Podcasts about LMS

Subscribe here: https://creators.spotify.com/pod/show/saeder/subscribe to access all exclusive Sae Bae Casts! Sae Bae Merch: https://sae-bae-shop.fourthwall.com Jeporite is an OSRS YouTuber & streamer, best known for his incredible Northern UIM series. https://www.youtube.com/@Jeporite https://www.twitch.tv/Jeporite https://whenisthenextepisodeofnorthernuim.com LMS clip: https://youtu.be/_P7bMvFor2k?si=uVW7-YSk06zn82fd&t=658 https://twitch.tv/saeder https://x.com/SaederRS

Most recruitment agencies believe in training. Very few build a structured system that consistently produces top billers. Larissa Gerlach experienced the hard version first. In year one, she earned $40,000 and questioned whether she would make it in recruitment at all. By year three, she had reached President's Club. Soon after, the CFO of a private equity-backed recruiting firm asked her to replicate her results across 25 offices. That request became the foundation of a national recruiter training programme. In this episode, Mark Whitby and Larissa unpack what actually drives recruiter performance, why activity metrics alone don't create top billers, and how recruitment business owners can build scalable training systems that reduce ramp-up time and increase recruiter billings. If you are serious about recruitment agency growth, search firm leadership, and building consistent performance inside your team, this conversation goes beyond theory. It's about systems. What You'll Discover • Why 200+ calls per week worked — and why most recruiters still fail at high activity • The difference between knowledge and live desk performance • How to turn individual billing success into a national training framework • Why daily role plays accelerate recruiter revenue • The three structural reasons founders struggle to implement training • Why cohort-based onboarding produces stronger long-term performance • How to build recruitment agency systems that scale beyond one top performer Episode Highlights [03:56] From fashion sales to recruitment after the 2009 recession [08:37] The $40,000 first year and the meeting where she nearly quit [12:35] Why most recruiters struggle in year one — and what actually starts to click [22:15] The 200-calls-per-week discipline that changed her trajectory [26:07] The CFO email that led to building a national sales training programme [28:17] What the training playbook looked like — from binder to LMS [35:51] Why daily role plays create elite performers [1:05:49] The three reasons most founders struggle to train their teams [1:10:29] Why group cohorts outperform one-to-one onboarding About Larissa Gerlach Larissa Gerlach is the founder of Vibrant Talent Group, an executive search firm specialising in marketing, product, and design roles across New York and San Francisco. She has over 15 years of experience across billing, business development, national learning and development, and agency leadership. At a private equity-backed recruiting firm, she became the fastest-growing salesperson in company history before leading national recruiter training initiatives. Resources Mentioned Recruiter Training Programme https://recruitmentcoach.com/training Seven Figure Freedom Scorecard https://recruitmentcoach.com/scorecard Recruiterflow https://recruitmentcoach.com/recruiterflow Trusted Voice Video https://recruitmentcoach.com/video Book a free strategy session with Mark Whitby https://recruitmentcoach.com/strategy-session If you want weekly conversations with recruitment business owners, executive search leaders, and top billers focused on recruitment agency revenue, recruiter performance, and long-term business resilience, follow The Resilient Recruiter on Apple Podcasts. The difference between average billers and elite teams is rarely motivation. It's structure.

Arrêtons de créer des catalogues de formations. C'est l'échec assuré.Anne-Marie Cuinier est experte en learning & marketing, autrice du livre Créer des expériences de formation engageantes et créatrice du podcast Learn & Enjoy. Et elle a une conviction forte concernant la formation : ce doit être une expérience. Alors il faut les penser comme telle.On peut investir des milliers d'euros dans des modules e-learning et créer des contenus ultra léchés, si personne ne s'inscrit, on passe à côté de l'essentiel. Comment faire ? Appliquer les principes du marketing à la formation. Et c'est tout ce qu'elle nous raconte dans le dernier épisode de Learning by Doing.Bonne écoute !À très vite,Prenez soin de vous !Plus d'info :Pour suivre Anne-Marie sur LinkedIn : https://www.linkedin.com/in/annemariecuinier/Pour écouter son podcast : https://open.spotify.com/playlist/6Mh56Tut5BBURtzsuIe2lAEt son livre Créer des expériences de formation engageantes: Pensez l'avant, accompagnez l'après, inspirez vos apprenants !Pour recevoir gratuitement notre sélection hebdo de conseils pratiques pour animer votre équipe, rendez-vous ici : https://teambakery.com/nlEt n'oubliez pas de laisser 5 étoiles et un gentil commentaire sur Apple Podcast et Spotify si l'épisode vous a plu.CHAPITRAGE00:00:00 - Intro00:02:16 - Présentation d'Anne-Marie et de son parcours.00:03:17 - Quel a été son déclic sur le sujet de la formation ?00:06:25 - Quelle place pour la formation en entreprise ?00:09:27 - Trois leviers pour rendre la formation désirable.00:16:36 - Quelques exemples de avant/après.00:20:51 - Qu'est-ce que la signature pédagogique ?00:25:30 - Par quoi commencer ?00:29:46 - À quel type d'entreprise s'adresser-t-elle ?00:32:30 - Ses trois conseils pour rendre son offre de formation plus lisible.00:34:59 - Quelques recommandations de contenu.00:36:37 - Questions de fin.Vous aimerez cet épisode si vous aimez : Outils du Manager • Happy Work • HBR on Leadership • Le Podcast de la Formation • MANAGEMENT & LEADERSHIP • Learn & EnjoyHébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Cafes, restaurants and car washes all use points and rewards to drive behaviors. Can we do the same with our Learning Management Systems? In this week's episode of The Mindtools L&D Podcast, Incentli's Jeff Campbell speaks to Ross G and Ross D about: how digital currencies give LMS administrators levers they can pull to drive behavior the role of extrinsic and intrinsic motivation on learning the impact of branded swag on learner advocacy In 'What I Learned This Week', Ross D discussed GitHub commits (super fun to bet on!) For more from Incentli, visit incentli.com. Incentli are a Mindtools Kineo partner, so if you would like to discuss integrating points and rewards with our Totara LMS please do get in touch by contacting custom@mindtools.com. For more from Mindtools Kineo, visit mindtools.com or kineo.com. There, you'll also find details of our Learning Management Systems, Content Hub for leaders and managers, and custom learning design service. Like the show? You'll LOVE our newsletter! Subscribe to The L&D Dispatch at lddispatch.com Connect with our speakers If you'd like to share your thoughts on this episode, connect with us on LinkedIn: Ross Garner Ross Dickie Jeff Campbell

Vous cherchez un rituel simple, rapide et terriblement efficace pour améliorer le fonctionnement de votre équipe ?Dans cette vidéo, je vous montre comment mettre en place une rétrospective (l'un des outils les plus puissants issus de l'Agile… et pourtant l'un des moins utilisés en management.)Au programme :

Tickets for AIEi Miami and AIE Europe are live, with first wave speakers announced!From pioneering software-defined networking to backing many of the most aggressive AI model companies of this cycle, Martin Casado and Sarah Wang sit at the center of the capital, compute, and talent arms race reshaping the tech industry. As partners at a16z investing across infrastructure and growth, they've watched venture and growth blur, model labs turn dollars into capability at unprecedented speed, and startups raise nine-figure rounds before monetization.Martin and Sarah join us to unpack the new financing playbook for AI: why today's rounds are really compute contracts in disguise, how the “raise → train → ship → raise bigger” flywheel works, and whether foundation model companies can outspend the entire app ecosystem built on top of them. They also share what's underhyped (boring enterprise software), what's overheated (talent wars and compensation spirals), and the two radically different futures they see for AI's market structure.We discuss:* Martin's “two futures” fork: infinite fragmentation and new software categories vs. a small oligopoly of general models that consume everything above them* The capital flywheel: how model labs translate funding directly into capability gains, then into revenue growth measured in weeks, not years* Why venture and growth have merged: $100M–$1B hybrid rounds, strategic investors, compute negotiations, and complex deal structures* The AGI vs. product tension: allocating scarce GPUs between long-term research and near-term revenue flywheels* Whether frontier labs can out-raise and outspend the entire app ecosystem built on top of their APIs* Why today's talent wars ($10M+ comp packages, $B acqui-hires) are breaking early-stage founder math* Cursor as a case study: building up from the app layer while training down into your own models* Why “boring” enterprise software may be the most underinvested opportunity in the AI mania* Hardware and robotics: why the ChatGPT moment hasn't yet arrived for robots and what would need to change* World Labs and generative 3D: bringing the marginal cost of 3D scene creation down by orders of magnitude* Why public AI discourse is often wildly disconnected from boardroom reality and how founders should navigate the noiseShow Notes:* “Where Value Will Accrue in AI: Martin Casado & Sarah Wang” - a16z show* “Jack Altman & Martin Casado on the Future of Venture Capital”* World Labs—Martin Casado• LinkedIn: https://www.linkedin.com/in/martincasado/• X: https://x.com/martin_casadoSarah Wang• LinkedIn: https://www.linkedin.com/in/sarah-wang-59b96a7• X: https://x.com/sarahdingwanga16z• https://a16z.com/Timestamps00:00:00 – Intro: Live from a16z00:01:20 – The New AI Funding Model: Venture + Growth Collide00:03:19 – Circular Funding, Demand & “No Dark GPUs”00:05:24 – Infrastructure vs Apps: The Lines Blur00:06:24 – The Capital Flywheel: Raise → Train → Ship → Raise Bigger00:09:39 – Can Frontier Labs Outspend the Entire App Ecosystem?00:11:24 – Character AI & The AGI vs Product Dilemma00:14:39 – Talent Wars, $10M Engineers & Founder Anxiety00:17:33 – What's Underinvested? The Case for “Boring” Software00:19:29 – Robotics, Hardware & Why It's Hard to Win00:22:42 – Custom ASICs & The $1B Training Run Economics00:24:23 – American Dynamism, Geography & AI Power Centers00:26:48 – How AI Is Changing the Investor Workflow (Claude Cowork)00:29:12 – Two Futures of AI: Infinite Expansion or Oligopoly?00:32:48 – If You Can Raise More Than Your Ecosystem, You Win00:34:27 – Are All Tasks AGI-Complete? Coding as the Test Case00:38:55 – Cursor & The Power of the App Layer00:44:05 – World Labs, Spatial Intelligence & 3D Foundation Models00:47:20 – Thinking Machines, Founder Drama & Media Narratives00:52:30 – Where Long-Term Power Accrues in the AI StackTranscriptLatent.Space - Inside AI's $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z[00:00:00] Welcome to Latent Space (Live from a16z) + Meet the Guests[00:00:00] Alessio: Hey everyone. Welcome to the Latent Space podcast, live from a 16 z. Uh, this is Alessio founder Kernel Lance, and I'm joined by Twix, editor of Latent Space.[00:00:08] swyx: Hey, hey, hey. Uh, and we're so glad to be on with you guys. Also a top AI podcast, uh, Martin Cado and Sarah Wang. Welcome, very[00:00:16] Martin Casado: happy to be here and welcome.[00:00:17] swyx: Yes, uh, we love this office. We love what you've done with the place. Uh, the new logo is everywhere now. It's, it's still getting, takes a while to get used to, but it reminds me of like sort of a callback to a more ambitious age, which I think is kind of[00:00:31] Martin Casado: definitely makes a statement.[00:00:33] swyx: Yeah.[00:00:34] Martin Casado: Not quite sure what that statement is, but it makes a statement.[00:00:37] swyx: Uh, Martin, I go back with you to Netlify.[00:00:40] Martin Casado: Yep.[00:00:40] swyx: Uh, and, uh, you know, you create a software defined networking and all, all that stuff people can read up on your background. Yep. Sarah, I'm newer to you. Uh, you, you sort of started working together on AI infrastructure stuff.[00:00:51] Sarah Wang: That's right. Yeah. Seven, seven years ago now.[00:00:53] Martin Casado: Best growth investor in the entire industry.[00:00:55] swyx: Oh, say[00:00:56] Martin Casado: more hands down there is, there is. [00:01:00] I mean, when it comes to AI companies, Sarah, I think has done the most kind of aggressive, um, investment thesis around AI models, right? So, worked for Nom Ja, Mira Ia, FEI Fey, and so just these frontier, kind of like large AI models.[00:01:15] I think, you know, Sarah's been the, the broadest investor. Is that fair?[00:01:20] Venture vs. Growth in the Frontier Model Era[00:01:20] Sarah Wang: No, I, well, I was gonna say, I think it's been a really interesting tag, tag team actually just ‘cause the, a lot of these big C deals, not only are they raising a lot of money, um, it's still a tech founder bet, which obviously is inherently early stage.[00:01:33] But the resources,[00:01:36] Martin Casado: so many, I[00:01:36] Sarah Wang: was gonna say the resources one, they just grow really quickly. But then two, the resources that they need day one are kind of growth scale. So I, the hybrid tag team that we have is. Quite effective, I think,[00:01:46] Martin Casado: what is growth these days? You know, you don't wake up if it's less than a billion or like, it's, it's actually, it's actually very like, like no, it's a very interesting time in investing because like, you know, take like the character around, right?[00:01:59] These tend to [00:02:00] be like pre monetization, but the dollars are large enough that you need to have a larger fund and the analysis. You know, because you've got lots of users. ‘cause this stuff has such high demand requires, you know, more of a number sophistication. And so most of these deals, whether it's US or other firms on these large model companies, are like this hybrid between venture growth.[00:02:18] Sarah Wang: Yeah. Total. And I think, you know, stuff like BD for example, you wouldn't usually need BD when you were seed stage trying to get market biz Devrel. Biz Devrel, exactly. Okay. But like now, sorry, I'm,[00:02:27] swyx: I'm not familiar. What, what, what does biz Devrel mean for a venture fund? Because I know what biz Devrel means for a company.[00:02:31] Sarah Wang: Yeah.[00:02:32] Compute Deals, Strategics, and the ‘Circular Funding' Question[00:02:32] Sarah Wang: You know, so a, a good example is, I mean, we talk about buying compute, but there's a huge negotiation involved there in terms of, okay, do you get equity for the compute? What, what sort of partner are you looking at? Is there a go-to market arm to that? Um, and these are just things on this scale, hundreds of millions, you know, maybe.[00:02:50] Six months into the inception of a company, you just wouldn't have to negotiate these deals before.[00:02:54] Martin Casado: Yeah. These large rounds are very complex now. Like in the past, if you did a series A [00:03:00] or a series B, like whatever, you're writing a 20 to a $60 million check and you call it a day. Now you normally have financial investors and strategic investors, and then the strategic portion always still goes with like these kind of large compute contracts, which can take months to do.[00:03:13] And so it's, it's very different ties. I've been doing this for 10 years. It's the, I've never seen anything like this.[00:03:19] swyx: Yeah. Do you have worries about the circular funding from so disease strategics?[00:03:24] Martin Casado: I mean, listen, as long as the demand is there, like the demand is there. Like the problem with the internet is the demand wasn't there.[00:03:29] swyx: Exactly. All right. This, this is like the, the whole pyramid scheme bubble thing, where like, as long as you mark to market on like the notional value of like, these deals, fine, but like once it starts to chip away, it really Well[00:03:41] Martin Casado: no, like as, as, as, as long as there's demand. I mean, you know, this, this is like a lot of these sound bites have already become kind of cliches, but they're worth saying it.[00:03:47] Right? Like during the internet days, like we were. Um, raising money to put fiber in the ground that wasn't used. And that's a problem, right? Because now you actually have a supply overhang.[00:03:58] swyx: Mm-hmm.[00:03:59] Martin Casado: And even in the, [00:04:00] the time of the, the internet, like the supply and, and bandwidth overhang, even as massive as it was in, as massive as the crash was only lasted about four years.[00:04:09] But we don't have a supply overhang. Like there's no dark GPUs, right? I mean, and so, you know, circular or not, I mean, you know, if, if someone invests in a company that, um. You know, they'll actually use the GPUs. And on the other side of it is the, is the ask for customer. So I I, I think it's a different time.[00:04:25] Sarah Wang: I think the other piece, maybe just to add onto this, and I'm gonna quote Martine in front of him, but this is probably also a unique time in that. For the first time, you can actually trace dollars to outcomes. Yeah, right. Provided that scaling laws are, are holding, um, and capabilities are actually moving forward.[00:04:40] Because if you can put translate dollars into capabilities, uh, a capability improvement, there's demand there to martine's point. But if that somehow breaks, you know, obviously that's an important assumption in this whole thing to make it work. But you know, instead of investing dollars into sales and marketing, you're, you're investing into r and d to get to the capability, um, you know, increase.[00:04:59] And [00:05:00] that's sort of been the demand driver because. Once there's an unlock there, people are willing to pay for it.[00:05:05] Alessio: Yeah.[00:05:06] Blurring Lines: Models as Infra + Apps, and the New Fundraising Flywheel[00:05:06] Alessio: Is there any difference in how you built the portfolio now that some of your growth companies are, like the infrastructure of the early stage companies, like, you know, OpenAI is now the same size as some of the cloud providers were early on.[00:05:16] Like what does that look like? Like how much information can you feed off each other between the, the two?[00:05:24] Martin Casado: There's so many lines that are being crossed right now, or blurred. Right. So we already talked about venture and growth. Another one that's being blurred is between infrastructure and apps, right? So like what is a model company?[00:05:35] Mm-hmm. Like, it's clearly infrastructure, right? Because it's like, you know, it's doing kind of core r and d. It's a horizontal platform, but it's also an app because it's um, uh, touches the users directly. And then of course. You know, the, the, the growth of these is just so high. And so I actually think you're just starting to see a, a, a new financing strategy emerge and, you know, we've had to adapt as a result of that.[00:05:59] And [00:06:00] so there's been a lot of changes. Um, you're right that these companies become platform companies very quickly. You've got ecosystem build out. So none of this is necessarily new, but the timescales of which it's happened is pretty phenomenal. And the way we'd normally cut lines before is blurred a little bit, but.[00:06:16] But that, that, that said, I mean, a lot of it also just does feel like things that we've seen in the past, like cloud build out the internet build out as well.[00:06:24] Sarah Wang: Yeah. Um, yeah, I think it's interesting, uh, I don't know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, um.[00:06:33] You raise money for compute, you pour that or you, you pour the money into compute, you get some sort of breakthrough. You funnel the breakthrough into your vertically integrated application. That could be chat GBT, that could be cloud code, you know, whatever it is. You massively gain share and get users.[00:06:49] Maybe you're even subsidizing at that point. Um, depending on your strategy. You raise money at the peak momentum and then you repeat, rinse and repeat. Um, and so. And that wasn't [00:07:00] true even two years ago, I think. Mm-hmm. And so it's sort of to your, just tying it to fundraising strategy, right? There's a, and hiring strategy.[00:07:07] All of these are tied, I think the lines are blurring even more today where everyone is, and they, but of course these companies all have API businesses and so they're these, these frenemy lines that are getting blurred in that a lot of, I mean, they have billions of dollars of API revenue, right? And so there are customers there.[00:07:23] But they're competing on the app layer.[00:07:24] Martin Casado: Yeah. So this is a really, really important point. So I, I would say for sure, venture and growth, that line is blurry app and infrastructure. That line is blurry. Um, but I don't think that that changes our practice so much. But like where the very open questions are like, does this layer in the same way.[00:07:43] Compute traditionally has like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer. But that might not, might not be the case here. It may be the case that you actually can't verticalize on the token string. Like you can't build an app like it, it necessarily goes down just because there are no [00:08:00] abstractions.[00:08:00] So those are kinda the bigger existential questions we ask. Another thing that is very different this time than in the history of computer sciences is. In the past, if you raised money, then you basically had to wait for engineering to catch up. Which famously doesn't scale like the mythical mammoth. It take a very long time.[00:08:18] But like that's not the case here. Like a model company can raise money and drop a model in a, in a year, and it's better, right? And, and it does it with a team of 20 people or 10 people. So this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before.[00:08:39] And I think everybody's trying to understand what the consequences are. So I think it's less about like. Big companies and growth and this, and more about these more systemic questions that we actually don't have answers to.[00:08:49] Alessio: Yeah, like at Kernel Labs, one of our ideas is like if you had unlimited money to spend productively to turn tokens into products, like the whole early stage [00:09:00] market is very different because today you're investing X amount of capital to win a deal because of price structure and whatnot, and you're kind of pot committing.[00:09:07] Yeah. To a certain strategy for a certain amount of time. Yeah. But if you could like iteratively spin out companies and products and just throw, I, I wanna spend a million dollar of inference today and get a product out tomorrow.[00:09:18] swyx: Yeah.[00:09:19] Alessio: Like, we should get to the point where like the friction of like token to product is so low that you can do this and then you can change the Right, the early stage venture model to be much more iterative.[00:09:30] And then every round is like either 100 k of inference or like a hundred million from a 16 Z. There's no, there's no like $8 million C round anymore. Right.[00:09:38] When Frontier Labs Outspend the Entire App Ecosystem[00:09:38] Martin Casado: But, but, but, but there's a, there's a, the, an industry structural question that we don't know the answer to, which involves the frontier models, which is, let's take.[00:09:48] Anthropic it. Let's say Anthropic has a state-of-the-art model that has some large percentage of market share. And let's say that, uh, uh, uh, you know, uh, a company's building smaller models [00:10:00] that, you know, use the bigger model in the background, open 4.5, but they add value on top of that. Now, if Anthropic can raise three times more.[00:10:10] Every subsequent round, they probably can raise more money than the entire app ecosystem that's built on top of it. And if that's the case, they can expand beyond everything built on top of it. It's like imagine like a star that's just kind of expanding, so there could be a systemic. There could be a, a systemic situation where the soda models can raise so much money that they can out pay anybody that bills on top of ‘em, which would be something I don't think we've ever seen before just because we were so bottlenecked in engineering, and this is a very open question.[00:10:41] swyx: Yeah. It's, it is almost like bitter lesson applied to the startup industry.[00:10:45] Martin Casado: Yeah, a hundred percent. It literally becomes an issue of like raise capital, turn that directly into growth. Use that to raise three times more. Exactly. And if you can keep doing that, you literally can outspend any company that's built the, not any company.[00:10:57] You can outspend the aggregate of companies on top of [00:11:00] you and therefore you'll necessarily take their share, which is crazy.[00:11:02] swyx: Would you say that kind of happens in character? Is that the, the sort of postmortem on. What happened?[00:11:10] Sarah Wang: Um,[00:11:10] Martin Casado: no.[00:11:12] Sarah Wang: Yeah, because I think so,[00:11:13] swyx: I mean the actual postmortem is, he wanted to go back to Google.[00:11:15] Exactly. But like[00:11:18] Martin Casado: that's another difference that[00:11:19] Sarah Wang: you said[00:11:21] Martin Casado: it. We should talk, we should actually talk about that.[00:11:22] swyx: Yeah,[00:11:22] Sarah Wang: that's[00:11:23] swyx: Go for it. Take it. Take,[00:11:23] Sarah Wang: yeah.[00:11:24] Character.AI, Founder Goals (AGI vs Product), and GPU Allocation Tradeoffs[00:11:24] Sarah Wang: I was gonna say, I think, um. The, the, the character thing raises actually a different issue, which actually the Frontier Labs will face as well. So we'll see how they handle it.[00:11:34] But, um, so we invest in character in January, 2023, which feels like eons ago, I mean, three years ago. Feels like lifetimes ago. But, um, and then they, uh, did the IP licensing deal with Google in August, 2020. Uh, four. And so, um, you know, at the time, no, you know, he's talked publicly about this, right? He wanted to Google wouldn't let him put out products in the world.[00:11:56] That's obviously changed drastically. But, um, he went to go do [00:12:00] that. Um, but he had a product attached. The goal was, I mean, it's Nome Shair, he wanted to get to a GI. That was always his personal goal. But, you know, I think through collecting data, right, and this sort of very human use case, that the character product.[00:12:13] Originally was and still is, um, was one of the vehicles to do that. Um, I think the real reason that, you know. I if you think about the, the stress that any company feels before, um, you ultimately going one way or the other is sort of this a GI versus product. Um, and I think a lot of the big, I think, you know, opening eyes, feeling that, um, anthropic if they haven't started, you know, felt it, certainly given the success of their products, they may start to feel that soon.[00:12:39] And the real. I think there's real trade-offs, right? It's like how many, when you think about GPUs, that's a limited resource. Where do you allocate the GPUs? Is it toward the product? Is it toward new re research? Right? Is it, or long-term research, is it toward, um, n you know, near to midterm research? And so, um, in a case where you're resource constrained, um, [00:13:00] of course there's this fundraising game you can play, right?[00:13:01] But the fund, the market was very different back in 2023 too. Um. I think the best researchers in the world have this dilemma of, okay, I wanna go all in on a GI, but it's the product usage revenue flywheel that keeps the revenue in the house to power all the GPUs to get to a GI. And so it does make, um, you know, I think it sets up an interesting dilemma for any startup that has trouble raising up until that level, right?[00:13:27] And certainly if you don't have that progress, you can't continue this fly, you know, fundraising flywheel.[00:13:32] Martin Casado: I would say that because, ‘cause we're keeping track of all of the things that are different, right? Like, you know, venture growth and uh, app infra and one of the ones is definitely the personalities of the founders.[00:13:45] It's just very different this time I've been. Been doing this for a decade and I've been doing startups for 20 years. And so, um, I mean a lot of people start this to do a GI and we've never had like a unified North star that I recall in the same [00:14:00] way. Like people built companies to start companies in the past.[00:14:02] Like that was what it was. Like I would create an internet company, I would create infrastructure company, like it's kind of more engineering builders and this is kind of a different. You know, mentality. And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider a GI, but others have not.[00:14:20] And so like there is always this tension with personnel. And so I think we're seeing more kind of founder movement.[00:14:27] Sarah Wang: Yeah.[00:14:27] Martin Casado: You know, as a fraction of founders than we've ever seen. I mean, maybe since like, I don't know the time of like Shockly and the trade DUR aid or something like that. Way back in the beginning of the industry, I, it's a very, very.[00:14:38] Unusual time of personnel.[00:14:39] Sarah Wang: Totally.[00:14:40] Talent Wars, Mega-Comp, and the Rise of Acquihire M&A[00:14:40] Sarah Wang: And it, I think it's exacerbated by the fact that talent wars, I mean, every industry has talent wars, but not at this magnitude, right? No. Yeah. Very rarely can you see someone get poached for $5 billion. That's hard to compete with. And then secondly, if you're a founder in ai, you could fart and it would be on the front page of, you know, the information these days.[00:14:59] And so there's [00:15:00] sort of this fishbowl effect that I think adds to the deep anxiety that, that these AI founders are feeling.[00:15:06] Martin Casado: Hmm.[00:15:06] swyx: Uh, yes. I mean, just on, uh, briefly comment on the founder, uh, the sort of. Talent wars thing. I feel like 2025 was just like a blip. Like I, I don't know if we'll see that again.[00:15:17] ‘cause meta built the team. Like, I don't know if, I think, I think they're kind of done and like, who's gonna pay more than meta? I, I don't know.[00:15:23] Martin Casado: I, I agree. So it feels so, it feel, it feels this way to me too. It's like, it is like, basically Zuckerberg kind of came out swinging and then now he's kind of back to building.[00:15:30] Yeah,[00:15:31] swyx: yeah. You know, you gotta like pay up to like assemble team to rush the job, whatever. But then now, now you like you, you made your choices and now they got a ship.[00:15:38] Martin Casado: I mean, the, the o other side of that is like, you know, like we're, we're actually in the job hiring market. We've got 600 people here. I hire all the time.[00:15:44] I've got three open recs if anybody's interested, that's listening to this for investor. Yeah, on, on the team, like on the investing side of the team, like, and, um, a lot of the people we talk to have acting, you know, active, um, offers for 10 million a year or something like that. And like, you know, and we pay really, [00:16:00] really well.[00:16:00] And just to see what's out on the market is really, is really remarkable. And so I would just say it's actually, so you're right, like the really flashy one, like I will get someone for, you know, a billion dollars, but like the inflated, um, uh, trickles down. Yeah, it is still very active today. I mean,[00:16:18] Sarah Wang: yeah, you could be an L five and get an offer in the tens of millions.[00:16:22] Okay. Yeah. Easily. Yeah. It's so I think you're right that it felt like a blip. I hope you're right. Um, but I think it's been, the steady state is now, I think got pulled up. Yeah. Yeah. I'll pull up for[00:16:31] Martin Casado: sure. Yeah.[00:16:32] Alessio: Yeah. And I think that's breaking the early stage founder math too. I think before a lot of people would be like, well, maybe I should just go be a founder instead of like getting paid.[00:16:39] Yeah. 800 KA million at Google. But if I'm getting paid. Five, 6 million. That's different but[00:16:45] Martin Casado: on. But on the other hand, there's more strategic money than we've ever seen historically, right? Mm-hmm. And so, yep. The economics, the, the, the, the calculus on the economics is very different in a number of ways. And, uh, it's crazy.[00:16:58] It's cra it's causing like a, [00:17:00] a, a, a ton of change in confusion in the market. Some very positive, sub negative, like, so for example, the other side of the, um. The co-founder, like, um, acquisition, you know, mark Zuckerberg poaching someone for a lot of money is like, we were actually seeing historic amount of m and a for basically acquihires, right?[00:17:20] That you like, you know, really good outcomes from a venture perspective that are effective acquihires, right? So I would say it's probably net positive from the investment standpoint, even though it seems from the headlines to be very disruptive in a negative way.[00:17:33] Alessio: Yeah.[00:17:33] What's Underfunded: Boring Software, Robotics Skepticism, and Custom Silicon Economics[00:17:33] Alessio: Um, let's talk maybe about what's not being invested in, like maybe some interesting ideas that you would see more people build or it, it seems in a way, you know, as ycs getting more popular, it's like access getting more popular.[00:17:47] There's a startup school path that a lot of founders take and they know what's hot in the VC circles and they know what gets funded. Uh, and there's maybe not as much risk appetite for. Things outside of that. Um, I'm curious if you feel [00:18:00] like that's true and what are maybe, uh, some of the areas, uh, that you think are under discussed?[00:18:06] Martin Casado: I mean, I actually think that we've taken our eye off the ball in a lot of like, just traditional, you know, software companies. Um, so like, I mean. You know, I think right now there's almost a barbell, like you're like the hot thing on X, you're deep tech.[00:18:21] swyx: Mm-hmm.[00:18:22] Martin Casado: Right. But I, you know, I feel like there's just kind of a long, you know, list of like good.[00:18:28] Good companies that will be around for a long time in very large markets. Say you're building a database, you know, say you're building, um, you know, kind of monitoring or logging or tooling or whatever. There's some good companies out there right now, but like, they have a really hard time getting, um, the attention of investors.[00:18:43] And it's almost become a meme, right? Which is like, if you're not basically growing from zero to a hundred in a year, you're not interesting, which is just, is the silliest thing to say. I mean, think of yourself as like an introvert person, like, like your personal money, right? Mm-hmm. So. Your personal money, will you put it in the stock market at 7% or you put it in this company growing five x in a very large [00:19:00] market?[00:19:00] Of course you can put it in the company five x. So it's just like we say these stupid things, like if you're not going from zero to a hundred, but like those, like who knows what the margins of those are mean. Clearly these are good investments. True for anybody, right? True. Like our LPs want whatever.[00:19:12] Three x net over, you know, the life cycle of a fund, right? So a, a company in a big market growing five X is a great investment. We'd, everybody would be happy with these returns, but we've got this kind of mania on these, these strong growths. And so I would say that that's probably the most underinvested sector.[00:19:28] Right now.[00:19:29] swyx: Boring software, boring enterprise software.[00:19:31] Martin Casado: Traditional. Really good company.[00:19:33] swyx: No, no AI here.[00:19:34] Martin Casado: No. Like boring. Well, well, the AI of course is pulling them into use cases. Yeah, but that's not what they're, they're not on the token path, right? Yeah. Let's just say that like they're software, but they're not on the token path.[00:19:41] Like these are like they're great investments from any definition except for like random VC on Twitter saying VC on x, saying like, it's not growing fast enough. What do you[00:19:52] Sarah Wang: think? Yeah, maybe I'll answer a slightly different. Question, but adjacent to what you asked, um, which is maybe an area that we're not, uh, investing [00:20:00] right now that I think is a question and we're spending a lot of time in regardless of whether we pull the trigger or not.[00:20:05] Um, and it would probably be on the hardware side, actually. Robotics, right? And the robotics side. Robotics. Right. Which is, it's, I don't wanna say that it's not getting funding ‘cause it's clearly, uh, it's, it's sort of non-consensus to almost not invest in robotics at this point. But, um, we spent a lot of time in that space and I think for us, we just haven't seen the chat GPT moment.[00:20:22] Happen on the hardware side. Um, and the funding going into it feels like it's already. Taking that for granted.[00:20:30] Martin Casado: Yeah. Yeah. But we also went through the drone, you know, um, there's a zip line right, right out there. What's that? Oh yeah, there's a zip line. Yeah. What the drone, what the av And like one of the takeaways is when it comes to hardware, um, most companies will end up verticalizing.[00:20:46] Like if you're. If you're investing in a robot company for an A for agriculture, you're investing in an ag company. ‘cause that's the competition and that's surprising. And that's supply chain. And if you're doing it for mining, that's mining. And so the ad team does a lot of that type of stuff ‘cause they actually set up to [00:21:00] diligence that type of work.[00:21:01] But for like horizontal technology investing, there's very little when it comes to robots just because it's so fit for, for purpose. And so we kinda like to look at software. Solutions or horizontal solutions like applied intuition. Clearly from the AV wave deep map, clearly from the AV wave, I would say scale AI was actually a horizontal one for That's fair, you know, for robotics early on.[00:21:23] And so that sort of thing we're very, very interested. But the actual like robot interacting with the world is probably better for different team. Agree.[00:21:30] Alessio: Yeah, I'm curious who these teams are supposed to be that invest in them. I feel like everybody's like, yeah, robotics, it's important and like people should invest in it.[00:21:38] But then when you look at like the numbers, like the capital requirements early on versus like the moment of, okay, this is actually gonna work. Let's keep investing. That seems really hard to predict in a way that is not,[00:21:49] Martin Casado: I think co, CO two, kla, gc, I mean these are all invested in in Harvard companies. He just, you know, and [00:22:00] listen, I mean, it could work this time for sure.[00:22:01] Right? I mean if Elon's doing it, he's like, right. Just, just the fact that Elon's doing it means that there's gonna be a lot of capital and a lot of attempts for a long period of time. So that alone maybe suggests that we should just be investing in robotics just ‘cause you have this North star who's Elon with a humanoid and that's gonna like basically willing into being an industry.[00:22:17] Um, but we've just historically found like. We're a huge believer that this is gonna happen. We just don't feel like we're in a good position to diligence these things. ‘cause again, robotics companies tend to be vertical. You really have to understand the market they're being sold into. Like that's like that competitive equilibrium with a human being is what's important.[00:22:34] It's not like the core tech and like we're kind of more horizontal core tech type investors. And this is Sarah and I. Yeah, the ad team is different. They can actually do these types of things.[00:22:42] swyx: Uh, just to clarify, AD stands for[00:22:44] Martin Casado: American Dynamism.[00:22:45] swyx: Alright. Okay. Yeah, yeah, yeah. Uh, I actually, I do have a related question that, first of all, I wanna acknowledge also just on the, on the chip side.[00:22:51] Yeah. I, I recall a podcast that where you were on, i, I, I think it was the a CC podcast, uh, about two or three years ago where you, where you suddenly said [00:23:00] something, which really stuck in my head about how at some point, at some point kind of scale it makes sense to. Build a custom aic Yes. For per run.[00:23:07] Martin Casado: Yes.[00:23:07] It's crazy. Yeah.[00:23:09] swyx: We're here and I think you, you estimated 500 billion, uh, something.[00:23:12] Martin Casado: No, no, no. A billion, a billion dollar training run of $1 billion training run. It makes sense to actually do a custom meic if you can do it in time. The question now is timelines. Yeah, but not money because just, just, just rough math.[00:23:22] If it's a billion dollar training. Then the inference for that model has to be over a billion, otherwise it won't be solvent. So let's assume it's, if you could save 20%, which you could save much more than that with an ASIC 20%, that's $200 million. You can tape out a chip for $200 million. Right? So now you can literally like justify economically, not timeline wise.[00:23:41] That's a different issue. An ASIC per model, which[00:23:44] swyx: is because that, that's how much we leave on the table every single time. We, we, we do like generic Nvidia.[00:23:48] Martin Casado: Exactly. Exactly. No, it, it is actually much more than that. You could probably get, you know, a factor of two, which would be 500 million.[00:23:54] swyx: Typical MFU would be like 50.[00:23:55] Yeah, yeah. And that's good.[00:23:57] Martin Casado: Exactly. Yeah. Hundred[00:23:57] swyx: percent. Um, so, so, yeah, and I mean, and I [00:24:00] just wanna acknowledge like, here we are in, in, in 2025 and opening eyes confirming like Broadcom and all the other like custom silicon deals, which is incredible. I, I think that, uh, you know, speaking about ad there's, there's a really like interesting tie in that obviously you guys are hit on, which is like these sort, this sort of like America first movement or like sort of re industrialized here.[00:24:17] Yeah. Uh, move TSMC here, if that's possible. Um, how much overlap is there from ad[00:24:23] Martin Casado: Yeah.[00:24:23] swyx: To, I guess, growth and, uh, investing in particularly like, you know, US AI companies that are strongly bounded by their compute.[00:24:32] Martin Casado: Yeah. Yeah. So I mean, I, I would view, I would view AD as more as a market segmentation than like a mission, right?[00:24:37] So the market segmentation is, it has kind of regulatory compliance issues or government, you know, sale or it deals with like hardware. I mean, they're just set up to, to, to, to, to. To diligence those types of companies. So it's a more of a market segmentation thing. I would say the entire firm. You know, which has been since it is been intercepted, you know, has geographical biases, right?[00:24:58] I mean, for the longest time we're like, you [00:25:00] know, bay Area is gonna be like, great, where the majority of the dollars go. Yeah. And, and listen, there, there's actually a lot of compounding effects for having a geographic bias. Right. You know, everybody's in the same place. You've got an ecosystem, you're there, you've got presence, you've got a network.[00:25:12] Um, and, uh, I mean, I would say the Bay area's very much back. You know, like I, I remember during pre COVID, like it was like almost Crypto had kind of. Pulled startups away. Miami from the Bay Area. Miami, yeah. Yeah. New York was, you know, because it's so close to finance, came up like Los Angeles had a moment ‘cause it was so close to consumer, but now it's kind of come back here.[00:25:29] And so I would say, you know, we tend to be very Bay area focused historically, even though of course we've asked all over the world. And then I would say like, if you take the ring out, you know, one more, it's gonna be the US of course, because we know it very well. And then one more is gonna be getting us and its allies and Yeah.[00:25:44] And it goes from there.[00:25:45] Sarah Wang: Yeah,[00:25:45] Martin Casado: sorry.[00:25:46] Sarah Wang: No, no. I agree. I think from a, but I think from the intern that that's sort of like where the companies are headquartered. Maybe your questions on supply chain and customer base. Uh, I, I would say our customers are, are, our companies are fairly international from that perspective.[00:25:59] Like they're selling [00:26:00] globally, right? They have global supply chains in some cases.[00:26:03] Martin Casado: I would say also the stickiness is very different.[00:26:05] Sarah Wang: Yeah.[00:26:05] Martin Casado: Historically between venture and growth, like there's so much company building in venture, so much so like hiring the next PM. Introducing the customer, like all of that stuff.[00:26:15] Like of course we're just gonna be stronger where we have our network and we've been doing business for 20 years. I've been in the Bay Area for 25 years, so clearly I'm just more effective here than I would be somewhere else. Um, where I think, I think for some of the later stage rounds, the companies don't need that much help.[00:26:30] They're already kind of pretty mature historically, so like they can kind of be everywhere. So there's kind of less of that stickiness. This is different in the AI time. I mean, Sarah is now the, uh, chief of staff of like half the AI companies in, uh, in the Bay Area right now. She's like, ops Ninja Biz, Devrel, BizOps.[00:26:48] swyx: Are, are you, are you finding much AI automation in your work? Like what, what is your stack.[00:26:53] Sarah Wang: Oh my, in my personal stack.[00:26:54] swyx: I mean, because like, uh, by the way, it's the, the, the reason for this is it is triggering, uh, yeah. We, like, I'm hiring [00:27:00] ops, ops people. Um, a lot of ponders I know are also hiring ops people and I'm just, you know, it's opportunity Since you're, you're also like basically helping out with ops with a lot of companies.[00:27:09] What are people doing these days? Because it's still very manual as far as I can tell.[00:27:13] Sarah Wang: Hmm. Yeah. I think the things that we help with are pretty network based, um, in that. It's sort of like, Hey, how do do I shortcut this process? Well, let's connect you to the right person. So there's not quite an AI workflow for that.[00:27:26] I will say as a growth investor, Claude Cowork is pretty interesting. Yeah. Like for the first time, you can actually get one shot data analysis. Right. Which, you know, if you're gonna do a customer database, analyze a cohort retention, right? That's just stuff that you had to do by hand before. And our team, the other, it was like midnight and the three of us were playing with Claude Cowork.[00:27:47] We gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like, aha moment. That sounds so boring. But you know, that's, that's the kind of thing that a growth investor is like, [00:28:00] you know, slaving away on late at night. Um, done in a few seconds.[00:28:03] swyx: Yeah. You gotta wonder what the whole, like, philanthropic labs, which is like their new sort of products studio.[00:28:10] Yeah. What would that be worth as an independent, uh, startup? You know, like a[00:28:14] Martin Casado: lot.[00:28:14] Sarah Wang: Yeah, true.[00:28:16] swyx: Yeah. You[00:28:16] Martin Casado: gotta hand it to them. They've been executing incredibly well.[00:28:19] swyx: Yeah. I, I mean, to me, like, you know, philanthropic, like building on cloud code, I think, uh, it makes sense to me the, the real. Um, pedal to the metal, whatever the, the, the phrase is, is when they start coming after consumer with, uh, against OpenAI and like that is like red alert at Open ai.[00:28:35] Oh, I[00:28:35] Martin Casado: think they've been pretty clear. They're enterprise focused.[00:28:37] swyx: They have been, but like they've been free. Here's[00:28:40] Martin Casado: care publicly,[00:28:40] swyx: it's enterprise focused. It's coding. Right. Yeah.[00:28:43] AI Labs vs Startups: Disruption, Undercutting & the Innovator's Dilemma[00:28:43] swyx: And then, and, but here's cloud, cloud, cowork, and, and here's like, well, we, uh, they, apparently they're running Instagram ads for Claudia.[00:28:50] I, on, you know, for, for people on, I get them all the time. Right. And so, like,[00:28:54] Martin Casado: uh,[00:28:54] swyx: it, it's kind of like this, the disruption thing of, uh, you know. Mo Open has been doing, [00:29:00] consumer been doing the, just pursuing general intelligence in every mo modality, and here's a topic that only focus on this thing, but now they're sort of undercutting and doing the whole innovator's dilemma thing on like everything else.[00:29:11] Martin Casado: It's very[00:29:11] swyx: interesting.[00:29:12] Martin Casado: Yeah, I mean there's, there's a very open que so for me there's like, do you know that meme where there's like the guy in the path and there's like a path this way? There's a path this way. Like one which way Western man. Yeah. Yeah.[00:29:23] Two Futures for AI: Infinite Market vs AGI Oligopoly[00:29:23] Martin Casado: And for me, like, like all the entire industry kind of like hinges on like two potential futures.[00:29:29] So in, in one potential future, um, the market is infinitely large. There's perverse economies of scale. ‘cause as soon as you put a model out there, like it kind of sublimates and all the other models catch up and like, it's just like software's being rewritten and fractured all over the place and there's tons of upside and it just grows.[00:29:48] And then there's another path which is like, well. Maybe these models actually generalize really well, and all you have to do is train them with three times more money. That's all you have to [00:30:00] do, and it'll just consume everything beyond it. And if that's the case, like you end up with basically an oligopoly for everything, like, you know mm-hmm.[00:30:06] Because they're perfectly general and like, so this would be like the, the a GI path would be like, these are perfectly general. They can do everything. And this one is like, this is actually normal software. The universe is complicated. You've got, and nobody knows the answer.[00:30:18] The Economics Reality Check: Gross Margins, Training Costs & Borrowing Against the Future[00:30:18] Martin Casado: My belief is if you actually look at the numbers of these companies, so generally if you look at the numbers of these companies, if you look at like the amount they're making and how much they, they spent training the last model, they're gross margin positive.[00:30:30] You're like, oh, that's really working. But if you look at like. The current training that they're doing for the next model, their gross margin negative. So part of me thinks that a lot of ‘em are kind of borrowing against the future and that's gonna have to slow down. It's gonna catch up to them at some point in time, but we don't really know.[00:30:47] Sarah Wang: Yeah.[00:30:47] Martin Casado: Does that make sense? Like, I mean, it could be, it could be the case that the only reason this is working is ‘cause they can raise that next round and they can train that next model. ‘cause these models have such a short. Life. And so at some point in time, like, you know, they won't be able to [00:31:00] raise that next round for the next model and then things will kind of converge and fragment again.[00:31:03] But right now it's not.[00:31:04] Sarah Wang: Totally. I think the other, by the way, just, um, a meta point. I think the other lesson from the last three years is, and we talk about this all the time ‘cause we're on this. Twitter X bubble. Um, cool. But, you know, if you go back to, let's say March, 2024, that period, it felt like a, I think an open source model with an, like a, you know, benchmark leading capability was sort of launching on a daily basis at that point.[00:31:27] And, um, and so that, you know, that's one period. Suddenly it's sort of like open source takes over the world. There's gonna be a plethora. It's not an oligopoly, you know, if you fast, you know, if you, if you rewind time even before that GPT-4 was number one for. Nine months, 10 months. It's a long time. Right.[00:31:44] Um, and of course now we're in this era where it feels like an oligopoly, um, maybe some very steady state shifts and, and you know, it could look like this in the future too, but it just, it's so hard to call. And I think the thing that keeps, you know, us up at [00:32:00] night in, in a good way and bad way, is that the capability progress is actually not slowing down.[00:32:06] And so until that happens, right, like you don't know what's gonna look like.[00:32:09] Martin Casado: But I, I would, I would say for sure it's not converged, like for sure, like the systemic capital flows have not converged, meaning right now it's still borrowing against the future to subsidize growth currently, which you can do that for a period of time.[00:32:23] But, but you know, at the end, at some point the market will rationalize that and just nobody knows what that will look like.[00:32:29] Alessio: Yeah.[00:32:29] Martin Casado: Or, or like the drop in price of compute will, will, will save them. Who knows?[00:32:34] Alessio: Yeah. Yeah. I think the models need to ask them to, to specific tasks. You know? It's like, okay, now Opus 4.5 might be a GI at some specific task, and now you can like depreciate the model over a longer time.[00:32:45] I think now, now, right now there's like no old model.[00:32:47] Martin Casado: No, but let, but lemme just change that mental, that's, that used to be my mental model. Lemme just change it a little bit.[00:32:53] Capital as a Weapon vs Task Saturation: Where Real Enterprise Value Gets Built[00:32:53] Martin Casado: If you can raise three times, if you can raise more than the aggregate of anybody that uses your models, that doesn't even matter.[00:32:59] It doesn't [00:33:00] even matter. See what I'm saying? Like, yeah. Yeah. So, so I have an API Business. My API business is 60% margin, or 70% margin, or 80% margin is a high margin business. So I know what everybody is using. If I can raise more money than the aggregate of everybody that's using it, I will consume them whether I'm a GI or not.[00:33:14] And I will know if they're using it ‘cause they're using it. And like, unlike in the past where engineering stops me from doing that.[00:33:21] Alessio: Mm-hmm.[00:33:21] Martin Casado: It is very straightforward. You just train. So I also thought it was kind of like, you must ask the code a GI, general, general, general. But I think there's also just a possibility that the, that the capital markets will just give them the, the, the ammunition to just go after everybody on top of ‘em.[00:33:36] Sarah Wang: I, I do wonder though, to your point, um, if there's a certain task that. Getting marginally better isn't actually that much better. Like we've asked them to it, to, you know, we can call it a GI or whatever, you know, actually, Ali Goi talks about this, like we're already at a GI for a lot of functions in the enterprise.[00:33:50] Um. That's probably those for those tasks, you probably could build very specific companies that focus on just getting as much value out of that task that isn't [00:34:00] coming from the model itself. There's probably a rich enterprise business to be built there. I mean, could be wrong on that, but there's a lot of interesting examples.[00:34:08] So, right, if you're looking the legal profession or, or whatnot, and maybe that's not a great one ‘cause the models are getting better on that front too, but just something where it's a bit saturated, then the value comes from. Services. It comes from implementation, right? It comes from all these things that actually make it useful to the end customer.[00:34:24] Martin Casado: Sorry, what am I, one more thing I think is, is underused in all of this is like, to what extent every task is a GI complete.[00:34:31] Sarah Wang: Mm-hmm.[00:34:32] Martin Casado: Yeah. I code every day. It's so fun.[00:34:35] Sarah Wang: That's a core question. Yeah.[00:34:36] Martin Casado: And like. When I'm talking to these models, it's not just code. I mean, it's everything, right? Like I, you know, like it's,[00:34:43] swyx: it's healthcare.[00:34:44] It's,[00:34:44] Martin Casado: I mean, it's[00:34:44] swyx: Mele,[00:34:45] Martin Casado: but it's every, it is exactly that. Like, yeah, that's[00:34:47] Sarah Wang: great support. Yeah.[00:34:48] Martin Casado: It's everything. Like I'm asking these models to, yeah, to understand compliance. I'm asking these models to go search the web. I'm asking these models to talk about things I know in the history, like it's having a full conversation with me while I, I engineer, and so it could be [00:35:00] the case that like, mm-hmm.[00:35:01] The most a, you know, a GI complete, like I'm not an a GI guy. Like I think that's, you know, but like the most a GI complete model will is win independent of the task. And we don't know the answer to that one either.[00:35:11] swyx: Yeah.[00:35:12] Martin Casado: But it seems to me that like, listen, codex in my experience is for sure better than Opus 4.5 for coding.[00:35:18] Like it finds the hardest bugs that I work in with. Like, it is, you know. The smartest developers. I don't work on it. It's great. Um, but I think Opus 4.5 is actually very, it's got a great bedside manner and it really, and it, it really matters if you're building something very complex because like, it really, you know, like you're, you're, you're a partner and a brainstorming partner for somebody.[00:35:38] And I think we don't discuss enough how every task kind of has that quality.[00:35:42] swyx: Mm-hmm.[00:35:43] Martin Casado: And what does that mean to like capital investment and like frontier models and Submodels? Yeah.[00:35:47] Why “Coding Models” Keep Collapsing into Generalists (Reasoning vs Taste)[00:35:47] Martin Casado: Like what happened to all the special coding models? Like, none of ‘em worked right. So[00:35:51] Alessio: some of them, they didn't even get released.[00:35:53] Magical[00:35:54] Martin Casado: Devrel. There's a whole, there's a whole host. We saw a bunch of them and like there's this whole theory that like, there could be, and [00:36:00] I think one of the conclusions is, is like there's no such thing as a coding model,[00:36:04] Alessio: you know?[00:36:04] Martin Casado: Like, that's not a thing. Like you're talking to another human being and it's, it's good at coding, but like it's gotta be good at everything.[00:36:10] swyx: Uh, minor disagree only because I, I'm pretty like, have pretty high confidence that basically open eye will always release a GPT five and a GT five codex. Like that's the code's. Yeah. The way I call it is one for raisin, one for Tiz. Um, and, and then like someone internal open, it was like, yeah, that's a good way to frame it.[00:36:32] Martin Casado: That's so funny.[00:36:33] swyx: Uh, but maybe it, maybe it collapses down to reason and that's it. It's not like a hundred dimensions doesn't life. Yeah. It's two dimensions. Yeah, yeah, yeah, yeah. Like and exactly. Beside manner versus coding. Yeah.[00:36:43] Martin Casado: Yeah.[00:36:44] swyx: It's, yeah.[00:36:46] Martin Casado: I, I think for, for any, it's hilarious. For any, for anybody listening to this for, for, for, I mean, for you, like when, when you're like coding or using these models for something like that.[00:36:52] Like actually just like be aware of how much of the interaction has nothing to do with coding and it just turns out to be a large portion of it. And so like, you're, I [00:37:00] think like, like the best Soto ish model. You know, it is going to remain very important no matter what the task is.[00:37:06] swyx: Yeah.[00:37:07] What He's Actually Coding: Gaussian Splats, Spark.js & 3D Scene Rendering Demos[00:37:07] swyx: Uh, speaking of coding, uh, I, I'm gonna be cheeky and ask like, what actually are you coding?[00:37:11] Because obviously you, you could code anything and you are obviously a busy investor and a manager of the good. Giant team. Um, what are you calling?[00:37:18] Martin Casado: I help, um, uh, FEFA at World Labs. Uh, it's one of the investments and um, and they're building a foundation model that creates 3D scenes.[00:37:27] swyx: Yeah, we had it on the pod.[00:37:28] Yeah. Yeah,[00:37:28] Martin Casado: yeah. And so these 3D scenes are Gaussian splats, just by the way that kind of AI works. And so like, you can reconstruct a scene better with, with, with radiance feels than with meshes. ‘cause like they don't really have topology. So, so they, they, they produce each. Beautiful, you know, 3D rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn't great.[00:37:50] It's just never, you know, it's always been meshes and like, things like unreal use meshes. And so I work on a open source library called Spark js, which is a. Uh, [00:38:00] a JavaScript rendering layer ready for Gaussian splats. And it's just because, you know, um, you, you, you need that support and, and right now there's kind of a three js moment that's all meshes and so like, it's become kind of the default in three Js ecosystem.[00:38:13] As part of that to kind of exercise the library, I just build a whole bunch of cool demos. So if you see me on X, you see like all my demos and all the world building, but all of that is just to exercise this, this library that I work on. ‘cause it's actually a very tough algorithmics problem to actually scale a library that much.[00:38:29] And just so you know, this is ancient history now, but 30 years ago I paid for undergrad, you know, working on game engines in college in the late nineties. So I've got actually a back and it's very old background, but I actually have a background in this and so a lot of it's fun. You know, but, but the, the, the, the whole goal is just for this rendering library to, to,[00:38:47] Sarah Wang: are you one of the most active contributors?[00:38:49] The, their GitHub[00:38:50] Martin Casado: spark? Yes.[00:38:51] Sarah Wang: Yeah, yeah.[00:38:51] Martin Casado: There's only two of us there, so, yes. No, so by the way, so the, the pri The pri, yeah. Yeah. So the primary developer is a [00:39:00] guy named Andres Quist, who's an absolute genius. He and I did our, our PhDs together. And so like, um, we studied for constant Quas together. It was almost like hanging out with an old friend, you know?[00:39:09] And so like. So he, he's the core, core guy. I did mostly kind of, you know, the side I run venture fund.[00:39:14] swyx: It's amazing. Like five years ago you would not have done any of this. And it brought you back[00:39:19] Martin Casado: the act, the Activ energy, you're still back. Energy was so high because you had to learn all the framework b******t.[00:39:23] Man, I f*****g used to hate that. And so like, now I don't have to deal with that. I can like focus on the algorithmics so I can focus on the scaling and I,[00:39:29] swyx: yeah. Yeah.[00:39:29] LLMs vs Spatial Intelligence + How to Value World Labs' 3D Foundation Model[00:39:29] swyx: And then, uh, I'll observe one irony and then I'll ask a serious investor question, uh, which is like, the irony is FFE actually doesn't believe that LMS can lead us to spatial intelligence.[00:39:37] And here you are using LMS to like help like achieve spatial intelligence. I just see, I see some like disconnect in there.[00:39:45] Martin Casado: Yeah. Yeah. So I think, I think, you know, I think, I think what she would say is LLMs are great to help with coding.[00:39:51] swyx: Yes.[00:39:51] Martin Casado: But like, that's very different than a model that actually like provides, they, they'll never have the[00:39:56] swyx: spatial inte[00:39:56] Martin Casado: issues.[00:39:56] And listen, our brains clearly listen, our brains, brains clearly have [00:40:00] both our, our brains clearly have a language reasoning section and they clearly have a spatial reasoning section. I mean, it's just, you know, these are two pretty independent problems.[00:40:07] swyx: Okay. And you, you, like, I, I would say that the, the one data point I recently had, uh, against it is the DeepMind, uh, IMO Gold, where, so, uh, typically the, the typical answer is that this is where you start going down the neuros symbolic path, right?[00:40:21] Like one, uh, sort of very sort of abstract reasoning thing and one form, formal thing. Um, and that's what. DeepMind had in 2024 with alpha proof, alpha geometry, and now they just use deep think and just extended thinking tokens. And it's one model and it's, and it's in LM.[00:40:36] Martin Casado: Yeah, yeah, yeah, yeah, yeah.[00:40:37] swyx: And so that, that was my indication of like, maybe you don't need a separate system.[00:40:42] Martin Casado: Yeah. So, so let me step back. I mean, at the end of the day, at the end of the day, these things are like nodes in a graph with weights on them. Right. You know, like it can be modeled like if you, if you distill it down. But let me just talk about the two different substrates. Let's, let me put you in a dark room.[00:40:56] Like totally black room. And then let me just [00:41:00] describe how you exit it. Like to your left, there's a table like duck below this thing, right? I mean like the chances that you're gonna like not run into something are very low. Now let me like turn on the light and you actually see, and you can do distance and you know how far something away is and like where it is or whatever.[00:41:17] Then you can do it, right? Like language is not the right primitives to describe. The universe because it's not exact enough. So that's all Faye, Faye is talking about. When it comes to like spatial reasoning, it's like you actually have to know that this is three feet far, like that far away. It is curved.[00:41:37] You have to understand, you know, the, like the actual movement through space.[00:41:40] swyx: Yeah.[00:41:40] Martin Casado: So I do, I listen, I do think at the end of these models are definitely converging as far as models, but there's, there's, there's different representations of problems you're solving. One is language. Which, you know, that would be like describing to somebody like what to do.[00:41:51] And the other one is actually just showing them and the space reasoning is just showing them.[00:41:55] swyx: Yeah, yeah, yeah. Right. Got it, got it. Uh, the, in the investor question was on, on, well labs [00:42:00] is, well, like, how do I value something like this? What, what, what work does the, do you do? I'm just like, Fefe is awesome.[00:42:07] Justin's awesome. And you know, the other two co-founder, co-founders, but like the, the, the tech, everyone's building cool tech. But like, what's the value of the tech? And this is the fundamental question[00:42:16] Martin Casado: of, well, let, let, just like these, let me just maybe give you a rough sketch on the diffusion models. I actually love to hear Sarah because I'm a venture for, you know, so like, ventures always, always like kind of wild west type[00:42:24] swyx: stuff.[00:42:24] You, you, you, you paid a dream and she has to like, actually[00:42:28] Martin Casado: I'm gonna say I'm gonna mar to reality, so I'm gonna say the venture for you. And she can be like, okay, you a little kid. Yeah. So like, so, so these diffusion models literally. Create something for, for almost nothing. And something that the, the world has found to be very valuable in the past, in our real markets, right?[00:42:45] Like, like a 2D image. I mean, that's been an entire market. People value them. It takes a human being a long time to create it, right? I mean, to create a, you know, a, to turn me into a whatever, like an image would cost a hundred bucks in an hour. The inference cost [00:43:00] us a hundredth of a penny, right? So we've seen this with speech in very successful companies.[00:43:03] We've seen this with 2D image. We've seen this with movies. Right? Now, think about 3D scene. I mean, I mean, when's Grand Theft Auto coming out? It's been six, what? It's been 10 years. I mean, how, how like, but hasn't been 10 years.[00:43:14] Alessio: Yeah.[00:43:15] Martin Casado: How much would it cost to like, to reproduce this room in 3D? Right. If you, if you, if you hired somebody on fiber, like in, in any sort of quality, probably 4,000 to $10,000.[00:43:24] And then if you had a professional, probably $30,000. So if you could generate the exact same thing from a 2D image, and we know that these are used and they're using Unreal and they're using Blend, or they're using movies and they're using video games and they're using all. So if you could do that for.[00:43:36] You know, less than a dollar, that's four or five orders of magnitude cheaper. So you're bringing the marginal cost of something that's useful down by three orders of magnitude, which historically have created very large companies. So that would be like the venture kind of strategic dreaming map.[00:43:49] swyx: Yeah.[00:43:50] And, and for listeners, uh, you can do this yourself on your, on your own phone with like. Uh, the marble.[00:43:55] Martin Casado: Yeah. Marble.[00:43:55] swyx: Uh, or but also there's many Nerf apps where you just go on your iPhone and, and do this.[00:43:59] Martin Casado: Yeah. Yeah. [00:44:00] Yeah. And, and in the case of marble though, it would, what you do is you literally give it in.[00:44:03] So most Nerf apps you like kind of run around and take a whole bunch of pictures and then you kind of reconstruct it.[00:44:08] swyx: Yeah.[00:44:08] Martin Casado: Um, things like marble, just that the whole generative 3D space will just take a 2D image and it'll reconstruct all the like, like[00:44:16] swyx: meaning it has to fill in. Uh,[00:44:18] Martin Casado: stuff at the back of the table, under the table, the back, like, like the images, it doesn't see.[00:44:22] So the generator stuff is very different than reconstruction that it fills in the things that you can't see.[00:44:26] swyx: Yeah. Okay.[00:44:26] Sarah Wang: So,[00:44:27] Martin Casado: all right. So now the,[00:44:28] Sarah Wang: no, no. I mean I love that[00:44:29] Martin Casado: the adult[00:44:29] Sarah Wang: perspective. Um, well, no, I was gonna say these are very much a tag team. So we, we started this pod with that, um, premise. And I think this is a perfect question to even build on that further.[00:44:36] ‘cause it truly is, I mean, we're tag teaming all of these together.[00:44:39] Investing in Model Labs, Media Rumors, and the Cursor Playbook (Margins & Going Down-Stack)[00:44:39] Sarah Wang: Um, but I think every investment fundamentally starts with the same. Maybe the same two premises. One is, at this point in time, we actually believe that there are. And of one founders for their particular craft, and they have to be demonstrated in their prior careers, right?[00:44:56] So, uh, we're not investing in every, you know, now the term is NEO [00:45:00] lab, but every foundation model, uh, any, any company, any founder trying to build a foundation model, we're not, um, contrary to popular opinion, we're

AI is taking the enterprise world by storm. But how is AI redefining the LMS? Find out what Docebo CEO, Alessio Artuffo, says on this episode of Talented Learning Show podcast! The post Podcast 108: How is AI Changing the LMS? appeared first on Talented Learning.

Send a textJoin hosts Alex Sarlin and Ben Kornell as they unpack a fast-moving week in education. From AI-native curriculum battles and literacy leadership shifts to voucher surges and national AI pilots reshaping special education. ✨ Episode Highlights:[00:01:48] ASU+GSV preview and the expanding global EdTech ecosystem[00:06:25] The 2026 EdTech AI Map launches with 240+ companies[00:07:14] Brisk introduces AI-powered curriculum integration[00:09:04] The race to own the AI layer in schools[00:13:10] Data ownership becomes the key AI battleground[00:16:59] Kira 2.0 expands into a full AI-native LMS[00:21:16] Texas ESA applications surge past 61,000[00:30:20] UK launches $23M AI pilot for special needs[00:33:40] Microsoft invests in AI teacher training[00:34:59] Google expands Gemini in education[00:35:57] UX emerges as EdTech's new advantage[00:36:43] The AI grad profile prioritizes human skills Plus, special guests:[00:38:33] Karl Rectanus, CEO of Really Great Reading, on literacy outcomes, science of reading implementation, and scaling impact [01:02:22] Dan Meyer, VP of User Growth of Amplify on AI skepticism, social AI in math classrooms, and keeping learning human-centered

Today on Around the School Table (xuno.com.au/podcasts), host Steve Davis is joined by James Thomas, Managing Director of Digital Education Partnerships Indonesia (DEPI) (depi.co.id). DEPI helps international edtech companies build trust and traction across Indonesia’s vast school landscape. It’s a market shaped by scale, complexity, and relationships. Early in the conversation, James breaks down what makes Indonesia unique. There are tens of millions of students and hundreds of thousands of schools. However, size is only one factor. Just as important is how schools communicate, buy, and implement systems. Next, the episode digs into a common assumption about “international schools”. Many people expect mature operations. Yet the reality can be mixed. James shares research across SPK schools (SPK stands for Satuan Pendidikan Kerja Sama, which translates to “Cooperative Education Unit.” These are Indonesian private schools officially licensed to deliver an international curriculum). While many use an LMS for learning, fewer rely on a dedicated school management system. As a result, critical workflows can remain manual. Importantly, James clarifies the difference between an LMS, an SMS, and an SIS layer. An LMS supports teaching and learning workflows. Meanwhile, an SMS runs operations like attendance, wellbeing, parent communication, events, and payments. Then, an SIS layer can connect systems without forcing a full replacement. Consequently, schools can reduce fragmentation and improve workflow reliability. Finance and payments emerge as a major pressure point. Schools often manage complex fee logic in spreadsheets. Unfortunately, one small change can trigger errors. In addition, many schools still depend on WhatsApp for parent communication. Because messages arrive constantly, teacher wellbeing can be impacted. The episode also explores safeguarding and reporting. James explains Indonesia’s TPPK mandate (TPPK stands for Tim Pencegahan dan Penanganan Kekerasan, which translates to Team for the Prevention and Handling of Violence) and why structured incident recording matters. Moreover, he shares why many rollouts fail. They’re treated as IT installs, not organisational change. Instead, James recommends phased implementation, clear ownership, and realistic priorities. If you’re a school leader reviewing systems, this episode offers a practical playbook. You’ll hear how to identify operational headaches, reduce risk, and build resilient school operations. Powered by: xuno.com.au.See omnystudio.com/listener for privacy information.

Today on Around the School Table (xuno.com.au/podcasts), host Steve Davis is joined by James Thomas, Managing Director of Digital Education Partnerships Indonesia (DEPI) (depi.co.id). DEPI helps international edtech companies build trust and traction across Indonesia’s vast school landscape. It’s a market shaped by scale, complexity, and relationships. Early in the conversation, James breaks down what makes Indonesia unique. There are tens of millions of students and hundreds of thousands of schools. However, size is only one factor. Just as important is how schools communicate, buy, and implement systems. Next, the episode digs into a common assumption about “international schools”. Many people expect mature operations. Yet the reality can be mixed. James shares research across SPK schools (SPK stands for Satuan Pendidikan Kerja Sama, which translates to “Cooperative Education Unit.” These are Indonesian private schools officially licensed to deliver an international curriculum). While many use an LMS for learning, fewer rely on a dedicated school management system. As a result, critical workflows can remain manual. Importantly, James clarifies the difference between an LMS, an SMS, and an SIS layer. An LMS supports teaching and learning workflows. Meanwhile, an SMS runs operations like attendance, wellbeing, parent communication, events, and payments. Then, an SIS layer can connect systems without forcing a full replacement. Consequently, schools can reduce fragmentation and improve workflow reliability. Finance and payments emerge as a major pressure point. Schools often manage complex fee logic in spreadsheets. Unfortunately, one small change can trigger errors. In addition, many schools still depend on WhatsApp for parent communication. Because messages arrive constantly, teacher wellbeing can be impacted. The episode also explores safeguarding and reporting. James explains Indonesia’s TPPK mandate (TPPK stands for Tim Pencegahan dan Penanganan Kekerasan, which translates to Team for the Prevention and Handling of Violence) and why structured incident recording matters. Moreover, he shares why many rollouts fail. They’re treated as IT installs, not organisational change. Instead, James recommends phased implementation, clear ownership, and realistic priorities. If you’re a school leader reviewing systems, this episode offers a practical playbook. You’ll hear how to identify operational headaches, reduce risk, and build resilient school operations. Powered by: xuno.com.au.See omnystudio.com/listener for privacy information.

Endlich wieder haufenweise News von A und O, Papers zu Datenschutzerklärungen in LMS und zu VR-Szenarian, Fundgrube, Veranstaltungstipps und Weltverbesserungsidee. Ach ja, H5P kommt auch vor.

Eric and Marty talk about how they use their phones, tablets and laptops at work Articles & ResourcesUsing Mac and iPad Together: Tips and Trickshttps://education.apple.com/resource/250012976How I turned my iPad into a work machinehttps://youtu.be/Uu8gU8gyCZUHow to Integrate Tablets into Existing Workflowshttps://www.lenovo.com/us/en/knowledgebase/how-to-integrate-tablets-into-existing-workflows/Four ways tablets can improve your workflowhttps://shop.lhagenda.com/uncategorized/four-ways-tablets-can-improve-workflow/7 ways to use your smartphones & tablets for workhttps://command-app.com/7-ways-to-use-your-smartphones-tablets-for-workSmartphone: Command Center & Capture Device·   Email triage and quick replies·   Calendar and scheduling·   Text communication·   Voice memo and idea capture·   Two-factor authentication·   Quick AI prompts·   Micro-tasks between meetingsTablet: Reading & Reflective Workspace·   Reading and annotating PDFs·   Dissertation and manuscript review·   Slide viewing and markup·   Handwritten note-taking·   Mind-mapping and idea sketchingLaptop/Desktop: Deep Work Engine·   Manuscript writing and grant drafting·   Data analysis·   Slide creation·   Course design and LMS work·   Video recording and editing·   Long-form AI prompt engineering Practical Takeaways·   Not all devices should do everything.·   Assign tasks by cognitive load.·   Reduce context switching.·   Protect deep-work environments.·   Let your workflow evolve as your academic role evolves.Final CommentsEmail: ThePodTalkNetwork@gmail.comWebsite: https://ThePodTalk.netYouTube: https://www.youtube.com/@TechSavvyProfessor

Et si la réunionite cachait aussi un manque de confiance en ses collaborateur·rices ?Laurent Perrin, le cofondateur de Front, a un avis assez tranché sur le communication au travail. Email, chat, réunions à répétition… on peut vite avoir l'impression de communiquer toujours plus, mais de collaborer de moins en moins (et ce n'est pas qu'une impression !). Eh oui, les dysfonctionnements de la communication sont souvent le symptôme d'un problème plus profond : la confiance.Alors dans ce nouvel épisode de Learning by Doing, Laurent revient sur la surcharge informationnelle dont les organisations sont souvent victimes, l'importance de la communication asynchrone comme levier de performance et explique quelques rituels d'équipe pour éviter la réunionite.Bonne écoute !À très vite,Prenez soin de vous !Plus d'info :Pour suivre Laurent sur LinkedIn : https://www.linkedin.com/in/laurent-perrin-9199505/Pour recevoir gratuitement notre sélection hebdo de conseils pratiques pour animer votre équipe, rendez-vous ici : https://teambakery.com/nlEt n'oubliez pas de laisser 5 étoiles et un gentil commentaire sur Apple Podcast et Spotify si l'épisode vous a plu.CHAPITRAGE :00:00:00 - Intro00:02:20 – Présentation de Laurent et de son parcours00:04:04 – Quelle est la mission de Front ?00:05:46 – Quels sont les points de douleur de leurs clients ?00:07:20 – Quel est le scénario typique d'un nouveau client aujourd'hui ?00:09:32 – Comment déceler les problèmes de communication dans son entreprise ?00:14:25 – Comment gérer l'asynchrone ?00:17:49 – Quels sont leurs rituels d'équipe chez Front ?00:27:09 – Le rôle de l'écrit chez Front00:30:46 – Comment utiliser l'IA ?00:44:49 – Son plus grand apprentissage00:46:57 – Sa routine d'apprentissage00:48:13 – Questionnaire de finVous aimerez cet épisode si vous aimez : Outils du Manager • Happy Work • HBR on Leadership • Le Podcast de la Formation • MANAGEMENT & LEADERSHIP • Learn & EnjoyHébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

Join Ivoclar (AND US!) this February at LMT Lab Day in Chicago. Ivoclar will be offering 16 different educational lectures over the three-day event, giving dental professionals plenty of opportunities to learn, connect, and grow. Visit labday.com/Ivoclar to view the full schedule and register, and be sure to stop by and see the Ivoclar team in the Windy City. Walking the Lab Day Chicago floor? Make it worth it. Stop by the FOLLOW-ME! hyperDENT booth (E-27, East Hall) and take part in their Milling Roadmap—a quick, scavenger-hunt-style activity that leads you to key milling partners like Axsys, Imagine, DOF, and Roland. Collect stamps at booths you're likely visiting anyway and get entered to win some great giveaways—including this year's grand prize: a foldable Honda electric scooter. You're already walking the floor. Now it might carry you. Come see and talk to Elvis and Barb at all these amazing shows in 2026* Cal-Lab Association Meeting in Chicago Feb 19-20 https://cal-lab.org/ LMT Lab Day Chicago Feb 19-21 https://lmtmag.com/lmtlabday Dental Lab Association of Texas Meeting in Dallas Apr 9-11 https://members.dlat.org/ exocad Insights in Mallorca, Spain Apr 30 - May 1 https://exocad.com/insights-2026 This week we sit down with Michael Joseph, a London-based lab owner whose journey through dental technology is anything but traditional. From delivering impressions on a scooter through the streets of London to completely rebuilding his lab as a fully digital operation, Michael shares a candid, honest look at what it really takes to survive—and thrive—in today's dental lab landscape. Michael walks listeners through his early days pouring hundreds of stone models by hand, navigating education with dyslexia, and eventually earning his degree in dental technology. After years at the bench, he pivoted into dental sales, working with companies like Orascoptic, Sirona, and Skillbond/Argen—experience that gave him deep insight into materials, equipment, and the business side of dentistry. That sales background ultimately fueled his return to lab ownership and helped him build a strong network of clinicians from day one. The conversation takes a powerful turn as Michael opens up about the challenges of Brexit, COVID, staffing losses, and personal upheaval that nearly ended his lab altogether. Instead of quitting, he made a bold decision: gut the lab completely and rebuild it from the ground up as a fully digital operation. Investing heavily in milling, printing, Exocad, and workflow automation, Michael shares how committing to systems, protocols, and vertical integration transformed not just his lab—but his mindset. Elvis and Barb dig into Michael's digital workflows, including photogrammetry, full-arch immediate load cases, remote design teams, LMS integration with GreatLab, and why reliability and consistency are the real competitive advantages. Michael also explains how peer referrals—not ads—became his strongest growth engine, and how simplifying communication with dentists through WhatsApp, QR codes, automation, and self-booking systems has completely changed the way his lab operates.### If you want to grow your business, you need clear insight into what's happening inside your operation and across your customer journey. That's where Icortica comes in. At Canadian Dental Labs, Icortica has become a cornerstone of how we operate—giving us at-a-glance visibility into performance, helping us focus our efforts, spot opportunities early, and solve problems before they grow. It takes the guesswork out of decision-making and shows us what to do next. Plus, the Icortica team is incredibly responsive and feels like a true partner in our success. If you're serious about growing your business and understanding your customers better, Icortica can get you there. Learn more at icortica.com/voices — Icortica, helping dental labs grow. 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. Starting the year strong in the dental lab isn't about goals—it's about results. Predictable productivity is what drives real profitability, and unpredictability is costly when remakes rise and production slows. That's why labs rely on Roland DGA's DGSHAPE milling solutions. With consistent accuracy, minimal downtime, and automation you can count on, systems like the DWX-53DC deliver reliable output day after day—making ROI measurable and growth predictable. See consistency in action at LMT Lab Day Chicago, Booth I-20. Choose DGSHAPE. Crafted with Japanese precision. Trusted by dental professionals worldwide. Learn more at rolanddental.comSpecial Guest: Michael Joseph.

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Learning platforms often drive channel training. But now, AI adds a whole new angle. Find out how to enable partners outside the LMS on this Talented Learning Show podcast! The post Podcast 107: How Can AI Enable Partners Outside the LMS? appeared first on Talented Learning.

Would you like access to our advanced agency training for FREE? https://www.agencymastery360.com/training Do you feel you're giving everything to your agency and only getting exhaustion as a result? Agencies grow best when they're built around clarity, empathy, and self-awareness. Whether it's pricing, boundaries, team management, or AI, the common thread is intention. Today's featured guest understands that you don't need to hustle harder. You need to design smarter, around who you are, how you work best, and what kind of business you actually want to run. She'll share her perspective on agency growth, self-awareness, leadership, and how AI should actually be used inside a modern agency and provide a real look at what it takes to build an agency that's profitable, human, and sustainable without losing yourself in the process. Ingrid Schneider is the CEO and founder of Stay in Your Lane, a fractional CMO and franchise development agency, and Train in Your Lane, an AI education company helping teams build real AI intuition. What started as fractional work after being laid off during the pandemic has grown into a 16-person team running full marketing departments, launching brands, building LMS platforms, and training companies like Ben & Jerry's and Ace Hardware on how to actually use AI to solve problems. In this episode, we'll discuss: Going from survival mode to self-worth: pricing and confidence. How to set boundaries and protect your brain. Design an agency that energizes you, not drains you. Managing people, not just performance with a human-first approach. Subscribe Apple | Spotify | iHeart Radio Sponsors and Resources This episode is brought to you by Wix Studio: If you're leveling up your team and your client experience, your site builder should keep up too. That's why successful agencies use Wix Studio — built to adapt the way your agency does: AI-powered site mapping, responsive design, flexible workflows, and scalable CMS tools so you spend less on plugins and more on growth. Ready to design faster and smarter? Go to wix.com/studio to get started. Building an Agency on Trust and Integrity Ingrid doesn't come from a tidy, linear career path. After being laid off as a CMO during the pandemic, she made the decision to not work for anyone else again. She started doing fractional CMO work to replace her salary, focusing on trust, authenticity, and doing the work well. What began as a solo operation three and a half years ago is now a full team serving a wide range of clients. Some rely on Ingrid's team to run their entire marketing department. Others bring them in for focused, fractional engagements. The growth didn't come from aggressive sales tactics—it came from being reliable, human, and honest about what they were good at. Learning Your Worth and Unlearning Survival Mode When Ingrid landed her first client, she charged $3,000 a month for two brands. And that client still complained about pricing. Like many agency owners, she was focused on replacing her salary, not building a business. Survival mode has a way of shrinking your sense of value. Learning her worth didn't come from a pricing spreadsheet. It came from personal work deconstructing old beliefs, recognizing her own capabilities, and understanding the impact she could have on others. Ingrid talks openly about how her upbringing and past experiences shaped her tendency to underprice herself and overextend. As her confidence grew, so did her standards. She began collecting people with grit, sometimes hiring for attitude over experience, and building a team she trusted deeply. The biggest lesson for her was: if you don't believe in your value, your pricing, and your agency, will reflect that. Preventing Agency Burnout: How to Set Boundaries Running a business can be incredibly stressful, which is why many owners can relate to being in fight or fly mode all the time. However, this is the worst thing for both your health and your business because chronic stress will affect your brain and get you to a point known as "flipping your lid." According to Ingrid, this term, which she learned from Dr. Daniel Siegel, describes what happens when stress pushes you into fight, flight, or freeze. Logic goes offline. Creativity disappears and everything feels harder. For agency owners, this shows up as exhaustion, impatience, and bad decisions, and healing will mean confronting the reality that you can't run a business well if your body and brain are in survival mode. In her case, Ingrid found healing by emphasizing boundaries as a leadership responsibility. Knowing where your value is best served, trusting your team, and recognizing when their lids are flipped allows you to lead with empathy instead of pressure. The agency doesn't need a burned-out hero. It needs a regulated, self-aware leader. Designing an Agency That Energizes You, Not Drains You This is a lesson that agency owners that currently feel miserable with their business and wanting to give up should learn. Drawing your boundaries will look different to everyone, but you can start by asking yourself what you want to do every day and what you never want to do again. Just draw a circle on a piece of paper and start writing. Inside: the work that gives you energy. Outside: everything that drains you. You'll see that most likely what you need is to redesign your agency around this. You can't be all things to all people. Agency that try usually end up miserable and unprofitable. Wins and losses both matter, but only if you're paying attention to what they're teaching you. Topline revenue means nothing if you hate how you're earning it. Sustainable growth comes from aligning what's good for the business with what actually fills your cup. That alignment is what keeps agencies alive long-term. Managing People, Not Just Performance with a Human-First Approach As an empath, Ingrid leads with a people-first approach rooted in Trust-Based Relational Intervention (TBRI). When something goes wrong, she looks at three things in order: herself, the system, and then the person. Are expectations clear? Do they have the resources they need? Is she showing up with patience? Perfectionism isn't the goal in her agency because perfection is stressful, unrealistic, and unnecessary. Instead, the focus is on doing really good work while protecting the team's mental energy. This is where AI comes in, not as a shortcut for thinking, but as a way to remove the minutia that burns people out. This has been the case for Ingrid, who enjoys managing people. If this is not your case, then focus on hiring people who can manage themselves. But remember you have to learn to let go if you want a self-managing team. There are countless ways to reach the same outcome and speed isn't always the metric that matters most. Sometimes the "slow" work produces the best results. Using AI to Empower Teams, Not Create More Noise Ingrid's approach focuses on education and the fact that everyone should be training their AI intuition to be able to understand how an AI tool works and how it could help them. She trained her own intuition by changing her social media algorithms to feed her AI micro-learnings. From there, it became about application: looking at every agency task and asking, Can AI help solve this better? Her team runs weekly "show and tell" sessions where they demo how they used AI to solve real problems. There's also an AI policy but it's framed as a permission slip, not a rulebook. Team members can experiment with tools on a company card, and if they prove value, the agency commits. The bigger point is this: if you're not empowering your team to use AI thoughtfully, you're holding them back. This isn't about pumping out more content—it's about freeing up human brains to do the work that actually matters. Do You Want to Transform Your Agency from a Liability to an Asset? Looking to dig deeper into your agency's potential? Check out our Agency Blueprint. Designed for agency owners like you, our Agency Blueprint helps you uncover growth opportunities, tackle obstacles, and craft a customized blueprint for your agency's success.

Vieillir ne devrait pas être un sujet en entreprise.Au contraire, l'âge devrait être un atout stratégique pour les organisations. Laeticia Vitaud est autrice et conférencière, spécialiste des mutations du travail et de la démographie. Et récemment, elle a publié un nouvel ouvrage “L'Atout Âge”, consacré à la diversité des âges en entreprise.Alors dans ce nouvel épisode de Learning by Doing, on explore pourquoi la vision “junior / senior” ne tient plus face aux réalités démographiques, comment recruter autrement, ce que signifie vraiment “manager l'intergénérationnel” et pourquoi la prise en compte du corps, du temps et de la sédentarité devient un enjeu managérial à part entière.Bonne écoute !À très vite,Prenez soin de vous !Plus d'info :Pour suivre Laetitia sur LinkedIn : https://www.linkedin.com/in/laetitia-vitaud-cadre-noir/Pour écouter le premier épisode enregistré ensemble : https://podcasts.audiomeans.fr/learning-by-doing-4f5c7395/10-laetitia-vitaud-cultiver-lartisanat-pour-redonner-du-sens-au-travail-e93025dbSon livre L'Atout AgePour recevoir gratuitement notre sélection hebdo de conseils pratiques pour animer votre équipe, rendez-vous ici : https://teambakery.com/nlEt n'oubliez pas de laisser 5 étoiles et un gentil commentaire sur Apple Podcast et Spotify si l'épisode vous a plu.CHAPITRAGE YTB00:00:00 - Intro00:01:33 – Présentation de Laetitia et de son dernier livre00:04:31 - Les “Questions Rapides”00:05:23 - De quel constat de départ est-elle partie pour écrire son dernier livre ?00:07:57 - Comment montre l'enjeu stratégique et financier de la diversité d'âge aux entreprises ?00:10:19 - La structure de son livre00:11:40 - Comment faire de la diversité d'âge une force ?00:20:08 - Comment convaincre sa direction RH ?00:26:24 - Quelques exemples de bonnes pratiques00:37:45 - Le binôme âge00:40:35 - Adapter son environnement de travail à différentes générations00:55:30 - Questions de finVous aimerez cet épisode si vous aimez : Outils du Manager • Happy Work • HBR on Leadership • Le Podcast de la Formation • MANAGEMENT & LEADERSHIP • Learn & EnjoyHébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

When done well, learning design grabs participants' attention like a great marketing campaign and engages them emotionally and intellectually to make learning stick.  That's the premise of “Think Like a Marketer, Train Like an L&D Pro,” a book written by our guests  Mike Taylor and Bianca Baumann.  In this episode, you will learn how to elevate traditional training approaches with marketing tools such as learner personas, journey mapping, and content strategy that come together in high impact learning campaigns.Show Notes: Co-authors Mike Taylor and Bianca Baumann share some of the top takeaways from their book, “Think Like a Marketer, Train Like an L&D Pro,” to help you create experiences that learners crave.L&D can learn a lot from marketing. Marketers know the value of making an emotional connection with their audience —L&D should too. Applying marketing principles can help learning designers create experiences that boost performance and influence behavior.Learner personas are foundational, not optional. Understanding who learners are, what they need, and what motivates them is critical to designing content that resonates and sticks.Journey mapping helps deliver learning at the right moment. Mapping the learner journey allows L&D teams to support employees before, during, and after key moments—not just during formal training events.Content strategy drives behavior change. When personas, journey maps, and content strategy work together, learning becomes more targeted, improving proficiency and long-term behavior change.It all comes together in a Learning Campaign. Smaller, well-timed content—resources, nudges, and reinforcements—create more impact than one-time standalone training events.Success is measured beyond completion rates. True learning impact shows up in measuring performance, confidence, and on-the-job behavior—not just in LMS reports.Learn more about Think Like a Marketer, Train Like an L&D ProConnect with Mike Connect with Bianca 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

La Liga Mexicana de Beisbol (LMB) y la Liga Mexicana de Sóftbol (LMS) lanzaron su campaña de Responsabilidad Social, ‘Unidos por el diamante'.La Mtra. Carla Román, Jefa de Oficina de la Liga Mexicana de Beisbol; y la Lic. Ivana Morett, Coordinadora de Marketing de la LMB, platican sobre este proyecto que visibilizará el trabajo de 11 organizaciones. Cada institución contará con un mes para promover su causa de manera digital en colaboración con la LMS, la LMB y los equipos que las conforman. ¿Cómo surge el proyecto?¿Cuáles son los objetivos?¿Cómo el beisbol es un agente de cambio para proyectos sociales?¿Cómo fue la designación de las 11 organizaciones?Te invitamos a que te suscribas a nuestra newsletter: El Míster donde podrás encontrar investigaciones y reportes referentes a la industria deportiva. Comparte y sigue nuestras redes sociales: X, Instagram y LinkedIn. Contacto: ivan@elmister.info - jair@elmister.info

We begin this episode with Andrew from the National Literacy Trust, who unveils the "National Year of Reading" campaign in response to a sharp decline in reading enjoyment among children. Andrew argues that in an AI-first world, literacy is more vital than ever, and shares how connecting texts to students' passions from gaming to sports can rekindle their "reader identity." Next, Connor Gaitley from Edmentum shifts the focus to "career-connected learning." Connor discusses how tools like Major Clarity are helping students explore diverse future pathways whether university or apprenticeships ensuring that education meets every learner where they are and closes no doors. We then explore practical AI tools for the classroom with Will from RedPen AI, a former teacher who turned his frustration with workload into innovation. Will explains how his platform bridges the gap between handwritten work and digital assessment, acting as a "second pair of eyes" to track progress without requiring 1:1 devices. Finally, Liz Crawford from Kahoot! joins us to discuss the platform's evolution from a simple quiz tool to an enterprise-ready learning ecosystem. Liz highlights their new AI generation features, deep integration with LMS platforms, and the 200+ independent research studies proving their impact on student outcomes. Although Bett UK 2026 has come to a close, the innovation continues. Visit https://uk.bettshow.com/ to explore the highlights and stay connected with the community. This episode is proudly sponsored by Edmentum – visit them at https://www.edmentum.com/ – and by Edruption, powering the future of learning at https://edruption.com/.

Have you ever gone into a closing meeting, a sales presentation, or even a prospecting call with total confidence? That mindset and feeling that everything's going to go your way, that nothing can go wrong, that you're absolutely going to win? I've been there. I know you have too. It's one of the greatest feelings ever. But let's juxtapose that against going into a meeting feeling insecure, where your focus is on everything that could go wrong versus everything that could go right. And then, as soon as something does go wrong, everything starts to spiral downward. There is absolutely nothing that can make or break a deal like confidence. In this Sales Gravy podcast episode, we're going to explore exactly where confidence comes from, why it matters so much in sales, and most importantly, what you can do to build the unshakeable confidence that closes deals. The Insecurity Death Spiral Recently, I learned a profound lesson about confidence. I was invited to play golf with a group of businesspeople in Florida. Beautiful day, sunshine, great course. It should have been perfect. Except I'm not a very good golfer. And these guys? They were good. Really good. The kind of golfers who carry single-digit handicaps and talk about their swing plane like it's a science project. So I'm standing on the first tee, watching them stripe their drives straight down the middle, and I can feel it happening. That little voice in my head starts whispering: "You don't belong here. You're going to embarrass yourself. Everyone's going to see how bad you are." I started strong enough. Made it through the first couple of holes without humiliating myself. But then I hit a bad shot. Then another. And instead of shaking it off like I normally would, I started fixating on those bad shots. That's when the downward spiral began. Every swing became an exercise in anxiety. I was so focused on not messing up that I couldn't help but mess up. My mechanics fell apart. My rhythm disappeared. By the end of the round, I had played one of the worst games of golf in my life. Not because I suddenly forgot how to swing a club, but because I let insecurity take over. Now, I managed to keep a smile on my face. We were playing golf in the Florida sunshine, after all. But inside, I was frustrated because I knew what had happened. I let my insecurity about being the weakest player in the group sabotage my entire game. And here's what hit me on the plane home: That's exactly what I see happen in sales all the time. One moment of uncertainty, one unexpected challenge, and suddenly, a salesperson who is perfectly capable starts spiraling. Their confidence evaporates. And with it goes their ability to perform. Why Confidence Matters in Sales In sales, there is nothing that sells like confidence. Nothing. Buyers lean into confidence. They're attracted to it. They trust it. And because of emotional contagion—your ability to transfer your emotions to another person—you basically take your confidence and hand it to the buyer, who then gains more confidence in you. Think about it. When you walk into a meeting radiating confidence, the buyer thinks, "This person knows what they're doing. They believe in what they're selling. I can trust them." But when you walk in feeling insecure, the buyer picks up on that too. They start thinking, "Why is this person nervous? What aren't they telling me? Maybe this isn't the right solution." In sales, because we can't always control the playing field and because we don't always feel like we should be where we are—especially when we're dealing with the C-suite or high-level decision makers, when we're in super competitive situations, or when we don't really know what we're talking about—one thing that goes wrong can create a cascade of other problems, creating a downward insecurity spiral that is real and deadly. The Ultimate Source of Confidence So the question is: Where does confidence come from? Where do you get it? Well, confidence by its very nature comes from the inside. It's a mindset. It's something that you believe, just like insecurity is a mindset that comes from the inside. Confidence is mostly created by certainty. When you feel certain that you can control the outcome, you feel more confident. When you're in situations that feel familiar, or you're talking about a product, your service, or some part of your offering that you totally understand, you feel more confident. When you've executed the sales process perfectly and built deep relationships with your customers, you feel more confident that they're going to buy from you. When you've practiced your presentation multiple times and know it by rote, you feel more confident. By the way, the same thing works in reverse. Uncertainty begets insecurity. When you walk into a situation, and you feel uncertain—and this happens to a lot of brand-new salespeople who don't know what to say or feel like they don't really understand the product offering, their industry, or their customer's business—it creates a level of insecurity. So the answer, if we want to be more confident, is to create more certainty. Certainty Creates Confidence Let me give you an example from my horrible, awful, terrible round of golf. In the middle of that terrible round, I got desperate for anything that would give me confidence. So I started playing entire holes with my 7-iron because that was the one club I felt I was certain I could hit. Except for putting, I would hit the 7-iron off the tee, on the fairway, and chip with it around the green. 150 yards at a time with my 7-iron, I could make it go straight down the fairway and hit the green. That certainty in that particular club helped me feel more confident, and my game actually improved when I stuck with what I knew worked. Now, in sales like golf, there is nothing you can be 100% certain about, simply because there are too many variables. We're dealing with human beings, nasty competitors, and a shifting landscape. Even in accounts that are in our pipeline, things are always changing. So for us as sales professionals, there's no absolute certainty. But there are ways you can boost certainty in order to gain more confidence. Four Ways to Create Certainty and Boost Confidence 1. Invest in Yourself Through Education If you get insecure when you're talking about things in your industry or about your product that you don't understand, then go educate yourself. Take the time to learn. Take classes. Go to your LMS and take e-learning. Read everything about your product. Become an expert—not just in your product, but in your industry. Also, learn about business. The more you can educate yourself about business, the more you gain business acumen, which makes you feel more confident in conversations with executives. When you know your stuff cold, understand your product inside and out, and can speak intelligently about your industry and your customer's business challenges, uncertainty evaporates, and with it, goes insecurity. 2. Plan Every Single Call Winging it is wickedly stupid on sales calls because when you wing it, you create uncertainty. So sit down and think about every single call. What am I going to do? What questions am I going to ask? What's my objective for being there? What am I going to close for at the end (targeted next step)? Build a plan, write it down, and review it in advance of your meeting. Planning creates certainty. 3. Murder Board Your Big Meetings Along with planning comes the concept of murder boarding, red teaming, or scenario playing. Murder boarding creates certainty around handling the unexpected. Especially in large presentations and closing calls, you need to start pulling the thread on everything that could possibly go wrong. Every objection you could get. Every pushback. Every hard question. Think about the different stakeholders who are going to be around the table, and the types of questions they're going to ask, and the potential things they may say. Then find somebody on your team or somebody in your household to role-play all those scenarios with. I've found that nothing gives me more confidence in big sales meetings than murder boarding. Because when I get into those situations—especially with objections or negotiations that can be super intimidating—the more I role-play those things, the better I am at them and the easier they are to deal with. In fact, they're far less difficult in real life than they were in the role-playing. 4. Keep a Full Pipeline This is powerful: There's nothing that makes you more confident than being able to sell like you don't have to sell. When you are fanatical about prospecting and build a full pipeline, it gives you lots of options. You know you can walk away from anything. You're detached from the outcome. When it doesn't make a difference if you win or lose, you gain immense confidence, which is why a full pipeline is the ultimate confidence builder. With Confidence, Mindset Matters When it comes to confidence, mindset matters. If you are obsessed with how you might fail or what you might do wrong, there's a tendency to get the thing you're focused on. It's called target obsession. Whatever we focus on, we tend to attract and move toward. So be careful what you're focused on. One of the things I do—and I know this is kind of weird, but it works—is before I walk into a sales meeting, I look into the mirror and tell myself, "I'm a great salesperson." I actually say the words out loud. It's a little bit cheesy. But by saying those words, changing my body language, pushing my shoulders up, my chin out—the power pose, as some would say—that actually begins to change my mindset and makes me feel more confident. Add to that eating well,

Have you ever gone into a closing meeting, a sales presentation, or even a prospecting call with total confidence? That mindset and feeling that everything’s going to go your way, that nothing can go wrong, that you’re absolutely going to win? I’ve been there. I know you have too. It’s one of the greatest feelings ever. But let’s juxtapose that against going into a meeting feeling insecure, where your focus is on everything that could go wrong versus everything that could go right. And then, as soon as something does go wrong, everything starts to spiral downward. There is absolutely nothing that can make or break a deal like confidence. In this Sales Gravy podcast episode, we’re going to explore exactly where confidence comes from, why it matters so much in sales, and most importantly, what you can do to build the unshakeable confidence that closes deals. The Insecurity Death Spiral Recently, I learned a profound lesson about confidence. I was invited to play golf with a group of businesspeople in Florida. Beautiful day, sunshine, great course. It should have been perfect. Except I’m not a very good golfer. And these guys? They were good. Really good. The kind of golfers who carry single-digit handicaps and talk about their swing plane like it’s a science project. So I’m standing on the first tee, watching them stripe their drives straight down the middle, and I can feel it happening. That little voice in my head starts whispering: “You don’t belong here. You’re going to embarrass yourself. Everyone’s going to see how bad you are.” I started strong enough. Made it through the first couple of holes without humiliating myself. But then I hit a bad shot. Then another. And instead of shaking it off like I normally would, I started fixating on those bad shots. That’s when the downward spiral began. Every swing became an exercise in anxiety. I was so focused on not messing up that I couldn’t help but mess up. My mechanics fell apart. My rhythm disappeared. By the end of the round, I had played one of the worst games of golf in my life. Not because I suddenly forgot how to swing a club, but because I let insecurity take over. Now, I managed to keep a smile on my face. We were playing golf in the Florida sunshine, after all. But inside, I was frustrated because I knew what had happened. I let my insecurity about being the weakest player in the group sabotage my entire game. And here’s what hit me on the plane home: That’s exactly what I see happen in sales all the time. One moment of uncertainty, one unexpected challenge, and suddenly, a salesperson who is perfectly capable starts spiraling. Their confidence evaporates. And with it goes their ability to perform. Why Confidence Matters in Sales In sales, there is nothing that sells like confidence. Nothing. Buyers lean into confidence. They’re attracted to it. They trust it. And because of emotional contagion—your ability to transfer your emotions to another person—you basically take your confidence and hand it to the buyer, who then gains more confidence in you. Think about it. When you walk into a meeting radiating confidence, the buyer thinks, “This person knows what they’re doing. They believe in what they’re selling. I can trust them.” But when you walk in feeling insecure, the buyer picks up on that too. They start thinking, “Why is this person nervous? What aren’t they telling me? Maybe this isn’t the right solution.” In sales, because we can’t always control the playing field and because we don’t always feel like we should be where we are—especially when we’re dealing with the C-suite or high-level decision makers, when we’re in super competitive situations, or when we don’t really know what we’re talking about—one thing that goes wrong can create a cascade of other problems, creating a downward insecurity spiral that is real and deadly. The Ultimate Source of Confidence So the question is: Where does confidence come from? Where do you get it? Well, confidence by its very nature comes from the inside. It’s a mindset. It’s something that you believe, just like insecurity is a mindset that comes from the inside. Confidence is mostly created by certainty. When you feel certain that you can control the outcome, you feel more confident. When you’re in situations that feel familiar, or you’re talking about a product, your service, or some part of your offering that you totally understand, you feel more confident. When you’ve executed the sales process perfectly and built deep relationships with your customers, you feel more confident that they’re going to buy from you. When you’ve practiced your presentation multiple times and know it by rote, you feel more confident. By the way, the same thing works in reverse. Uncertainty begets insecurity. When you walk into a situation, and you feel uncertain—and this happens to a lot of brand-new salespeople who don’t know what to say or feel like they don’t really understand the product offering, their industry, or their customer’s business—it creates a level of insecurity. So the answer, if we want to be more confident, is to create more certainty. Certainty Creates Confidence Let me give you an example from my horrible, awful, terrible round of golf. In the middle of that terrible round, I got desperate for anything that would give me confidence. So I started playing entire holes with my 7-iron because that was the one club I felt I was certain I could hit. Except for putting, I would hit the 7-iron off the tee, on the fairway, and chip with it around the green. 150 yards at a time with my 7-iron, I could make it go straight down the fairway and hit the green. That certainty in that particular club helped me feel more confident, and my game actually improved when I stuck with what I knew worked. Now, in sales like golf, there is nothing you can be 100% certain about, simply because there are too many variables. We’re dealing with human beings, nasty competitors, and a shifting landscape. Even in accounts that are in our pipeline, things are always changing. So for us as sales professionals, there’s no absolute certainty. But there are ways you can boost certainty in order to gain more confidence. Four Ways to Create Certainty and Boost Confidence 1. Invest in Yourself Through Education If you get insecure when you’re talking about things in your industry or about your product that you don’t understand, then go educate yourself. Take the time to learn. Take classes. Go to your LMS and take e-learning. Read everything about your product. Become an expert—not just in your product, but in your industry. Also, learn about business. The more you can educate yourself about business, the more you gain business acumen, which makes you feel more confident in conversations with executives. When you know your stuff cold, understand your product inside and out, and can speak intelligently about your industry and your customer’s business challenges, uncertainty evaporates, and with it, goes insecurity. 2. Plan Every Single Call Winging it is wickedly stupid on sales calls because when you wing it, you create uncertainty. So sit down and think about every single call. What am I going to do? What questions am I going to ask? What’s my objective for being there? What am I going to close for at the end (targeted next step)? Build a plan, write it down, and review it in advance of your meeting. Planning creates certainty. 3. Murder Board Your Big Meetings Along with planning comes the concept of murder boarding, red teaming, or scenario playing. Murder boarding creates certainty around handling the unexpected. Especially in large presentations and closing calls, you need to start pulling the thread on everything that could possibly go wrong. Every objection you could get. Every pushback. Every hard question. Think about the different stakeholders who are going to be around the table, and the types of questions they’re going to ask, and the potential things they may say. Then find somebody on your team or somebody in your household to role-play all those scenarios with. I’ve found that nothing gives me more confidence in big sales meetings than murder boarding. Because when I get into those situations—especially with objections or negotiations that can be super intimidating—the more I role-play those things, the better I am at them and the easier they are to deal with. In fact, they’re far less difficult in real life than they were in the role-playing. 4. Keep a Full Pipeline This is powerful: There’s nothing that makes you more confident than being able to sell like you don’t have to sell. When you are fanatical about prospecting and build a full pipeline, it gives you lots of options. You know you can walk away from anything. You’re detached from the outcome. When it doesn’t make a difference if you win or lose, you gain immense confidence, which is why a full pipeline is the ultimate confidence builder. With Confidence, Mindset Matters When it comes to confidence, mindset matters. If you are obsessed with how you might fail or what you might do wrong, there’s a tendency to get the thing you’re focused on. It’s called target obsession. Whatever we focus on, we tend to attract and move toward. So be careful what you’re focused on. One of the things I do—and I know this is kind of weird, but it works—is before I walk into a sales meeting, I look into the mirror and tell myself, “I’m a great salesperson.” I actually say the words out loud. It’s a little bit cheesy. But by saying those words, changing my body language, pushing my shoulders up, my chin out—the power pose, as some would say—that actually begins to change my mindset and makes me feel more confident. Add to that eating well, getting plenty of sleep (sleep really does wonders for your confidence), exercising, and making sure, before you go into a big presentation, that you’re not going in on an empty stomach. How to Overcome Insecurity in the Moment I sell every single day, and I’ve been doing this for 30 years. I know what it’s like to walk into a meeting with a prospect or customer and feel insecure. It happens to me still. But here’s the thing: I’m very careful not to let people see me sweat because insecurity and sales make a poor mixture. Because emotions are contagious and people have a tendency to respond in kind, I want to avoid transferring my insecurity to them, causing them to feel uncertain about me. So I’m very careful with my body language, eye contact, voice inflection, and how fast I speak. One tactic I use when I feel insecure is to slow down, pause, and ask a question. This gives me a moment to regain my composure and manage my body language. Build Confidence with Knowledge, Planning, Practice, and Pipeline Confidence isn’t something you’re born with. It’s something you build through preparation, knowledge, practice, and a full pipeline. The good news is that all of these things are within your control. You can choose to educate yourself, to plan, practice, and prospect. Here’s what I want you to do this week: First, identify your gaps. Where do you feel uncertain in your sales process? Is it product knowledge? Industry knowledge? Objection handling? Closing? Write it down. Second, create a learning plan. For each gap you identified, create a specific plan to fill it. What books will you read? What training will you take? Who will you shadow or learn from? Third, plan your next three calls. Don’t wing another call this week. Sit down and plan your next three sales conversations. Write out your objectives, your questions, and your close. Fourth, murder board your biggest opportunity. If you’ve got a major presentation or closing call coming up, spend an hour this week role-playing every possible scenario with a colleague. Fifth, evaluate your pipeline. Is it full enough that you can sell without desperation? If not, block time this week for serious prospecting. This is how you build the kind of unshakeable confidence that buyers respond to, competitors fear, and that feels so good. And remember, when it’s time to go home, and you’re tired and worn out, always stop and make one more call. Because that one more call gives you the confidence that you can walk in any door, anytime, stand toe to toe with any buyer, and have a winning sales conversation. Over a million sales professionals and sales teams have become more confident prospectors with the Fanatical Prospecting system. Learn more here.

Kicking off 2026, let's cut through the noise and focus on what actually matters: edtech tools that can genuinely move the needle for educators. Rather than chasing trends or shiny new platforms, this episode is about intentional, practical technology use that supports feedback, collaboration, creativity, engagement, organization, and student voice.#EdTech Thought: Shrinking the Engagement GapThe episode tackles the growing disconnect between students' highly interactive digital lives outside of school and the passive digital experiences they often encounter in classrooms.Chris challenges the idea that more screen time equals more engagement and introduces the 80/20 Producer Strategy:For every 80% of the time students spend consuming informationEnsure at least 20% is spent creating something with value beyond the gradebookThe core message:Engagement in 2026 isn't about flashy tools. It's about student agency. When students create, design, build, and solve real problems, the engagement gap begins to close.Six Tools to Level Up in 2026#1 Mote — Rethinking FeedbackFeedback is essential but time-consuming. Mote allows educators to leave quick voice comments directly inside Google Docs, Slides, and LMS platforms.Why it matters:Faster than typingMore personal and humanAccessible through audio + transcriptionLevel-Up Question:Where in your workflow could your voice be more effective than your keyboard?#2 FigJam — Making Thinking VisibleFigJam is a collaborative digital whiteboard that turns learning into an active, visible process.Use it to:Brainstorm and organize ideasCapture student thinking in real timeSupport collaboration for both synchronous and asynchronous workLevel-Up Question:How often do students visually share their thinking before submitting a final product?#3 Canva — Creativity That CommunicatesCanva has evolved into a full creation and communication platform, allowing students to demonstrate learning visually and professionally.Classroom possibilities include:Infographics and explainer visualsDigital portfoliosEthical media creation and storytellingLevel-Up Question:Are students creating content — or just consuming it?#4 Curipod, Pear Deck & Nearpod — Real-Time EngagementThese tools transform traditional presentations into interactive learning experiences through polls, questions, and formative checks.Why they work:Immediate insight into student understandingNo extra gradingIncreased accountability without pressureLevel-Up Question:How often do you

Chris Badgett offers a comprehensive overview to the top WordPress plugins in this episode for creating and expanding an LMS website that goes beyond the LifterLMS core. He begins by emphasizing that LifterLMS itself handles almost all of the fundamental LMS features, such as creating courses, student dashboards, user accounts, memberships, e-commerce with PayPal or […] The post Best WordPress Plugins for LMS Websites Besides LifterLMS appeared first on LMScast.

Dan MacDonald is the founder and CEO of BIS Safety Software, based in Edmonton, Canada. He didn't start in safety or software—he came from retail and leadership training before an unexpected pivot led him into online safety systems. That shift eventually became a long-term bet on a "un-sexy" problem that companies can't ignore.  Today, BIS Safety serves more than 2.5 million users across high-risk industries like construction, mining, transportation, and energy. The company generates roughly $25M CAD in annual revenue, employs about 200 people globally, and runs one of the stickiest SaaS platforms you'll find—with less than 1% annual logo churn. After nearly 20 years of bootstrapped growth, Dan is beginning a staged exit, starting with a minority secondary sale and planning a control transaction in a few years. Along the way, he shares hard-earned lessons about product obsession, compounding customer retention, and why steady execution beats hype. Key Takeaways Extreme Retention Matters — Less than 1% annual churn created compounding growth without aggressive sales spending. Product Over Sales — BIS focused on usability and depth first, letting word-of-mouth drive most early growth. Customer-Funded Start — Early customers paid to build the first LMS, avoiding dilution and premature fundraising. Long Bootstrap Reality — The first decade involved no salary, deep personal risk, and constant financial pressure. Enterprise Power, End User Simplicity — Power users get depth, while users see an interface designed to feel effortless. Quote from Dan MacDonald, Founder and CEO of BIS Safety Software "So at that time when I started the business, there was an awakening kind of moment of realization. It hit me big. I'm reading hundreds of business books and reading about Bill Hewlett, Dave Packard, Sam Walton, and many others. "I'm listening to the things they're saying, the ways they're thinking, And all I'm thinking is, my God, they think like me, they're just like me, they're normal people, they're just like me. And that was kind of the first awakening realization to say, they're not superhuman! That gave me the confidence to build the business and believe in the future. I never thought there's a pot of gold at the end of the rainbow it was never about the money. It was just some burning thing inside me that just I need to do this.I was just driven to do to do this. I felt like it was just this is what I'm meant to do." Links Dan MacDonald on LinkedIn BIS Safety Software on LinkedIn BIS Safety Software website Podcast Sponsor – Lighter Capital This podcast is sponsored by Lighter Capital. In the last 15 years, Lighter Capital has helped over 600 software and SaaS founders secure simple, non-dilutive financing to grow a little faster—without giving up any precious equity or board seats to investors.  Simple debt funding from Lighter Capital can range from $50K to $10 million, with straightforward terms, no personal guarantees or covenants, and up to a 4-year payback period. Go to LighterCapital.com to apply and get a quick pre-qualification. Then talk with their experienced team to create a practical funding plan to achieve your goals.  The Practical Founders Podcast Tune into the Practical Founders Podcast for weekly in-depth interviews with founders who have built valuable software companies without big funding. Subscribe to the Practical Founders Podcast using your favorite podcast app or view on our YouTube channel. Get the weekly Practical Founders newsletter and podcast updates at practicalfounders.com. Practical Founders CEO Peer Groups Be part of a committed and confidential group of practical founders creating valuable software companies without big VC funding.  A Practical Founders Peer Group is a committed and confidential group of founders/CEOs who want to help you succeed on your terms. Each Practical Founders Peer Group is personally curated and moderated by Greg Head.

Join in and hear from one of our community's newer college graduates - Mr. Wes Carroll. The LMS Carnival, LMS, The Cow Path and mountain athletics are all covered in this great discussion, in part, about growing up on the mountain. Hear how he now reflects back on it as a recent college graduate - a unique perspective that parents of young children may find interesting. Young parents of little ones today may enjoy hearing how the childhood experiences are processed and remembered - for example Wes was a huge baseball player on Lookout and then in middle school at McCallie. Dad Dan even helped coach - like many do today. Wes talks about the huge impact his parents - both Mom and Dad had on him and also what it was like heading off to college and realizing that feeling of more independence - a section that near-graduates may enjoy in particular.Wes shares about how his childhood interests have become life passions and how he is at a major-events point in his life with a couple of different events happening simultaneously. This is a fun episode and (like his fun dad, Dan - look for earlier episode with Dan Carroll) you will likely relate and remember these stages in many of our own lives - exciting times.All in all, a community child, growing up, gradating college and starting his first major job after school - this is an inspiring tale for young folks in high school, those currently in college and for young parents who may wonder how the activities race and busy events cycle impacts their children - and how its remembered. A fun and enjoyable episode that many can enjoy and relate to - well done Wes!Thank you and we are all pulling for you and wishing you the very best in these next exciting chapters of your life.  Great seeing the children of this community thriving in the world. Godspeed Wes.Spread the word! Find us at ...theMountainEcho.orgPlease "Like" and 'subscribe' for notification of new episodes on your media player's podcast menu. Also, on regular, full length, non-bonus episodes, many thanks for closing music featuring the Dismembered Tennesseans and vocals by the amazing Laura Walker singing Tennessee Waltz. Opening fiddle music played by the late Mr. Fletcher Bright.

professorjrod@gmail.comWhat if the scariest hacks of 2025 never looked like hacks at all? We break down five real-world scenarios where attackers didn't smash locks—they used the keys we handed them. From an AI-cloned voice that sailed through a wire transfer to a building's HVAC console that quietly held elevators and doors hostage, the common thread is hard to ignore: trust. Trusted voices, trusted vendors, trusted “boring” systems, trusted sessions, and trusted APIs became the most valuable attack surface of the year.We start with a “boring” phone call that proves how caller ID and confidence can defeat policy when culture doesn't empower people to challenge authority. Then we step into the mechanical room: cloud dashboards for HVAC and badge readers, vendor-shared credentials, and thin network segmentation made physical denial of service as simple as logging in. The pivot continues somewhere few teams watch—libraries—where an unpatched management system bridged city HR, school portals, and public access with zero alarms, because nothing looked broken.Authentication takes a hit next. MFA worked, yet attackers won by stealing active LMS session tokens from a neglected component and riding valid access for weeks. No failed logins, no brute force—just continuation that our tools rarely question. Finally, we open the mobile app and watch the traffic. Clean, well-formed API calls mapped pricing rules, loyalty balances, and inventory signals at scale. Not a single malformed request, but plenty of business logic abuse that finance noticed before security did.If you care about cybersecurity, IT operations, or the CompTIA mindset, the takeaways are clear: shorten trust windows, verify context continuously, rotate and scope vendor access, segment OT from IT, treat libraries and civic tech as real attack surface, bind tokens to devices, and put rate limits and behavior analytics at the heart of your API strategy. Ready to rethink where your defenses are blind? Listen now, share with your team, and tell us which assumption you'll challenge first. And if this helped, subscribe, leave a review, and pass it on to someone who needs a wake-up call.Support the showArt By Sarah/DesmondMusic by Joakim KarudLittle chacha ProductionsJuan Rodriguez can be reached atTikTok @ProfessorJrodProfessorJRod@gmail.com@Prof_JRodInstagram ProfessorJRod

Jeffrey welcomes Ola Iyinolakan, Chief Executive Officer, Stakestack AI, Hollywood, CA. Can you introduce yourself and share the founding story and mission of Stakestack? How does Stakestack's AI-driven platform improve workforce learning and compliance compared to traditional methods? What specific challenges faced by businesses in Michigan, especially in manufacturing automotive, does Stakestack address? What collaborations in Michigan's ecosystem have helped accelerate Stakestack's growth? How do you envision artificial intelligence impacting the broader Michigan community, including workers, businesses, and education, and what actions should be taken to ensure everyone benefits from AI-driven changes? Looking forward, what's most important to Stakestack going forward? » Visit MBN website: www.michiganbusinessnetwork.com/ » Subscribe to MBN's YouTube: www.youtube.com/@MichiganbusinessnetworkMBN » Like MBN: www.facebook.com/mibiznetwork » Follow MBN: twitter.com/MIBizNetwork/ » MBN Instagram: www.instagram.com/mibiznetwork/ Stakestack AI is revolutionizing workplace training and higher education with an adaptive AI-powered learning platform designed for the individual learner. Our dynamic AI agent moves beyond traditional one-size-fits-all modules, leveraging AI-driven course creation and personalized assessments that adjust seamlessly to each user's unique pace and learning style. The platform integrates seamlessly with existing corporate LMS systems, allowing HR teams and managers to easily create impactful training using intuitive AI tools such as text-to-video, text-to-image, and cognitive assessments. Stakestack AI empowers organizations to deliver personalized, engaging, and cost-effective training solutions that continuously evolve with every learner and align with business goals and workforce. Join us in redefining education, making it smarter, more engaging, affordable, and truly transformative.

In this episode of the HR L&D Podcast, host Nick Day sits down with John Canapel, President and COO of Cypher Learning, to explore how AI is reshaping learning and development and what this means for the future of HR.The conversation covers why traditional LMS models are no longer fit for purpose, how AI native learning platforms are collapsing months of work into minutes, and why personalization, engagement, and learning in the flow of work are now critical. John shares how AI agents, microlearning, and conversational learning can help organizations close skill gaps faster while protecting IP and maintaining strong governance.You will hear practical insights on AI driven course creation, learning agents, gamification, and skills based career pathways, alongside real world examples showing improved engagement, faster onboarding, stronger compliance, and higher retention. The discussion also looks at how the role of the L&D professional is evolving from content creation to designing scalable learning systems and managing behavior change.Whether you are an HR leader, L&D professional, or business executive, this episode will help you understand how to move from slow, compliance driven training to high impact, human centered learning powered by AI. Expect clear guidance on where to start, how to assess AI readiness, and how to build a workforce that can upskill faster and perform at a higher level in a rapidly changing world.John's Kannapell LinkedIn: https://www.linkedin.com/in/kannapell/ Nick Day's LinkedIn: https://www.linkedin.com/in/nickday/ Find your ideal candidate with our job vacancy system: https://jgarecruitment.ck.page/919cf6b9eaSign up to the HR L&D Newsletter - https://jgarecruitment.ck.page/23e7b153e7

Corporate learning used to measure success by the size of its course catalogue and the number of completions. That world is fading. Employees now have access to commercial-grade learning inside tools like ChatGPT and Gemini, and leaders expect proof that learning actually shifts performance, culture and results. Lori Niles-Hofmann thinks this is the reckoning the profession has needed for years. Lori is a long-time learning strategist and co-founder of Eight Levers, with more than twenty years of experience in L&D across international banking, consulting and marketing. She specializes in large-scale digital learning transformation and helps organizations use data, platforms and design to make learning a business driver instead of a content factory. Her book, "The Eight Levers of EdTech Transformation: A Field Guide to the New Future-Focused L&D," lays out a practical model for CLOs who know that the role must evolve. In this episode of Leadership NOW, we talk about: • Why L&D will be under extreme pressure from external learning experiences if it does not change • What it means to stop being a course factory and start running campaigns built around triggers and performance • Her view of the LMS as invisible middleware, living inside tools like Copilot, rather than a portal people “go to” • How to work with HR, IT and finance as part of a skills supply chain instead of a standalone training shop • The learning–work continuum, where every task can become a learning opportunity that feeds directly into output • Learning triage, closed-loop reporting and how data can move L&D from order taker to strategic partner Lori also shares why she believes we are only millimeters away from truly contextualized, personalized learning experiences at scale, and what learning leaders must do now to be ready. Find out more: Lori Niles-Hofmann: https://www.loriniles.com/ Dan Pontefract and the Leadership NOW podcast: https://www.danpontefract.com

2026 is a pivotal year for education and training in the SBCA Academy. In this solo episode, Ashley Baker, SBCA's Director of Education highlights the progress made in 2025, and sheds light on what's next. Updated and advanced truss technician training, leadership development, and seamless LMS integration elevate the Academy and make learning even more efficient for companies and their teams. Whether you're a longtime member or simply curious about the industry, this episode offers a clear and exciting look at what's ahead and why it matters.

TL;DRAI literacy is becoming a baseline skill. This episode explores how organizations and individuals are actually building AI capability at work, with a focus on:* Self-directed learning and AI education at scale* Personalized learning journeys versus one-size-fits-all training* The shift from basic AI use to agentic workflows* The role of human strengths—creativity, judgment, and adaptability—in an AI-driven workplaceIn this episode, I'm joined by Erica Salm Rench, an AI educator and leader at Sidecar AI.Sidecar is an AI education platform and learning management system (LMS) designed to help organizations educate their employees on AI through self-directed learning. It combines structured courses, role-based learning paths, and hands-on use cases so individuals can build AI capability at their own pace while organizations raise overall AI fluency.Our conversation explores what AI education actually looks like beyond hype—how people are learning it, how organizations are rolling it out, and why understanding AI is quickly becoming a career differentiator rather than a technical specialty.AI Education Has Shifted from “What Is It?” to “How Do I Use It?”Erica explains that the conversation around AI in associations has changed dramatically over the last several years. Early on, organizations were hesitant to even talk about AI. Today, the question is no longer what is AI? but how can we use it to advance our mission, improve operations, and better serve our members?That shift brings a new challenge: helping people move from curiosity to competence in a way that feels approachable rather than overwhelming.Meeting People Where They AreOne of the strongest themes in our discussion is the importance of meeting learners at their current level of comfort and knowledge. AI education isn't one-size-fits-all.This means combining:* Foundational AI concepts* Role-specific applications (marketing, events, operations)* A growing library of real-world use cases* Ongoing updates as tools evolveThe goal isn't to turn everyone into a AI engineer—it's to help people understand what's possible and apply AI meaningfully in their day-to-day work.From Prompting to Agentic WorkWe spend time talking about the evolution from simple AI use cases—like writing emails or summarizing content—to agentic AI, where systems take action on a user's behalf.This shift matters because it fundamentally changes how work gets done. Instead of just assisting with tasks, AI begins to:* Automate multi-step workflows* Scale work that previously required human labor* Act as a force multiplier rather than a one-off toolWe agree that while much of this is still clunky today, the direction is clear: agents are becoming a core part of how work will be organized.Personalized Learning Is the Future of EducationA major insight from the episode is that personalized learning journeys will define the next phase of education—especially in fast-moving domains like AI.Erica describes how Sidecar uses AI within its learning environment to:* Act as a learning assistant* Answer questions in real time* Reinforce concepts* Help learners connect theory to applicationThis mirrors a broader trend: education becoming less about static courses and more about continuous, adaptive support.The Psychology of Learning AI at WorkWe talk openly about fear—fear of job loss, fear of falling behind, fear of not being “technical enough.” Erica makes the case that leaders have a responsibility to educate their teams, not just for organizational performance, but for people's long-term career resilience.From a psychological perspective, AI education:* Reduces anxiety by replacing uncertainty with understanding* Increases confidence and autonomy* Helps people see AI as a collaborator, not a threatSpending even 20–30 minutes a day learning AI can quickly change how people see their own future at work.Human Strengths Still Matter More Than EverOne of my favorite parts of the conversation is where we zoom out to the human side of all this. As AI removes technical barriers, the differentiator becomes human qualities—creativity, resilience, judgment, adaptability, and the ability to ask good questions.AI doesn't replace these traits. It amplifies them.Used well, AI allows people to overcome past limitations, work around weaknesses, and bring their ideas to life faster than ever before.What Listeners Should Take AwayAI literacy is becoming a baseline skill. The people who thrive won't be the most technical, but the most curious, adaptable, and intentional about learning how to work alongside intelligent systems.Education—done thoughtfully and continuously—is the bridge between fear and opportunity.Where to Find EricaErica is highly active on LinkedIn and can be found through Sidecar AI, where she and her team are building education-first pathways into AI for associations, nonprofits, and mission-driven organizations. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit charleshandler.substack.com

Dr. Stephanie Valentine (University of Nebraska–Lincoln) joins Sharona and Boz to tell the origin story behind TeachFront—a grading-and-feedback platform she and her students built after getting buried in spreadsheets trying to make standards-based / ungrading-style systems work at scale.They dig into what shifted when grades stopped being “points to litigate” and became feedback for growth, what went wrong (and what finally worked) in those early semesters, and why most LMS gradebooks still force instructors to “hack” systems designed for averages. Stephanie explains how TeachFront supports iterative feedback, reassessments, flexible mastery scales (including specs-style checkboxes), and clearer student-facing progress visuals—without putting points front-and-center.LinksPlease note - any books linked here are likely Amazon Associates links. Clicking on them and purchasing through them helps support the show. Thanks for your support!TeachFront.comYoutube Webinar on Using TeachFrontEpisode 120 - Learning Takes Time with Wendy SmithResourcesThe Center for Grading Reform - seeking to advance education in the United States by supporting effective grading reform at all levels through conferences, educational workshops, professional development, research and scholarship, influencing public policy, and community building.The Grading Conference - an annual, online conference exploring Alternative Grading in Higher Education & K-12.Some great resources to educate yourself about Alternative Grading:The Grading for Growth BlogThe Grading ConferenceThe Intentional Academia BlogRecommended Books on Alternative Grading:Grading for Growth, by Robert Talbert and David ClarkSpecifications Grading, by Linda NilsenUndoing the Grade, by Jesse StommelFollow us on Bluesky, Facebook and Instagram - @thegradingpod. To leave us a...

It's YOUR time to #EdUp with Dr. Richard W. E. Georges, President, H. Lavity Stoutt Community CollegeIn this episode, President Series #429, recorded Live from the Middle States Commission on Higher Education⁠ 2025 Annual ConferenceYOUR host is ⁠⁠Dr. Joe SallustioHow does the only community college in the British Virgin Islands serve 40,000 people across 50 islands with 800 credit students & over 1,000 continuing ed students achieving 90% employment rates?Why did Hurricane Irma in 2017 force HLSCC to move everything to the cloud (LMS, operations, HR & finance) which made them one of the better prepared institutions in their region to respond to COVID without too much loss?How is the transformation phase after recovery & discovery bringing the first fully owned bachelor's degree in education & residential campus with standalone government grants? 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 get early, ad-free access & exclusive leadership content to help support the show? Then ⁠⁠​subscribe today​⁠⁠ to lock in YOUR $5.99/m lifetime supporters rate! This offer ends December 31, 2025! 

Alex is a team leader working with a mix of experienced adjusters and brand-new claims professionals, some assigned to the field and others working behind a desk. Alex has to make sure his team has the training they need to approach their work with confidence. Luckily PLRB.org's Education Hub has everything they need to succeed.   Notable Timestamps [ 00:10 ] - The PLRB Education Hub supports team leaders like Alex with training for both new and experienced adjusters to build confidence in handling claims. [ 01:25 ] - Update #1: A new critical thinking course will help adjusters analyze information, decide when to bring in experts, and resolve claims fairly and in good faith. [ 02:20 ] - Update #2: The annual "Claims Resolution" webinar series will address ethics of automation, bad faith in AI, and how emerging tech affects investigations. [ 03:35 ] - Update #3: A new PLRB designation program aims to take adjusters from entry level through line-of-business-specific training with elective options. [ 05:05 ] - The Education Hub offers 200+ recorded webinars, podcasts, modules, and downloadable slide decks as an on-demand claims knowledge library. [ 06:35 ] - "Test Your Claims Knowledge" microlearning modules use flashcards, definitions, photos, and scenarios for quick, interactive training. [ 08:45 ] - Member companies can integrate PLRB courses, webinars, microlearnings, and even this podcast directly into their own LMS platforms. [ 12:55 ] - PLRB will help members curate custom courses by combining videos, quizzes, and interactives in any sequence to match specific training goals. [ 14:10 ] - The library includes 100+ non-CE modules, about 200 podcasts, some 250 recorded webinars, plus many shorter video series for flexible learning. [ 16:25 ] - Mike summarizes the key points above. Your PLRB Resources Upcoming Events: PLRB Conferences & More! https://www.plrb.org/events PLRB Education HUB: https://members.plrb.org/education Listeners can email education@plrb.org for help navigating resources, requesting new content, or getting tailored curriculum support. Employees of member companies also have access to a searchable legal database, hundreds of hours of video trainings, building code materials, weather data, and even the ability to have your coverage questions answered by our team of attorneys (https://www.plrb.org/ask-plrb/) at no additional charge to you or your company. Subscribe to this Podcast Your Podcast App - Please subscribe and rate us on your favorite podcast app YouTube - Please like and subscribe at @plrb LinkedIN - Please follow at "Property and Liability Resource Bureau" Send us your Scenario! Please reach out to us at 630-509-8704 with your scenario! This could be your "adjuster story" sharing a situation from your claims experience, or a burning question you would like the team to answer. In any case, please omit any personal information as we will anonymize your story before we share. Just reach out to scenario@plrb.org.  Legal Information The views and opinions expressed in this resource are those of the individual speaker and not necessarily those of the Property & Liability Resource Bureau (PLRB), its membership, or any organization with which the presenter is employed or affiliated. The information, ideas, and opinions are presented as information only and not as legal advice or offers of representation. Individual policy language and state laws vary, and listeners should rely on guidance from their companies and counsel as appropriate. Music: "Piece of Future" by Keyframe_Audio. Pixabay. Pixabay License. Font: Metropolis by Chris Simpson. SIL OFL 1.1. Icons: FontAwesome (SIL OFL 1.1) and Noun Project (royalty-free licenses purchased via subscription). Sound Effects: Pixabay (Pixabay License) and Freesound.org (CC0).

Season 2, Episode 18Guests:Courtney Hurley OLY — Three-time Olympian (team bronze), Head Coach, Duke City Fencing; program lead at Menaul SchoolChris Ferrara — Assistant Head of School & Upper School Director, Menaul School (Albuquerque, NM)What we coverCourtney's transition from Olympian to coach: passing on elite habits, culture-building, and motivationThe liberal-arts lens (Chris): teaching how to think, not what to think; “high challenge, high support” academicsWhy daily, in-school training matters: Menaul's 70–85 minute fencing block every day, plus after-school lessons and club boutingTime management that works: block schedules, LMS support on travel days, concrete checkpoints from advisors/homeroomThe parent role: authoritative (not authoritarian); building habits and internal drive without a pressure cookerCompetition as fuel: why early meets accelerate learning and buy-inBuilding a regional pipeline: growing Albuquerque's fencing scene—and why a smaller state can be a strategic advantageThe three-weapon vision: adding dedicated foil/saber coaches, strength training, and an NCAA-style structureScholar-athlete outcomes: how varsity-level sport correlates with college success—and how fencing fits college admissionsLinksLearn more about Menaul School's fencing program (with Duke City Fencing):https://www.menaulschool.org/fencing-find-your-edge/Timestamps0:00 — Two perspectives, one goal: student-athletes who thrive1:17 — Courtney: fencing was “who I am”—why coaching was the natural next step2:30 — Chris: the liberal-arts case for scholar-athletes (mind–body–spirit)4:57 — Using fencing strategically in college planning5:24 — Courtney's scholar-athlete path: school support + travel reality7:21 — Western travel culture & flexible academics (LMS on the road)8:36 — Teaching time management: high challenge, high support10:16 — Coach's role: priorities, buy-in, and aligning goals11:30 — A week in the life: daily fencing block (70–85 min), block classes, after-school lessons14:55 — Culture shift in ABQ: from hobby to competitive16:18 — Why daily training compresses learning curves17:12 — The three-to-five-year plan: three weapons, S&C, university-style structure18:26 — Why athletics belong in school: GPA + varsity sport = college success20:45 — Life skills from fencing: perseverance, interviews, careers22:09 — The parent balance: building habits & ownership26:26 — Making fencing the best part of the day (present-moment focus)27:55 — What Courtney gets from coaching: a new challenge, new results30:07 — Fit questions for families considering Menaul33:00 — Why boarding + fencing can unlock opportunity34:51 — Putting Albuquerque on the map—competitively36:36 — A small-state advantage in college admissionsQuotable“You're a club before you're a team—culture keeps kids showing up. But daily reps inside the school day? That's what accelerates progress.” — Courtney Hurley“The #1 predictor of college success is GPA; the #2 is participation in varsity-level sports.” — Chris FerraraCall to actionCurious about the school-day fencing model? Explore Menaul's program and how it pairs with Duke City Fencing: https://www.menaulschool.org/fencing-find-your-edge/CreditsHost: Bryan Wendell • Guests: Courtney Hurley OLY & Chris Ferrara --First to 15: The Official Podcast of USA FencingHost: Bryan WendellCover art: Manna CreationsTheme music: Brian Sanyshyn

Every few years, the world of product management goes through a phase shift. When I started at Microsoft in the early 2000s, we shipped Office in boxes. Product cycles were long, engineering was expensive, and user research moved at the speed of snail mail. Fast forward a decade and the cloud era reset the speed at which we build, measure, and learn. Then mobile reshaped everything we thought we knew about attention, engagement, and distribution.Now we are standing at the edge of another shift. Not a small shift, but a tectonic one. Artificial intelligence is rewriting the rules of product creation, product discovery, product expectations, and product careers.To help make sense of this moment, I hosted a panel of world class product leaders on the Fireside PM podcast:• Rami Abu-Zahra, Amazon product leader across Kindle, Books, and Prime Video• Todd Beaupre, Product Director at YouTube leading Home and Recommendations• Joe Corkery, CEO and cofounder of Jaide Health • Tom Leung (me), Partner at Palo Alto Foundry• Lauren Nagel, VP Product at Mezmo• David Nydegger, Chief Product Officer at OvivaThese are leaders running massive consumer platforms, high stakes health tech, and fast moving developer tools. The conversation was rich, honest, and filled with specific examples. This post summarizes the discussion, adds my own reflections, and offers a practical guide for early and mid career PMs who want to stay relevant in a world where AI is redefining what great product management looks like.Table of Contents* What AI Cannot Do and Why PM Judgment Still Matters* The New AI Literacy: What PMs Must Know by 2026* Why Building AI Products Speeds Up Some Cycles and Slows Down Others* Whether the PM, Eng, UX Trifecta Still Stands* The Biggest Risks AI Introduces Into Product Development* Actionable Advice for Early and Mid Career PMs* My Takeaways and What Really Matters Going Forward* Closing Thoughts and Coaching Practice1. What AI Cannot Do and Why PM Judgment Still MattersWe opened the panel with a foundational question. As AI becomes more capable every quarter, what is left for humans to do. Where do PMs still add irreplaceable value. It is the question every PM secretly wonders.Todd put it simply: “At the end of the day, you have to make some judgment calls. We are not going to turn that over anytime soon.”This theme came up again and again. AI is phenomenal at synthesizing, drafting, exploring, and narrowing. But it does not have conviction. It does not have lived experience. It does not feel user pain. It does not carry responsibility.Joe from Jaide Health captured it perfectly when he said: “AI cannot feel the pain your users have. It can help meet their goals, but it will not get you that deep understanding.”There is still no replacement for sitting with a frustrated healthcare customer who cannot get their clinical data into your system, or a creator on YouTube who feels the algorithm is punishing their art, or a devops engineer staring at an RCA output that feels 20 percent off.Every PM knows this feeling: the moment when all signals point one way, but your gut tells you the data is incomplete or misleading. This is the craft that AI does not have.Why judgment becomes even more important in an AI worldDavid, who runs product at a regulated health company, said something incredibly important: “Knowing what great looks like becomes more essential, not less. The PM's that thrive in AI are the ones with great product sense.”This is counterintuitive for many. But when the operational work becomes automated, the differentiation shifts toward taste, intuition, sequencing, and prioritization.Lauren asked the million dollar question. “How are we going to train junior PMs if AI is doing the legwork. Who teaches them how to think.”This is a profound point. If AI closes the gap between junior and senior PMs in execution tasks, the difference will emerge almost entirely in judgment. Knowing how to probe user problems. Knowing when a feature is good enough. Knowing which tradeoffs matter. Knowing which flaw is fatal and which is cosmetic.AI is incredible at writing a PRD. AI is terrible at knowing whether the PRD is any good.Which means the future PM becomes more strategic, more intuitive, more customer obsessed, and more willing to make thoughtful bets under uncertainty.2. The New AI Literacy: What PMs Must Know by 2026I asked the panel what AI literacy actually means for PMs. Not the hype. Not the buzzwords. The real work.Instead of giving gimmicky answers, the discussion converged on a clear set of skills that PMs must master.Skill 1: Understanding context engineeringDavid laid this out clearly: “Knowing what LMS are good at and what they are not good at, and knowing how to give them the right context, has become a foundational PM skill.”Most PMs think prompt engineering is about clever phrasing. In reality, the future is about context engineering. Feeding models the right data. Choosing the right constraints. Deciding what to ignore. Curating inputs that shape outputs in reliable ways.Context engineering is to AI product development what Figma was to collaborative design. If you cannot do it, you are not going to be effective.Skill 2: Evals, evals, evalsRami said something that resonated with the entire panel: “Last year was all about prompts. This year is all about evals.”He is right.• How do you build a golden dataset.• How do you evaluate accuracy.• How do you detect drift.• How do you measure hallucination rates.• How do you combine UX evals with model evals.• How do you decide what good looks like.• How do you define safe versus unsafe boundaries.AI evaluation is now a core PM responsibility. Not exclusively. But PMs must understand what engineers are testing for, what failure modes exist, and how to design test sets that reflect the real world.Lauren said her PMs write evals side by side with engineering. That is where the world is going.Skill 3: Knowing when to trust AI output and when to override itTodd noted: “It is one thing to get an answer that sounds good. It is another thing to know if it is actually good.”This is the heart of the role. AI can produce strategic recommendations that look polished, structured, and wise. But the real question is whether they are grounded in reality, aligned with your constraints, and consistent with your product vision.A PM without the ability to tell real insight from confident nonsense will be replaced by someone who can.Skill 4: Understanding the physics of model changesThis one surprised many people, but it was a recurring point.Rami noted: “When you upgrade a model, the outputs can be totally different. The evals start failing. The experience shifts.”PMs must understand:• Models get deprecated• Models drift• Model updates can break well tuned prompts• API pricing has real COGS implications• Latency varies• Context windows vary• Some tasks need agents, some need RAG, some need a small finetuned modelThis is product work now. The PM of 2026 must know these constraints as well as a PM of the cloud era understood database limits or API rate limits.Skill 5: How to construct AI powered prototypes in hours, not weeksIt now takes one afternoon to build something meaningful. Zero code required. Prompt, test, refine. Whether you use Replit, Cursor, Vercel, or sandboxed agents, the speed is shocking.But this makes taste and problem selection even more important. The future PM must be able to quickly validate whether a concept is worth building beyond the demo stage.3. Why Building AI Products Speeds Up Some Cycles and Slows Down OthersThis part of the conversation was fascinating because people expected AI to accelerate everything. The panel had a very different view.Fast: Prototyping and concept validationLauren described how her teams can build working versions of an AI powered Root Cause Analysis feature in days, test it with customers, and get directional feedback immediately.“You can think bigger because the cost of trying things is much lower,” she said.For founders, early PMs, and anyone validating hypotheses, this is liberating. You can test ten ideas in a week. That used to take a quarter.Slow: Productionizing AI featuresThe surprising part is that shipping the V1 of an AI feature is slower than most expect.Joe noted: “You can get prototypes instantly. But turning that into a real product that works reliably is still hard.”Why. Because:• You need evals.• You need monitoring.• You need guardrails.• You need safety reviews.• You need deterministic parts of the workflow.• You need to manage COGS.• You need to design fallbacks.• You need to handle unpredictable inputs.• You need to think about hallucination risk.• You need new UI surfaces for non deterministic outputs.Lauren said bluntly: “Vibe coding is fast. Moving that vibe code to production is still a four month process.”This should be printed on a poster in every AI startup office.Very Slow: Iterating on AI powered featuresAnother counterintuitive point. Many teams ship a great V1 but struggle to improve it significantly afterward.David said their nutrition AI feature launched well but: “We struggled really hard to make it better. Each iteration was easy to try but difficult to improve in a meaningful way.”Why is iteration so difficult.Because model improvements may not translate directly into UX improvements. Users need consistency. Drift creates churn. Small changes in context or prompts can cause large changes in behavior.Teams are learning a hard truth: AI powered features do not behave like typical deterministic product flows. They require new iteration muscles that most orgs do not yet have.4. The PM, Eng, UX Trifecta in the AI EraI asked whether the classic PM, Eng, UX triad is still the right model. The audience was expecting disagreement. The panel was surprisingly aligned.The trifecta is not going anywhereRami put it simply: “We still need experts in all three domains to raise the bar.”Joe added: “AI makes it possible for PMs to do more technical work. But it does not replace engineering. Same for design.”AI blurs the edges of the roles, but it does not collapse them. In fact, each role becomes more valuable because the work becomes more abstract.• PMs focus on judgment, sequencing, evaluation, and customer centric problem framing• Engineers focus on agents, systems, architecture, guardrails, latency, and reliability• Designers focus on dynamic UX, non deterministic UX patterns, and new affordances for AI outputsWhat does changeAI makes the PM-Eng relationship more intense. The backbone of AI features is a combination of model orchestration, evaluation, prompting, and context curation. PMs must be tighter than ever with engineering to design these systems.David noted that his teams focus more on individual talents. Some PMs are great at context engineering. Some designers excel at polishing AI generated layouts. Some engineers are brilliant at prompt chaining. AI reveals strengths quickly.The trifecta remains. The skill distribution within it evolves.5. The Biggest Risks AI Introduces Into Product DevelopmentWhen we asked what scares PMs most about AI, the conversation became blunt and honest. Risk 1: Loss of user trustLauren warned: “If people keep shipping low quality AI features, user trust in AI erodes. And then your good AI product suffers from the skepticism.”This is very real. Many early AI features across industries are low quality, gimmicky, or unreliable. Users quickly learn to distrust these experiences.Which means PMs must resist the pressure to ship before the feature is ready.Risk 2: Skill atrophyTodd shared a story that hit home for many PMs. “Junior folks just want to plug in the prompt and take whatever the AI gives them. That is a recipe for having no job later.”PMs who outsource their thinking to AI will lose their judgment. Judgment cannot be regained easily.This is the silent career killer.Risk 3: Safety hazards in sensitive domainsDavid was direct: “If we have one unsafe output, we have to shut the feature off. We cannot afford even small mistakes.”In healthcare, finance, education, and legal industries, the tolerance for error is near zero. AI must be monitored relentlessly. Human in the loop systems are mandatory. The cycles are slower but the stakes are higher.Risk 4: The high bar for AI compared to humansJoe said something I have thought about for years: “AI is held to a much higher standard than human decision making. Humans make mistakes constantly, but we forgive them. AI makes one mistake and it is unacceptable.”This slows adoption in certain industries and creates unrealistic expectations.Risk 5: Model deprecation and instabilityRami described a real problem AI PMs face: “Models get deprecated faster than they get replaced. The next model is not always GA. Outputs change. Prompts break.”This creates product instability that PMs must anticipate and design around.Risk 6: Differentiation becomes hardI shared this perspective because I see so many early stage startups struggle with it.If your whole product is a wrapper around an LLM, competitors will copy you in a week. The real differentiation will not come from using AI. It will come from how deeply you understand the customer, how you integrate AI with proprietary data, and how you create durable workflows.6. Actionable Advice for Early and Mid Career PMsThis was one of my favorite parts of the panel because the advice was humble, practical, and immediately useful.A. Develop deep user empathy. This will become your biggest differentiator.Lauren said it clearly: “Maintain your empathy. Understand the pain your user really has.”AI makes execution cheap. It makes insight valuable.If you can articulate user pain precisely.If you can differentiate surface friction from underlying need.If you can see around corners.If you can prototype solutions and test them in hours.If you can connect dots between what AI can do and what users need.You will thrive.Tactical steps:• Sit in on customer support calls every week.• Watch 10 user sessions for every feature you own.• Talk to customers until patterns emerge.• Ask “why” five times in every conversation.• Maintain a user pain log and update it constantly.B. Become great at context engineeringThis will matter as much as SQL mattered ten years ago.Action steps:• Practice writing prompts with structured context blocks.• Build a library of prompts that work for your product.• Study how adding, removing, or reordering context changes output.• Learn RAG patterns.• Learn when structured data beats embeddings.• Learn when smaller local models outperform big ones.C. Learn eval frameworksThis is non negotiable.You need to know:• Precision vs recall tradeoffs• How to build golden datasets• How to design scenario based evals for UX• How to test for hallucination• How to monitor drift• How to set quality thresholds• How to build dashboards that reflect real world input distributionsYou do not need to write the code.You do need to define the eval strategy.D. Strengthen your product senseYou cannot outsource product taste.Todd said it best: “Imagine asking AI to generate 20 percent growth for you. It will not tell you what great looks like.”To strengthen your product sense:• Review the best products weekly.• Take screenshots of great UX patterns.• Map user flows from apps you admire.• Break products down into primitives.• Ask yourself why a product decision works.• Predict what great would look like before you design it.The PMs who thrive will be the ones who can recognize magic when they see it.E. Stay curiousRami's closing advice was simple and perfect: “Stay curious. Keep learning. It never gets old.”AI changes monthly. The PM who is excited by new ideas will outperform the PM who clings to old patterns.Practical habits:• Read one AI research paper summary each week.• Follow evaluation and model updates from major vendors.• Build at least one small AI prototype a month.• Join AI PM communities.• Teach juniors what you learn. Nothing accelerates mastery faster.F. Embrace velocity and side projectsTodd said that some of his biggest career breakthroughs came from solving problems on the side.This is more true now than ever.If you have an idea, you can build an MVP over a weekend. If it solves a real problem, someone will notice.G. Stay close to engineeringNot because you need to code, but because AI features require tighter PM engineering collaboration.Learn enough to be dangerous:• How embeddings work• How vector stores behave• What latency tradeoffs exist• How agents chain tasks• How model versioning works• How context limits shape UX• Why some prompts blow up API costsIf you can speak this language, you will earn trust and accelerate cycles.H. Understand the business deeplyJoe's advice was timeless: “Know who pays you and how much they pay. Solve real problems and know the business model.”PMs who understand unit economics, COGS, pricing, and funnel dynamics will stand out.7. Tom's Takeaways and What Really Matters Going ForwardI ended the recording by sharing what I personally believe after moderating this discussion and working closely with a variety of AI teams over the past 2 years.Judgment becomes the most valuable PM skillAs AI gets better at analysis, synthesis, and execution, your value shifts to:• Choosing the right problem• Sequencing decisions• Making 55 45 calls• Understanding user pain• Making tradeoffs• Deciding when good is good enough• Defining success• Communicating vision• Influencing the orgAgents can write specs.LLMs can produce strategies.But only humans can choose the right one and commit.Learning speed becomes a competitive advantageI said this on the panel and I believe it more every month.Because of AI, you now have:• Infinite coaches• Infinite mentors• Infinite experts• Infinite documentation• Infinite learning loopsA PM who learns slowly will not survive the next decade. Curiosity, empathy, and velocity will separate great from goodMany panelists said versions of this. The common pattern was:• Understand users deeply• Combine multiple tools creatively• Move quickly• Learn constantlyThe future rewards generalists with taste, speed, and emotional intelligence.Differentiation requires going beyond wrapper appsThis is one of my biggest concerns for early stage founders. If your entire product is a wrapper around a model, you are vulnerable.Durable value will come from:• Proprietary data• Proprietary workflows• Deep domain insight• Organizational trust• Distribution advantage• Safety and reliability• Integration with existing systemsAI is a component, not a moat.8. Closing ThoughtsHosting this panel made me more optimistic about the future of product management. Not because AI will not change the job. It already has. But because the fundamental craft remains alive.Product management has always been about understanding people, making decisions with incomplete information, telling compelling stories, and guiding teams through ambiguity and being right often.AI accelerates the craft. It amplifies the best PMs and exposes the weak ones. It rewards curiosity, empathy, velocity, and judgment.If you want tailored support on your PM career, leadership journey, or executive path, I offer 1 on 1 career, executive, and product coaching at tomleungcoaching.com.OK team. Let's ship greatness. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit firesidepm.substack.com

Education might be the most underrated marketing weapon in business, and today, we're pulling back the curtain on exactly why. In this episode of the Marketing Boost Solutions Podcast, featured guest Christopher Dundy, Marine veteran and CEO of Flagship LMS, reveals how training can transform your business from chaotic and reactive to scalable and unstoppable. With more than 1,000 custom e-learning courses built and full-service LMS platforms launched in under a week, Chris brings the kind of experience entrepreneurs rarely get access to. He breaks down the strategies top organizations use to boost retention, strengthen customer trust, and engineer consistent, repeatable systems that fuel growth.Whether you're training customers, affiliates, or your internal teams, Chris shows how automation, micro-learning, and smart systems turn education into a true marketing asset, one that multiplies loyalty, accelerates sales, and sets your brand apart. This conversation will completely reshape how you think about scaling, onboarding, and creating advocates at every level. If you've ever wanted a blueprint for building long-term trust at scale, this episode delivers it. This isn't just education … It's a strategy. And it will change the way you grow.

What should training providers expect from new global learning systems? Find out as John Leh talks with LMS innovator Nick Eriksen on this Talented Learning Show podcast! The post Podcast 106: How Innovative Tools Improve Global Learning Results appeared first on Talented Learning.

This week, we sit down with Seth Smith, founder of the rapidly growing lab software company Greatlab.io (https://www.greatlab.io/), and Ryan Alexander from Vitality Dental Arts (https://www.vitalitydentalarts.com/), who's been living the GreatLab life since May and has plenty to say about it. Seth shares the long, winding road from e-commerce to dentistry, to clear aligners, to scanners, and finally to building what he hopes becomes the most modern, integrated, and speed-driven LMS in the industry. He talks workflow obsession, eliminating downloads, killing paper dockets, listening to lab pain points, and why he's visited over 100 labs (and keeps going). Ryan brings the real-world perspective from a 100-tech lab that went through multiple LMS transitions before landing on GreatLab. He explains how their booking teams shrank, inbound calls dropped by 50%, audits disappeared, and technicians suddenly found computers they “didn't have” once the system made their jobs easier. From the CRM that kills phone tag to ScanHub pulling every scanner into one feed, Ryan breaks down exactly what changed on the bench, in customer service, and across production. We also dig into bad scans (yes, 20% of them), doctor communication, automatic file routing, task automation, shipping integrations, data migration fears, and why some labs should not switch systems unless they're truly ready to modernize. If you've ever wondered what a cloud-based, automation-heavy, lab-built-from-the-ground-up LMS looks like—or why another lab described GreatLab as “a Ferrari while everyone else is a Civic”—this episode lays it all out. Learn more or request a demo: greatlab.io Find them in Vegas at NADL Visions (https://www.nadl.org/nadl-vision-21) and in Chicago at Lab Day (https://lmtmag.com/lmtlabday)! Happy Holidays from Ivoclar! As the year comes to a close, all of us at Ivoclar want to extend our heartfelt gratitude to the incredible Voices From the Bench community. Thank you for your partnership, your trust, and the support you've shown throughout the year. From our Ivoclar family to yours, we wish you a joyful, healthy, and safe holiday season. May your days be merry, your nights be bright, and your smiles shine like freshly fallen snow. Ho, ho, ho — Happy Holidays from Ivoclar! Elvis and Barb are gearing up for their chat with the HyperDent Dude himself, Jordan Greenberg from FOLLOW-ME! Technology (https://www.follow-me-tech.com/). At LabFest, Elvis found out that every hyperDENT (https://www.follow-me-tech.com/hyperdent/) license comes with Template Editor Lite — a built-in feature that lets you make safe, customized tweaks to your milling strategies. Whether you want to prioritize surface quality or speed, this tool gives you the control to fine-tune your results while FOLLOW-ME! keeps everything validated and reliable. Because in the end, us lab techs love to tinker — and hyperDENT makes it easy to choose your own CAM-venture. Special Guests: Ryan Alexander and Seth Smith.

Information access: While many have Copilot licenses, usage is low beyond basic tasks like email and meeting summaries. The main challenge with adoption is providing guidance within apps like PowerPoint, Excel, Dynamics, and Word so users can access help exactly when they need it. This is something Rehmani's company, VisualSP, and his training platform, copilottrainingpackage.com, specialize in. "I'm a big proponent of giving people 'at the moment need' information," he notes.Training paths: Copilottrainingpackage.com enables users to go down different "training paths," explains Rehmani. Specifically, there are pre-built PowerPoint training modules covering key topics like prompt creation and preventing hallucinations. Additionally, there's learning management system (LMS)-ready video content on Copilot use cases in Word, Excel, and other tools for on-demand learning. Finally, the platform offers optional live training sessions for trainers and power users to ensure effective adoption and ROI from Copilot. "At the end of the day, it's all about making Copilot into ROI and not just an expense layer."What to expect: Rehmani describes the "anatomy" of the program. It uses seven modules to teach trainers and power users how to craft effective prompts, reduce Copilot errors, and apply specific workflows for high-impact ROI. Then, participants share this knowledge internally, enabling time savings and efficiency across their organizations.End-of-year pricing: Users can take advantage of this resource with special pricing through the end of the year. Users can purchase the standalone package for $4,950 or the package and live training for $8,950, all of which could be delivered in 2026, explains Rehmani. Visit Cloud Wars for more.

Steve Sullivan joins me for a lively conversation about podcasting, tutor videos, and digital A&P teaching. We explore how he humanizes online learning, why students crave multiple approaches, and what he's learned after 23 years of teaching. From LMS-independent course design to global podcast reach, Steve shares practical strategies and inspiring stories that can help any A&P instructor evolve their teaching. 0:00:00 | Introduction 0:00:49 | This Episode 0:02:28 | Becoming Steve Sullivan 0:06:41 | Your Teaching Voice* 0:07:30 | Why Start a Podcast? 0:14:03 | Farewell to TAPP ed* 0:15:45 | Growing a Podcast & Growing Through It 0:19:56 | Authors Alert * 0:21:05 | Digital Teaching That Actually Helps 0:30:59 | When Our Tools Disappear* 0:32:48 | A&P Tools That Fit Any Textbook 0:48:36 | Collaboration Audit* 0:49:14 | What 23 Years of A&P Reveals 1:01:10 | Innovation Check * 1:01:44 | Staying Connected * Breaks ★ If you cannot see or activate the audio player, go to: theAPprofessor.org/podcast-episode-156.html ❓ Please take the anonymous survey: theAPprofessor.org/survey ☝️ Questions & Feedback: 1-833-LION-DEN (1-833-546-6336)

Continuum is solving the multi-party return problem in B2B supply chain—a transaction involving distributors, manufacturers, and end users that previously took 30-45 days and now completes in 30-45 seconds. In this episode of Category Visionaries, we sat down with Alex Witcpalek, CEO and Founder of Continuum, to unpack how he's building what he calls "reverse EDI" in a market of 1.5 million distribution and manufacturing companies across North America. After 13 years selling technology into this space, Alex is now growing 8x year-over-year by turning customers into the primary acquisition channel through network effects. Topics Discussed: Why multi-party returns require replicating order management, warehouse management, and procurement systems simultaneously The tactical sequencing of building network businesses: solving for independent value, achieving critical mass, then activating network effects How Continuum navigates deep ERP integrations (SAP, Oracle, NetSuite, Epicor) plus bespoke business logic across multiple supply chain tiers Facebook retargeting, BDR outbound, events, and customer referrals as the four channels driving growth in a non-PLG market Why business model differentiation is the only remaining moat when technical barriers collapse Building domain expertise distribution systems using AI-powered LMS fed by sales call recordings GTM Lessons For B2B Founders: Choose problems where you can capture 100% of addressable market, not fractional share: Alex deliberately avoided competing in CRM, sales order automation, or accounts payable—categories where even dominant players cap at 25-30% market penetration. Instead, he targeted multi-party reverse logistics, a greenfield problem no one else was solving. This strategic choice eliminates competitive displacement risk and allows every prospect conversation to focus on change management rather than competitive differentiation. Founders should map their TAM against competitive saturation: markets where you can own the entire category create fundamentally different growth trajectories than fighting for fragments. Sequence network businesses: independent value → critical mass → network activation: Alex was told by investors 18 months in that network effects "weren't going to work." His insight: "When you don't have a network, you don't sell the network. It's just in your plans and how you're building." Continuum sold P&L impact, manual labor reduction, and customer experience improvements to early adopters while building network infrastructure invisibly. Only after achieving density in specific verticals (HVAC, electrical, plumbing) did they surface the network value proposition. This sequencing prevents the cold-start problem—founders building marketplace or network businesses must design standalone value that makes the first 100 customers successful independent of network density. Exploit high pain thresholds in legacy industries as competitive barriers: Supply chain companies accept 30-45 day return cycles, manual warranty claims on paper, and playing "guess who" by phone to find inventory across distributor branches. Alex notes they have "extremely high pain threshold" from living with broken systems for decades. While this creates longer education cycles, it also means competitors won't enter (too hard) and once you prove ROI, switching costs become prohibitive. Founders should reframe customer inertia: industries tolerating obvious inefficiencies offer category creation opportunities with built-in moats, not just sales friction. Business model architecture is the only defensible moat—technical differentiation is dead: Alex is building his own e-signature platform (Continue Sign) and AI LMS using vibe coding to prove technical moats no longer exist. Continuum's defensibility comes entirely from network lock-in: displacing them requires disconnecting manufacturers like Carrier, Daikin, and Bosch plus their entire distributor ecosystems simultaneously. He references EDI (1960s technology still dominant today) as proof that network effects create permanent advantages. Founders must architect switching costs, network density, or proprietary data advantages into their business model—technology alone provides zero protection in the AI era. Match channel strategy to actual ICP behavior, not SaaS conventions: Continuum's top lead source is customer-driven network growth—distributors recruiting manufacturers and vice versa. Facebook retargeting works because their 50+ year-old supply chain buyers "are trying to comment on their grandkids' pictures," not scrolling LinkedIn. BDR outbound still delivers high win rates in an industry where business happens on handshakes, making events critical. This channel mix would fail for PLG products but works perfectly for enterprise cycles with $40K ACVs and 90-day sales processes. Founders should ethnographically research where their specific buyers actually spend attention rather than defaulting to LinkedIn, content marketing, or PLG based on what works in adjacent categories. Use 90-day enterprise cycles and multi-stakeholder complexity as qualification, not friction: Continuum runs enterprise sales motions for $40K deals because multi-party returns touch 16 constituents across sales, customer service, fleet, supply chain, warehouse, purchasing, and finance. Rather than trying to simplify buying, Alex uses this complexity as a filter—companies willing to coordinate VP of Supply Chain, COO, and CFO alignment are serious buyers. He layers three value propositions (P&L impact, labor reduction, customer experience) knowing different stakeholders weight them differently. Founders selling into complex environments should embrace multi-threading as a qualification mechanism that improves win rates and reduces churn, not overhead to eliminate. //  Sponsors:  Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire  Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM  

How do schools prepare for the changing landscape of both education and business with the pace of advancements in technology and specifically in artificial intelligence (AI)? What lessons were learned from the rapid shift to digital that happened during the pandemic and how can that knowledge improve the way higher education works today?Shawn Miller is the Associate Provost for Digital Learning and Strategy at Rice University. Shawn serves as the key steward of Rice's digital strategy where he leverages best practices already in place across the University and also introduces new approaches and collaborations to be scaled.Shawn and host David Mansouri discuss the transformative impact of digital learning and AI on higher education. Shawn shares his career journey, from his time at the University of Texas at El Paso (UTEP) and Duke University, through to his current role at Rice. Their conversation explores Rice's vision for digital education, the integration of AI tools in learning, and the future of teaching and learning at Rice. Shawn also highlights the challenges and ethical concerns related to AI, including the aspects of AI in education that he is more interested in than using it to just continue the way things were taught before. Shawn also lays out his view of some essential skills students need to thrive in an AI-powered world.Let us know you're listening by filling out this form. We will be sending listeners Beyond the Hedges Swag every month.Episode Guide:01:01 Shawn Miller's introduction and background06:16 The Vision for Digital Learning at Rice14:23 Impact of COVID-19 on Digital Learning19:30 Integrating AI into Education at Rice23:47 Promising AI Applications in Teaching26:19 AI's Role in Learning and Analytics28:55 Challenges and Ethical Concerns of AI33:14 Skills for an AI-Powered World35:52 Future of Teaching and Learning at Rice38:51 Rapid Fire QuestionsBeyond The Hedges is a production of Rice University and is produced by University FM.Episode Quotes:Rethinking education in the age of AI27:39: What's really most frustrating to me about the first wave of AI education tools that we got thrown at us, right, as institutions—and I'm talking even about startups—they're mostly founded on the idea that whatever we are doing now in classes and in teaching is somehow the right way to do it, right? So, it's like, how can you speed up creating better multiple-choice tests, right? Or how could you grade all these papers that you've got to grade, right? Well, maybe the outcome for that class isn't that you should write a paper in the first place, right? But now is our chance to ask that. And I know this is frustrating for faculty…[28:61] But it's a good opportunity for us to, but then it's been frustrating to have all these edtech ventures come out where it's like, “But AI could make all the things better!” And it's like, yeah, but you're talking about making traditional education faster, cheaper, more productive. You're not talking about helping people learn better.What's a better question for AI in education11:16: Maybe the answer for AI is not what can you have the AI do that you used to do, as much as what can I do even more of or even better. And I think that's a good mindset for us to be in, in education.The pandemic digital experience15:34: I think you have two things that people tend to say about the pandemic digital learning experience. One is that it was horrible, and they'd never want to do it again. Then, for those who knew about online learning or had done it before the pandemic, they'll say, “Well, that's because no one did it right,” quote unquote. And I think we can honor both of those viewpoints. But I'd also say that we learned a few things, right? One thing is most faculty learned how to use the LMS and Zoom. And if you think back pre-COVID, how many people could launch a webinar or call a virtual meeting, right? And how many staff did it take to set up a global web conference? It was incredibly expensive. It took a lot of time. You had to schedule it, and now people just trigger these things, right? I think the second thing we learned is that hybrid work can definitely work. And I've gone on record a few times saying that the future of work maybe parallels the future of hybrid and online learning.Show Links:Rice Digital Learning and StrategyRice AlumniAssociation of Rice Alumni | FacebookRice Alumni (@ricealumni) | X (Twitter)Association of Rice Alumni (@ricealumni) | Instagram Host Profiles:David Mansouri | LinkedInDavid Mansouri '07 | Alumni | Rice UniversityDavid Mansouri (@davemansouri) | XDavid Mansouri | TNScoreGuest Profiles:Shawn Miller | Faculty ProfileShawn Miller | LinkedIn ProfileShawn Miller | Social Profile on X

In this bonus episode recorded live at EDUCAUSE in Nashville, Dustin spoke with Ryan Lufkin, VP of Global Academic Strategy at Instructure, about the current crossroads in higher ed, technology, and workforce readiness. The conversation touches on findings from the latest State of Learning and Readiness report, the real (and perceived) gaps in student skill preparedness, and why now is the time for institutions to embrace AI—not fear it. Ryan also offers a behind-the-scenes look at Canvas Career and how Instructure is broadening its mission beyond just a learning management system.Guest Name: Ryan Lufkin - VP of Global Academic Strategy at InstructureGuest Social: LinkedInGuest Bio: Ryan Lufkin is the VP of Global Academic Strategy at Instructure (makers of Canvas, the leading education technology used by schools across the country). Ryan has been working with colleges and universities in the educational technology space for over 20 years, beginning with Utah-based start-up Campus Pipeline, the first html portal for higher education. He has worked for large ERP solutions like SungardHE and Ellucian helping evolve those systems to the Cloud, driven mobile adoption in teaching and learning technology, and developed curriculum for both corporate and higher education institutions. Ryan has helped lead Canvas' evolution from an LMS to a full learning management platform to support the challenges facing colleges and universities of all sizes. - - - -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.

Power is the new bottleneck, reasoning got real, and the business finally caught up. In this wide-ranging conversation, I sit down with Nathan Benaich, Founder and General Partner at Air Street Capital, to discuss the newly published 2025 State of AI report—what's actually working, what's hype, and where the next edge will come from. We start at the physical layer: energy procurement, PPAs, off-grid builds, and why water and grid constraints are turning power—not GPUs—into the decisive moat.From there, we move into capability: reasoning models acting as AI co-scientists in verifiable domains, and the “chain-of-action” shift in robotics that's taking us from polished demos to dependable deployments. Along the way, we examine the market reality—who's making real revenue, how margins actually behave once tokens and inference meet pricing, and what all of this means for builders and investors.We also zoom out to the ecosystem: NVIDIA's position vs. custom silicon, China's split stack, and the rise of sovereign AI (and the “sovereignty washing” that comes with it). The policy and security picture gets a hard look too—regulation's vibe shift, data-rights realpolitik, and what agents and MCP mean for cyber risk and adoption.Nathan closes with where he's placing bets (bio, defense, robotics, voice) and three predictions for the next 12 months. Nathan BenaichBlog - https://www.nathanbenaich.comX/Twitter - https://x.com/nathanbenaichSource: State of AI Report 2025 (9/10/2025)Air Street CapitalWebsite - https://www.airstreet.comX/Twitter - https://x.com/airstreetMatt Turck (Managing Director)Blog - https://www.mattturck.comLinkedIn - https://www.linkedin.com/in/turck/X/Twitter - https://twitter.com/mattturckFIRSTMARKWebsite - https://firstmark.comX/Twitter - https://twitter.com/FirstMarkCap(0:00) – Cold Open: “Gargantuan money, real reasoning”(0:40) – Intro: State of AI 2025 with Nathan Benaich(02:06) – Reasoning got real: from chain-of-thought to verified math wins(04:11) – AI co-scientist: hypotheses, wet-lab validation, fewer “dumb stochastic parrots” (04:44) – Chain-of-action robotics: plan → act you can audit(05:13) – Humanoids vs. warehouse reality: where robots actually stick first(06:32) – The business caught up: who's making real revenue now(08:26) – Adoption & spend: Ramp stats, retention, and the shadow-AI gap(11:00) – Margins debate: tokens, pricing, and the thin-wrapper trap(14:02) – Bubble or boom? Wall Street vs. SF vibes (and circular deals)(19:54) – Power is the bottleneck: $50B/GW capex and the new moat(21:02) – PPAs, gas turbines, and off-grid builds: the procurement game(23:54) – Water, grids, and NIMBY: sustainability gets political(25:08) – NVIDIA's moat: 90% of papers, Broadcom/AMD, and custom silicon(28:47) – China split-stack: Huawei, Cambricon, and export zigzags(30:30) – Sovereign AI or “sovereignty washing”? Open source as leverage(40:40) – Regulation & safety: from Bletchley to “AI Action”—the vibe shift(44:06) – Safety budgets vs. lab spend; models that game evals(44:46) – Data rights realpolitik: $1.5B signals the new training cost(47:04) – Cyber risk in the agent era: MCP, malware LMs, state actors(50:19) – Agents that convert: search → commerce and the demo flywheel(54:18) – VC lens: where Nathan is investing (bio, defense, robotics, voice)(68:29) – Predictions: power politics, AI neutrality, end-to-end discoveries(1:02:13) – Wrap: what to watch next & where to find the report (stateof.ai)

Have you ever thought about what we need to understand regarding the use of LM potencies?   Join us for our latest episode, in which Gabriel will discuss his practice, which is based on homeopathic potencies known as LMs, and its various benefits, as well as his wonderful experience practicing homeopathy and his idea of sharing this knowledge with native Amazonians.   Gabriel Cambraia Neiva, Ph.D., RSHom, is a homeopath who graduated from the North West College of Homeopathy in Manchester. Following the principles of classical homeopathy, Gabriel has been treating children and adults for the last few years, both in the United Kingdom and in Brazil. From mental health to respiratory and skin complaints, Gabriel supports patients to achieve better health during chronic and acute conditions. Gabriel also offers workshops on homeopathic prescribing. He is a registered member of the Society of Homeopaths, UK, where his practice is based. Gabriel's practice is mostly based on homeopathic potencies called LMs, which are gentle, water-based remedies. According to the father of homeopathy, the physician Samuel Hahnemann, these remedies are the ones most perfected, as there are hardly any aggravations. Reactions are seen faster, and the duration of treatment is drastically reduced. Although usually prescribing one remedy at a time, according to the classical science of homeopathy, there are cases in which support remedies might be needed - these are usually prescribed in lower, centesimal potencies.   Check out these episode highlights: 02:09 - How was Gabriel first introduced to homeopathy 03:21 - His incredible story of how homeopathy helped his son 05:15 - What sparked his interest in LM potencies 11:49 - The usual successful treatment with LMs 13:27 - The various advantages of using LM potencies 16:48 - The ideal starting point in using LMs 18:53 - The proper way of administering LMs 27:44 - Homeopathy as a first line of healthcare in Brazil 30:01 - Homeopathy and shamanism in the Amazon   Know more about Gabriel https://homeopathia.org/   If you would like to support the Homeopathy Hangout Podcast, please consider making a donation by visiting www.EugenieKruger.com and click the DONATE button at the top of the site. Every donation about $10 will receive a shout-out on a future episode.   Join my Homeopathy Hangout Podcast Facebook community here: https://www.facebook.com/groups/HelloHomies   Here is the link to my free 30-minute Homeopathy@Home online course: https://www.youtube.com/watch?v=vqBUpxO4pZQ&t=438s   Upon completion of the course - and if you live in Australia - you can join my Facebook group for free acute advice (you'll need to answer a couple of questions about the course upon request to join): www.facebook.com/groups/eughom

In this episode of the Reshaping Learning podcast from SchoolDay, host Kristi Hemingway sits down with Matt Glanville, Director of Assessment at the International Baccalaureate. Together they explore how AI is reshaping education—from reducing grading burdens and providing real-time feedback to supporting differentiated assessment and improving student writing. Matt also shares the IB's perspective on using AI ethically to augment—not replace—human judgment, and how schools can harness these tools to empower teachers and better prepare students for a rapidly changing world. This episode is sponsored by Scribo from Literatu—AI for writing that builds real connections. Scribo helps teachers see what students need, gives students personalized feedback, and shows schools real progress. Trusted worldwide and award-winning, Scribo works with any LMS. Try it free for one month at literatu.com.   Resources: Get started with a free trial from our episode sponsor, Scribo from Literatu   More great stuff: Explore SchoolDay's Career Academy and visit our blog.