Podcasts about LMS

Votre manager donne directement des consignes à votre équipe sans vous prévenir ?Vous découvrez les décisions après tout le monde, les priorités changent sans que vous soyez informé et votre équipe ne sait plus vraiment qui décide ? Ce type de situation est beaucoup plus fréquent qu'on ne le pense. Et contrairement à ce que l'on pourrait croire, le problème n'est pas une question d'ego ou de reconnaissance. Le véritable risque, c'est la confusion que cela crée dans le fonctionnement de l'équipe.Alors dans cet épisode, je vous partage une méthode concrète pour réagir lorsque votre manager vous "bypass", sans rentrer dans un rapport de force. Au programme :

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.

On parle beaucoup de management d'équipe mais beaucoup moins de savoir manager son propre manager.Quand les attentes sont floues, que les priorités changent sans arrêt ou que les arbitrages tardent, c'est souvent toute l'équipe qui en subit les conséquences. Pourtant, il existe des leviers simples pour construire une relation plus saine, plus claire et plus efficace avec son manager.Alors voici 5 repères concrets pour mieux travailler avec son manager :

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.

Faut-il tout dire à son équipe quand on est manager ?C'est une question qui revient souvent et il n'existe pas de réponse toute faite. Entre une réorganisation qui se prépare, un recrutement gelé ou des objectifs qui risquent de ne pas être atteints, comment communiquer sans créer de l'inquiétude ? Et à l'inverse, est-ce qu'en gardant le silence, on ne laisse pas simplement la place aux rumeurs ?Au programme de cet épisode :

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

Ils sont passés de 25 à plus de 100 collaborateurs en quelques années. De 5 à 40 millions d'euros de chiffre d'affaires. Et tout ça, en conservant une culture d'entreprise extrêmement forte.

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

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. 

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

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

What happens when the law meets a general-purpose cultural machine? In this episode, hosts Matteo Iuorio and Sofia Debernardi sit down with intellectual property expert Professor Giancarlo Frosio to unpack the massive legal battleground surrounding generative AI. We start with the immediate legal technicalities—separating the liability of tech companies training models from the liability of users prompting them—before sliding into the gripping, high-stakes philosophical landscape of what happens to human labor, law, and purpose as we race toward Artificial General Intelligence (AGI) and superintelligence. Key Takeaways The Two Legal Battlegrounds:Copyright issues with AI are split into two distinct phases: theTraining Stage(ingesting data to extract patterns) and theOutput Stage(whether an AI-generated result is "substantially similar" to a protected work).Strict Liability & The Neutral Tool Dilemma:Copyright is a strict liability offense. Professor Frosio shares his perspective that AI labs are placing "neutral, general-purpose tools" on the market. Therefore, legal liability for an infringing output should ideally sit with the user prompting it—provided the developer implemented standard safeguards.The Geopolitical AI Arms Race:Stricter text and data-mining copyright regulations in regions like Europe can function as a bottleneck for local tech development, inadvertently pushing the dominance of the AI "arms race" exclusively toward the US and China.The Looming Threat to Purpose:As the operational capabilities of AI shift from narrow tasks to holistic human replication (AGI) and beyond (superintelligence), society faces a massive conundrum: if artificial entities can outperform human intellectual labor completely, what is left for humanity's sense of purpose? Terminology Glossary LLM (Large Language Model): Note: Mentioned contextually as "LMS" during the interview recording. These are AI programs trained on vast amounts of text data to understand, summarize, generate, and predict new content. Substantial Similarity: A fundamental legal doctrine used by courts to determine if an unauthorized reproduction has taken too much protectable expression from an original copyrighted work. AGI vs. Superintelligence: Narrow AI handles specific single tasks. Artificial General Intelligence (AGI) can holistically apply knowledge to any task like a human. Superintelligence refers to a theoretical future entity whose collective intellect far surpasses the capacity of the human brain. References & Links to Explore Learn more about Professor Frosio's work and research at theGlobal Intellectual Property and Technology Centre (GIP Tech).Check out the landmark pending litigation referenced in the episode:Getty Images v. Stability AIin the UK.Learn about the European Union's framework discussed by reading the official documentation on theEU Artificial Intelligence Act (AI Act).To explore the philosophical warnings mentioned by the "Godfather of AI" Geoffrey Hinton on AGI and systemic alignment risks, check out hisNobel Prize lecturesand recent AI safety advocacy.Read up on the historic sci-fi themes referenced at the end of the episode via Isaac Asimov's classicFoundation Series.

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

Recorded May 8, 2026 This week on Off the Rails, the gang celebrates finals week the only way higher ed AV/IT knows how: by watching a major cloud platform wobble and quietly whispering sweet nothings to the on-prem rack. The Instructure Canvas mess kicks off a discussion about cloud dependency, LMS integrations, sketchy APIs, cyber insurance, and the comforting lie that "hosted" means "not our problem." Then it's on to upcoming NWMET and InfoComm sessions about building local AI tools for AV design, documentation, math, and workflows… because sometimes the best cloud strategy is "don't." The main topic tackles higher ed media production studios, virtual production spaces, visualization labs, esports rooms, and all the other shiny innovation boxes campuses love to build before remembering someone has to staff, fund, maintain, and explain them. The crew digs into automation, realistic expectations, revenue potential, student involvement, and why your $3,000 LED volume is adorable. Finally, a listener's question about HyFlex classroom audio leads to ceiling mics, Catchbox, lectern mics, Dante, and the timeless truth that microphones are happiest when they're disappointing someone.   News story: https://www.kcur.org/education/2026-05-07/hackers-hit-university-of-missouri-system-and-9-000-other-canvas-schools   AVSF Presentations: NWMET Conference: https://www.nwmet.org "Weaponizing AI for AV and IT Design" Weds May 20, 100p InfoComm: https://www.infocommshow.org "From Prompt to Project: Applying AI to Higher Ed AV Design" Thurs June 17, 1000a   RDL Dante Headphone Amplifier: https://rdlnet.com/product/av-nh1/   Alternate show titles: It's just text / and 9,000 other schools!  Find me every vulnerability This is real; it could happen It's a really cool concept There's so much leakage that can happen What is happening in North Carolina? There's a few variants of this strain that goes around We call that Bro-Jo We gotta fill this hole The secret word is… Public-Private Partnerships The bottom people get it shoved in their face We're not building these spaces for good enough Magic with a Pac-Man button Braggart…    We stream live every Friday at about 315p Eastern/1215p Pacific and you can listen to everything we record over at AVSuperFriends.com    ▀▄▀▄▀ CONTACT LINKS ▀▄▀▄▀ ► Website: https://www.avsuperfriends.com ► Twitter: https://twitter.com/avsuperfriends ► LinkedIn: https://www.linkedin.com/company/avsuperfriends ► YouTube: https://www.youtube.com/@avsuperfriends ► Bluesky: https://bsky.app/profile/avsuperfriends.bsky.social ► Email: mailbag@avsuperfriends.com ► RSS: https://avsuperfriends.libsyn.com/rss   Donate to AVSF: https://www.avsuperfriends.com/support

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/

Peter and Eden kick off a chaotic week — Eden's dealing with the Shiny Hunters ransomware attack on Canvas (the university LMS that runs basically everything, currently being held hostage for the second time) while Peter is just weary from step counts. The bulk of the episode is a genre-spanning music deep dive: Eden assigns four critic-darling albums neither of them would normally reach for (Robyn, Ella Langley, Wendy Eisenberg, and Mandy Indiana), Peter assigns one desert-island pick Eden hasn't heard yet. Between the new releases, a Diablo 4 expansion, Cobalt lore, and the Dungeon Crawler Carl comic selling out on Free Comic Book Day, it's a very full episode.SHOW NOTESCanvas Ransomware Crisis — Eden, who works in university IT, breaks down the Shiny Hunters attack on Canvas, the dominant learning management system used by ~54% of schools. The attackers took the platform down twice, demanded ransom, and threatened to release data from 9,000+ schools by May 12th. Eden spent Free Comic Book Day week in Zoom calls, prepping faculty for a likely third outage.New Metal Releases — Peter covers recent drops: new Sevendust (pretty okay, Lajon Witherspoon sounds great), Draconian's Insomnolent Ruin (gothic death-doom, better than their 2020 album), and a Testament remaster of Practice What You Preach — which apparently had notoriously bad 80s mastering on every prior version.What Else Peter's Been Into — Currently watching The Good Place (season two, laughing out loud), reading the new MurderBot novella System Collapse (more existential ennui, building toward a Preservation vs. Barishastranza showdown), and very much hooked on Vampire Crawlers, a $10 roguelike deck-builder with a dungeon crawl structure that he calls at least as good as Slay the Spire.Free Comic Book Day at Eden's Shop — The comic shop where Eden works had its best day ever — beating last year's record by ~$3K. The Dungeon Crawler Carl issue zero sold out by 11:15 AM and was flipping on eBay for $30+. Eden's boss is now planning to order ten copies of the forthcoming OGN.Eden's Media Check-In — Went back to Wuthering Waves (best combat of any free-to-play open world; Cyberpunk Edgerunners crossover incoming), read They Were Eleven by Moto Hagio (70s shoujo sci-fi, recently translated, thoroughly recommended), and briefly installed/uninstalled Neverness to Everness after the devs were caught using AI-generated assets and their "replacement" assets were also AI-generated.The Music Listening Project — Robyn, Sexistential — Eden's clear favorite of the four assigned albums. Robyn's first album in eight years sounds like Body Talk Part 4, which is exactly what she apparently aimed for. Both hosts agree it goes down smooth and does exactly what dance-pop is supposed to do. Peter's pick of the bunch.Ella Langley, Dandelion — Peter's least favorite ("I fucking hated every note on this shitty ass shit album"), not softened much by the 19-song runtime. Eden also wanted to like it more than they did. Peter's wife, who has a master's in vocal performance, concurred on the voice. Both prefer Kacey Musgraves's Middle of Nowhere, which dropped right after Eden finalized the listening list.Wendy Eisenberg, self-titled — A folk/chamber-folk record Eden found genuinely enjoyable, especially in quieter guitar-forward moments. Peter couldn't get past what he describes as chronically unsupported vocals (no diaphragm engagement). Mid-episode, Eden Googles and discovers Wendy uses they/them pronouns — quick correction mid-stream.Mandy Indiana, URGH — Noise rock with French lyrics; alienating by design, and for once that assessment is meant charitably. Peter could see putting it on if he just wants sound, not music. Eden started strong but felt bludgeoned by the end. Album art apparently smears skulls and faces across the screen in real time — which tracks.Cobalt, Slow Forever (2016) — Peter's desert island pick, his most-listened album of the last two years. Eden had never heard it and came away genuinely impressed. Peter gives a brief history: Cobalt's Gin (2009) as foundational American black metal, the band's turmoil around the previous vocalist's behavior, Charlie Fell (of Lord Mantis) stepping in, Eric Wunder doing all instruments himself, and the resulting pivot from black metal to progressive sludge with blackened overtones. Peter closes with a passage from "King Rust." Eric Wunder passed away earlier this year — Slow Forever as a final statement.

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

Your organisation has probably spent years building a learning library. Courses, videos, SCORM files, PDFs — hundreds of them, living in the LMS or scattered across SharePoint. You can enrol in them. You can sit through them. What you can't do is ask them a question and get an answer in seconds, at the moment you actually need one. The knowledge is there. It just isn't retrievable. That's the problem Mike Alcock, founder of Talvi, has set out to solve. In this episode, Mike takes John through how Talvi works. They also cover Mike's own unlikely route into learntech: a Civil Engineering degree at Sheffield, a detour through an insulation factory in Newcastle, and three successive software businesses each arriving ahead of the market. And they have a searching conversation about what tools like Talvi mean for the LMS and for the instructional designer — neither of whom emerges entirely unscathed. Is the technology now genuinely good enough to make learning in the flow of work a practical reality, rather than a conference agenda perennial?.   TIMESTAMPS 00:00 - Start 02:14 - Intro 04:15 - What is Talvi for? 16:20 - What's the journey for a learning leader adopting Talvi? 20:47 - Mike's story: from civil engineering to learntech 30:19 - What will tools like Talvi do to the LMS? 39:50 - Explanation of terms: RAG, vector databases… 49:01 - End   CONNECT WITH LEARNING HACK LinkedIn: linkedin.com/in/johnhelmer X: @johnhelmer Threads: @jphelmer Bluesky: @johnhelmer.bsky.social Website: learninghackpodcast.com

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?

Today, we check in a year after the first Unsupervised Learning x Latent Space Crossover special to discuss everything that has changed (there is a lot) in the world of AI. This episode was recorded just after AIE Europe, but before the Cursor-xAI deal.Unsupervised Learning is a podcast that interviews the sharpest minds in AI about what's real today, what will be real in the future and what it means for businesses and the world - helping builders, researchers and founders deconstruct and understand the biggest breakthroughs.Thanks to Jacob and the UL production team for hosting and editing this!Jacob Effron* LinkedIn: https://www.linkedin.com/in/jacobeffron/* X: https://x.com/jacobeffronFull Episode on Their YouTubeWe discuss:* swyx's view from the center of the AI engineering zeitgeist: OpenClaw, harness engineering, context engineering, evals, observability, GPUs, multimodality, and why conference tracks now reveal what matters most in AI* Whether AI infrastructure has finally stabilized: why “skills” may be the minimal viable packaging format for agents, why infra companies have had to reinvent themselves every year, and why application companies have had an easier time surviving model volatility* The vertical vs. horizontal AI startup debate: why application companies can act as the outsourced AI team for enterprises, why some horizontal companies still matter, and why sandboxes may be the clearest reinvention of classic cloud infrastructure for the AI era* The “agent lab” playbook: starting with frontier models, specializing for your domain, then training your own models once you have enough data, workload, and user behavior to justify the cost and latency savings* Why domain-specific model training is real, not just marketing: how companies like Cursor and Cognition can get users to choose their in-house models, and why search, domain specialization, and distillation are becoming more important* Open models, custom chips, and alternative inference infrastructure: why swyx has turned more bullish on open source, why non-NVIDIA hardware is suddenly getting real attention, and why every 10x speedup can unlock new product experiences* What it means to sell to agents instead of humans: why agent experience may mostly just be good developer experience by another name, why APIs and docs matter more than ever, and how pretraining-data incumbents are compounding advantages in an agent-first world* Why memory and personalization may become the next big wedge: today's models mostly reward frequency of mentions, but in the future, swyx expects product choice to be shaped much more by personalized memory systems* The state of the AI coding wars: why coding has become one of the largest and fastest-growing categories in AI, how Anthropic, OpenAI, Cursor, and Cognition have all ridden the wave, and why the category may still have more room to run* Capability exploration vs. efficiency: why the industry is still in a token-maxing, experiment-heavy phase where people are rewarded for spending more rather than less* Claude Code vs. Codex and the strange stickiness of coding products: why first magical product experiences may matter more than expected, and why the bigger mystery may be why only a few names have emerged as real winners so far* What the end state of the coding market might look like: two major players, a longer tail of niche products, and possible disruption if Microsoft, Mistral, xAI, or the Chinese labs push harder into coding* Where application companies still have room against the labs: why frontier labs are trying to expand into verticals like finance and healthcare, but still leave space for focused companies that own the workflow and the last mile* Why coding may be a preview of every other AI market: the first category to truly go parabolic, the clearest example of foundation model companies colliding with application companies, and a template for how future vertical AI markets may develop* Why AI valuations now feel unbounded: from billion-dollar ARR products built in a year to trillion-dollar market caps, swyx and Jacob unpack how the AI market has broken traditional startup intuitions about scale and durability* Consumer AI vs. coding AI: why ChatGPT's consumer category may have plateaued on frequency and product design, while coding continues to feel like a daily-use category with real momentum* The next product frontier beyond coding: consumer agents, computer use, and “coding agents breaking containment,” with swyx's thesis that 2025 was the year of coding agents and 2026 may be the year they begin to do everything else* Whether foundation models are really killing startup categories: why swyx is less worried for early founders, more worried for mid-size startups and traditional SaaS, and why building something ambitious may now be the best job interview for a frontier lab* AI vs. SaaS and the internal culture war around adoption: the tension between AI-native employees who want to rip out expensive software and skeptics who think quick AI-built replacements create fragile systems* Why traditional SaaS may be under real pressure: swyx's own experience spending six figures on event and sponsor management software, the temptation to rebuild it cheaply with AI, and the broader question of whether teams will trust custom AI-native replacements* Biosafety, security, and frontier model access: why swyx raised biosafety at a dinner with Anthropic's Mike Krieger, why Krieger argued security is the bigger issue, and what restricted model releases reveal about Anthropic vs. OpenAI* The era of giant models: why 10T+ parameter systems may only be a temporary rationing phase before bigger clusters arrive, why labs may increasingly keep their most powerful models private for distillation, and why scale alone no longer feels like a complete answer* Memory as the slowest scaling factor in AI: why context windows have improved far more slowly than people hoped, why million-token context still has not changed most real workflows, and why memory may be the key bottleneck for the next generation of systems* What swyx changed his mind on in the past year: becoming more bullish on open models, more convinced that the top tier of agent startups behaves very differently from the median AI company, and more optimistic about fine-tuning and specialized model adaptation* “Dark factories” and zero-human-review coding: the next frontier after zero human-written code, where models not only write the code but ship it without human review, forcing companies to rethink testing and verification from first principles* Why RL and post-training may matter more than people assumed: even if the resulting models get thrown out every few months, the data, workflows, and domain-specific improvements persist* Synthetic rubrics, Doctor GRPO, and multi-turn RL: why reinforcement learning is becoming much more domain-specific and multi-step than many people realize, opening the door to much deeper customization* The next frontier after coding: memory, personalization, and world models, including why swyx thinks world models matter not just for robotics or gaming, but for giving AI something closer to lived understanding* Fei-Fei Li, spatial intelligence, and the Good Will Hunting analogy: the idea that today's LLMs may know everything by reading it all, but still lack the lived experience that turns knowledge into a deeper kind of intelligenceTimestamps* 00:00:00 Intro preview: AI coding wars, startup pressure, and market structure* 00:00:28 Welcome to the Latent Space × Unsupervised Learning crossover* 00:01:17 What AI builders are focused on now: OpenClaw, harnesses, and infra* 00:04:33 Why AI infra is harder than apps, and where startups can still win* 00:06:39 Should companies train their own models?* 00:09:28 Open models, custom chips, and the new inference race* 00:11:25 Designing products for agents, not just humans* 00:16:49 The state of the AI coding wars in 2026* 00:19:27 Capability exploration, token-maxing, and why coding is going parabolic* 00:21:41 What the end state of the coding market could look like* 00:23:50 Where app companies still have room against the labs* 00:27:02 Why AI valuations and market swings feel unprecedented* 00:28:56 Consumer AI vs. coding AI, and why sticky products still matter* 00:32:28 What the next breakthrough product experience might be* 00:32:53 2026 thesis: coding agents break containment and eat the world* 00:35:27 Are foundation models wiping out startup categories?* 00:37:33 AI vs. SaaS, vibe coding, and internal team tensions* 00:40:01 Biosafety, security, and the politics of restricted model releases* 00:42:19 Giant models, compute constraints, and the limits of scale* 00:44:30 Memory as the real bottleneck in AI* 00:44:57 Why swyx changed his mind on open models* 00:47:44 Dark factories and the future of zero-human-review coding* 00:49:36 Why post-training and RL may matter more than people think* 00:51:50 Memory, world models, and the next frontier of intelligence* 00:53:54 The Good Will Hunting analogy for LLMs* 00:54:21 OutroTranscript[00:00:00] swyx: Isn't that crazy? That number is just mind boggling.[00:00:03] Jacob Effron: What is the state of the AI coding wars today?[00:00:05] swyx: We're in a phase of sort of like capability exploration. The general thesis that I have been pursuing now is that the same way that 2025 was a year coding agents 2026 is coding agents breaking containments to do everything else.[00:00:16] Jacob Effron: Do you worry about the foundation models just getting into a bunch of these startup categories?[00:00:21] swyx: Mid-size startups. Yes.[00:00:23] Jacob Effron: What do you think the end state of this market is[00:00:25] swyx: for the market structure to, to significantly change? There would be[00:00:28] Jacob Effron: today on unsupervised learning. We had a, a fun episode and what's really become an annual tradition, a crossover episode with our friends at Latent space.Swix and I sat down and we talked about everything happening in the AI ecosystem today. What we thought of the various changes at the model layer, what's happening in the infra world, the coding wars, and a bunch of other things. It's a ton of fun to do this with someone I really respect and another great podcaster in the game.Without further ado, here's our episode. Well switch. This is, uh, super fun to be back with another unsupervised learning, uh, latent space crossover episode.[00:01:02] swyx: Yeah,[00:01:02] Jacob Effron: I feel like a lot of places we could start, but you know, one thing I always find fascinating, uh, about the way you spend your time is you obviously are like at the epicenter of this engineering movement and community, and you run these events and conferences and put on these.Awesome talks and, and I think just have a great pulse on the zeitgeist of what's going on.[00:01:16] swyx: Yeah.[00:01:17] Jacob Effron: Maybe to, to start just what are the biggest topics people are thinking about right now?[00:01:21] swyx: Yeah, so I just came back from London, uh, where we did a IE Europe and we're doing roughly one per quarter now, which Yeah, you've[00:01:27] Jacob Effron: really up[00:01:27] swyx: the, hopefully[00:01:28] Jacob Effron: up the, up the pace.[00:01:29] swyx: It's trying. We're trying to match AI speed, youknow?[00:01:30] Jacob Effron: Yeah, exactly. The tops would be completely different, I imagine. Uh,[00:01:33] swyx: yeah. You know, I definitely curate the tracks, like you can see what I think. When you see the track list and the, the speakers that I invite, obviously Open Claw is like the story of the last four or five months, and then be, be just below that.I would consider harness engineering, context engineering to be two related topics in agents and rag. And then there's a long tail of Evergreen stuff like evals, observability, GPUs, uh, and uh, LM infra and just general, just in general. We also have other updates on like multimodality and, uh, generative media, let's call it.Um, but I definitely, the, the first three that I mentioned are top of mind people. Yeah.[00:02:13] Jacob Effron: I think harness is particular like, so interesting. Um, you know, there was this tweet from Harrison Chase, the, the lane chain, CEO, that, that caught my eye recently where he said, you know, it finally feels like we have stability, uh, around the infrastructure for, uh, you know, around ai.And I think what. He basically was implying his like, look over the past two, three years as a company at the epicenter of AI infrastructure, it was a bit like playing whack-a-mole, right? You were constantly moving around with, however, the building patterns were evolving[00:02:36] swyx: for Harrison for sure. Right? Like he's basically had to reinvent the company every year since he started Lang Chain.Right? It was Lang chain, Ang graph and LP agents and like, uh, I think he's like one of the most nimble, adept sharp people about this. Yeah. Yeah.[00:02:49] Jacob Effron: Saying now, now is finally the time stability[00:02:51] swyx: this. Yeah.[00:02:52] Jacob Effron: Yeah. Um, do you buy that or what have you kind of make of that take?[00:02:56] swyx: I think that. It, it's very expensive to say this Time is different sometimes, but when you're just writing code, like it's actually okay to just like try to make a call and I think it may not even matter if this call is right or not.Like I just don't even care that much because you can be right on a thesis, but if you don't, you don't figure out how to monetize the thesis, then who cares if you said something first that said, um, it does feel like, for example. Uh, we went through a lot of different ways of passion packaging integrations up with, uh, with agents.And it feels like we've landed at skills, which is like the minimal viable format. Yeah. Which is just a markdown file, uh, with some scripts attached to it, and I don't see how it can be more simple than that. And so there is some justification for. The stability around harnesses. I feel like there may be more adaptation with regards to maybe like the real time elements or subagents or memory or any of those like agent disciplines, let's call it in, in agent engineering.Uh, but if, if the thesis is that, okay, you just want agents are LMS with tools in the loop with a file system, what they can do. Retrieval with, with skills and all these like standard tooling that now seems to be relatively consensus then probably. That makes sense. Um, I just think like there's no point trying to stake your reputation on this thesis that we're there because if it changes again, just change with it.It's fine.[00:04:33] Jacob Effron: Yeah. It's always, you know, I've always been struck by how that is. Much more challenging for infrastructure companies and application companies. Like obviously I think, yeah. You know, on the application side you've seen, you know, Brett Taylor from Sierra Max, from Lara. Like, they're like, look, we build, you know, what's ahead of the models and we're willing to throw everything out every three months, you know, as the models get better and better.Exactly. Yeah. But the thing you at least have there is you have. Uh, you have an end customer, right? That's like decently sticky. Um, you know, they will mostly stick, you know, they'll, they'll give you a shot at least of, of building these things. What I've always found more challenging, uh, at, at the kind of like, you know, reinvent yourself every three months of the infrastructure layer, it's like, you know, developers are definitely a, a pickier audience maybe than an accounting firm or, uh, you know, a bank.Yeah. And so it's definitely a, a, a more challenging position to be in to, to have to constantly reinvent yourself.[00:05:17] swyx: Yeah. Yeah. Yeah. And, and like when they turn, it's like. Very complete. Like, they'll leave to like the, the hot new thing, uh, because there's like no defensibility, I guess. Like e even, even if you are a database, like, uh, people can migrate workloads off databases.Like it's, it's a, it's a known thing. Uh, so I think like basically what we're talking about is the vertical versus horizontal, uh, debate in, in AI startups. And uh, the way I think about it also is just that like when you are. Um, Lara, when you are a bridge, like you are the outsource AI team, right? You, you are, your job is to apply whatever state ofthe art AI methods.[00:05:55] Jacob Effron: Yeah. Like this translation layer between model capabilities and your[00:05:57] swyx: own customers. Yeah. To, to the end customers and like, well, if they didn't have you, they would've to hire in house and they're not gonna hire in house so they have you. And like, I think that's like a reasonable, like very robust to any whatever trends and, and discoveries that people make in, in the engineering layer.I do think like there is, um. It like sort of useful horizontal companies being built, but they're all. Very much like, sort of like the reinventions of classic cloud in the AI era and the, the primary one being sandboxes. Yeah. Um, which like, it's another form of compute guys, like, let's not get too excited about it.But I mean, like the, the workloads are enormous.[00:06:38] Jacob Effron: Right.[00:06:38] swyx: Yeah.[00:06:39] Jacob Effron: It's interesting, and I feel like as, as part of this, you know, the questions that folks are asking around infrastructure, there's a lot around, you know, the extent to which companies should have their own AI teams and what they should be doing in-house.And, you know, uh, I think there's questions around should people be training their own models? Should people be doing, you know, rl, uh, in-house based on the data they have? I feel like, you know, one has to evolve their takes on this every, every three months with paces. But where, where are you at on this today?[00:07:00] swyx: I think, well, I mean actually all models have gone up. Um, and obviously I'm involved in cognition and also cursors doing, doing, uh, a lot of own model training. And I think that that is some part of the, what I've been calling the agent lab playbook, where you start off with the state of the art models from, uh, from the big labs and you, uh, specialize for your domain.But once you have enough workload and enough high quality data from your users, then you can obviously train your own models and like save a lot on cost and latency and all that, all that good stuff. Um, you also get like a marketing bonus of like calling it some fancy name and putting out some research[00:07:38] Jacob Effron: from my seat.I can't tell how much of it is like actual, you know, value that's provided to the end user. And how much of it is that marketing bonus? Right. It seems some combination of the[00:07:45] swyx: I think it's both.[00:07:46] Jacob Effron: Yeah.[00:07:46] swyx: Um, no, no. There, there actually is real value. Um, and you, you know that for a number of reasons. Like one, even when it's not subsidized, people do choose it as like one of the top four or five.This is both composer two and, uh, suite 1.6 I one of the top five models. Like in a, in a fair market? In a free market, yeah. In a, in a, in a model switch. Or people do choose it and like, it's not subsidized. Like, so that's as good as it gets. Uh, but beyond that, like domain specific models, for example. For search with, with both, which both companies have absolutely makes, makes a ton of sense.Everyone says like, yeah, we should always, always do this. And honestly like, I think the infrastructure for that is becoming easier with, um, like thinking machines tinker thing as well as primary like, uh, lab stuff. Yeah, I mean like, this is one of those like reversal of the, the bitter lesson where you first bootstrap on the large models and the general purpose models to get big.And as you get very well-defined workloads that are just high quantity but not high variance, um, then you just distill down to a smaller model and run that on your own. Right. Which like totally makes sense.[00:08:50] Jacob Effron: What I'm less clear on is the kind of DIY RL use case, which I think is really mostly around, you know, improved, uh, quality for, for different things.Obviously there's probably like more efficient ways to, you know, get a smaller model that's that's faster and cheaper. And it'll be interesting to see whether. You know, obviously you had, you know, uh, two, three years ago this whole case of companies that were, you know, pre-training and claiming better outcomes in, in their domains than getting kind of cooked as each model iteration improved.You know, I wonder whether that's a, a similar story plays out in the, uh, in, in the, our all space. Yeah, for the focus on, on on pure outcomes and quality, not the cost side, which clearly your own models for cost at scale makes a ton of sense.[00:09:28] swyx: I think there are this, there are two sides of the same coin.Like you basically always want to hold, uh, quality constant or trade off a little bit of quality for a drastic decreasing cost. And that's true for everyone. Uh, one element I wanted to bring out, which is very much in favor of open models, is custom chips. So this would be cereus, but also talu. And then there's a huge range of stuff in between.This has been a huge story this past year on just like everything non Nvidia is getting bid up, including like freaking MatX is working for, which is very, which is very rewarding for me, but I think one of those things where like, oh, like the suddenly, because the number of alternative. Hard, uh, hardware is increasing and the inference that you can get is insanely high.Like, um, we're talking thousands of tokens per second instead of less than a hundred. So the trade off for qua quality doesn't hold as much anymore because the speed is so high.[00:10:24] Jacob Effron: Have you seen a lot of companies go all in on the alternative chip?[00:10:26] swyx: So cognition has Yeah. On Cerebras, uh, and, and so has OpenAIUm, uh, and so no, I don't think so beyond that, uh, and that, do you think that's like a, that's mostly, that's foreshadowing of, that's, yeah. I used to be kind of a skeptic in terms of like, okay, so what if I get my inference at a hundred to a hundred tokens per second sped up to 200 tokens per second. It's only two X faster.It's not that big a deal. Um, but when you, uh, I think every 10 x does unlock a different usage pattern. Um, and you, we have proof in Talas and, and some of the others. That you can actually, um, drastically imp improve inference speed and what happens from there? I don't even really know, like it's, it's so hard to predict when entire applications just appear at once.Yeah. Uh, and it also isn't that expensive, right? So like, um, this is one of those things where like, I, I think the, the investment cycle is gonna be multi-year. Um, and I. Would caution people to not dismiss it too, too quickly.[00:11:25] Jacob Effron: Yeah. I mean, one other like infra question I was curious to get your thoughts on is obviously it seems increasingly a lot of the cutting edge infra companies are building for agents as the buyers of their product or users of their product, right?[00:11:35] swyx: Ooh,[00:11:36] Jacob Effron: and[00:11:37] swyx: another huge theme. Yeah. Yeah.[00:11:38] Jacob Effron: And I'm trying to figure out like what. What, what do you have to do differently about selling into agents? Um, are they just the ultimate rational developers? Uh, or is there, you know,[00:11:46] swyx: no, absolutely not. Um, I think they are easily prompt, injected and, uh, very tuned towards like, basically com compounding existing winners.[00:11:57] Jacob Effron: Yeah,[00:11:57] swyx: so like if, like, congrats if you won the lottery for getting into the training data right before 2023, because now you're like installed in there for the foreseeable future. But yeah. Uh, you know, one stat that Versal, uh, CTO Malta dropped at my conference was that there are now, uh, 60% of traffic to Elle's, um, like app arch, like admin app architecture for like configuring versal applications, uh, is bought.It's not, it's not human. Uh, so like your primary customer is agents now. Um, and it's mostly co like mostly coding agents, mostly people using CLI on CP or whatever. But yeah, I mean, I think. More. I, I think step one, if it doesn't exist as an API that agents can use, it doesn't exist. Right, right. Which I think is like, uh, it's a good hygiene thing anyway, to, to make everything API available, but not as like an extra, um.Push on like products, people to not only work on the ui, um, you should probably work on the on SCLI stuff. Beyond that, I think honestly there is like, so I, I come from the sensibility of, I think everything that you are trying to do for agents experience now, which is the term that Matt Bowman and Nullify is trying to coin, is the same thing that you should have been doing for developer experience.That you should have had good docs, you should have had a consistent API, uh, that is. Mostly stateless. Um, you should have, I guess, discoverable or progressive disclosure or like search or like whatever. And so now that people have energy in like finding these customers to do that, that's great. Um, do I believe in.Extending beyond that into something like a EO, um, for gaming The chatbots? Not necessarily, but obviously there's gonna be huge advantages when people who figure out the short term wins. Yeah. And short term wins can compound.[00:13:43] Jacob Effron: Do you think these compounding advantages to like the, the pre-training data cutoff companies, like, you know, obviously over some period of time, I imagine that doesn't persist.And so as you think about like. I dunno, three, four years from now what the, you know, selection criteria end up being. Do you think it still mirrors exactly what you were saying before? Like it's exactly what you should have been doing all along to sell a good product to developers?[00:14:01] swyx: It could be, except that I think in three, four years we'll probably have much better memory and personalization.So then general a EO or GEO doesn't really matter as much. So I think whatever memory or personalization system we end up with will probably d determine what you end up choosing much more. Than, than what is currently the case, which is just frequency of mentions, let's call it. Yeah,[00:14:26] Jacob Effron: yeah.[00:14:26] swyx: Uh, so you just spa quantity and I think that's, I mean, that's something I'm looking forward to.I do think, like, like, you know, I, I think that the fundamental exercise to work through for yourself is if you start a new, um, sort of. Uh, disruptor company. Now there's a, there's a big incumbent that everyone knows, like, like superb base. Super base is like, kind of like the Postgres, like database, uh, incumbent.If you wanna start like new superb base, how would you compete with them? And I don't necessarily have the answer, but I, I, I do think like people, like resend like relatively new. I think they would start like 20, 23 and still there was, there was a recent survey where like, people. Checked what Claude recommends by default.If you just don't prompt it with anything, just say, gimme an email provider and says, resent as in like 70, 70% of each cases. Like the fact that you can get in there with like such a relatively short existence, I think is, is encouraging.[00:15:14] Jacob Effron: Yeah.[00:15:14] swyx: I do think like. Um, you do want to do whatever it is to, to like to, to get in that Very short mentions this because, um, it's not gonna be 20 of them, it's gonna be like three.[00:15:26] Jacob Effron: No, definitely. It feels like, uh, you know, probably more, more consolidation than ever. Uh, or, or kind of like, you know, uh, a winner take most market than maybe the, the, the physics of go-to market in the past. Yeah. Might have, uh, enabled.[00:15:38] swyx: The other thing also is like, semantic association is gonna be very important, uh, in the sense that like, you want to do like the combo articles where you're like, use my thing with for sale, with blah, blah.And like that all gets picked up in a, in a corpus. And so that's. Probably one thing that you, you wanna do? Well, I don't know what else. Uh, it's, it's, it's, it's one of those things where like, I think I feel, I feel I'm behind, uh, I don't know how you feel about this, but like,[00:16:04] Jacob Effron: I think AI is just everyone constantly feeling like they're behind some, uh,[00:16:08] swyx: yeah.With,[00:16:09] Jacob Effron: I wanna meet the person that doesn't feel behind,[00:16:11] swyx: but like with, with ax, right? Like, so, so like, my, my stance was that exactly what I said before, like everything that you, that you should do for agents is something that you should have done for humans anyway. Yeah. And so. To the extent that you're just getting it more energy to, to do things for agents, great.But like, uh, it's hard to articulate what new thing apart from just like more spam, um, that you should be doing. Anyway, that would be my take right now. Um, I I, I do think like there, there will be more turns at this. I think the personalization turn that is coming, um, will be big. And I don't know what that looks like because like basically we're kind of, we feel kind of tapped out on the memory side of things.[00:16:49] Jacob Effron: Yeah. I, I guess since we last chatted, you know, you, you took this role over at cognition, um, and you've obviously have a, have a front row seat to the AI coding space today. You know, I feel like coding in many ways. You know, people view it as this, like, I mean, besides being like the, the mother of all markets and this massive opportunity, I think it's kinda a preview of like, what's to come for many other spaces.Both. Yeah. You know, I feel like agents are most advanced in coding. I also feel like the, you know, competition between foundation models and application companies, you know, and, uh, mirrors what we may see in other spaces. And so maybe for our listeners, can you just lay out like what is the state of the AI coding wars today?[00:17:25] swyx: Um, it is massive, right? Like, uh, and I don't think necessarily, last time we talked about this, we appreciated the size of what[00:17:32] Jacob Effron: No, I wish we did.[00:17:33] swyx: I state of AI coding wars today, um, both opening eye philanthropic have made it their p serials to competing coding. Um, and. Tropic is like 2.5 billion in a RR just from Cloud Code.The way they recognize a RR is. Opt for debate, uh, open ai. I don't think the, a public number is known, but let's call it 2 billion as well. And then cursor is like, rumored to be 2 billion, you know? And, and those, those are like the public numbers that are known? Yeah. Um, so like huge markets that have just been created in the past one year.Like, like anthropic, just like Claude Code just recently celebrated their one year anniversary, which is, yeah, pretty nice. Um, so, and then I think, like the other thing that I see is there's, there's some other people who are like, oh, here's like the, the sort of relative penetration of, uh, Claude use cases, right?Like, and it's like coding 50% and then legal, whatever. Health, uh, it's like the, the remaining ones. And there was a very popular tweet that was like, okay, I'll look at the, the empty space and all these other use cases. If you are a new founder today, you should be betting on the other stuff because on, on a sort of catch up Yeah.Theory and my. Consider my, my pushback is the same pushback that, uh, I had on app over Google, which is like, well, well why is this time different? Like, why, if it went from let's say 10 to 50% in the past year, why can't I keep going? Uh, and like getting that wrong is actually a very painful one because you could have just did, did the momentum bet.Instead of the mean reversion bed. So I, I, I think that that is the, the state of things now that people are very, very much into psychosis. Um, they're are getting rewarded for spending more rather than spending less. And I think we're not in that phase of efficiency. We're in a phase of sort of like capability exploration.So I think people who are more crazy, who are more. Uh, creative, um, get rewarded comparatively. Yeah.[00:19:27] Jacob Effron: Well, it's interesting. I mean, it feels like behind these like token maxing, leaderboards and whatnot is this, it's like the first phase of this transition from a workforce perspective is you just gotta show your employer like, Hey, I, I use these tools.[00:19:37] swyx: Here's my nu number of tokens I cost, and that's it. They don't care about the quality. Right. It is, uh, maybe distasteful to someone who cares about the craft and, and all that. Um, but directionally everyone just wants you to go up regardless. And so, um, there it is not very discerning. It's, and it's probably very sloppy, but I think it's net fine because we're still probably underusing ai just in generally.Yeah. Um, and so I think that's like very interesting. Like we had on the podcast, uh, Ryan La Poplar from OBI, who spends a billion tokens a day. Yeah. Um, and that's for those county home, it's like something like 10,000 worth, $10,000 worth a day of API tokens. If they, they did market rates, um, and like most of us can't afford that.Yeah. But like. And, and, and probably a lot of what he does is slop.[00:20:25] Jacob Effron: Right.[00:20:25] swyx: But like, he's going to dis, he's like, if there were a new capability, he would discover it first before you because he was, he was trying and you were not trying. Right. And like, you only do things that work like, well, good for you.But like the, the people who are going to discover the next hot thing are living at the edge.[00:20:42] Jacob Effron: Right and increase in living at the edge of just having the compute budget to like run these experiments. I mean, kind of similar to what living at the edge on the research side has always been. You know, it was constrained in many ways by the amount of compute you had to run these experiments.It feels similarly on the, almost on the builder or like actualizing these tools now.[00:20:56] swyx: Yeah. The other thing that's, I mean, very obvious is philanthropic is kind of like the high price premium player. Um, that where, you know. Restricting limits or restricting model releases even is like the name of the game.Whereas Codex is like, come on in guys, use our SDK, use our login and we don't care. We're gonna reset limits. Whatever you do want to try to exploit the subsidies where you can get it. And definitely Codex is super subsidized right now. Gemini also very subsidized. Um, and. Comparatively, like, I think you should make, Hey, I guess while, while that's going on, it's not that bad to be a capabilities explorer on just the $200 a month plan from Cloud Code or from OpenAI.Um, and, uh, I I, I, my sense is that people aren't even there yet.[00:21:41] Jacob Effron: How do you think this, like, market ultimately plays? I mean, it's obviously such a big market that, you know, any slice of that market is interesting for, for anyone going after it. But I think what, what makes people so interesting in the coding market particularly is it feels like it's kind of this.Foreshadowing of what will happen in other, you know, any other kind of application market that the foundation models eventually turn to and are all their models against and gather data around. And so how do you think, you know, like does there end up being room for lots of different kinds of players or like, what do you think the end state of this market is and is that, do you think that's applicable to other markets?[00:22:10] swyx: I feel like there will be, I mean. Status quo is probably the most likely outcome, which is there are two big players and there's a small range of longer tail people that, um, fit other use cases that the, the two big players don't. That feels right to me. I think that, um, for it to, for the market structure to, to significantly change there would be, there needs to be significant change in like the economics or like the, the brand building or like the, the, the, the value propositions of the, of the companies involved and I.Haven't seen any in the last six months that, that have really changed the stories materially. So I feel like they would just keep going until something, something else happens. Something else happens, meaning like Microsoft wakes up and like goes like. Guys, we have GitHub, we have, uh, you know, we, we, we'll, we'll do something much bigger here than other, other than just copilot.Um, and, uh, that would be a big change. Um, MSL has put out a model now, and I was in a breakfast with, uh, Alex Wang, where they were like, yeah, like, we, we really, really want to go after the coding use case. We haven't done anything yet, but like, don't underestimate them. Right. Um, and, and similarly for the Chinese labs.Um, I think they're trying to go after it. Like ZAI is doing stuff. GLM uh, ZI and GLM is same thing. Um, uh, and, and so it's, so like everyone's trying to get a piece of that pie. I, I feel like the, the status quo has been pretty stable for the past, like almost a year I'll say.[00:23:39] Jacob Effron: Yeah. And is the room for the, not like, you know, for, for the application companies more on like the enterprise side or like where do the, where do the, like what surface area do the model companies leave for application companies?[00:23:50] swyx: Yeah, that's a good one. Um. It's very much evolving. Um, it, I, I, I will say because opening I did not have this, the, this level of attention on coding. Yeah. Uh, a year ago. We just don't have that much history. Right. Um, and it seems like, for example, so the big push at Open I now is the Super app. Um, is that a consumer thing?Is that like a products like. Portfolio rationalization thing, how much is that gonna take away attention from coding at the time when they actually do want to put more coding? I think it's, it's very unclear. So I do think like there's, there's all these, like in both big labs, there's. Uh, sorry. Both of the, and, and drop and, and deep minus and XAI are are separate cases.Um, they are trying to see the other time expansion areas. So cloud code for finance. Yeah. Um, uh, cloud cowork, all those, all those things. Whereas I think cursor and cognition are like comparatively just focused on coding and so I, I do think they leave space and I do think for the other verticals that also means the same thing.Right. That, uh, that they're not gonna be that. Um, intensely focused on, on, on that domain. Except for, I, I think I would mark out finance and healthcare as like the next ones, um, that they're clearly going after. Uh, I, I would say comparatively, healthcare seems more thorny. There, there, there've been some announcements about it, but like, I would respect the, the finance work a lot more just because like the, the path to money is a lot clearer.[00:25:12] Jacob Effron: Yeah, no, I mean, obviously like, I, I think, you know, maybe similar to, to the space that's being left in these other domains, you know, there's obviously. Uh, a lot that's required to actually implement these tools in enterprises, uh, versus, you know, maybe just giving them, uh, giving model access to, to folks outta the box.[00:25:27] swyx: Yeah, yeah. Yeah. So the, the agent lab thing is like, we'll do the last mile for you. Whereas I think the model labs tend to just trust the model and, and be minimalist about it. Both of them work.[00:25:38] Jacob Effron: Yeah.[00:25:38] swyx: I, I don't, I don't necessarily think one, uh, beats the other, uh, for every, for every use case. Um, all I, all I do know is that it does seem like.Uh, the large enterprises do want a dedicated partner that isn't just the model labs, which is kind of interesting.[00:25:55] Jacob Effron: We, we've been in this phase of, of pure capability exploration. And so I think nothing has been, you know, better for the large labs, right? I mean, they're always gonna be, uh, uh, the frontier of, of capability exploration.And so I think have a very good relationship with a lot of these enterprises. But ultimately over time, like. The, uh, the incentive structure of these labs is always gonna be maximal, you know, token consumption for, uh, for the end customers they work with. And there's just, I think, so few companies that have actually gotten to massive scale.Maybe coding again is the most interesting. So it's the first space that really is just completely gone, you know? Yeah. You must love it every day. Like absolutely insane. And. I think it[00:26:32] swyx: gets even. Okay. I mean, like, I think we, we say good things about crystal cognition, but the sheer liftoff of like both end UPIC and open ai.‘cause they, they, they have independent valuations. I mean, let's throw an XEI in there because it's now I ping at 1.2 trillion. That number is just mind boggling. Like I, I feel like in normal investing or normal startups, there's kind of like a ceiling market cap or valuation. Totally. That, that like you, you reach and you go like, all right, let's, it's gonna be chiller from now on.And these guys are not slow down. No.[00:27:02] Jacob Effron: Well, I also think the dynamic is fascinating about some of these later stage companies is, is, you know, in the past, I feel like in, in venture world, if you got to a certain level of scale, the question around you was really more a valuation question. And this is like why there was different phase, like, you know, types of venture people did and like the late stage growth people were just incredible at like, you know, a little bit of what's the ultimate market opportunity of this company, but also what's the right way to, to value it.Like we know it's, it's in some bands of an outcome that is like. Sure there's some variance to it, but it's like relatively understood what that bands is and then maybe you get over time surprised to the upside. Whereas any kind of like later, even the labs themselves, any later stage company, the bands of which that company might be worth right now, even in a year or two years are so massive because of how fast the ecosystem changes that it's like.Even for later stage companies, every three months could be an existential level event to the upside to the downside. Yeah. Um, and I think that, like, you are obviously seeing it in the, in the positive with code, which, you know, if you think about a company like philanthropic, you know, that. For a while, it was like unclear if they were going to have access to enough capital, um, to really stay in the, in the race, right?And then coding hit at the exact right time. They had the perfect model for it. They executed brilliantly. Um, and you know, now are, are, you know, uh, you know, one of the most valuable companies in the world.[00:28:13] swyx: Uh, at the same time, I, I don't find, I, I have zero sympathy for opening eye because they're crushing it and they're all rich.You know, this is like a high class champagne problem to have to, uh, to be number two at coding or whatever. Like, who cares? Like, you're, you're doing great.[00:28:27] Jacob Effron: Yeah. It's funny though. I can't even, I mean, you would be closer to this, uh, you know, even that you're in the AI coding space, but it's like a lot of people I talk to think Codex is just as good, if not better than Claude Code.Right. I think one thing that I've been really surprised by, and maybe, maybe Cloud Code is a better product in some ways, I'm curious your thoughts is just in consumer AI with chat GBT. You saw this big first mover advantage, right? Where admittedly today, like, I don't know, Claude Gemini. Great products.Not sure, not abundantly clear chat GBTs any better, but like. People stick with chat, GBT, it's the first thing to introduce them.[00:28:56] swyx: They stay, but they're not growing anymore. I don't know if you've seen[00:28:59] Jacob Effron: Right. But that to me is more of like a, a, a product problem than it is. They're not like, it's not like they've like lost share to someone else.My understanding is the overall problem with consumer AI today is much more of a how do you take this tool and, you know, for, for folks like us, like knowledge workers, it's like this incredible magic tool, but it's not necessarily a daily active use tool for a lot of people around the world today. And what are the like products?It's, it's kind of a category wide problem. Like in coding, for example, like. The entire space has gone parabolic. There may be some relative growth in, uh, in other consumer AI players, but it's not like consumer AI as a category is like going parabolic and they're not capturing most of that thing. I think it's actually the larger problem is much more, hey, the category has kind of hit a bit of a plateau of people haven't figured out how to bring, you know, tons more users on board.Yeah, yeah. Or increase the frequency of those users. And so it seems more of a category wide problem than it is, you know, a massive market share of change. I was gonna draw the comparison to, to the coding space where Claude Co is the first product, obviously, to introduce people to this magical experience.You know, by all accounts, codex is, is pretty damn close to as good, if not better. Um, but like still that first product, you, you would've thought that would not be a super sticky, uh, you know, product surface area. And it actually has, it turns out, I, it feels like the first lab to introduce you and experience really does, uh, keep a lot of, uh, a lot of the focus.[00:30:12] swyx: I, I think. M maybe it's like still, still early days. You know, Chad, BT is like three plus years old and Yeah. Cloud code is only one. Just turned a year. Yeah. So give it time, you know? Yeah. Like, yeah. I mean, definitely sometimes a lot of people have switched from to Codex. Maybe that will keep going. I, it's like really hard to tell.Uh, yeah. I, I, I do, I do think that. Because we are in this like, high volatility, high temperature phase. Um, the loyalty and stickiness to first movers and category creators, I don't think is as high as it might be in some other, uh, areas in our careers that we've looked at.[00:30:47] Jacob Effron: Yeah. Though, I mean, I've been surprised by the cloud code thing.I, I would've thought that, like, in many ways I always worried about the[00:30:52] swyx: enterprise. You think you would've been gone by now?[00:30:53] Jacob Effron: Not gone. But I would've, I I always worried that the, that the consumer business of these companies would be quite sticky. And then the enterprise API business. Uh, was actually like, you know, in some ways like your least loyal buyers, like they would, they would move to,[00:31:05] swyx: right, right.But, but they worked out that it wasn't the enterprise API it was enterprise product.[00:31:09] Jacob Effron: Totally. And maybe that was the, that was the secret that like, but the amount of lock-in or just default behavior that has happened in that space, uh, is, is more than I might've imagined with two products that by all accounts are pretty damn similar.Yeah.[00:31:22] swyx: No fight there. Uh, I will say I do think that Codex is still in like a catch up. Like in terms of personal experience. Um, the only thing I like out of, out of Codex is the, is like Spark and like yeah. Uh, the, I, I feel like the skills integration is a little bit better. I feel like, uh, the, the speed is a bit better.Maybe ‘cause it's in, is written in rust or whatever. Um, very minor things that you like. Almost like telling yourself rather than like objectively assessing between two, two of them. I, I, I do think, like vibes wise, I think that's going on. Um, the, the, you know, I, I feel like the, the missing questions, uh, in, in this whole debate is like, why is this so concentrated in only two names, right?Yeah. Like, um, how, where, like, where is the Gemini? You know, presence, where's the Xai presence? Um, and like they are trying, it's just they haven't made that much progress yet.[00:32:12] Jacob Effron: But what the, what the Claude Co moment does show, and it actually in some ways makes you a little more bullish on the potential for someone else to catch up because it does feel like if you're the first person to introduce some magical net new product experience, that that actually might be stickier than one might have imagined.[00:32:27] swyx: Right, right, right. Okay. Yeah.[00:32:28] Jacob Effron: And so it's, everyone can believe they have shot[00:32:29] swyx: that. What do you think that new product experience might be like? I, I, it's, it's like, and this is a failure of imagination on my part. Like, I always wonder, like, people always say this like, well, the, the thing that will save us is like being first to the next new thing.Like what is it?[00:32:41] Jacob Effron: Yeah.[00:32:42] swyx: It's like,[00:32:45] Jacob Effron: I dunno, something around like, uh, consumer agent, computer use, like hybrid. I think, obviously, I think we're like scratching the surface on the consumer side.[00:32:53] swyx: So my, my current theory is like the. Open claw is like a vision of things to come.[00:32:58] Jacob Effron: Totally.[00:32:58] swyx: Um, and uh, it's good that O open I has like the association with open claw, but by no means do they have the rights to win it.The general thesis that I have been pursuing now is that the year the same way that 2025 was the year of coding agents, 2026 is coding agents breaking containment to do everything else. Um, and so coding agents continue to still win, but because they generate software and software eats the world, so like, it's kind of like the trans.Associated property of like software, eat the world, coding agents, eat software, therefore coding agents eat the world. Um, which is like an interesting,[00:33:30] Jacob Effron: yeah, and breaking containment always an easier phase phrase in the consumer context than the enterprise one. You've seen people run these really cool, uh, experiments in their own personal lives.I think like,[00:33:37] swyx: yes.[00:33:38] Jacob Effron: Figuring out, you know, how you, obviously everyone's focused, you know, on the enterprise side now around how you create these experiences. I feel like the vibes, you know, people love to have these narratives of like, everything is completely shifted. It's like I actually, you know, open AI.Organizationally, uh, you know, volatility aside is, you know, great products, great team, great models like everyone else in the world is incentivized for there to be. Two, three more. Everyone would love more like great model companies. And so I feel like the, the natural forces of the world revolt when any one company, you know, is too much the star of the show, right?There's so many people in the ecosystem that are incentivized for that not to happen. And so I think I'd be shocked if we don't have. Uh, uh, reversion of vibes, not maybe completely the other way, but at least a little bit more equal at some point over the next six, 12 months.[00:34:24] swyx: I, I think there's just a kind of different stages when, when you talk about the world, one wanting more model companies, I talked think about like the neo labs.[00:34:30] Jacob Effron: Yeah.[00:34:31] swyx: And I mean, I don't know, is it fair to say none of them have really broken through in the past year?[00:34:35] Jacob Effron: I think that's totally fair,[00:34:37] swyx: which is rough. Um, and well, how are we gonna, how are we gonna grow that diversity in, in, in choice, like. Um, that's, this is it.[00:34:46] Jacob Effron: Yeah. It'll be really interesting to see what, what, what ends up happening with that.And you've seen, you know, folks like Nvidia, you know, very incentivized to make sure there's, there's a broader platform of, of other model providers.[00:34:57] swyx: I think, uh, I don't know people say this, but I, I, I don't think they try it hard. Nvidia tries harder to build neo clouds[00:35:05] Jacob Effron: Yeah.[00:35:06] swyx: Than neo labs.[00:35:07] Jacob Effron: Well, they try pretty damn hard to build neo Cloud, so[00:35:09] swyx: that's,[00:35:09] Jacob Effron: yeah.[00:35:10] swyx: But like, you know, let's call it like the, the core weaves of the world, much happier place in the, you know, than any neo lab built on top of them.[00:35:18] Jacob Effron: Yeah. That one might argue it's, it's easier to, to enable a neo cloud to be successful than it is. Uh, you can't will a neo lab into existence the same way you, soNvidia[00:35:25] swyx: has more direct control over it.Uh, for sure.[00:35:27] Jacob Effron: What else is kind of catching your eye today on the startup side? I mean, you worry, there's obviously this whole narrative of like, you know, the foundation models, you know, they announced a product and every stock goes down 15%. Like[00:35:36] swyx: Yeah.[00:35:37] Jacob Effron: Do you, do you worry about the foundation models just kind of eating into to a bunch of these startup categories?[00:35:43] swyx: Not really. I, I think actually like. As, uh, there's, there's, okay, there's, there's, there's the, there's the point of view of like being an investor in startups, and there's a point of view of like, do you wanna start something? And I think honestly, like the, the downside for all these is so. Minimal in, in a sense of like, the worst you do is you just get hired into one of these labs anyway.So I, I think the, the market for people who just do things and try things and try to execute in like a competent way, even if like it doesn't work out commercially, even if it just wasn't that great anyway. Like, but like that's your job interview to go into, into one of these things anyway, so, um, I don't feel that.From a, from a very, very small startup perspective, mid-size startups. Yes. Uh, I will say there's been a lot of dead, um, LM Infra, a lot of LM infra consolidation like the, the, uh, lang fuses of the world getting absorbed into, into click house. And I, I think. Like people have maybe worked out the domain specific playbook, uh, and like, I think that's okay.Um, and, and yeah, I'm not that, not that worried about, uh, okay. So, um, I, I would say I'd be more worried about traditional SaaS, like low NPSS. This is the whole AI versus SaaS debate that has, that's been going on. Uh, and, and like literally I'm going through that exact thing in my company where, so I like kind of.Thinking through this on a very visceral, visceral level, right? On one hand you have the people who say you vibe coders don't appreciate the amount of work that goes into A-A-C-R-M and like, yeah, you think you can rip out Salesforce? So did the 30 entrepreneurs before you, right? Like, like, you know, you classically underestimate the things that you don't.Deeply, no. And, and, and target audience is not you. Uh, at the same time, like we have never been able to build software so easily and customize software so easily and like Yeah, you're not gonna use 90% of the things in Salesforce. So like, yeah. What's the typical, so what have you, what[00:37:33] Jacob Effron: have you done internally?[00:37:34] swyx: So we have there the main SaaS that we do for event management and sponsor management. That's, and we paid 200 KA year for that. Not, not huge, but like chunky for, for, for my, my scale. Um, and like, yeah, I could probably spend 2000 and, and build like a custom version of that. Um, the, the, the trick has been dealing with my, the rest of my team and getting them on board.Yeah. ‘cause I'm the most ethical person on my team, but like, I can't make that decision myself. And I think in the same way I've been telling with other CEOs team leaders as well, it's like, well you can be super cloud pilled. You can be super LM psychosis and that you think that's okay, but you like you have to bring your team with you.And I think like there, the sort of widening disparity in LM psychosis in companies is causing real s real riffs because. And on one hand, on one hand, the people who are less AI native are not getting with the picture. They're not, they're actually like behind, they're actually not waking up to the fact that like you, everything you think is necessary is not actually that necessary.And in fact, exactly would be better of you if you just like held your nose and went in and when came out the other side. Yeah, only talking to agents in natural language and like your life would actually be better and you just, you're just like close-minded. There's that perspective. The other perspective is, oh, you vibe coder.You, you did this in a weekend and you got the 80% solution and now the rest of your employees. Have to pick up the rest of your s**t, right, that you, that you thought you were, you were such hot, amazing, uh, uh, at, but like, actually you didn't figure it out. And like, actually LMS are still useless at this and blah, blah, blah.So like, I think there's this huge debate going on in every company right now. Um, and like, um, you know, I have a small microcosm of it, but like, yeah, it, it's making me hesitate to, to pull the trigger. But like I will at some point, it's like maybe I've put it off for one year, but not like five. Yeah, but like, so, so like SaaS is definitely getting squeezed.Um, it does make me wonder, like, I, I do think that there's an opportunity for a more AI native, um, system of record thing that is not just Postgres. Um, or not just MongoDB, although both are very good. Maybe it's like a convex or like people Yeah. Bring up convex a lot. I don't know, like, like, I, I just feel like the sort of quote unquote firebase of, of AI apps isn't really a thing yet.Um, beyond what we have. Uh, which, which is fine. It's, it's, it's just. We could probably start in a more sort of rapid iteration cycle first before scaling up to like a Postgres or MongoDB, which are more sort of old tech. I was at a dinner with, uh, Mike Krieger, the CPO of en philanthropic, and, and he, we were just kind of going around the room going like, what are people most worried about?Yeah. And, uh, for me, uh, I, instead of security, I brought up biosafety. Yeah,[00:40:21] Jacob Effron: classic.[00:40:22] swyx: Um, actually, like I said, it was. Cliche and classic, and the rest of the table were, were like, what do you mean? Someone sitting at home can manufacture a virus that wipes out half of humanity,[00:40:32] Jacob Effron: almost like the OG Jeffrey Hinton.Like, this is why you should be scared.[00:40:35] swyx: I'm like, yeah, like the read the, you know, risk reports. Like this is like the thing. Um, I think, and Mike was just sitting there knowing he was sitting on Mythos and going like, actually it's security. Um, and I think like, um, I think the, there's, there's, part of it is.A very good marketing. Like too good. Yeah, like I would actually advise and topic to tune down the marketing because also it's, it is just a very good model and you don't have to make so many marketing claims around it. At the same time, it is not really a private model. If you give it to 40 companies.Each of whom have like 10,000 employees or whatever. Right. It's not, it's not private, it's, it's like there's bad actors in there.[00:41:18] Jacob Effron: Yeah. Hopefully, hopefully not as, uh, as bad as releasing it widely, but, uh, no, I mean, it's an interesting. You know, it's an interesting case study for how all, I mean, many model releases might, I mean, you know, this might be the first model release that looks like the rest of ‘em from from now on, right?[00:41:31] swyx: It, it, so it's, it's the, there's an overall product strategy, uh, for anthropic of like bundle, uh, you know, restrict access bundle, uh, product with model maybe.Whereas, uh, OpenAI has definitely been a lot more sort of. Philosophically aligned on like, we will just enable access everywhere and we don't know what you, what will come out of it. Right.[00:41:51] Jacob Effron: Right. Though, I mean, this current moment, uh, obviously the cynical take is also just ties to the amount of compute that both companies[00:41:56] swyx: Yeah.Right, right, right. Yeah, I think, I think that's true. I I do think like the, the, this is the, the, the scale, the dawn of like larger than 10 trillion parameter models is very interesting. I don't think it, I think it's a temporary phenomenon because we have much larger compute clusters coming online for everyone over the next like three, five years.It's, and this is like already written in, in the cards.[00:42:18] Jacob Effron: Yeah.[00:42:19] swyx: So to the extent that like, you know, will we have rationing of models, uh, above 10 trillion, uh, in like two years? I don't think so. I think everyone will have no, we'll just[00:42:29] Jacob Effron: have rationing of the next phase.[00:42:30] swyx: Right. Right. But like, that's as it should be almost like, um.My, my classic example, which I, this is just me theorizing, not anything confirmed by Google. When Google announced Gemini, they actually announced three sizes, which was Flash Pro Ultra. They never released Ultra. They only have Pro and Flash. Um, so my theory is they have ultra sitting in a basement and they just could distilling from it for, for flashing pro.Um, which like, yeah, I mean, I, I actually think that's. As it should be for any lab that they, that they do that.[00:43:02] Jacob Effron: Yeah. Just because those are the models that people actually wanna end up using. And it's just like cost prohibit.[00:43:06] swyx: It is more, yeah, it's cost. Yeah. It's, it's not the want, it's just, just, just the cost.Um, I do think, like, uh, it is interesting that, uh, for a while I was, I was considering the theory that models capped out at two, 2 trillion, and I think that's proving to be wrong. And well then if I'm wrong, how wrong? How wrong am I? Do we do 200 trillion? Do we do two quarter trillion, whatever? Um, and I don't think we have the straight answer to that, but like, uh, it's interesting that we are continuing to scale number of pers when everyone kind of assu like can see that we're not going to get like the next thousand or 1 million x from this paradigm.So like the others, like the alias of the world are working on other. Um, model architecture improvements. We need a different scaling law, I guess, because like, we're, I, I feel like people already already feel like we're tapped out on this. Like the, the end, the end state of this is we turn most of the world into data centers and like, I don't know.I don't know if we want that.[00:44:08] Jacob Effron: Yeah, I mean, uh, if the, if, if, if the return of intelligence are there, maybe, uh, maybe not so bad.[00:44:13] swyx: I, I, I think there, there's just a sheer amount of like, like un scalability that like is wrangling people's sensibilities right now. Um, especially in terms of like context lengths.Um, my classic quote is that context length is like the slowest scaling factor in, in lms.[00:44:30] Jacob Effron: Yeah.[00:44:30] swyx: Um, we, like, we took maybe. Three years to go from like 4,000 context length to a million and that's about it. Yeah. Like Gemini has had a million token context length for two years now. Um, and no one's using it.Like, so like yeah, it's memory. Memory is probably gonna be the, the biggest limiting constraint on all these things.[00:44:50] Jacob Effron: Yeah. Certainly seems that way. I guess I'm curious over the last year since you recorded last, like what's one thing you've changed your mind on?[00:44:57] swyx: I feel like I was kind of bearish on open models like last year.Um, in a sense of, like, I, I had just done the podcast with an Al[00:45:07] Jacob Effron: Yeah.[00:45:08] swyx: Of Braintrust where he, and he, I mean, you know, he has a good cross section of all the top AI companies and he says market share of open source is 5% and going down. Um, I think that's changed. I think it's going up. Um, and even if,[00:45:22] Jacob Effron: even though the capability gap does seem to be increasing.Spending on the[00:45:26] swyx: time. It's hard to tell. Yeah, it's, it's really hard to tell. ‘cause like, okay, for, for listeners, capability gap increasing is like on public benchmarks. And let's say you're comparing mythos versus like, I don't know, G-T-O-S-S or like GLM 5.1. And, um, it's, it is really hard to tell. ‘cause even if they were closing, you will also not believe that they were closing that much because it's very easy to gain the benchmarks.Yeah. So you just don't really, really know. Um, all you know is like. Uh, there's somewhat objective open router stats on like what people choose in a free market. And people do choose some of these open models in significant volume, except that a lot of them are heavily discounted. So you need to kind of like price adjust, uh, these things.So even if, even if that were true, which I, I'm not sure, like I, I, I feel like the numbers just up now instead of down. Uh, I think the. Separation between what the top tier agent labs

Dans cet épisode, Delphine Zanelli reçoit Catherine Testa. Autrice, conférencière et entrepreneure, elle a créé le média L'Optimisme puis un think tank consacré au bien-être au travail et au futur du travail. Elle publie un nouveau livre sur l'optimisme dix ans après son premier ouvrage.En avant-première, Catherine Testa partage les enseignements de son nouveau livre Optimisme mode d'emploi, à paraître le 21 mai, et montre comment l'optimisme au travail nourrit l'action managériale.Alors qu'elle finalisait ce nouvel ouvrage, elle a fait l'amitié à Delphine Zanelli d'accorder cet entretien. Un échange où l'optimisme au travail éclaire l'énergie, la confiance et la capacité d'agir dans les périodes d'incertitude.Au fil de l'épisode, Catherine Testa propose une définition claire de l'optimisme au travail. Une manière de regarder la réalité avec lucidité, d'accueillir les difficultés, puis de choisir un espace d'action. Elle rappelle une idée forte : le véritable opposé de l'optimisme, c'est l'inaction.Nous parlons de ce qui influence profondément nos comportements et nos décisions : la culture d'entreprise, l'ambiance d'équipe, les récits familiaux, la santé mentale, la charge mentale collective, les contenus que nous consommons chaque jour, les peurs diffusées autour de nous. Pourquoi certaines équipes retrouvent de l'élan dans des périodes tendues quand d'autres se figent ? Pourquoi un manager transforme l'énergie d'un collectif ? Pourquoi la confiance progresse dans certains environnements et s'érode dans d'autres ?L'épisode met aussi en lumière le rôle des pratiques managériales. Un manager façonne un climat de travail, soutient l'engagement des collaborateurs, redonne du sens, ouvre des perspectives et aide chacun à reconnaître sa singularité. Catherine Testa rappelle que chaque personne occupe une fonction, et apporte aussi une manière unique de coopérer, de créer du lien, d'anticiper, d'apaiser une tension ou de faire avancer un projet.Autre thème central : l'intelligence artificielle et l'évolution des métiers. Catherine Testa propose une question décisive : “quelle est ma complémentarité ?”. Une réflexion utile pour les managers, les DRH, les RH, les assistantes, les fonctions support et toutes celles et ceux qui souhaitent continuer à contribuer avec impact dans un monde en transformation.Cet échange apporte des repères concrets sur le leadership, le management, la transformation du management, la confiance, la performance collective, l'engagement des collaborateurs et le futur du travail. Il montre aussi comment l'optimisme au travail nourrit l'action managériale et relance l'énergie collective dans les périodes floues.

The jump from high school to college is bigger than most families realize. In this conversation, I talk with Dr. Tara Williams about what neurodivergent students really need as they prepare for college and why so many of them struggle in that transition. We unpack the shift from high school supports to college systems, where students are suddenly expected to manage accommodations, communicate with professors, understand FERPA, and advocate for themselves in a much more independent way. Tara explains why waiting until the summer before college can create unnecessary stress, and why self-advocacy has to start getting practiced much earlier. We also talk about executive functioning in real life, not as a buzzword, but as the day to day challenge of keeping up with emails, assignments, schedules, accommodations, and decisions. Tara shares practical tools for helping students build those skills, along with a powerful reminder that college success is not just about getting into the "right" major or pushing through what is not working. Sometimes the real win is helping a student find the path that actually fits how they learn, think, and thrive. Key Takeaways College accommodations work very differently from high school supports. Students are expected to initiate the process, submit documentation, schedule meetings, and communicate with professors themselves. The summer before college is already a high pressure time to begin. Families need to know that accommodation offices may book far in advance, and waiting too long can mean starting the semester without support. Self-advocacy needs to be practiced before college. Students can start by emailing teachers, asking about missed work, and learning how to communicate their needs while still in middle school or high school. Executive functioning support is not one skill. It includes calendars, planning, batching tasks, reminders, follow through, and figuring out what systems a student will actually use. Parents may need support building these systems too. Many adults are trying to help their child with tools they were never taught themselves. A good system has to fit the person. Google Calendars, Post-its, color coding, batching emails, and breaking tasks down can all work, but only if the student will actually use them. Technology makes sustained attention harder for everyone. Notifications, learning platforms, email, and constant digital access all increase cognitive load for students and adults alike. Accommodations should be available even if a student does not use them every time. Signing up matters. The student can decide when they need the support. Sometimes the issue is not just skill, but fit. A student may be in the wrong major, the wrong course path, or a program chosen for them rather than with them. College success is often about redirection, not failure. Finding a path that matches a student's real strengths and interests can change everything. About Dr. Tara Williams Dr. Tara Williams is the owner and founder of Innovative Collegiate Consultants, Inc. She earned her PhD in Synthetic Inorganic Chemistry from the University of Sussex in Falmer, United Kingdom, and is currently a tenured professor at College of the Canyons in Santa Clarita, California, where she has taught for the past twenty years. Since 2010, she has worked with neurodivergent students across the United States after noticing how many were struggling with the transition from K-12 support systems to college environments that require far more self-advocacy. Dr. Williams and her team specialize in executive functioning coaching with a strong academic focus, supporting students with accommodations, course planning, email and LMS management, housing, internships, jobs, and more. Her work helps neurodivergent and neurotypical students build confidence, advocate for themselves, and thrive in school and college. About Your Host, Gabriele Nicolet I'm Gabriele Nicolet, toddler whisperer, speech therapist, parenting life coach, and host of Complicated Kids. Each week, I share practical, relationship-based strategies for raising kids with big feelings, big needs, and beautifully different brains. My goal is to help families move from surviving to thriving by building connection, confidence, and clarity at home. Complicated Kids Resources and Links Website: www.gabrielenicolet.com Schedule a free intro call: Book here YouTube: Subscribe here Tell the Story (anti-anxiety tool): Learn more Instagram: Follow here Facebook: Connect here LinkedIn: View profile Free "Orchid Kid" Checklist: Download here Enjoying the show? If Complicated Kids has been helpful, the best way to support the podcast is to follow, rate, and leave a quick review. It helps other parents find the show and it means a lot. If there's a topic you'd love to hear covered on a future episode, you can always reach out at podcast@complicatedkids.com. I love hearing what's on your mind and what would support your family. Thank you for being here.

In this bonus episode recorded live at the Collegis Education DisruptED summit in Phoenix, we spoke with Denise Aberle-Cannata, Provost at the College of Western Idaho (CWI). They talk about building a student success model centered on the full student lifecycle—from inquiry through graduation and career placement. By mapping the student journey, the institution identified gaps and redesigned processes to create a more seamless, supportive experience grounded in relationships, not just systems.  The conversation emphasizes the importance of culture, change management, and cross-campus ownership in making student success initiatives stick. While technology played a supporting role in improving efficiency and visibility, the real impact came from aligning people, processes, and purpose around helping every student succeed. Guest Name: Denise Aberle-Cannata - Provost at College of Western Idaho Guest Social: LinkedIn Guest Bio: Since joining the College of Western Idaho in 2019, Denise Aberle-Cannata has led large-scale academic and operational transformation at Idaho's largest community college, serving more than 34,000 students annually. Her work has focused on redesigning the student experience and modernizing how education is delivered — including launching a college-wide CRM to strengthen coordinated student support, launching new LMS, refining and scaling high-quality online learning, and expanding flexible academic models that improve student momentum and completion. - - - -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.

Looking for daily inspiration?  Get a quote from the top leaders in the industry in your inbox every morning.   Marc Dixon is the managing director and co-founder USA of Study Academy USA. He got his start in the attractions industry in the mid-90s at Lagoon in Utah, then spent about two decades with Kodak in event imaging solutions before moving into other attraction tech businesses and, ultimately, e-learning. Today, Study Academy USA partners with organizations like IAAPA to build learning management systems and convert proven training content into trackable, scalable online courses for attractions. In this interview, Marc talks about technology in attractions, AI's purpose in the industry, and learning to take risks. Technology in attractions “We get to provide smiles and great experiences and memories.” Marc frames attraction technology as a means to capture, enhance, and scale what guests come for in the first place. He walks through the evolution of on-ride and experiential photography, from analog systems to digital, and from green screens to background removal and image enhancement. Even with smartphones everywhere, he argues the souvenir imaging business hasn't disappeared because guests still want content they cannot create themselves, especially on rides and in curated photo moments. He also connects that same “tech serves the experience” mindset to his current work with Study Academy USA. By building modern LMS tools and e-learning content for attraction operators and associations, the goal is to give attractions another practical way to train at scale while still supporting the on-the-job behaviors that make guest experiences great. AI's purpose in the industry “I've heard the statement that AI is not going to replace your job, but people using AI will.” Marc's view is that AI should primarily enhance work, not replace it outright. In imaging, he points to AI-driven improvements like background replacement and photo cleanup that reduce friction and raise quality, even when small mistakes happen in capture. In training, he sees AI as a way to make learning data more useful by pulling insights from LMS results and highlighting where teams are struggling, so leaders can coach more precisely. He's also clear about responsible use. Marc says he would never copy and paste AI output without reviewing it because it still needs to reflect his voice and intent. His biggest concern is people trusting AI blindly instead of treating it as a tool that speeds up work while still requiring human judgment. Learning to take risks “Learn to take risks, man, go for it. If there's something you're really passionate about and you want to try, what's the worst that can happen?” When Marc describes choosing entrepreneurship over a comfortable corporate role, he makes it clear that the risk is real and not always glamorous. There are good days and bad days, and some paths do not work out the way you expect. But he emphasizes persistence and adaptability, saying the wins come from believing it will work and being willing to pivot until you find the path that does. His advice to younger professionals is direct: take the risk, try the thing, and treat missteps as learning opportunities rather than permanent failures. That mindset, he says, is what keeps founders moving forward when uncertainty shows up.   Marc can be reached on LinkedIn, as well as by email at marc@studyacademyusa.com. To learn more about Study Academy USA, visit www.studyacademyusa.com. This podcast wouldn't be possible without the incredible work of our faaaaaantastic team:   Scheduling and correspondence by Kristen Karaliunas   To connect with AttractionPros: AttractionPros.com AttractionPros@gmail.com AttractionPros on Facebook AttractionPros on LinkedIn AttractionPros on Instagram AttractionPros on Twitter (X)

Some cancers get billions in funding, massive awareness campaigns, and life-saving research. Leiomyosarcoma isn't one of them. It's a rare, aggressive cancer that most people have never even heard of — but Dr. Mitch Achee & Annie Achee are hoping to change that. In this episode, they  explain  what LMS is, why it's so difficult to treat, and  how the National Leiomyosarcoma Foundation is filling the gaps through patient education, a 24/7 support hotline, and global research efforts. Truman Charities is proud to partner with the NLMSF for the upcoming Derby Party on May 2nd, where every dollar raised goes directly to patients and research. Purchase your Truman Charities Derby Tickets at HERE Connect with The National Leiomyosarcoma Foundation:WebsiteFacebookHotline: 303-808-3437Connect with Jamie at Truman Charities:FacebookInstagramLinkedInWebsiteYouTubeEmail: info@trumancharities.comThis episode was post produced by Podcast Boutique https://podcastboutique.com/

Episode SummaryIn this EDUCAUSE episode, Dr. Vanessa Kenon from UTSA, Tonya Bennett from the University of Pennsylvania, and Tim Boltz from Carahsoft get into the tension every higher ed IT leader is sitting with right now - when to move on AI, when to wait for policy, and how to keep curiosity alive before the feds rewrite the rulebook.FeaturingDr. Vanessa Kenon is Associate Vice President for Information Technology at the University of Texas at San Antonio - leading IT through a major university merger while keeping innovation and compliance from pulling the institution in opposite directions.Tonya Bennett is Director of Educational Technology at the University of Pennsylvania - managing the LMS-centered EdTech ecosystem across 12 schools and bringing a master's in law to every AI governance conversation she's in.Tim Boltz leads the Education Vertical at Carahsoft - 17 years in, representing 1,500 manufacturers, and building the cooperative purchasing infrastructure that lets institutions stop waiting on 12-18 month RFQ cycles.Timestamps(1:00) Bold Careers & ServiceNow University - 400+ students served and 150 chasing 25 spots(8:00) TASSCC - how Texas built its own version of EDUCAUSE and why vendor partnerships made it work(11:00) Financial pressures in higher ed - why leaning into IT investment beats pulling back(15:00) Frictionless EdTech at UPenn - one credential, every platform, zero manual steps(20:00) UTSA's experiential learning engine - DoD contractors, RackSpace, Dell & eSports(26:00) Carahsoft's easy button - cooperative purchasing vehicles already live across all 50 states(29:00) AI's legal wild west - agentic AI, IP liability & who's responsible when the agent acts(35:00) Curiosity vs. compliance at UTSA -keeping innovation alive without losing governance(39:00) Closing trends - community over commodity, workforce readiness & what's next for Higher EdListen now: YouTube x Apple x SpotifyWhenever you're ready, there are 3 ways you can connect with TechTables:1.

Everyone is talking about AI these days. Often these conversations are about how AI might upend education, or work, or social life, or maybe civilization itself. But among cognitive scientists and psychologists the conversation inevitably drifts toward other questions. What does this latest generation of AI tell us about the human mind? Is it putting old ideas and theories to rest? Is it ushering in new ones? Will AI—in other words—also upend cognitive science? My guests today are Dr. Mike Frank and Dr. Gary Lupyan. Mike is a Professor of Psychology at Stanford University, where his lab focuses on language learning and cognition in children. Gary is a Professor of Psychology at the University of Wisconsin Madison, where his lab studies language and its role in augmenting human cognition. Both Gary and Mike have more recently been thinking a lot about AI and how it is challenging and deepening our understanding of the human mind.   In this conversation, we talk about being interested in AI as cognitive scientists—while also being concerned about the technology as people. We discuss the linguistic abilities of frontier LLMs compared to the linguistic abilities of adult humans. We talk about a glaring "data gap" here—the fact that, even though LLMs often rival human abilities, they require orders of magnitude more data to do so. We contrast the capabilities of large language models with so-called BabyLMs. We consider the fact that, as LLMs master language, they also master other abilities—capacities for mathematical reasoning, causal understanding, possibly theory of mind, and more. And we talk about why language might be an especially potent form of input for an AI. Along the way, we touch on reference and the symbol grounding problem, the Platonic Representation Hypothesis, stimulus computability, confabulated citations, pattern matching and jabberwocky, the poverty of the stimulus argument, congenital blindness, Quine's topiary, the limits of in principle demonstrations, the WEIRD problem, and what the astonishing sophistication of disembodied AIs might suggest about the role of bodily experience in human cognition. Before we get to it, one small request: we're currently running a short survey of our listeners. You can find the link in our show notes. If you have a few minutes, we'd really love your input!  Alright friends, here's my conversation with Mike Frank and Gary Lupyan. I think you'll enjoy it!   Notes 5:00 – For more discussion of "stochastic parrots" and other ways of framing AI systems, see our recent episode with Melanie Mitchell. For the "octopus test," see here. 8:00 – "BabyLMs" are—in contrast to large LMs (aka LLMs)—models that are trained on a more human-scale amount of linguistic input. For more on the BabyLM community, see here. 12:00 – For broad discussion of the use of AIs as "cognitive models," see this paper by Dr. Frank and a colleague. The same paper discusses the idea of "stimulus computability."  18:00 – For Dr. Frank's "baby steps" paper, see here. 20:00 – For more on how Claude understands line breaks, see Anthropic's analysis of the issue here.  23:00 – For work on human-like grammaticality judgments in LLMs, see this paper by a team including Dr. Lupyan.  24:00 – See here for an influential paper on, among other things, how LLMs refute the idea that syntax is unlearnable. The article titled 'How linguistics learned to stop worrying and love the language models' is here; Dr. Lupyan's commentary—'Large language models have learned to use language'—here. 29:00 – For some of Dr. Lupyan's work on the "abstractness" of even concrete concepts, see here. 35:00 – For a classic paper on the so-called symbol grounding problem, see here.  37:00 – For the preprint putting forth the "Platonic Representation Hypothesis," see here. 40:30 – For more on the data gap between children and LLMs—and what accounts for it—see Dr. Frank's paper here. 45:00 – For a sampling of Dr. Frank and colleagues' work comparing language models to children, see here, here, and here. For more on the LEVANTE project, a collaborative effort spearheaded by Dr. Frank, see here. 48:00 – For the preprint—'The Unreasonable Effectiveness of Pattern Matching,' by Dr. Lupyan and a colleague—see here. 55:00 – For more on Dr. Lupyan's perspective on the centrality of language in human cognition, see here. See also this more recent paper, considering the question in light of LLMs.  58:00 – For our earlier episode with Dr. Marina Bedny, see here. For the recent paper by Dr. Bedny and colleagues considering their research on congenital blindness in light of LLMs, see here. 1:01:00 – For classic work on language learning in blind children, see here. 1:02:00 – For a paper by Dr. Lupyan and colleagues on "hidden" individual differences, see here. 1:03:00 – For more on "multiple realizability," see here. For our earlier episode with Dr. Eric Turkheimer, see here. 1:09:00 – For more on the work of Dr. Frank's collaborator, Dan Yamins, see here. 1:14:00 – See our earlier episode with Dr. M.J. Crockett for more discussion of the "WEIRD problem" around scientific uses of AI. In the same episode, we discussed how new cognitive scientific methods focus attention on questions that can be studied with those methods.    Recommendations The Mind at Play, by Jimmy Soni & Rob Goodman On Desire, by William Irvine Patterns, thinking, and cognition, by Howard Margolis Open Encyclopedia of Cognitive Science The BabyLM workhops/community (e.g, the entry on LLMs)   Many Minds is a project of the Diverse Intelligences Summer Institute, which is made possible by a generous grant from the John Templeton Foundation to Indiana University. The show is hosted and produced by Kensy Cooperrider, with help from Assistant Producer Urte Laukaityte and with creative support from DISI Directors Erica Cartmill and Jacob Foster. Our artwork is by Ben Oldroyd. Subscribe to Many Minds on Apple, Stitcher, Spotify, Pocket Casts, Google Play, or wherever you listen to podcasts. You can also now subscribe to the Many Minds newsletter here! We welcome your comments, questions, and suggestions. Feel free to email us at: manymindspodcast@gmail.com. For updates about the show, visit our website or follow us on Bluesky (@manymindspod.bsky.social).

Hey Voices from the Bench community! Jessica Love here, sending a shoutout from Utah! If you're passionate about creating natural, beautiful smiles—but want to simplify your workflow without sacrificing aesthetics—this is for you. I'm honored to be part of Ivoclar's development team introducing a powerful new stain and glaze system featuring Structure Paste, IPS e.max Ceram Art. Create stunning depth and lifelike color in as little as one firing. Let's continue to innovate, simplify, and create meaningful change—one smile at a time. When it comes to digital dentures, design is easy—manufacturing is where things get messy. That's why the Elevate Denture Solution brings it all together. Built by Roland DGSHAPE, Ivoclar, and FOLLOW-ME! Technology Group, it combines machine, materials, and CAM into one fully optimized workflow—so you get consistent, high-quality results without the guesswork. Want to simplify production and scale with confidence? Check it out at rollanddga.com/elevate. "Live" from the Ivoclar ballroom at Lab Day 2026, Elvis and Barb dives into conversations that perfectly capture what this industry is all about—innovation, relationships, and a whole lot of nerding out. We kick things off with Frederic Rapp, who went from growing up in his dad's basement lab in France to scaling it into one of the largest labs in Europe. After selling the business, he found his way back into the industry through innovation—helping labs unlock the gold mine sitting inside their own data with icortica. From dashboards to AI-driven insights and even voice-activated notes in the parking lot, it's all about working smarter, not harder… and maybe not looking like an idiot when you walk into a doctor's office. Then things shift to a great partnership with Casey Baldwin and Darin Lockaby, where we get into a seriously cool collaboration between Ivoclar and DESS. Think plug-and-play workflows that let labs mill their own abutments in-house—FDA compliant, streamlined, and actually simple. With margins tighter than ever, this kind of control over production isn't just nice… it's becoming necessary. From scaling labs to scaling data, from implants to AI, this episode is packed with insight, laughs, and a clear message: the labs that embrace technology (without losing the human touch) are the ones that are going to win. Join us at exocad Insights 2026, happening April 30–May 1, 2026, on the stunning island of Mallorca, Spain. This two-day event features powerhouse keynotes, hands-on workshops, live software demos, and top-tier industry showcases—all in one unforgettable setting. Barb and Elvis will be on site bringing you exclusive interviews, plus don't miss the Women in Dentistry Lunch, celebrating career growth, wellbeing, and the real stories shaping our profession. And of course, cap it all off with the legendary exoGlam Night under the stars. Tickets are limited. Visit exocad.com/insights-2026 and use code VFTBPalma15 for 15% off.Special Guests: Casey Baldwin, Darin Lockaby, and Frederic Rapp.