a hand wavy way of answering that question. Yeah. But it does mean that we should be re-imagining, uh, what the job to be done is, uh, very fundamentally [00:27:28] Vince Menzione: cy, how are you thinking about it since you’re building the, the, the toolkit at Microsoft? [00:27:32] Cyril Belikoff: Yeah. Um, yeah. Well, John says absolutely is right, particularly around marketplace and how that sort of flow and ecosystem will work. Um, in addition to that, it’s. Not only about how marketplace or about developers ’cause the developer, uh, scenario or persona was the first globally to see the value of AI and agents in the flow of their work. [00:27:55] Cyril Belikoff: It was the first to like, oh, one developer can now do much more. I can be empowered, I can build agents to work on my behalf. I can, uh, I can be an architect instead of a hands-on coder. Right. It’s changed the profile of what developers do exactly what. Uh, John mentioned what he’s doing himself. That’s just one profile of a user. [00:28:17] Cyril Belikoff: Th there are many other profiles. A sales person, a marketer, a uh, CFO team, hr. Each of these are literally going through the same transformation. They’re one beat behind developers because developers, you know, was really tech enabled and tech stack driven, and the, and the opportunity was, was obvious quite quickly. [00:28:39] Cyril Belikoff: These are coming really quickly. This is not like the internet adoption cycle that took many multiple years. This is like, we’re talking months. So, and even inside Microsoft, we have our own, what we call, uh, frontier Marketing Internal Initiative to re, um, rewire marketers and how they go about their daily job and stop doing it this way and do it this way. [00:29:04] Cyril Belikoff: Just pick up M 365 copilot with. Coworking Claude, uh, embedded and just go do the work. Why do you have to outsource this or create this? Just edit the video right there yourself. Like, why do you have to just go do the, as a marketer you want to create a beautiful piece of content, just go and create it. [00:29:21] Cyril Belikoff: ’cause it can create it for you now. [00:29:22] Vince Menzione: Yeah, [00:29:23] Cyril Belikoff: three months ago, literally three months ago, it couldn’t do that. And so what is it gonna be in two months for a marketer or a salesperson or finance person? I mean, just co-pilot in Excel. What that is doing to the financial business is in, like if you, any financial person will tell you they live and breathe through in Excel, like it’s just, it’s their equivalent of, it runs [00:29:44] Vince Menzione: most businesses [00:29:44] Cyril Belikoff: their dev tool, right? [00:29:45] Cyril Belikoff: It’s their thing. It’s really [00:29:46] Vince Menzione: is. [00:29:47] Cyril Belikoff: So this is happening over and over again. Again, if you can package those things up and package a package, that piece of IP on a marketplace is, is not only gonna be a big system, it could be a very, very small piece of. Functionality in a flow for a marketer or a salesperson that is so valuable that can be solved many times on a marketplace. [00:30:08] Cyril Belikoff: And in many cases, everyone’s a software company at this point. Everybody can go and package something up. And so I would be stunned if we don’t see cross pollination from system integrators to channel partners, all just publishing on marketplaces based on, oh, I’ve done this thing four times. I cannot do it 400 times. [00:30:26] Cyril Belikoff: Let me just package it up and put it on a marketplace. So. Yeah, I’m sure you know, John alluded to that. It’s, there’s a lot of exciting times. [00:30:33] Vince Menzione: No, the future [00:30:33] Jon Yoo: is [00:30:33] Vince Menzione: great. What do you [00:30:34] Jon Yoo: totally, I mean, it’s, it’s a case of like, how do, what does being marketplace native also mean? [00:30:38] Cyril Belikoff: Yes. [00:30:38] Jon Yoo: You know, and not, I feel like I’ve seen so many people just use it as a, Hey, we’re opportunistically there and if we get some leads out of it, great. [00:30:45] Jon Yoo: Or if there’s some deals, great, but they’re not really leaning in and being marketplace native the way, you know, and right now there’s, this might be uncomfortable, but there’s no excuses. You know, like creating a partner marketing content with your brand guidelines. It should not take so many time. Or it’s a 10 minute exercise. [00:31:02] Jon Yoo: Exactly. Exactly. Three [00:31:03] Cyril Belikoff: prompts. [00:31:05] Vince Menzione: It used to be that ops used to get involved because, oh, we, we know that they have a commitment. Let’s go run it through the marketplace. Right now, what you both have been suggesting here is it becomes a discoverable process. It becomes part of your normal go to market strategy, and you’re, you’re driving pr, product led growth and SEO and all the things you need to do running a a corporation and modern corporation today. [00:31:26] Cyril Belikoff: Yeah, [00:31:26] Vince Menzione: exactly. So, what’s the future like? Where, where do we go in 12 months with this? What do you think? What do you predict? We, we get out a Ouija board or a crystal ball here. What, what, what do we think? [00:31:38] Cyril Belikoff: I think more broadly in the industry, you’re gonna, some of the scenarios that John alluded to, agent to agent interactions, uh, many agents acting on behalf of humans and on behalf of organizations, uh, and doing. [00:31:55] Cyril Belikoff: Simple things and quite complex things. Uh, and those need to be, uh, managed carefully, uh, with, uh, the right, uh, engagements and built carefully. And in, in many cases, customers will look to partners that are quote unquote certified on, uh, uh, HyperCloud platform. Uh, as a way to get going quickly, but also get the, the quality that they need. [00:32:21] Cyril Belikoff: Um, and I think instead of buying this monolithic, massive application, I think we will see lots more of smaller applications being built that have cleaner open, uh, you know, MC, you know, MCP type agent interfaces that can just work together, uh, without, you know. More complicated integration work, [00:32:42] Vince Menzione: cobbled together your own solutions as opposed to big [00:32:45] Cyril Belikoff: monolithic [00:32:45] Vince Menzione: applications. [00:32:46] Cyril Belikoff: Yeah, there’ll be a much more agile, [00:32:48] Vince Menzione: yeah. [00:32:48] Cyril Belikoff: Uh, personalization. I mean, a lot of people saying SaaS is dead. It’s not quite dead. But I think that SaaS will get significantly more agile, more custom and more personal, uh, for, uh, for customers. [00:33:03] Jon Yoo: I, I would agree with that. Um, I mean, I’m biased, but I think marketplace are gonna be even more relevant than ever. [00:33:08] Jon Yoo: I mean, it’s already highly relevant, but, uh, as you, you know, I think in the past the, the narrative was, well, there’s a new generation of buyers and they’re millennials. They, they wanted, you know, uh, I always talk about consumer experience to B2B sales, you know, and, and now it’s like agents that’s that on steroids. [00:33:24] Jon Yoo: Um, but what I, you know, beyond that, I think, uh, there, there’s a lot of talk of like SAS apocalypse or software companies getting. Destroyed by, you know, the, the, the AI labs or SaaS is dead or whatever. I think SaaS is gonna, I mean, there’s gonna be way more software companies because the cost to build is a lot easier. [00:33:46] Jon Yoo: That means that competition will be. Even more fierce than ever. And you have all these AI native companies that are coming out with a quicker time to market, quicker time to value, and they have some recursive loops that makes the product even that much better. Um, so what that means for everyone is like, one, you gotta go back to the, the core differentiations. [00:34:06] Jon Yoo: Or like in the past, maybe the, the time to build was the differentiation, but today it’s, or you know, there’s some other, obviously the core elements, but then there’s. Distribution. Yeah. So how do you partner with Microsoft, for example? How do you have your own self-improving like distribution model? That makes sense. [00:34:22] Jon Yoo: Marketplace being a huge component of that. Two is obviously there, there’s a piece of like network and data. That’s what we think about of hey, what makes our product better as more, more people use it. Um, because yeah, competition is crazy fierce and it finally goes into times deployment. That’s why you see these companies like. [00:34:41] Jon Yoo: Open AI anthropic that are competing for their enterprise, uh, pie. And instead of doing it themselves, they’re, I mean, I think OpenAI just did a joint venture of like $4.1 billion into the deployment company. Anthropics doing the same, they’re surrounding themselves with the ecosystem and channel is going to be more relevant than than ever, as long as you know how to enable AI services and know how to deliver on this technology to the, to the broader world. [00:35:07] Jon Yoo: And so. Channel awesome. Marketplace, awesome. You know, competition’s gonna be fierce. Success is not going anywhere. [00:35:16] Vince Menzione: Good conversation, gentlemen. [00:35:18] Jon Yoo: Awesome. [00:35:18] Vince Menzione: I’ve been told we’re over time. I wanted to open it up to questions. Um, but I do feel like we, yeah, I’ll get, I’ll get yelled at. But this was incredible. Um, some great, I mean, the, the pa it, it’s terrific to see. [00:35:36] Vince Menzione: How far we’ve come in, so shorter period of time, and it’s only gonna continue to get better. I think the one question I’ll have is like, what, what would hold any of these companies back at this point? It feels like it’s such a compelling reason we need to move forward. Is there, is there anything, like why would, why would we hold back? [00:35:54] Cyril Belikoff: Um, you know, some of the discussions we have, um, is about how to balance today’s world with tomorrow’s world a little bit. [00:36:01] Vince Menzione: Yeah. [00:36:02] Cyril Belikoff: Today’s business model with tomorrow’s business model, today’s financial results with tomorrow’s financial results. Um, and there are very different approaches to all of this. [00:36:13] Cyril Belikoff: Those AI natives, they’re like, there is no yesterday. There’s only tomorrow. Um, there those companies that realize they’re being threatened by AI natives and so they have to move quickly. Um, and, uh. Figure out a, a, a business model and then someone else who wants to do a bit of both and bridge into it. [00:36:32] Vince Menzione: Yeah. [00:36:32] Cyril Belikoff: Um, and just within that frame there are different ways to tackle it, whether it’s create two teams, one’s the future team, one’s the current team, and, you know, may the best team win, um, makes sense with the customer and that, uh, or, uh, give the, give the customer the choice and have the teams going together. [00:36:49] Cyril Belikoff: So there are lots of different approaches. [00:36:51] Vince Menzione: Right. So great to have you. Did you have some, did you have a comment to make on that? [00:36:55] Jon Yoo: Uh, no. Just, uh, unwillingness to lean in and learn something new. Yeah. [00:36:59] Vince Menzione: Yeah. [00:37:00] Jon Yoo: I’m, I’m, I’m much more in the burn all boats buckets. Yeah, [00:37:03] Vince Menzione: I know. Me too. [00:37:03] Jon Yoo: Of, uh, no, no old team and new team. [00:37:05] Jon Yoo: Just new team and you know, that’s just push forward. [00:37:07] Vince Menzione: Well, great to have two amazing leaders on stage with [00:37:10] Cyril Belikoff: us. Thanks [00:37:10] Vince Menzione: so [00:37:10] Cyril Belikoff: much. [00:37:10] Vince Menzione: So thank you [00:37:11] Jon Yoo: so much. Yeah, thank [00:37:11] Vince Menzione: you [00:37:15] Jon Yoo: so much. [00:37:16] Vince Menzione: Don’t forget. Ultimate Partner Live is coming soon, October 26th through October 28th in Reston, Virginia. I hope to see you there. [00:37:28] I.

L'intelligence artificielle façonne le futur du travail. Au-delà de la simple vitesse d'exécution, ce futur du travail requiert une intention claire. Bâtir le futur du travail exige un esprit critique.Dans cet épisode, Delphine Zanelli reçoit Barbara Sessa, Présidente, Directrice Générale de Mastercard France, et Chloé Beauvalet, Directrice Générale du groupe Outsourcia.L'arrivée des agents conversationnels déclenche une course frénétique à la productivité. Les entreprises veulent exécuter plus vite. Pourtant, déléguer une tâche à la machine revient souvent à lui déléguer notre raisonnement. Une question floue produit une réponse vide. L'urgence se déplace de la production pure vers la formulation du problème.Cette rupture redéfinit les repères de l'entreprise. La figure de l'expert technique s'efface. Le collectif a désormais besoin de profils capables d'adopter une approche maïeutique. Il devient impératif d'écouter, de questionner la pratique de leadership et d'orienter la machine. Le métier de manager évolue pour faire émerger la singularité humaine, celle que l'outil ne peut pas imiter.Ce bouleversement interroge directement la transmission. Si les missions simples d'exécution disparaissent, comment former les jeunes recrues à la pensée complexe ? L'intégration des juniors devient un casse-tête pour le management. Il faut d'urgence recréer des espaces d'apprentissage et repenser leur exposition aux réalités du terrain.Barbara Sessa et Chloé Beauvalet dirigent des secteurs massivement automatisés, du paiement mondial à la relation client externalisée. Leurs constats s'éloignent des prédictions théoriques pour s'ancrer dans l'opérationnel. Elles démontrent l'importance vitale de préserver la diversité humaine face à la standardisation technologique pour réussir la transformation du management.Cet épisode donne des éléments concrets pour redéfinir la valeur de ses équipes face aux outils technologiques, identifier les processus à automatiser et adapter l'accompagnement des profils juniors.CHAPITRAGE :(00:00) L'illusion de la productivité (06:39) Le manager face à la machine (14:24) Le pivot vers l'approche maïeutique (22:06) La fin des tâches d'exécution (25:36) Le défi de la formation des jeunes (30:08) L'avenir de l'apprentissage (35:29) Cultiver l'esprit critique de son équipe

حلقة جديدة من البودكاسترز مع إسلام سامي، مؤسس سينكولوجي، في حوار مهم عن مستقبل التعليم في مصر، ومشاكل المدارس، وإزاي التكنولوجيا والذكاء الاصطناعي بقوا جزء أساسي من تطوير العملية التعليمية والإدارية داخل المدارس. اتكلمنا عن الفرق بين نظام التعليم زمان ودلوقتي، وليه مدارس كتير في مصر لسه بتعتمد على الورق والطرق التقليدية، وإزاي أنظمة زي إل إم إس وإي آر بي ممكن تغيّر تجربة الطالب، ولي الأمر، المدرس، وإدارة المدرسة بالكامل. إسلام سامي شرح لنا إزاي سينكولوجي وإيديوسينك بيقدموا سيستم متكامل للمدارس، من إدارة الطلاب والحسابات والدفع الأونلاين، لحد التصحيح، تدريب المدرسين، والذكاء الاصطناعي اللي بيساعد في التعليم والإدارة. وكمان اتكلمنا عن بداية سينكولوجي من مدرسة في طنطا، وتأثير كورونا على التعليم، وصعوبة انتشار التكنولوجيا في المدارس المصرية. حلقة مهمة لكل ولي أمر، مدرس، صاحب مدرسة، أو أي حد مهتم بمستقبل التعليم، التحول الرقمي، وإيدتك في مصر. New episode of Elpodcasters with Eslam Sami , founder of Syncology, for an important conversation about the future of education in Egypt, the challenges facing schools, and how technology and artificial intelligence are becoming essential in improving both the educational and administrative systems inside schools. We discuss the difference between traditional education and today's digital learning systems, why many schools in Egypt still rely on paper-based processes, and how systems like LMS and ERP can transform the experience for students, parents, teachers, and school management. Islam Sami explains how Syncology and EduSync provide an integrated school management system, covering everything from student affairs, accounting, and online payments to correction, teacher training, artificial intelligence tools, and full digital transformation. We also talk about the story behind Syncology, how it started from a school in Tanta, how COVID-19 changed education, and why technology adoption in Egyptian schools is still a major challenge. This episode is for every parent, teacher, school owner, entrepreneur, and anyone interested in education, EdTech, artificial intelligence, and the future of schools in Egypt. روابط Synclogy: Youtube Channel: https://www.youtube.com/@syncology Linkedin: https://www.linkedin.com/company/syncology-eservices/ Facebook: https://www.facebook.com/SYNC0L0GY Instagram: https://www.instagram.com/syncology_eservices?fbclid=IwY2xjawRioGNleHRuA2FlbQIxMABicmlkETF4TWhaUDRydVZteExpa3pac3J0YwZhcHBfaWQQMjIyMDM5MTc4ODIwMDg5MgABHoB4zOYk6NzDNv_XSJcH_G5WUWyNPiqB8HwzHgaSbgFiPIBWF-Of_NQrOj1L_aem_1_oEwMG-NKfjPyi8lXGIrA Website: https://www.syncology.tech رابط موقعنا, انضم إلى مجتمعنا: https://www.elpodcasters.com/ our website link, join our community: https://www.elpodcasters.com/ ‎اسمعوا البودكاسترز على | Listen to El-Podcasters on Spotify - https://anchor.fm/elpodcasters Apple - https://podcasts.apple.com/eg/podcast/el-podcasters/id1633419184 Anghami - https://play.anghami.com/podcast/1029463712 El-Podcasters Social Media | منصات التواصل الإجتماعي للبودكاسترز: Instagram - https://www.instagram.com/elpodcasters Tiktok - https://www.tiktok.com/@elpodcasters Facebook- https://www.facebook.com/elpodcasters Linkedin - https://www.linkedin.com/company/elpodcasters/ X - https://www.twitter.com/elpodcasters Snapchat - https://snapchat.com/t/3Zbo2vzS Bassel Alzaro - https://www.instagram.com/basselalzaro https://www.facebook.com/BasselAlzaroX https://snapchat.com/t/CoWlatfk Karim Rihan - https://www.instagram.com/karimrihann Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

Learning and development has spent decades creating courses, launching platforms, and chasing the next technology trend. But what if the problem isn't the technology at all?As AI reshapes how people access information, many traditional assumptions about workplace learning are being challenged. Employees no longer need to sit through generic training to find answers. They expect learning to be personalized, contextual, and available exactly when they need it.In this episode, Lori Niles-Hofmann joins Naomi Titleman Colla to explore why L&D is facing an existential moment, what organizations are getting wrong about skills development, and how AI could fundamentally change the way learning happens at work. Together, they discuss the shift from course creation to intelligent learning ecosystems, why skills management should be treated with the same precision as a supply chain, and how HR leaders can move from order-taking to strategic enablement.If you're responsible for developing people in an environment where business priorities, technology, and skills requirements are changing faster than ever, this conversation offers a practical and thought-provoking look at what comes next.Resources & References Mentioned

What if educating your people so well that they could leave was exactly the point? At Your Health, that's not a risk to manage — it's the philosophy that built an entire learning ecosystem. In this episode, Jamie talks with Aubrey Wall, who came to Your Health from a background in education and now leads Your Health University, the organization's learning management system and continuous-development engine. Aubrey brings an educator's eye to a fast-evolving healthcare environment, where best practice changes by the day and meeting patients where they are demands that staff never stop learning. Here's what you'll hear: Why a healthcare company runs 12-month, Department of Labor–registered apprenticeships — including programs in management, value-based care, population health, and hospice aide preparation How gamification is being built into nurse instruction (straight from Aubrey's dissertation research) The difference between Your Health University (your classroom) and the Hub (your resource library) How LinkedIn Learning delivered roughly $4.2 million in CEUs to staff last year Meeting Leah — the new AI assistant that helps employees find exactly the right course If you've ever believed growing your people is a cost rather than the whole point, this conversation will change how you think. Press play, then go ask Leah a question. www.YourHealth.Org

In this episode, James Whelan and Heath Moss discuss market updates, small cap investing strategies, recent travel experiences, and insights into commodities like oil and gold, along with sports and World Cup predictions.Also, would you prefer the nasal spray or the tongue strip for ED?Mentions of LTP, LMS, ILT, MRZSupport this show http://supporter.acast.com/the-bip-show. Hosted on Acast. See acast.com/privacy for more information.

76% des dirigeants interrogés par le CJD se déclarent inquiets de la situation politique. Pourtant certains tiennent, avancent, transforment. Ce que le leadership exige vraiment dans un monde où les tempêtes ne s'arrêtent plus.Dans cet épisode, Delphine Zanelli reçoit Mathieu Hetzer, président national du Centre des Jeunes Dirigeants (CJD), et Quitterie Idiart, vice-présidente du CJD, tous deux également dirigeants de leur propre entreprise.Les dirigeants se réveillent à 3h ou 4h du matin. Le "petit vélo" recommence. Le stress est permanent, l'injonction contradictoire aussi : gérer la trésorerie aujourd'hui, transformer le modèle demain, embarquer les équipes maintenant, penser au territoire dans dix ans. Le baromètre du CJD de mars 2026, 560 répondants, le chiffre précisément : ressenti global à 5,9 sur 10, 60% sans visibilité sur leur marché, 76% inquiets de la situation politique. Mathieu Hetzer formule la question que cette réalité pose : "Pouvons-nous encore diriger en quête de performance permanente dans ce monde instable ?"La réponse de Quitterie Idiart passe par un concept précis. La robustesse. Garder des marges de manœuvre pour faire face aux chocs qui vont arriver, plutôt qu'optimiser dans un monde qui n'existe plus. Concrètement : passer de la spécialisation à la polyvalence, travailler simultanément sur les trois horizons (activité présente, nouvelles pistes, activité de demain), construire des équipes capables d'absorber plutôt que de seulement exécuter. Cette transformation du management est au cœur de la commission nationale "Sur le chemin de la robustesse" lancée par le CJD. Les pratiques managériales portées par le mouvement s'inscrivent dans une vision du futur du travail où la polyvalence et la responsabilité distribuée remplacent l'optimisation à court terme.L'échange aborde aussi la gouvernance partagée comme levier concret de leadership et d'engagement des collaborateurs. Mathieu Hetzer l'a mise en place dans sa propre entreprise : stratégie co-construite via des ateliers d'intelligence collective, décision finale qui reste celle du dirigeant, mais charge mentale distribuée. Un collaborateur qui co-décide ne peut plus se désolidariser de la direction prise. Son engagement est directement en jeu. Le CJD fonctionne sur un principe de confiance et de bienveillance sans complaisance, un cadre qui permet aux dirigeants de parler vrai entre pairs. Le rôle politique du dirigeant, au sens de contribution active à la cité, est également exploré dans l'échange.Mathieu Hetzer et Quitterie Idiart parlent à la fois depuis le terrain de leurs propres entreprises et depuis un mouvement de 6 000 dirigeants fondé en 1938. Le CJD a produit un baromètre chiffré en mars 2026. Ce sont des données directement issues du terrain. Le leadership qu'ils décrivent n'est pas une posture. C'est une pratique quotidienne, construite dans la durée.Cet épisode donne des éléments concrets pour identifier les premiers pas vers un modèle d'entreprise plus robuste, expérimenter la gouvernance partagée sans renoncer à sa responsabilité de dirigeant, et comprendre pourquoi le sens, le lien et la joie deviennent des leviers opérationnels pour faire tenir les équipes dans la durée.CHAPITRAGE :(00:00) Introduction : diriger dans un monde qui a changé de nature(04:25) Ce qui empêche vraiment de dormir : le moralomètre CJD 2026(09:14) L'injonction contradictoire : gérer le court terme et transformer le long terme(17:00) Le rôle politique du dirigeant dans la cité(24:00) Du je au nous : se transformer pour mieux prendre soin de ses équipes(29:39) De la performance à la robustesse : un nouveau cadre pour piloter(38:22) Gouvernance partagée et intelligence collective en pratique(49:19) Renaissance, valanche et joie : vers un souffle collectif

Send us Fan MailRobin Daniels is the Chief Business Officer at Zensai and a seasoned tech executive with stints at Salesforce, LinkedIn, Box and WeWork. Recorded live from the floor of HumanX 2026, this lightning round explores what it really takes to create an environment where people are motivated to grow, learn and do their best work every day.Robin and host Dan Turchin dig into why most LMS platforms have failed employees, how AI is changing the relationship between learning and performance, and why investing in people is not just the right thing to do but a proven path to better business outcomes.What You'll LearnWhy 80% of employees are disengaged and what organizations can do about itHow AI-powered learning delivers the right skills at the right moment, not generic compliance trainingHow Zensai uses AI to coach managers and strengthen the employee-manager relationshipWhy proving the link between learning and performance is the key to making L&D a strategic priorityWhy the future belongs to humans who combine technical skills with taste, judgment and soft skills 

93 % des entreprises ont commencé à déployer l'IA. 30 % savent vraiment ce qu'elles en font.Dans cet épisode de L'Entreprise de demain, Delphine Zanelli reçoit Brice Gaillard, directeur général d'Apolearn, plateforme de digital learning partenaire de cette saison, pour une conversation au cœur du futur du travail. Brice accompagne des équipes RH et formation depuis dix ans. Il a intégré l'IA dans sa plateforme il y a trois ans pour résoudre de vrais problèmes opérationnels. Ce terrain lui donne un point de vue que les discours généraux sur l'IA n'ont pas.Ce qu'il observe chez ses clients : le problème du déploiement de l'IA n'est jamais là où on croit. L'IA ne tolère pas le flou. Ce qu'on n'a pas formulé, elle ne peut pas l'inventer. Déployer l'IA exige de mettre à plat ses processus, son expertise, ses façons de faire. Tout ce qui était implicite dans l'organisation doit devenir explicite. Et c'est là que ça coince.La première question à poser n'est donc pas "comment utiliser l'IA" mais "pour quoi faire". Quelle stratégie business, et comment l'IA vient en être au service. 74 % des dirigeants espéraient une hausse de chiffre d'affaires. 20 % y sont parvenus. L'écart tient souvent à l'absence de réponse à cette question.Quand cette question n'est pas posée, les équipes trouvent leurs propres réponses. 55 % des salariés utilisent l'IA sans le dire à leur employeur. 1 prompt sur 12 contient des données sensibles, des coordonnées de clients, des données d'employés, des secrets industriels. Ce shadow IA n'est pas un problème de mauvaise volonté. C'est la conséquence d'un déploiement construit sans les équipes. (Source : Capgemini Research Institute, 2025)Et si l'IA prend en charge une part croissante des tâches techniques, que reste-t-il de spécifiquement humain ? C'est là que le futur du travail prend une tournure inattendue. Dans la Silicon Valley en ce moment, les formations qui progressent le plus vite sont les soft skills. Apprendre à apprendre, maîtriser la langue, développer l'esprit critique. Brice y voit une transformation du management en profondeur : ce qui était secondaire devient central.Pour développer ces compétences, les approches traditionnelles montrent leurs limites. Le catalogue annuel, conçu une fois par an, déconnecté des usages réels du terrain. Brice défend une logique de formation continue, portée par les experts métiers eux-mêmes, plus proche de la réalité opérationnelle. Une approche que l'IA rend possible à l'échelle, en particulier pour le tutorat, impossible à déployer massivement sans ressources supplémentaires.Ce qui amène la question que presque personne ne pose encore. Les tâches qu'on automatise aujourd'hui, rédiger des briefs, synthétiser, analyser, ce sont exactement les tâches qui permettaient aux juniors d'apprendre en faisant. Les juniors d'aujourd'hui sont les seniors de demain. L'engagement des collaborateurs, leur développement, le futur du travail de toute l'organisation en dépendent. Brice défend un principe simple : ce n'est pas parce qu'on peut automatiser qu'il faut le faire.Un échange concret pour tout DRH, manager ou responsable formation qui veut comprendre ce que l'IA révèle vraiment de son organisation et construire son leadership sur ce sujet avec des repères solides.(01:09) L'IA agentique : ce que c'est vraiment (03:06) La pire façon de déployer l'IA (10:48) Où en sont vraiment les entreprises (16:52) Les compétences qui vont compter demain (26:03) Repenser la formation : du catalogue au terrain (33:39) Les juniors face à l'IA (39:09) 5 clés pour intégrer l'IA avec succès

Eric Casaburi — founder of Serotonin Centers and former builder of Retro Fitness — is back to break down one of the most exciting business models emerging at the intersection of fitness and longevity medicine. In this episode, Eric walks us through the creation of SLIM Gym (Serotonin Light Impact Model), a turnkey longevity clinic concept that plugs directly into existing gym and fitness studio spaces (think 200–500 square feet of unused office or childcare rooms). It delivers hormone replacement therapy, medical weight loss, GLP-1 protocols, peptide therapy, IV therapy, and comprehensive lab work to gym members—without the gym owner ever touching a medical compliance headache. Eric shares the real data behind why active gym members on longevity protocols retain at dramatically higher rates, why GLP-1s may actually be a "gateway drug" into fitness culture, how Serotonin handles HIPAA compliance and nurse practitioner training through a robust internal LMS, and why he believes the next major wave in the fitness industry isn't a new piece of equipment — it's the full integration of preventative health and performance medicine on the gym floor. Key Takeaways: 

Chris Badgett argues in this LMScast episode why LMS and CRM should be handled as a single, integrated system rather than as distinct tools by any professional online learning company. He highlights that whereas CRM technologies handle marketing, automation, and customer connections, platforms like LifterLMS handle the learning process. WP Fusion, which serves as the […] The post Your Course Site Is Half-Built Without WP Fusion appeared first on LMScast.

This week we talk about what's going on with the rubium mining and smithing xp rates, changes to sailing combat, and we do a Q&A.If you're struggling, find a crisis helpline in your country here: https://findahelpline.comUse code "BUNE20" for 20% off your order from https://mitchiefox.com! Episode 6 of Michelle's GM series: https://youtu.be/iCbKp7Bp-ew?si=gAe668HY5X4H1MGrEPISODE TIME STAMPS00:00 Intro & personal/community updates16:10 Rubium skilling updates47:31 LMS & Sailing changes1:14:22 Q&A1:40:54 OutroEpisode notes:https://secure.runescape.com/m=news/a=97/rubium-mining--smithing-changes-?oldschool=1https://secure.runescape.com/m=news/a=97/changes-to-sailing--lms-rewards-eligibility?oldschool=1Support the podcast on Patreon: https://www.patreon.com/bunebapeWatch live on Twitch: https://www.twitch.tv/bunebapeWatch live on YouTube: https://www.youtube.com/@BuneBape/streamsCheck out our side channel for variety games: https://www.youtube.com/@SmallBapeWatch Rob live on Twitch: https://www.twitch.tv/smallbapeJoin Our Community Discord at: https://discord.gg/bunebapeHelp buy cosplay supplies: https://throne.com/bunebapeDid you enjoy the content or have any questions? Let us know by commenting and check out more content you might enjoy at the links below.Podcast: open.spotify.com/show/4B3zj5EwqpatWmUre5wV6V?si=HfDE6IY5SqWLjlmdsJyXKQInstagram: instagram.com/bunebapeTwitter: twitter.com/bunebapeosrsTikTok: tiktok.com/@bunebapeosrsMerch: bunebape.comBusiness Inquiries:Bunebape@gmail.comTags:#osrs #oldschoolrunescape #osrspodcast #bunebape #runescapepodcast #podcast

MSD's Ian Wagner speaks with Latin Metals CEO Keith Henderson at Deutsche Gold Messe in Frankfurt. Henderson outlines the company's prospect-generator model, which prioritizes asset-level dilution over shareholder dilution by bringing in partners to fund high-risk exploration. Latin Metals is focused on Argentina and Peru, with projects spanning copper, gold and broader base-metal opportunities. Henderson highlights the company's 500,000-hectare sediment-hosted copper play in Argentina and the Organullo porphyry project, where Moxico is funding major drilling and studies. Latin Metals trades as LMS on the TSX-V and LMSQF on the OTCQB.

Quand une entreprise est traversée par une crise majeure, comment reconstruit-on le management et la confiance des équipes ? Comment redonner aux managers le plaisir de leur rôle quand personne ne leur a jamais donné les outils pour l'exercer vraiment ?Dans cet épisode, Delphine Zanelli reçoit Fanny Barbier, DRH du groupe Emeis, anciennement Orpea.Fanny Barbier arrive en août 2022 dans une organisation en état de choc. Double traumatisme : les séquelles du Covid, puis la la publication du livre Les Fossoyeurs qui a exposé les pratiques internes du groupe au grand public. Les managers ne savent pas s'ils vont rester. Les soignants s'interrogent sur le sens de leur engagement. Certains salariés viennent prendre soin de patients en EHPAD et dorment le soir dans leur voiture. Dans ce contexte, la mission de Fanny Barbier est à la fois immédiate et de long terme : reconstruire la confiance dans une organisation de plus de 1 000 établissements, tout en continuant à faire fonctionner les établissements au quotidien.La reconstruction passe d'abord par des preuves concrètes. Revalorisation salariale après 15 ans sans négociation salariale, webcasts transparents avec l'ensemble des collaborateurs, séparations rapides des profils non alignés avec les nouvelles valeurs. Le principe directeur est simple et constant : on dit ce qu'on fait, on fait ce qu'on dit. Fanny Barbier décrit aussi la création d'une école de management co-construite avec les managers eux-mêmes. L'appel à candidatures est posté à 9h. À midi, 300 volontaires ont répondu. Ce chiffre dit quelque chose de précis : les managers ne refusent pas d'apprendre. Ils refusent d'être ignorés.L'épisode aborde aussi le programme Amy, construit autour de quatre piliers concrets : logement d'urgence, soutien aux proches, accès à la santé, aide face aux difficultés financières. Ce programme part d'un constat que Fanny Barbier formule sans détour : des soignants qui arrivent chaque matin pour prendre soin de résidents, alors qu'eux-mêmes ne savent pas où ils vont dormir le soir. Une politique RH centrée sur l'individu, pas sur la catégorie.Fanny Barbier apporte sur ces sujets une précieuse perspective : celle d'une DRH entrée dans l'entreprise en pleine crise ouverte, sans illusion sur la durée du chemin, et convaincue que la considération est une conviction de direction avant d'être un dispositif RH. Son parcours dans les relations sociales lui a appris que la confiance ne se déclare pas. Elle se construit par des actes visibles, répétés, cohérents.Une masterclass. Cet épisode donne des éléments concrets pour reconstruire l'engagement d'une équipe après une rupture de confiance, concevoir une formation managériale à partir du vécu réel des managers, et bâtir une politique RH qui prend en compte l'individu dans toutes ses dimensions.CHAPITRAGE :(03:11) Arriver dans la tempête: les premières décisions (08:13) Reconstruire la confiance: les gages concrets (11:22) La considération comme conviction fondatrice (19:56) L'école de management: construire avec les managers (27:28) Quand les valeurs ne sont plus négociables (33:40) Le programme Amy: prendre soin de ceux qui prennent soin (40:13) Quel leader Fanny Barbier veut être dans 10 ans

This week on the podcast, we welcome Heather Stefanski, Chief Learning and Development Officer at McKinsey & Company. We explore how organizations like McKinsey are reimagining employee development for the age of AI, shifting learning into the flow of work, focusing on systems and purposeful apprenticeships, and embedding L&D directly into workflow design. You'll also hear all about the evolving skill sets for L&D teams and the importance of updating how we measure development. You will want to hear this episode if you are interested in...00:00 Integrating development into AI assistants04:49 Heather's role at McKinsey08:32 Developing skills in the workplace16:08 Designing developmental workflows with AI24:56 Understanding skill proficiency levels26:25 Building agentic development solutions30:53 Assessing AI proficiency levels33:18 Future skills focus at McKinsey42:55 AI in performance evaluations53:13 Using AI for feedback and reviewRethinking Language: Why Development Surpasses TrainingOne of the first shifts Heather Stefanski identifies is a deliberate move away from talking about “training” or even just “learning.” Instead, McKinsey centers its L&D strategy on development, a more holistic approach that encompasses formal programs, feedback mechanisms, leadership modeling, and real-time experiences in the flow of work.For McKinsey, development is inseparable from business outcomes, and employee development is critical to the firm's value proposition. This means McKinsey designs work intentionally to be developmental, combining upskilling, leadership building, and project experiences into a seamless ecosystem.Purposeful ApprenticeshipHeather discusses embedding rituals, such as performance check-ins and feedback sessions, directly into core workflows to build a system grounded in purposeful practices. By standardizing these rituals, McKinsey can even quantify the impact of great teachers on advancement, and L&D becomes part of organizational culture rather than a siloed function.The New Learning Tech StackOne of the most exciting transformations is McKinsey's ongoing work to blend learning seamlessly into technology-enabled workflows. Rather than relying solely on traditional LMS platforms, McKinsey is embedding learning designers into business teams that are building agentic workflows—AI-powered systems that guide, prompt, and provide real-time feedback as employees work.AI agents are being designed to do more than just increase productivity. Heather emphasizes that agents should also foster professional development by challenging users, prompting reflective questions, and offering immediate coaching. This shift pushes L&D professionals to evolve their skills, requiring fluency not just in instructional design but in data analysis and collaborative workflow engineering.What Skills Do Employees Still Need?As AI tools automate routine tasks, think aligning PowerPoint columns or data cleanup, McKinsey is strategically deciding what to stop teaching, redirecting focus to what keeps the firm distinctive: problem solving, judgment, metacognition, systems thinking, and authentic leadership. Purposeful abandonment of now-obsolete skills is as vital as doubling down on those that matter, ensuring development keeps pace with the shifting demands of knowledge work. Resources & People MentionedLisa Christensen on LinkedIn mckinsey.comCursorCLO Lift Group Connect with Heather StefanskiHeather Stefanski at McKinsey & Company Heather Stefanski on LinkedIn Connect With RedThread ResearchWebsite: RedThread ResearchOn LinkedInSubscribe to WORKPLACE STORIES

NCM Event Conversations feat. Eric Glass from CallRevuIn this special Series NCM Event Conversations we grabbed a few of our favorite guest to talk about their experiences and whats happening in our Industry. In this interview Lou Ramirez and Fred Lennartz speak with Eric Glass from CallRevu to discuss why the event's intimate, education-focused setting helps dealers share best practices. Glass explains CallRevu's evolution from Test Track, an AI simulation role-play tool, into the Test Track Learning Lab with full LMS capabilities for courses, learning paths, and certifications, and how Call Coach listens to live calls to recommend training and simulations in real time. NCM Fixed Ops Summit, an experience built for leaders ready to elevate performance, profitability, and the customer journey through modern systems and by utilizing AI-driven innovation. 

The FBI warns attackers are abusing Microsoft OAuth authentication. India pushes faster patching as AI speeds up cyberattacks. Iranian hackers blend phishing with SEO poisoning. Anthropic's AI finds thousands of open source flaws, while AI also reshapes bug bounties and fuels supply-chain attacks hitting thousands of GitHub repos. Plus, a new LMS zero-day, bulletproof hosting arrests in the Netherlands, FTC action over bogus “active listening” claims, and another busy week for cyber funding and M&A. Our guest is Kurtis Minder, author, joining us to discuss his book "Cyber Recon: My Life in Cyber Espionage and Ransomware Negotiation.” Please disregard all searches for disregard. Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Today we are joined by Kurtis Minder, author, joining us to discuss his book "Cyber Recon: My Life in Cyber Espionage and Ransomware Negotiation." Selected Reading FBI warns of Kali365 phishing service targeting Microsoft 365 accounts (Bleeping Computer) India's CERT-In Sets 12-Hour Patch Deadline for Exposed Flaws (Infosecurity Magazine) Iran-Linked Hackers Target US Aviation with Phishing and SEO Poisoning Campaign (Infosecurity Magazine) Anthropic: Mythos Detected 23,000 Potential Vulnerabilities Across 1,000 OSS Projects (SecurityWeek)  HackerOne takes an axe to its bug bounty rewards (The Register) Automated 'Megalodon' Campaign Spreads GitHub Repo Backdoors (GovInfo Security) Hackers Exploited KnowledgeDeliver Zero-Day for Web Shell Deployment (SecurityWeek) Admins of Bulletproof Hosting Service Used by Russian Hackers Arrested in Netherlands (SecurityWeek) FTC to Require Cox Media Group, Two Other Firms to Pay Nearly $1 Million to Settle Charges They Deceived Customers About “Active Listening” AI-Powered Marketing Service (Federal Trade Commission) Socket raises $60 million in Series C funding. (N2K Pro Business Briefing) You can no longer Google the word 'disregard' (TechCrunch) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Hello voices from the bench community, John Wilson here and I wanted to share some news about the evolution of the Programill lineup. Most importantly, Ivoclar's new PrograMill 7. What stands out right away is the reduced air consumption this mill requires, but what you'll notice first is that impressive new touchscreen. For us, the biggest advantage has been increased spindle power. My laboratory's known for these larger cases with complex geometries, and I can tell you that extra power really makes a difference. Next time you see your Ivoclar representative, be sure to ask about the PrograMill 7 and tell them John Wilson sent you. Thank you. At exocad Insights in beautiful Mallorca, we finally caught up with Felix from Imagine USA—and the timing couldn't have been better. As an exocad dealer on the front lines of digital dentistry, Felix shared his excitement about the strong turnout, the familiar faces, and most importantly, the innovation coming from exocad. What stood out most? The new exocad Hub and its cloud-based capabilities, along with powerful AI-driven tools inside DentalDB designed for efficient batch processing. For Felix and the Imagine team, it's not just about seeing what's new—it's about putting it to the test. By running new features through their own production facility first, they ensure real-world performance before bringing solutions to their customers. Beyond the technology, Felix emphasized the value of being there in person—connecting face-to-face with partners, having meaningful conversations, and stepping back to see where the industry is headed. And of course, doing it all in Mallorca doesn't hurt either. This week at the Dental Laboratory Association of Texas Meeting 2026, the microphones stayed hot as three completely different conversations all circled around the same thing: how fast the dental lab industry is evolving. First up, the crew sat down with Tony Aliatim from Axis Dental Milling to talk about going from biomedical engineering and printing silicone heart models for surgeons… to becoming one of the go-to names in dental milling. From industrial machining roots in Michigan to AI-powered calibration systems and Straumann plug-and-play workflows, Tony breaks down how VersaMill machines are helping labs mill everything from zirconia to implant abutments faster, smarter, and safer. Along the way, the conversation dives into HyperDent, trade show madness, wet vs dry milling nightmares, and why dental technicians may not realize how close this industry really is to aerospace-level manufacturing. Then things shifted from mills to maintenance with Rebekah Serrago and Chris Wilson from Garland Dental Services. What started decades ago as a garage-based repair business fixing handpieces has grown into one of the industry's best-kept secrets for equipment sales, service, and support. Rebekah shares the story of growing up folding flyers for her father's repair company before eventually becoming CEO and expanding Garland into a massive online sales and service operation supporting everything from ovens to mills. Chris joins in to talk preventative maintenance, service certifications, keeping ancient ovens alive, and why labs desperately need dealers that actually understand the equipment they sell. It's equal parts family-business story, repair shop wisdom, and hilarious behind-the-scenes dental lab banter. Finally, the future officially arrived when the podcast crew sat down with Antoine Coppens from Relu and orthodontic lab owner Christian Saurman of New England Orthodontic Laboratory. What started as four engineering students experimenting with AI in Belgium somehow turned into fully automated dental workflows capable of designing surgical guides, night guards, models, and restorations in minutes. The conversation explores how AI is reshaping lab workflows, reducing manual design time, integrating directly into LMS systems, and even learning individual lab preferences. Christian explains how his custom-built orthodontic lab management system helped eliminate workflow chaos and automate huge portions of production, while Antoine gives a fascinating look into where dental AI is headed next. Between AI-generated appliances, automated scan checks, and self-learning workflows, this episode feels less like science fiction and more like a preview of what labs will look like over the next five years.Special Guests: Antoine Coppens, Chris Wilson, Christian Saurman, Rebekah Serrago, and Tony Aliatim.

Scott Kramer of Infios talks about intelligent supply chain execution; why visibility & optimization are key building blocks; & leveraging AI the right way.  IN THIS EPISODE WE DISCUSS:   [02.25] An introduction to Scott, his role at Infios, and his background. [03.20] An overview of Infios, who they are, and what they do. [03.50] The ethos that has informed Infios's journey with AI and new technology. "From the beginning, our solution has been based around adaptability and openness. And now, with the advent of not just AI but AMRs, that openness has allowed us to navigate these waters more easily. We're flexible in how we can operate in this environment." [05.51] Why visibility and optimization are the building blocks for AI-enhanced execution. "Visibility and optimization are still important… But when we add the agents, they can understand what's going on, prescribe solutions, and take away a lot of the heavy lifting of research and analysis." [06.41] What Infios's recent supply chain execution readiness report reveals about how leaders are thinking about execution and optimization, particularly around manual workflows. "Some workflows have been optimized, but only within their silo. They may have optimized a workflow for transportation or warehouse – but how do you connect them?" [09.14] The biggest issue in the market right now with AI understanding and adoption. "People are asking us: "What are you doing right now with AI?" And that's the wrong question." [11.15] Where Scott sees companies on the reactive vs proactive scale right now, why mindset is still a limiting factor, and how visibility is changing. "People are still thinking in the ways they've traditionally solved problems. They're thinking about automating a single task; they're not thinking about connecting the dots together." [13.25] How leaders are thinking about the future of technology. "The art of what's possible has changed." [15.03] Why many AI pilots still aren't getting off the ground, and how we can actually create value from AI investments. "I've seen all too often: 'I have a great technology, now let me go and search for a problem.' We really need to start with the problem, then define the technology. AI is an amazing tool, when leveraged correctly." [18.13] What intelligent, connected execution should actually look like. [20.54] Training AI slowly, how bias is holding us back, and discovering what's really possible with AI. [22.53] The benefits to teams and businesses when they achieve intelligent, connected execution. [24.18] The small steps teams can take now to position for success. "Don't think of it as an LMS problem, WMS problem or TMS problem. Ultimately, it's a customer problem." RESOURCES AND LINKS MENTIONED:   Head over to Infios' website now to find out more and discover how they could help you too. You can also connect with Infios and keep up to date with the latest over on LinkedIn or YouTube, or you can connect with Scott on LinkedIn. If you enjoyed this episode and want to hear more from Infios, check out 520: Enter the New Era of Supply Chain Management, with Infios or 532: Turning Purposeful AI into Business Outcomes, with Infios. Check out our other podcasts HERE.

In this LMScast episode, Chris Badgett discusses how clever popups may be effective tools for increasing course sales. Also, learner success on an LMS website. Chris illustrates why popups should assist, support, and customize the student experience rather than just interrupting users for marketing goals by using Popup Maker in conjunction with LifterLMS. He investigates […] The post How To Increase Course Sales and Learner Results With Smart Popups appeared first on LMScast.

What happens when a fintech leader decides that serving the association community means doing far more than processing payments? And in an environment where associations are under pressure to deliver more value with limited resources, how can they create learning and connections that truly help members thrive?In this episode of Associations Thrive, host Joanna Pineda interviews Wade Tetsuka, President of U.S. Transactions Corporation (UST) and UST Education. Wade discusses:How UST helps associations accept credit card and ACH payments through AMS, LMS, and event platforms, while also helping reduce fees and improve service.Why payment processing becomes an especially important decision point when associations are changing AMS platforms.How UST Education began as simple peer-to-peer lunch roundtables for association IT directors and grew into a major educational platform.How the pandemic accelerated UST Education's virtual programming and enabled it to serve association professionals across the country.Why Wade believes companies should connect with the communities they serve in a more meaningful way, and how education became that “sweet spot” for him.Why education is the common thread across Wade's work, board service, and leadership philosophy, and why he sees it as “the great equalizer in society.”What the AANHPI association community means to Wade, and why representation and visibility matter for future Asian American leaders.References:UST Website

Le métier de manager attire de moins en moins. Et pourtant, ceux qui l'exercent veulent continuer à le faire. Dans cet épisode, Delphine Zanelli reçoit Samuel Durand, explorateur et réalisateur de documentaires sur les transformations du travail, auteur du sixième film Management, Work It Out.Son documentaire part d'une question posée en anglais : Why do good people become bad bosses ? Pourquoi des gens bien deviennent-ils de mauvais managers ? La réponse de Samuel Durand n'est pas dans les individus. Elle est dans le système. Un système de promotion qui place des personnes à des postes pour lesquels elles n'ont pas été préparées, c'est la loi de Peter. Un rôle non clarifié. Des outils absents. Et une envie d'être manager qu'on ne travaille jamais vraiment. C'est ainsi que le métier de manager se retrouve exercé sans préparation réelle.Samuel Durand identifie trois repères concrets issus du documentaire. Se connaître soi-même d'abord : la relation managériale est une relation humaine, et la connaissance de soi conditionne la qualité des interactions. Passer du temps sur le terrain ensuite : c'est ce que disent tous les collaborateurs interviewés dans le film. La confiance, la considération et la compréhension du travail réel se construisent là, pas en réunion. Redéfinir le droit à l'erreur enfin : Blaise Agresti, ancien dirigeant du PGHM, en donne une définition précise. Ce n'est pas laisser faire sans cadre. C'est confier une tâche un cran au-dessus des compétences actuelles pour permettre de progresser.L'épisode revient sur le moment d'émotion du documentaire avec Jean-Michel Frixon, ouvrier chez Michelin pendant 43 ans, dont le témoignage est aujourd'hui utilisé dans toutes les usines du groupe pour sensibiliser les managers à l'impact de leur comportement. Et sur la phrase affichée dans les bureaux de Michelin : "Le chef s'occupe de nous, nous on s'occupe du reste." Chez Michelin, ce système fonctionne à un point où les usines tournent seules, la nuit et le week-end, sans manager sur place.Samuel Durand évoque également son prochain projet, déjà en préparation. Il répond aussi aux trois questions signature du podcast.Cet épisode donne des éléments concrets pour comprendre pourquoi des managers compétents peinent dans leur rôle, pourquoi la formation seule ne suffit pas, et comment construire les conditions qui donnent envie d'exercer le métier de manager dans la durée.CHAPITRAGE(00:00) Introduction (01:07) Samuel Durand, explorateur des transformations du travail (03:43) "Why good people become bad bosses ?" la question du documentaire (04:06) La loi de Peter : quand le système crée le problème (06:35) Former les managers ou déclencher leur envie ? (08:38) Le rapport Gallup et le désengagement des managers (13:50) Jean-Michel, 43 ans chez Michelin — un témoignage fondateur (17:45) Les 3 repères concrets pour mieux manager (19:53) L'intention avant les outils (22:27) L'entreprise sans manager : le cas Indaero (25:05) "Le chef s'occupe de nous, nous on s'occupe du reste" (27:31) L'impact concret que ce documentaire peut avoir (33:17) Prochain documentaire : la reconnaissance et l'argent (38:35) Questions signature

In this episode of The Learning & Development Podcast, David James is joined by Becky Willis to break down the essential components of her new book, 7 Steps to Better Learning Engagement: A Blueprint for Creating Impactful L&D. Together, they explore what it truly takes for an organization to shift its attention toward L&D and, more importantly, how to convert that attention into tangible business impact. Becky reflects on the transition from traditional L&D hurdles to a streamlined, step-by-step guide for modern professionals. The conversation covers the entire lifecycle of a successful initiative—from securing executive buy-in and refining strategy to optimizing learning technology. They also delve into the "marketing" side of the industry, discussing how to leverage internal champions and communication tactics to ensure L&D moves from the periphery to the heart of organizational performance. Take your L&D to the next level   Take advantage of thousands of hours of analysis. Hundreds of conversations with industry innovators and 25+ years of hands-on global L&D leadership.   It's all distilled into one framework to help you level up L&D. Access the L&D Maturity Model here - https://360learning.com/maturity-model   KEY TAKEAWAYS ●      Reposition L&D as a business function, not a course factory - move from “we create training” to “we solve business problems,” and prove it using time to proficiency, product quality, and profit KPIs.  ●      Start with one senior sponsor, co‑solve a pressing business issue, prove impact with data and stories, then scale support via a Learning Advisory Board or similar council.   ●      Modern, collaborative, AI‑enabled platforms with strong UX can transform engagement and visibility. But don´t bolt AI onto legacy LMSs and risk turning L&D into a “dinosaur on wheels.”  ●      Market L&D internally like a product - use champions and visible executives and continually showcase success stories. ●      Go way beyond courses, design for moments of need. Build experience sharing, in‑the‑flow support and AI‑enabled coaching - employees need to get exactly what they need, when they need it. BEST MOMENTS “If you have bolted on AI that's just added into there, onto your dinosaur, LMS, then what you have is a dinosaur on wheels.” “I think the mindset of going from I create courses to I solve business problems is a big step.” “The sweet spot of learning and development is to be there at the moment of need - to influence the moment of apply.” Becky Willis Bio Becky Willis is a founder and the chief learning officer at Tractus Learning. She helps guide Tractus customers to implement successful digital learning. She is also the founder of WillLearn Consulting, where she helps companies plan, design, and develop high-performance digital learning ecosystems. Previously, she was the vice president of engagement at EdCast and led learning innovation at Hewlett Packard. You can follow and contact Becky via: LinkedIn: https://www.linkedin.com/in/beckywillis/ Website: https://tractuslearning.com/ VALUABLE RESOURCES The Learning And Development Podcast - https://podcasts.apple.com/gb/podcast/the-learning-development-podcast/id1466927523 L&D Master Class Series: https://360learning.com/blog/l-and-d-masterclass-home ABOUT THE HOST David James  David has been a People Development professional for more than 20 years, most notably as Director of Talent, Learning & OD for The Walt Disney Company across Europe, the Middle East & Africa.  As well as being the Chief Learning Officer at 360Learning, David is a prominent writer and speaker on topics around modern and digital L&D.  CONTACT METHOD  ●      Twitter:  https://twitter.com/davidinlearning ●      LinkedIn: https://www.linkedin.com/in/davidjameslinkedin ●      L&D Collective: https://360learning.com/the-l-and-d-collective ●      Blog: https://360learning.com/blog ●      L&D Master Class Series: https://360learning.com/blog/l-and-d-masterclass-home This Podcast has been brought to you by Disruptive Media. https://disruptivemedia.co.uk/

Boutique fitness is going global, but it's not just a growth play. International expansion is a full-scale operational, cultural and strategic transformation with real opportunities and risks. Get the inside track from someone who's actually built it at scale in Episode 729: Live from The HFA Show 2026: Lise Kuecker and Ashley Vasquez, VP of International at [solidcore]. 80/20 rule: maintain brand standards while allowing localized adaptation Tech reality check: your CRM, app and LMS may not adapt internationally Cultural nuance: what works in one market can completely flop in another Partnership stakes: the right local partner can make—or break—your success  Speed vs. control: franchising scales fast, but consistency must be protected Most brands underestimate the complexity of expanding internationally. You need determination, flexibility and mastery of endless details. Episode 729 is your reality check. Catch you there. With grit and gratitude, Lisé   LINKS: https://studiogrow.co/ https://www.instagram.com/studiogrowco https://www.linkedin.com/company/studio-growco/ https://open.spotify.com/show/04zR1tRiRhQUdIfvLbh60N https://www.youtube.com/@studiogrowco/videos

On this episode of The Association Podcast, we welcome Lacey Pope, MBA, CAE, Customer Success Manager at Web Scribble, to discuss her career journey in associations and her transition to the industry partner side. Lacey shares how she entered the association world through temp work, earned her CAE, and later drove process improvements at the Oncology Nursing Society that increased live support and boosted customer satisfaction by nearly 10%. She reflects on leading membership technology modernization at Shriners International—including AI translation tools, a new LMS, project management platform, and Power BI reporting—work that earned her recognition at the AWTC Awards. The conversation also explores hiring in the age of AI, daily AI use in customer success, and how associations can build stronger workforce development pipelines beyond a basic job board. 00:00 Welcome and Introductions 00:36 Rapid Fire Questions 02:17 Lacey Association Journey 03:26 Process Improvement Wins 04:55 Career Pivot and Web Scribble 07:22 Awards and Title Tradeoffs 09:32 Vendor Side and Member Value 12:05 Meet Mabel Topic Wheel 13:23 Hiring in the Age of AI 17:44 Daily AI and Policies 20:01 Using AI On The Side 20:51 Five-Year Career Pivot 22:17 Recruited To WebScribble 23:00 Industry Credibility Matters 24:58 Networking And Job Boards 27:02 Daily Wins And Parenting 28:21 AWTC Recognition And Belonging 30:52 Recognition And Community Growth 33:24 Customer Success Trends 34:51 Advice For Student Pipelines 36:42 What WebScribble Does 38:32 Final Thanks And Wrap

It's YOUR time to #EdUp with Erin Shy, CEO, WatermarkIn this episode, sponsored by the ​HigherEd PodCon​ II happening July 16 & 17, & the 2026 AcOps Conference July 29-31 by CoursedogYOUR host is Dr. Joe SallustioHow does a system of record for the provost finally exist when every other leader has their SIS, CRM, LMS & ERP but the provost has been eating last for decades?Why does the anecdote trap hurt higher ed when provosts can tell you about one great student but can't answer how many graduates from this program are now employed?What makes storytelling at scale impossible without clean, connected data when the honest answer to ROI of a political science degree right now may be we don't know?Listen in to #EdUpThank YOU so much for tuning in. Join us on the next episode for YOUR time to EdUp!Connect with YOUR EdUp Team - ⁠⁠⁠⁠⁠ ⁠⁠⁠⁠Elvin Freytes⁠⁠⁠⁠⁠⁠⁠⁠⁠ & ⁠⁠⁠⁠⁠⁠⁠⁠⁠ Dr. Joe Sallustio⁠⁠⁠⁠● Join YOUR EdUp community at The EdUp ExperienceWe make education YOUR business!P.S. Want to access to EdUp Leadership, the only intelligence platform built exclusively from presidential conversations in higher ed?